Prognostics for Ground Support Systems: Case Study on Pneumatic Valves

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Goebel, Kai

2011-01-01

Prognostics technologies determine the health (or damage) state of a component or sub-system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such information enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics methodology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the flow of propellant, so failures may have a significant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle filtering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.

Prognostics of Power Electronics, Methods and Validation Experiments

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose R.; Biswas, Gautam; Goebel, Kai

2012-01-01

Abstract Failure of electronic devices is a concern for future electric aircrafts that will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. As a result, investigation of precursors to failure in electronics and prediction of remaining life of electronic components is of key importance. DC-DC power converters are power electronics systems employed typically as sourcing elements for avionics equipment. Current research efforts in prognostics for these power systems focuses on the identification of failure mechanisms and the development of accelerated aging methodologies and systems to accelerate the aging process of test devices, while continuously measuring key electrical and thermal parameters. Preliminary model-based prognostics algorithms have been developed making use of empirical degradation models and physics-inspired degradation model with focus on key components like electrolytic capacitors and power MOSFETs (metal-oxide-semiconductor-field-effect-transistor). This paper presents current results on the development of validation methods for prognostics algorithms of power electrolytic capacitors. Particularly, in the use of accelerated aging systems for algorithm validation. Validation of prognostics algorithms present difficulties in practice due to the lack of run-to-failure experiments in deployed systems. By using accelerated experiments, we circumvent this problem in order to define initial validation activities.

Predicting stabilizing treatment outcomes for complex posttraumatic stress disorder and dissociative identity disorder: an expertise-based prognostic model.

PubMed

Baars, Erik W; van der Hart, Onno; Nijenhuis, Ellert R S; Chu, James A; Glas, Gerrit; Draijer, Nel

2011-01-01

The purpose of this study was to develop an expertise-based prognostic model for the treatment of complex posttraumatic stress disorder (PTSD) and dissociative identity disorder (DID). We developed a survey in 2 rounds: In the first round we surveyed 42 experienced therapists (22 DID and 20 complex PTSD therapists), and in the second round we surveyed a subset of 22 of the 42 therapists (13 DID and 9 complex PTSD therapists). First, we drew on therapists' knowledge of prognostic factors for stabilization-oriented treatment of complex PTSD and DID. Second, therapists prioritized a list of prognostic factors by estimating the size of each variable's prognostic effect; we clustered these factors according to content and named the clusters. Next, concept mapping methodology and statistical analyses (including principal components analyses) were used to transform individual judgments into weighted group judgments for clusters of items. A prognostic model, based on consensually determined estimates of effect sizes, of 8 clusters containing 51 factors for both complex PTSD and DID was formed. It includes the clusters lack of motivation, lack of healthy relationships, lack of healthy therapeutic relationships, lack of other internal and external resources, serious Axis I comorbidity, serious Axis II comorbidity, poor attachment, and self-destruction. In addition, a set of 5 DID-specific items was constructed. The model is supportive of the current phase-oriented treatment model, emphasizing the strengthening of the therapeutic relationship and the patient's resources in the initial stabilization phase. Further research is needed to test the model's statistical and clinical validity.

Transitions in Prognostic Awareness Among Terminally Ill Cancer Patients in Their Last 6 Months of Life Examined by Multi-State Markov Modeling.

PubMed

Hsiu Chen, Chen; Wen, Fur-Hsing; Hou, Ming-Mo; Hsieh, Chia-Hsun; Chou, Wen-Chi; Chen, Jen-Shi; Chang, Wen-Cheng; Tang, Siew Tzuh

2017-09-01

Developing accurate prognostic awareness, a cornerstone of preference-based end-of-life (EOL) care decision-making, is a dynamic process involving more prognostic-awareness states than knowing or not knowing. Understanding the transition probabilities and time spent in each prognostic-awareness state can help clinicians identify trigger points for facilitating transitions toward accurate prognostic awareness. We examined transition probabilities in distinct prognostic-awareness states between consecutive time points in 247 cancer patients' last 6 months and estimated the time spent in each state. Prognostic awareness was categorized into four states: (a) unknown and not wanting to know, state 1; (b) unknown but wanting to know, state 2; (c) inaccurate awareness, state 3; and (d) accurate awareness, state 4. Transitional probabilities were examined by multistate Markov modeling. Initially, 59.5% of patients had accurate prognostic awareness, whereas the probabilities of being in states 1-3 were 8.1%, 17.4%, and 15.0%, respectively. Patients' prognostic awareness generally remained unchanged (probabilities of remaining in the same state: 45.5%-92.9%). If prognostic awareness changed, it tended to shift toward higher prognostic-awareness states (probabilities of shifting to state 4 were 23.2%-36.6% for patients initially in states 1-3, followed by probabilities of shifting to state 3 for those in states 1 and 2 [9.8%-10.1%]). Patients were estimated to spend 1.29, 0.42, 0.68, and 3.61 months in states 1-4, respectively, in their last 6 months. Terminally ill cancer patients' prognostic awareness generally remained unchanged, with a tendency to become more aware of their prognosis. Health care professionals should facilitate patients' transitions toward accurate prognostic awareness in a timely manner to promote preference-based EOL decisions. Terminally ill Taiwanese cancer patients' prognostic awareness generally remained stable, with a tendency toward developing higher states of awareness. Health care professionals should appropriately assess patients' readiness for prognostic information and respect patients' reluctance to confront their poor prognosis if they are not ready to know, but sensitively coach them to cultivate their accurate prognostic awareness, provide desired and understandable prognostic information for those who are ready to know, and give direct and honest prognostic information to clarify any misunderstandings for those with inaccurate awareness, thus ensuring that they develop accurate and realistic prognostic knowledge in time to make end-of-life care decisions. © AlphaMed Press 2017.

Prognostic model based on nailfold capillaroscopy for identifying Raynaud's phenomenon patients at high risk for the development of a scleroderma spectrum disorder: PRINCE (prognostic index for nailfold capillaroscopic examination).

PubMed

Ingegnoli, Francesca; Boracchi, Patrizia; Gualtierotti, Roberta; Lubatti, Chiara; Meani, Laura; Zahalkova, Lenka; Zeni, Silvana; Fantini, Flavio

2008-07-01

To construct a prognostic index based on nailfold capillaroscopic examinations that is capable of predicting the 5-year transition from isolated Raynaud's phenomenon (RP) to RP secondary to scleroderma spectrum disorders (SSDs). The study involved 104 consecutive adult patients with a clinical history of isolated RP, and the index was externally validated in another cohort of 100 patients with the same characteristics. Both groups were followed up for 1-8 years. Six variables were examined because of their potential prognostic relevance (branching, enlarged and giant loops, capillary disorganization, microhemorrhages, and the number of capillaries). The only factors that played a significant prognostic role were the presence of giant loops (hazard ratio [HR] 2.64, P = 0.008) and microhemorrhages (HR 2.33, P = 0.01), and the number of capillaries (analyzed as a continuous variable). The adjusted prognostic role of these factors was evaluated by means of multivariate regression analysis, and the results were used to construct an algorithm-based prognostic index. The model was internally and externally validated. Our prognostic capillaroscopic index identifies RP patients in whom the risk of developing SSDs is high. This model is a weighted combination of different capillaroscopy parameters that allows physicians to stratify RP patients easily, using a relatively simple diagram to deduce the prognosis. Our results suggest that this index could be used in clinical practice, and its further inclusion in prospective studies will undoubtedly help in exploring its potential in predicting treatment response.

Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.

PubMed

Jampathong, Nampet; Laopaiboon, Malinee; Rattanakanokchai, Siwanon; Pattanittum, Porjai

2018-03-09

Prognostic models have been increasingly developed to predict complete recovery in ischemic stroke. However, questions arise about the performance characteristics of these models. The aim of this study was to systematically review and synthesize performance of existing prognostic models for complete recovery in ischemic stroke. We searched journal publications indexed in PUBMED, SCOPUS, CENTRAL, ISI Web of Science and OVID MEDLINE from inception until 4 December, 2017, for studies designed to develop and/or validate prognostic models for predicting complete recovery in ischemic stroke patients. Two reviewers independently examined titles and abstracts, and assessed whether each study met the pre-defined inclusion criteria and also independently extracted information about model development and performance. We evaluated validation of the models by medians of the area under the receiver operating characteristic curve (AUC) or c-statistic and calibration performance. We used a random-effects meta-analysis to pool AUC values. We included 10 studies with 23 models developed from elderly patients with a moderately severe ischemic stroke, mainly in three high income countries. Sample sizes for each study ranged from 75 to 4441. Logistic regression was the only analytical strategy used to develop the models. The number of various predictors varied from one to 11. Internal validation was performed in 12 models with a median AUC of 0.80 (95% CI 0.73 to 0.84). One model reported good calibration. Nine models reported external validation with a median AUC of 0.80 (95% CI 0.76 to 0.82). Four models showed good discrimination and calibration on external validation. The pooled AUC of the two validation models of the same developed model was 0.78 (95% CI 0.71 to 0.85). The performance of the 23 models found in the systematic review varied from fair to good in terms of internal and external validation. Further models should be developed with internal and external validation in low and middle income countries.

Prediction of risk of recurrence of venous thromboembolism following treatment for a first unprovoked venous thromboembolism: systematic review, prognostic model and clinical decision rule, and economic evaluation.

PubMed

Ensor, Joie; Riley, Richard D; Jowett, Sue; Monahan, Mark; Snell, Kym Ie; Bayliss, Susan; Moore, David; Fitzmaurice, David

2016-02-01

Unprovoked first venous thromboembolism (VTE) is defined as VTE in the absence of a temporary provoking factor such as surgery, immobility and other temporary factors. Recurrent VTE in unprovoked patients is highly prevalent, but easily preventable with oral anticoagulant (OAC) therapy. The unprovoked population is highly heterogeneous in terms of risk of recurrent VTE. The first aim of the project is to review existing prognostic models which stratify individuals by their recurrence risk, therefore potentially allowing tailored treatment strategies. The second aim is to enhance the existing research in this field, by developing and externally validating a new prognostic model for individual risk prediction, using a pooled database containing individual patient data (IPD) from several studies. The final aim is to assess the economic cost-effectiveness of the proposed prognostic model if it is used as a decision rule for resuming OAC therapy, compared with current standard treatment strategies. Standard systematic review methodology was used to identify relevant prognostic model development, validation and cost-effectiveness studies. Bibliographic databases (including MEDLINE, EMBASE and The Cochrane Library) were searched using terms relating to the clinical area and prognosis. Reviewing was undertaken by two reviewers independently using pre-defined criteria. Included full-text articles were data extracted and quality assessed. Critical appraisal of included full texts was undertaken and comparisons made of model performance. A prognostic model was developed using IPD from the pooled database of seven trials. A novel internal-external cross-validation (IECV) approach was used to develop and validate a prognostic model, with external validation undertaken in each of the trials iteratively. Given good performance in the IECV approach, a final model was developed using all trials data. A Markov patient-level simulation was used to consider the economic cost-effectiveness of using a decision rule (based on the prognostic model) to decide on resumption of OAC therapy (or not). Three full-text articles were identified by the systematic review. Critical appraisal identified methodological and applicability issues; in particular, all three existing models did not have external validation. To address this, new prognostic models were sought with external validation. Two potential models were considered: one for use at cessation of therapy (pre D-dimer), and one for use after cessation of therapy (post D-dimer). Model performance measured in the external validation trials showed strong calibration performance for both models. The post D-dimer model performed substantially better in terms of discrimination (c = 0.69), better separating high- and low-risk patients. The economic evaluation identified that a decision rule based on the final post D-dimer model may be cost-effective for patients with predicted risk of recurrence of over 8% annually; this suggests continued therapy for patients with predicted risks ≥ 8% and cessation of therapy otherwise. The post D-dimer model performed strongly and could be useful to predict individuals' risk of recurrence at any time up to 2-3 years, thereby aiding patient counselling and treatment decisions. A decision rule using this model may be cost-effective for informing clinical judgement and patient opinion in treatment decisions. Further research may investigate new predictors to enhance model performance and aim to further externally validate to confirm performance in new, non-trial populations. Finally, it is essential that further research is conducted to develop a model predicting bleeding risk on therapy, to manage the balance between the risks of recurrence and bleeding. This study is registered as PROSPERO CRD42013003494. The National Institute for Health Research Health Technology Assessment programme.

Refining prognosis in lung cancer: A report on the quality and relevance of clinical prognostic tools

PubMed Central

Mahar, Alyson L.; Compton, Carolyn; McShane, Lisa M.; Halabi, Susan; Asamura, Hisao; Rami-Porta, Ramon; Groome, Patti A.

2015-01-01

Introduction Accurate, individualized prognostication for lung cancer patients requires the integration of standard patient and pathologic factors, biologic, genetic, and other molecular characteristics of the tumor. Clinical prognostic tools aim to aggregate information on an individual patient to predict disease outcomes such as overall survival, but little is known about their clinical utility and accuracy in lung cancer. Methods A systematic search of the scientific literature for clinical prognostic tools in lung cancer published Jan 1, 1996-Jan 27, 2015 was performed. In addition, web-based resources were searched. A priori criteria determined by the Molecular Modellers Working Group of the American Joint Committee on Cancer were used to investigate the quality and usefulness of tools. Criteria included clinical presentation, model development approaches, validation strategies, and performance metrics. Results Thirty-two prognostic tools were identified. Patients with metastases were the most frequently considered population in non-small cell lung cancer. All tools for small cell lung cancer covered that entire patient population. Included prognostic factors varied considerably across tools. Internal validity was not formally evaluated for most tools and only eleven were evaluated for external validity. Two key considerations were highlighted for tool development: identification of an explicit purpose related to a relevant clinical population and clear decision-points, and prioritized inclusion of established prognostic factors over emerging factors. Conclusions Prognostic tools will contribute more meaningfully to the practice of personalized medicine if better study design and analysis approaches are used in their development and validation. PMID:26313682

Serum C-reactive protein (CRP) as a simple and independent prognostic factor in extranodal natural killer/T-cell lymphoma, nasal type.

PubMed

Li, Ya-Jun; Li, Zhi-Ming; Xia, Yi; Huang, Jia-Jia; Huang, Hui-Qiang; Xia, Zhong-Jun; Lin, Tong-Yu; Li, Su; Cai, Xiu-Yu; Wu-Xiao, Zhi-Jun; Jiang, Wen-Qi

2013-01-01

C-reactive protein (CRP) is a biomarker of the inflammatory response, and it shows significant prognostic value for several types of solid tumors. The prognostic significance of CRP for lymphoma has not been fully examined. We evaluated the prognostic role of baseline serum CRP levels in patients with extranodal natural killer (NK)/T-cell lymphoma (ENKTL). We retrospectively analyzed 185 patients with newly diagnosed ENKTL. The prognostic value of the serum CRP level was evaluated for the low-CRP group (CRP≤10 mg/L) versus the high-CRP group (CRP>10 mg/L). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) were evaluated and compared with the newly developed prognostic model. Patients in the high-CRP group tended to display increased adverse clinical characteristics, lower rates of complete remission (P<0.001), inferior progression-free survival (PFS, P = 0.001), and inferior overall survival (OS, P<0.001). Multivariate analysis demonstrated that elevated serum CRP levels, age >60 years, hypoalbuminemia, and elevated lactate dehydrogenase levels were independent adverse predictors of OS. Based on these four independent predictors, we constructed a new prognostic model that identified 4 groups with varying OS: group 1, no adverse factors; group 2, 1 factor; group 3, 2 factors; and group 4, 3 or 4 factors (P<0.001). The novel prognostic model was found to be superior to both the IPI in discriminating patients with different outcomes in the IPI low-risk group and the KPI in distinguishing between the low- and intermediate-low-risk groups, the intermediate-low- and high-intermediate-risk groups, and the high-intermediate- and high-risk groups. Our results suggest that pretreatment serum CRP levels represent an independent predictor of clinical outcome for patients with ENKTL. The prognostic value of the new prognostic model is superior to both IPI and KPI.

Serum C-Reactive Protein (CRP) as a Simple and Independent Prognostic Factor in Extranodal Natural Killer/T-Cell Lymphoma, Nasal Type

PubMed Central

Xia, Yi; Huang, Jia-Jia; Huang, Hui-Qiang; Xia, Zhong-Jun; Lin, Tong-Yu; Li, Su; Cai, Xiu-Yu; Wu-Xiao, Zhi-Jun; Jiang, Wen-Qi

2013-01-01

Background C-reactive protein (CRP) is a biomarker of the inflammatory response, and it shows significant prognostic value for several types of solid tumors. The prognostic significance of CRP for lymphoma has not been fully examined. We evaluated the prognostic role of baseline serum CRP levels in patients with extranodal natural killer (NK)/T-cell lymphoma (ENKTL). Methods We retrospectively analyzed 185 patients with newly diagnosed ENKTL. The prognostic value of the serum CRP level was evaluated for the low-CRP group (CRP≤10 mg/L) versus the high-CRP group (CRP>10 mg/L). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) were evaluated and compared with the newly developed prognostic model. Results Patients in the high-CRP group tended to display increased adverse clinical characteristics, lower rates of complete remission (P<0.001), inferior progression-free survival (PFS, P = 0.001), and inferior overall survival (OS, P<0.001). Multivariate analysis demonstrated that elevated serum CRP levels, age >60 years, hypoalbuminemia, and elevated lactate dehydrogenase levels were independent adverse predictors of OS. Based on these four independent predictors, we constructed a new prognostic model that identified 4 groups with varying OS: group 1, no adverse factors; group 2, 1 factor; group 3, 2 factors; and group 4, 3 or 4 factors (P<0.001). The novel prognostic model was found to be superior to both the IPI in discriminating patients with different outcomes in the IPI low-risk group and the KPI in distinguishing between the low- and intermediate-low-risk groups, the intermediate-low- and high-intermediate-risk groups, and the high-intermediate- and high-risk groups. Conclusions Our results suggest that pretreatment serum CRP levels represent an independent predictor of clinical outcome for patients with ENKTL. The prognostic value of the new prognostic model is superior to both IPI and KPI. PMID:23724031

Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agarwal, Vivek; Lybeck, Nancy J.; Pham, Binh

Research and development efforts are required to address aging and reliability concerns of the existing fleet of nuclear power plants. As most plants continue to operate beyond the license life (i.e., towards 60 or 80 years), plant components are more likely to incur age-related degradation mechanisms. To assess and manage the health of aging plant assets across the nuclear industry, the Electric Power Research Institute has developed a web-based Fleet-Wide Prognostic and Health Management (FW-PHM) Suite for diagnosis and prognosis. FW-PHM is a set of web-based diagnostic and prognostic tools and databases, comprised of the Diagnostic Advisor, the Asset Faultmore » Signature Database, the Remaining Useful Life Advisor, and the Remaining Useful Life Database, that serves as an integrated health monitoring architecture. The main focus of this paper is the implementation of prognostic models for generator step-up transformers in the FW-PHM Suite. One prognostic model discussed is based on the functional relationship between degree of polymerization, (the most commonly used metrics to assess the health of the winding insulation in a transformer) and furfural concentration in the insulating oil. The other model is based on thermal-induced degradation of the transformer insulation. By utilizing transformer loading information, established thermal models are used to estimate the hot spot temperature inside the transformer winding. Both models are implemented in the Remaining Useful Life Database of the FW-PHM Suite. The Remaining Useful Life Advisor utilizes the implemented prognostic models to estimate the remaining useful life of the paper winding insulation in the transformer based on actual oil testing and operational data.« less

Prognostic and predictive implications of Sokal, Euro and EUTOS scores in chronic myeloid leukaemia in the imatinib era-experience from a tertiary oncology centre in Southern India.

PubMed

Kuntegowdanahalli, Lakshmaiah Chinnagiriyappa; Kanakasetty, Govind Babu; Thanky, Aditi Harsh; Dasappa, Lokanatha; Jacob, Linu Abraham; Mallekavu, Suresh Babu; Lakkavalli, Rajeev Krishnappa; Kadabur, Lokesh N; Haleshappa, Rudresha Antapura

2016-01-01

Chronic myeloid leukaemia (CML) is a myeloproliferative disorder. Over the years many prognostic models have been developed to better risk stratify this disease at baseline. Sokal, Euro, and EUTOS scores were developed in varied populations initially receiving various therapies. Here we try to identify their predictive and prognostic implication in a larger population of Indian patients with CML-CP (chronic phase) in the imatinib era.

Development and Validation of a Lifecycle-based Prognostics Architecture with Test Bed Validation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hines, J. Wesley; Upadhyaya, Belle; Sharp, Michael

On-line monitoring and tracking of nuclear plant system and component degradation is being investigated as a method for improving the safety, reliability, and maintainability of aging nuclear power plants. Accurate prediction of the current degradation state of system components and structures is important for accurate estimates of their remaining useful life (RUL). The correct quantification and propagation of both the measurement uncertainty and model uncertainty is necessary for quantifying the uncertainty of the RUL prediction. This research project developed and validated methods to perform RUL estimation throughout the lifecycle of plant components. Prognostic methods should seamlessly operate from beginning ofmore » component life (BOL) to end of component life (EOL). We term this "Lifecycle Prognostics." When a component is put into use, the only information available may be past failure times of similar components used in similar conditions, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I Prognostics). As the component operates, it begins to degrade and consume its available life. This life consumption may be a function of system stresses, and the failure distribution should be updated to account for the system operational stress levels (Type II Prognostics). When degradation becomes apparent, this information can be used to again improve the RUL estimate (Type III Prognostics). This research focused on developing prognostics algorithms for the three types of prognostics, developing uncertainty quantification methods for each of the algorithms, and, most importantly, developing a framework using Bayesian methods to transition between prognostic model types and update failure distribution estimates as new information becomes available. The developed methods were then validated on a range of accelerated degradation test beds. The ultimate goal of prognostics is to provide an accurate assessment for RUL predictions, with as little uncertainty as possible. From a reliability and maintenance standpoint, there would be improved safety by avoiding all failures. Calculated risk would decrease, saving money by avoiding unnecessary maintenance. One major bottleneck for data-driven prognostics is the availability of run-to-failure degradation data. Without enough degradation data leading to failure, prognostic models can yield RUL distributions with large uncertainty or mathematically unsound predictions. To address these issues a "Lifecycle Prognostics" method was developed to create RUL distributions from Beginning of Life (BOL) to End of Life (EOL). This employs established Type I, II, and III prognostic methods, and Bayesian transitioning between each Type. Bayesian methods, as opposed to classical frequency statistics, show how an expected value, a priori, changes with new data to form a posterior distribution. For example, when you purchase a component you have a prior belief, or estimation, of how long it will operate before failing. As you operate it, you may collect information related to its condition that will allow you to update your estimated failure time. Bayesian methods are best used when limited data are available. The use of a prior also means that information is conserved when new data are available. The weightings of the prior belief and information contained in the sampled data are dependent on the variance (uncertainty) of the prior, the variance (uncertainty) of the data, and the amount of measured data (number of samples). If the variance of the prior is small compared to the uncertainty of the data, the prior will be weighed more heavily. However, as more data are collected, the data will be weighted more heavily and will eventually swamp out the prior in calculating the posterior distribution of model parameters. Fundamentally Bayesian analysis updates a prior belief with new data to get a posterior belief. The general approach to applying the Bayesian method to lifecycle prognostics consisted of identifying the prior, which is the RUL estimate and uncertainty from the previous prognostics type, and combining it with observational data related to the newer prognostics type. The resulting lifecycle prognostics algorithm uses all available information throughout the component lifecycle.« less

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

PubMed

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

External validation of a Cox prognostic model: principles and methods

PubMed Central

2013-01-01

Background A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. Methods We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. Results We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Conclusions Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model. PMID:23496923

Prevalence and prognostic significance of hyperkalemia in hospitalized patients with cirrhosis.

PubMed

Maiwall, Rakhi; Kumar, Suman; Sharma, Manoj Kumar; Wani, Zeeshan; Ozukum, Mulu; Sarin, Shiv Kumar

2016-05-01

The prevalence and clinical significance of hyponatremia in cirrhotics have been well studied; however, there are limited data on hyperkalemia in cirrhotics. We evaluated the prevalence and prognostic significance of hyperkalemia in hospitalized patients with cirrhosis and developed a prognostic model incorporating potassium for prediction of liver-related death in these patients. The training derivative cohort of patients was used for development of prognostic scores (Group A, n = 1160), which were validated in a large prospective cohort of cirrhotic patients. (Group B, n = 2681) of cirrhosis. Hyperkalemia was seen in 189 (14.1%) and 336 (12%) in Group A and Group B, respectively. Potassium showed a significant association that was direct with creatinine (P < 0.001) and urea (P < 0.001) and inverse with sodium (P < 0.001). Mortality was also significantly higher in patients with hyperkalemia (P = 0.0015, Hazard Ratio (HR) 1.3, 95% confidence interval 1.11-1.57). Combination of all these parameters into a single value predictor, that is, renal dysfunction index predicted mortality better than the individual components. Combining renal dysfunction index with other known prognostic markers (i.e. serum bilirubin, INR, albumin, hepatic encephalopathy, and ascites) in the "K" model predicted both short-term and long-term mortality with an excellent accuracy (Concordance-index 0.78 and 0.80 in training and validation cohorts, respectively). This was also superior to Model for End-stage Liver Disease, Model for End-stage liver disease sodium (MELDNa), and Child-Turcott-Pugh scores. Cirrhotics frequently have impaired potassium homeostasis, which has a prognostic significance. Serum potassium correlates directly with serum creatinine and urea and inversely with serum sodium. The model incorporating serum potassium developed from this study ("K"model) can predict death in advanced cirrhotics with an excellent accuracy. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Current state of prognostication and risk stratification in myelodysplastic syndromes.

PubMed

Zeidan, Amer M; Gore, Steven D; Padron, Eric; Komrokji, Rami S

2015-03-01

Myelodysplastic syndromes (MDS) are characterized by significant biologic and clinical heterogeneity. Because of the wide outcome variability, accurate prognostication is vital to high-quality risk-adaptive care of MDS patients. In this review, we discuss the current state of prognostic schemes for MDS and overview efforts aimed at utilizing molecular aberrations for prognostication in clinical practice. Several prognostic instruments have been developed and validated with increasing accuracy and complexity. Oncologists should be aware of the inherent limitations of these prognostic tools as they counsel patients and make clinical decisions. As more therapies are becoming available for MDS, the focus of model development is shifting from prognostic to treatment-specific predictive instruments. In addition to providing additional prognostic data beyond traditional clinical and pathologic parameters, the improved understanding of the genetic landscape and pathophysiologic consequences in MDS may allow the construction of treatment-specific predictive instruments. How to best use the results of molecular mutation testing to inform clinical decision making in MDS is still a work in progress. Important steps in this direction include standardization in performance and interpretation of assays and better understanding of the independent prognostic importance of the recurrent mutations, especially the less frequent ones.

Development of an On-board Failure Diagnostics and Prognostics System for Solid Rocket Booster

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry G.; Osipov, Vyatcheslav V.; Timucin, Dogan A.; Uckun, Serdar

2009-01-01

We develop a case breach model for the on-board fault diagnostics and prognostics system for subscale solid-rocket boosters (SRBs). The model development was motivated by recent ground firing tests, in which a deviation of measured time-traces from the predicted time-series was observed. A modified model takes into account the nozzle ablation, including the effect of roughness of the nozzle surface, the geometry of the fault, and erosion and burning of the walls of the hole in the metal case. The derived low-dimensional performance model (LDPM) of the fault can reproduce the observed time-series data very well. To verify the performance of the LDPM we build a FLUENT model of the case breach fault and demonstrate a good agreement between theoretical predictions based on the analytical solution of the model equations and the results of the FLUENT simulations. We then incorporate the derived LDPM into an inferential Bayesian framework and verify performance of the Bayesian algorithm for the diagnostics and prognostics of the case breach fault. It is shown that the obtained LDPM allows one to track parameters of the SRB during the flight in real time, to diagnose case breach fault, and to predict its values in the future. The application of the method to fault diagnostics and prognostics (FD&P) of other SRB faults modes is discussed.

Prognostic score to predict mortality during TB treatment in TB/HIV co-infected patients.

PubMed

Nguyen, Duc T; Jenkins, Helen E; Graviss, Edward A

2018-01-01

Estimating mortality risk during TB treatment in HIV co-infected patients is challenging for health professionals, especially in a low TB prevalence population, due to the lack of a standardized prognostic system. The current study aimed to develop and validate a simple mortality prognostic scoring system for TB/HIV co-infected patients. Using data from the CDC's Tuberculosis Genotyping Information Management System of TB patients in Texas reported from 01/2010 through 12/2016, age ≥15 years, HIV(+), and outcome being "completed" or "died", we developed and internally validated a mortality prognostic score using multiple logistic regression. Model discrimination was determined by the area under the receiver operating characteristic (ROC) curve (AUC). The model's good calibration was determined by a non-significant Hosmer-Lemeshow's goodness of fit test. Among the 450 patients included in the analysis, 57 (12.7%) died during TB treatment. The final prognostic score used six characteristics (age, residence in long-term care facility, meningeal TB, chest x-ray, culture positive, and culture not converted/unknown), which are routinely collected by TB programs. Prognostic scores were categorized into three groups that predicted mortality: low-risk (<20 points), medium-risk (20-25 points) and high-risk (>25 points). The model had good discrimination and calibration (AUC = 0.82; 0.80 in bootstrap validation), and a non-significant Hosmer-Lemeshow test p = 0.71. Our simple validated mortality prognostic scoring system can be a practical tool for health professionals in identifying TB/HIV co-infected patients with high mortality risk.

Application of Model-based Prognostics to a Pneumatic Valves Testbed

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Kulkarni, Chetan S.; Gorospe, George

2014-01-01

Pneumatic-actuated valves play an important role in many applications, including cryogenic propellant loading for space operations. Model-based prognostics emphasizes the importance of a model that describes the nominal and faulty behavior of a system, and how faulty behavior progresses in time, causing the end of useful life of the system. We describe the construction of a testbed consisting of a pneumatic valve that allows the injection of faulty behavior and controllable fault progression. The valve opens discretely, and is controlled through a solenoid valve. Controllable leaks of pneumatic gas in the testbed are introduced through proportional valves, allowing the testing and validation of prognostics algorithms for pneumatic valves. A new valve prognostics approach is developed that estimates fault progression and predicts remaining life based only on valve timing measurements. Simulation experiments demonstrate and validate the approach.

Prognostic modelling options for remaining useful life estimation by industry

NASA Astrophysics Data System (ADS)

Sikorska, J. Z.; Hodkiewicz, M.; Ma, L.

2011-07-01

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

A Linearized Prognostic Cloud Scheme in NASAs Goddard Earth Observing System Data Assimilation Tools

NASA Technical Reports Server (NTRS)

Holdaway, Daniel; Errico, Ronald M.; Gelaro, Ronald; Kim, Jong G.; Mahajan, Rahul

2015-01-01

A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA's Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.

Diagnosis and Prognosis of Weapon Systems

NASA Technical Reports Server (NTRS)

Nolan, Mary; Catania, Rebecca; deMare, Gregory

2005-01-01

The Prognostics Framework is a set of software tools with an open architecture that affords a capability to integrate various prognostic software mechanisms and to provide information for operational and battlefield decision-making and logistical planning pertaining to weapon systems. The Prognostics NASA Tech Briefs, February 2005 17 Framework is also a system-level health -management software system that (1) receives data from performance- monitoring and built-in-test sensors and from other prognostic software and (2) processes the received data to derive a diagnosis and a prognosis for a weapon system. This software relates the diagnostic and prognostic information to the overall health of the system, to the ability of the system to perform specific missions, and to needed maintenance actions and maintenance resources. In the development of the Prognostics Framework, effort was focused primarily on extending previously developed model-based diagnostic-reasoning software to add prognostic reasoning capabilities, including capabilities to perform statistical analyses and to utilize information pertaining to deterioration of parts, failure modes, time sensitivity of measured values, mission criticality, historical data, and trends in measurement data. As thus extended, the software offers an overall health-monitoring capability.

State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

NASA Astrophysics Data System (ADS)

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

2017-09-01

Integrating prognostics to a real application requires a certain maturity level and for this reason there is a lack of success stories about development of a complete Prognostics and Health Management system. In fact, the maturity of prognostics is closely linked to data and domain specific entities like modeling. Basically, prognostics task aims at predicting the degradation of engineering assets. However, practically it is not possible to precisely predict the impending failure, which requires a thorough understanding to encounter different sources of uncertainty that affect prognostics. Therefore, different aspects crucial to the prognostics framework, i.e., from monitoring data to remaining useful life of equipment need to be addressed. To this aim, the paper contributes to state of the art and taxonomy of prognostics approaches and their application perspectives. In addition, factors for prognostics approach selection are identified, and new case studies from component-system level are discussed. Moreover, open challenges toward maturity of the prognostics under uncertainty are highlighted and scheme for an efficient prognostics approach is presented. Finally, the existing challenges for verification and validation of prognostics at different technology readiness levels are discussed with respect to open challenges.

Investigating the Effect of Damage Progression Model Choice on Prognostics Performance

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil; Narasimhan, Sriram; Saha, Sankalita; Saha, Bhaskar; Goebel, Kai

2011-01-01

The success of model-based approaches to systems health management depends largely on the quality of the underlying models. In model-based prognostics, it is especially the quality of the damage progression models, i.e., the models describing how damage evolves as the system operates, that determines the accuracy and precision of remaining useful life predictions. Several common forms of these models are generally assumed in the literature, but are often not supported by physical evidence or physics-based analysis. In this paper, using a centrifugal pump as a case study, we develop different damage progression models. In simulation, we investigate how model changes influence prognostics performance. Results demonstrate that, in some cases, simple damage progression models are sufficient. But, in general, the results show a clear need for damage progression models that are accurate over long time horizons under varied loading conditions.

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

PubMed

Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

2016-08-30

 To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.  External validation of all published prognostic models in large scale, prospective, multicentre cohort study.  31 independent midwifery practices and six hospitals in the Netherlands.  Women recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded.  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Composite prognostic models across the non-alcoholic fatty liver disease spectrum: Clinical application in developing countries

PubMed Central

Lückhoff, Hilmar K; Kruger, Frederik C; Kotze, Maritha J

2015-01-01

Heterogeneity in clinical presentation, histological severity, prognosis and therapeutic outcomes characteristic of non-alcoholic fatty liver disease (NAFLD) necessitates the development of scientifically sound classification schemes to assist clinicians in stratifying patients into meaningful prognostic subgroups. The need for replacement of invasive liver biopsies as the standard method whereby NAFLD is diagnosed, graded and staged with biomarkers of histological severity injury led to the development of composite prognostic models as potentially viable surrogate alternatives. In the present article, we review existing scoring systems used to (1) confirm the presence of undiagnosed hepatosteatosis; (2) distinguish between simple steatosis and NASH; and (3) predict advanced hepatic fibrosis, with particular emphasis on the role of NAFLD as an independent cardio-metabolic risk factor. In addition, the incorporation of functional genomic markers and application of emerging imaging technologies are discussed as a means to improve the diagnostic accuracy and predictive performance of promising composite models found to be most appropriate for widespread clinical adoption. PMID:26019735

Intercomparisons of Prognostic, Diagnostic, and Inversion Modeling Approaches for Estimation of Net Ecosystem Exchange over the Pacific Northwest Region

NASA Astrophysics Data System (ADS)

Turner, D. P.; Jacobson, A. R.; Nemani, R. R.

2013-12-01

The recent development of large spatially-explicit datasets for multiple variables relevant to monitoring terrestrial carbon flux offers the opportunity to estimate the terrestrial land flux using several alternative, potentially complimentary, approaches. Here we developed and compared regional estimates of net ecosystem exchange (NEE) over the Pacific Northwest region of the U.S. using three approaches. In the prognostic modeling approach, the process-based Biome-BGC model was driven by distributed meteorological station data and was informed by Landsat-based coverages of forest stand age and disturbance regime. In the diagnostic modeling approach, the quasi-mechanistic CFLUX model estimated net ecosystem production (NEP) by upscaling eddy covariance flux tower observations. The model was driven by distributed climate data and MODIS FPAR (the fraction of incident PAR that is absorbed by the vegetation canopy). It was informed by coarse resolution (1 km) data about forest stand age. In both the prognostic and diagnostic modeling approaches, emissions estimates for biomass burning, harvested products, and river/stream evasion were added to model-based NEP to get NEE. The inversion model (CarbonTracker) relied on observations of atmospheric CO2 concentration to optimize prior surface carbon flux estimates. The Pacific Northwest is heterogeneous with respect to land cover and forest management, and repeated surveys of forest inventory plots support the presence of a strong regional carbon sink. The diagnostic model suggested a stronger carbon sink than the prognostic model, and a much larger sink that the inversion model. The introduction of Landsat data on disturbance history served to reduce uncertainty with respect to regional NEE in the diagnostic and prognostic modeling approaches. The FPAR data was particularly helpful in capturing the seasonality of the carbon flux using the diagnostic modeling approach. The inversion approach took advantage of a global network of CO2 observation stations, but had difficulty resolving regional fluxes such as that in the PNW given the still sparse nature of the CO2 measurement network.

Health-related quality-of-life parameters as independent prognostic factors in advanced or metastatic bladder cancer.

PubMed

Roychowdhury, D F; Hayden, A; Liepa, A M

2003-02-15

This retrospective analysis examined prognostic significance of health-related quality-of-life (HRQoL) parameters combined with baseline clinical factors on outcomes (overall survival, time to progressive disease, and time to treatment failure) in bladder cancer. Outcome and HRQoL (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30) data were collected prospectively in a phase III study assessing gemcitabine and cisplatin versus methotrexate, vinblastine, doxorubicin, and cisplatin in locally advanced or metastatic bladder cancer. Prespecified baseline clinical factors (performance status, tumor-node-metastasis staging, visceral metastases [VM], alkaline phosphatase [AP] level, number of metastatic sites, prior radiotherapy, disease measurability, sex, time from diagnosis, and sites of disease) and selected HRQoL parameters (global QoL; all functional scales; symptoms: pain, fatigue, insomnia, dyspnea, anorexia) were evaluated using Cox's proportional hazards model. Factors with individual prognostic value (P <.05) on outcomes in univariate models were assessed for joint prognostic value in a multivariate model. A final model was developed using a backward selection strategy. Patients with baseline HRQoL were included (364 of 405, 90%). The final model predicted longer survival with low/normal AP levels, no VM, high physical functioning, low role functioning, and no anorexia. Positive prognostic factors for time to progressive disease were good performance status, low/normal AP levels, no VM, and minimal fatigue; for time to treatment failure, they were low/normal AP levels, minimal fatigue, and no anorexia. Global QoL was a significant predictor of outcome in univariate analyses but was not retained in the multivariate model. HRQoL parameters are independent prognostic factors for outcome in advanced bladder cancer; their prognostic importance needs further evaluation.

Developing and validating a novel metabolic tumor volume risk stratification system for supplementing non-small cell lung cancer staging.

PubMed

Pu, Yonglin; Zhang, James X; Liu, Haiyan; Appelbaum, Daniel; Meng, Jianfeng; Penney, Bill C

2018-06-07

We hypothesized that whole-body metabolic tumor volume (MTVwb) could be used to supplement non-small cell lung cancer (NSCLC) staging due to its independent prognostic value. The goal of this study was to develop and validate a novel MTVwb risk stratification system to supplement NSCLC staging. We performed an IRB-approved retrospective review of 935 patients with NSCLC and FDG-avid tumor divided into modeling and validation cohorts based on the type of PET/CT scanner used for imaging. In addition, sensitivity analysis was conducted by dividing the patient population into two randomized cohorts. Cox regression and Kaplan-Meier survival analyses were performed to determine the prognostic value of the MTVwb risk stratification system. The cut-off values (10.0, 53.4 and 155.0 mL) between the MTVwb quartiles of the modeling cohort were applied to both the modeling and validation cohorts to determine each patient's MTVwb risk stratum. The survival analyses showed that a lower MTVwb risk stratum was associated with better overall survival (all p < 0.01), independent of TNM stage together with other clinical prognostic factors, and the discriminatory power of the MTVwb risk stratification system, as measured by Gönen and Heller's concordance index, was not significantly different from that of TNM stage in both cohorts. Also, the prognostic value of the MTVwb risk stratum was robust in the two randomized cohorts. The discordance rate between the MTVwb risk stratum and TNM stage or substage was 45.1% in the modeling cohort and 50.3% in the validation cohort. This study developed and validated a novel MTVwb risk stratification system, which has prognostic value independent of the TNM stage and other clinical prognostic factors in NSCLC, suggesting that it could be used for further NSCLC pretreatment assessment and for refining treatment decisions in individual patients.

Prognostic model for survival in patients with early stage cervical cancer.

PubMed

Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J; Stalpers, Lukas J A; Schilthuis, Marten S; van der Steeg, Jan Willem; Burger, Matthé P M; Buist, Marrije R

2011-02-15

In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment. A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic. The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85). In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients. Copyright © 2010 American Cancer Society.

Construction of a new, objective prognostic score for terminally ill cancer patients: a multicenter study.

PubMed

Suh, Sang-Yeon; Choi, Youn Seon; Shim, Jae Yong; Kim, Young Sung; Yeom, Chang Hwan; Kim, Daeyoung; Park, Shin Ae; Kim, Sooa; Seo, Ji Yeon; Kim, Su Hyun; Kim, Daegyeun; Choi, Sung-Eun; Ahn, Hong-Yup

2010-02-01

The goal of this study was to develop a new, objective prognostic score (OPS) for terminally ill cancer patients based on an integrated model that includes novel objective prognostic factors. A multicenter study of 209 terminally ill cancer patients from six training hospitals in Korea were prospectively followed until death. The Cox proportional hazard model was used to adjust for the influence of clinical and laboratory variables on survival time. The OPS was calculated from the sum of partial scores obtained from seven significant predictors determined by the final model. The partial score was based on the hazard ratio of each predictor. The accuracy of the OPS was evaluated. The overall median survival was 26 days. On the multivariate analysis, reduced oral intake, resting dyspnea, low performance status, leukocytosis, elevated bilirubin, elevated creatinine, and elevated lactate dehydrogenase (LDH) were identified as poor prognostic factors. The range of OPS was from 0.0 to 7.0. For the above cutoff point of 3.0, the 3-week prediction sensitivity was 74.7%, the specificity was 76.5%, and the overall accuracy was 75.5%. We developed the new OPS, without clinician's survival estimates but including a new prognostic factor (LDH). This new instrument demonstrated accurate prediction of the 3-week survival. The OPS had acceptable accuracy in this study population (training set). Further validation is required on an independent population (testing set).

Prognostic risk stratification derived from individual patient level data for men with advanced penile squamous cell carcinoma receiving first-line systemic therapy.

PubMed

Pond, Gregory R; Di Lorenzo, Giuseppe; Necchi, Andrea; Eigl, Bernhard J; Kolinsky, Michael P; Chacko, Raju T; Dorff, Tanya B; Harshman, Lauren C; Milowsky, Matthew I; Lee, Richard J; Galsky, Matthew D; Federico, Piera; Bolger, Graeme; DeShazo, Mollie; Mehta, Amitkumar; Goyal, Jatinder; Sonpavde, Guru

2014-05-01

Prognostic factors in men with penile squamous cell carcinoma (PSCC) receiving systemic therapy are unknown. A prognostic classification system in this disease may facilitate interpretation of outcomes and guide rational drug development. We performed a retrospective analysis to identify prognostic factors in men with PSCC receiving first-line systemic therapy for advanced disease. Individual patient level data were obtained from 13 institutions to study prognostic factors in the context of first-line systemic therapy for advanced PSCC. Cox proportional hazards regression analysis was conducted to examine the prognostic effect of these candidate factors on progression-free survival (PFS) and overall survival (OS): age, stage, hemoglobin, neutrophil count, lymphocyte count, albumin, site of metastasis (visceral or nonvisceral), smoking, circumcision, regimen, ECOG performance status (PS), lymphovascular invasion, precancerous lesion, and surgery following chemotherapy. The effect of different treatments was then evaluated adjusting for factors in the prognostic model. The study included 140 eligible men. Mean age across all men was 57.0 years. Among them, 8.6%, 21.4%, and 70.0% of patients had stage 2, 3, and 4 diseases, respectively; 40.7% had ECOG PS ≥ 1, 47.4% had visceral metastases, and 73.6% received cisplatin-based chemotherapy. The multivariate model of poor prognostic factors included visceral metastases (P<0.001) and ECOG PS ≥ 1 (P<0.001) for both PFS and OS. A risk stratification model constructed with 0, 1, and both poor prognostic factors was internally validated and demonstrated moderate discriminatory ability (c-statistic of 0.657 and 0.677 for OS and PFS, respectively). The median OS for the entire population was 9 months. Median OS was not reached, 8, and 7 months for those with 0, 1, and both risk factors, respectively. Cisplatin-based regimens were associated with better OS (P = 0.017) but not PFS (P = 0.37) compared with noncisplatin-based regimens after adjusting for the 2 prognostic factors. In men with advanced PSCC receiving first-line systemic therapy, visceral metastases and ECOG PS ≥ 1 were poor prognostic factors. A prognostic model including these factors exhibited moderate discriminatory ability for outcomes and warrants external validation. Patients receiving cisplatin-based regimens exhibited better outcomes compared with noncisplatin-based regimens after adjusting for prognostic factors. © 2013 Published by Elsevier Inc.

Next-generation prognostic assessment for diffuse large B-cell lymphoma

PubMed Central

Staton, Ashley D; Kof, Jean L; Chen, Qiushi; Ayer, Turgay; Flowers, Christopher R

2015-01-01

Current standard of care therapy for diffuse large B-cell lymphoma (DLBCL) cures a majority of patients with additional benefit in salvage therapy and autologous stem cell transplant for patients who relapse. The next generation of prognostic models for DLBCL aims to more accurately stratify patients for novel therapies and risk-adapted treatment strategies. This review discusses the significance of host genetic and tumor genomic alterations seen in DLBCL, clinical and epidemiologic factors, and how each can be integrated into risk stratification algorithms. In the future, treatment prediction and prognostic model development and subsequent validation will require data from a large number of DLBCL patients to establish sufficient statistical power to correctly predict outcome. Novel modeling approaches can augment these efforts. PMID:26289217

Next-generation prognostic assessment for diffuse large B-cell lymphoma.

PubMed

Staton, Ashley D; Koff, Jean L; Chen, Qiushi; Ayer, Turgay; Flowers, Christopher R

2015-01-01

Current standard of care therapy for diffuse large B-cell lymphoma (DLBCL) cures a majority of patients with additional benefit in salvage therapy and autologous stem cell transplant for patients who relapse. The next generation of prognostic models for DLBCL aims to more accurately stratify patients for novel therapies and risk-adapted treatment strategies. This review discusses the significance of host genetic and tumor genomic alterations seen in DLBCL, clinical and epidemiologic factors, and how each can be integrated into risk stratification algorithms. In the future, treatment prediction and prognostic model development and subsequent validation will require data from a large number of DLBCL patients to establish sufficient statistical power to correctly predict outcome. Novel modeling approaches can augment these efforts.

New prognostic model for extranodal natural killer/T cell lymphoma, nasal type.

PubMed

Cai, Qingqing; Luo, Xiaolin; Zhang, Guanrong; Huang, Huiqiang; Huang, Hui; Lin, Tongyu; Jiang, Wenqi; Xia, Zhongjun; Young, Ken H

2014-09-01

Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis, requiring risk stratification in affected patients. We designed a new prognostic model specifically for ENKTL to identify high-risk patients who need more aggressive therapy. We retrospectively reviewed 158 patients who were newly diagnosed with ENKTL. The estimated 5-year overall survival rate was 39.4 %. Independent prognostic factors included total protein (TP) <60 g/L, fasting blood glucose (FBG) >100 mg/dL, and Korean Prognostic Index (KPI) score ≥2. We constructed a new prognostic model by combining these prognostic factors: group 1 (64 cases (41.0 %)), no adverse factors; group 2 (58 cases (37.2 %)), one adverse factor; and group 3 (34 cases (21.8 %)), two or three adverse factors. The 5-year overall survival (OS) rates of these groups were 66.7, 23.0, and 5.9 %, respectively (p < 0.001). Our new prognostic model had a better prognostic value than did the KPI model alone (p < 0.001). Our proposed prognostic model for ENKTL, including the newly identified prognostic indicators, TP and FBG, demonstrated a balanced distribution of patients into different risk groups with better prognostic discrimination compared with the KPI model alone.

Real-Time Prognostics of a Rotary Valve Actuator

NASA Technical Reports Server (NTRS)

Daigle, Matthew

2015-01-01

Valves are used in many domains and often have system-critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and prediction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remaining useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.

Discovery and Validation of Prognostic Biomarker Models to Guide Triage among Adult Dengue Patients at Early Infection

PubMed Central

Tolfvenstam, Thomas; Thein, Tun-Linn; Naim, Ahmad Nazri Mohamed; Ling, Ling; Chow, Angelia; Chen, Mark I-Cheng; Ooi, Eng Eong; Leo, Yee Sin; Hibberd, Martin L.

2016-01-01

Background Dengue results in a significant public health burden in endemic regions. The World Health Organization (WHO) recommended the use of warning signs (WS) to stratify patients at risk of severe dengue disease in 2009. However, WS is limited in stratifying adult dengue patients at early infection (Day 1–3 post fever), who require close monitoring in hospitals to prevent severe dengue. The aim of this study is to identify and validate prognostic models, built with differentially expressed biomarkers, that enable the early identification of those with early dengue infection that require close clinical monitoring. Methods RNA microarray and protein assays were performed to identify differentially expressed biomarkers of severity among 92 adult dengue patients recruited at early infection from years 2005–2008. This comprised 47 cases who developed WS after first presentation and required hospitalization (WS+Hosp), as well as 45 controls who did not develop WS after first presentation and did not require hospitalization (Non-WS+Non-Hosp). Independent validation was conducted with 80 adult dengue patients recruited from years 2009–2012. Prognostic models were developed based on forward stepwise and backward elimination estimation, using multiple logistic regressions. Prognostic power was estimated by the area under the receiver operating characteristic curve (AUC). Results The WS+Hosp group had significantly higher viral load (P<0.001), lower platelet (P<0.001) and lymphocytes counts (P = 0.004) at early infection compared to the Non-WS+Non-Hosp group. From the RNA microarray and protein assays, the top single RNA and protein prognostic models at early infection were CCL8 RNA (AUC:0.73) and IP-10 protein (AUC:0.74), respectively. The model with CCL8, VPS13C RNA, uPAR protein, and with CCL8, VPS13C RNA and platelets were the best biomarker models for stratifying adult dengue patients at early infection, with sensitivity and specificity up to 83% and 84%, respectively. These results were tested in the independent validation group, showing sensitivity and specificity up to 96% and 54.6%, respectively. Conclusions At early infection, adult dengue patients who later presented WS and require hospitalization have significantly different pathophysiology compared with patients who consistently presented no WS and / or require no hospitalization. The molecular prognostic models developed and validated here based on these pathophysiology differences, could offer earlier and complementary indicators to the clinical WHO 2009 WS guide, in order to triage adult dengue patients at early infection. PMID:27286230

A hybrid prognostic model for multistep ahead prediction of machine condition

NASA Astrophysics Data System (ADS)

Roulias, D.; Loutas, T. H.; Kostopoulos, V.

2012-05-01

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

Model-Based Prognostics of Hybrid Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal

2015-01-01

Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

PubMed Central

Parker, Joel S.; Mullins, Michael; Cheang, Maggie C.U.; Leung, Samuel; Voduc, David; Vickery, Tammi; Davies, Sherri; Fauron, Christiane; He, Xiaping; Hu, Zhiyuan; Quackenbush, John F.; Stijleman, Inge J.; Palazzo, Juan; Marron, J.S.; Nobel, Andrew B.; Mardis, Elaine; Nielsen, Torsten O.; Ellis, Matthew J.; Perou, Charles M.; Bernard, Philip S.

2009-01-01

Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy. PMID:19204204

A prognostic index for natural killer cell lymphoma after non-anthracycline-based treatment: a multicentre, retrospective analysis.

PubMed

Kim, Seok Jin; Yoon, Dok Hyun; Jaccard, Arnaud; Chng, Wee Joo; Lim, Soon Thye; Hong, Huangming; Park, Yong; Chang, Kian Meng; Maeda, Yoshinobu; Ishida, Fumihiro; Shin, Dong-Yeop; Kim, Jin Seok; Jeong, Seong Hyun; Yang, Deok-Hwan; Jo, Jae-Cheol; Lee, Gyeong-Won; Choi, Chul Won; Lee, Won-Sik; Chen, Tsai-Yun; Kim, Kiyeun; Jung, Sin-Ho; Murayama, Tohru; Oki, Yasuhiro; Advani, Ranjana; d'Amore, Francesco; Schmitz, Norbert; Suh, Cheolwon; Suzuki, Ritsuro; Kwong, Yok Lam; Lin, Tong-Yu; Kim, Won Seog

2016-03-01

The clinical outcome of extranodal natural killer T-cell lymphoma (ENKTL) has improved substantially as a result of new treatment strategies with non-anthracycline-based chemotherapies and upfront use of concurrent chemoradiotherapy or radiotherapy. A new prognostic model based on the outcomes obtained with these contemporary treatments was warranted. We did a retrospective study of patients with newly diagnosed ENKTL without any previous treatment history for the disease who were given non-anthracycline-based chemotherapies with or without upfront concurrent chemoradiotherapy or radiotherapy with curative intent. A prognostic model to predict overall survival and progression-free survival on the basis of pretreatment clinical and laboratory characteristics was developed by filling a multivariable model on the basis of the dataset with complete data for the selected risk factors for an unbiased prediction model. The final model was applied to the patients who had complete data for the selected risk factors. We did a validation analysis of the prognostic model in an independent cohort. We did multivariate analyses of 527 patients who were included from 38 hospitals in 11 countries in the training cohort. Analyses showed that age greater than 60 years, stage III or IV disease, distant lymph-node involvement, and non-nasal type disease were significantly associated with overall survival and progression-free survival. We used these data as the basis for the prognostic index of natural killer lymphoma (PINK), in which patients are stratified into low-risk (no risk factors), intermediate-risk (one risk factor), or high-risk (two or more risk factors) groups, which were associated with 3-year overall survival of 81% (95% CI 75-86), 62% (55-70), and 25% (20-34), respectively. In the 328 patients with data for Epstein-Barr virus DNA, a detectable viral DNA titre was an independent prognostic factor for overall survival. When these data were added to PINK as the basis for another prognostic index (PINK-E)-which had similar low-risk (zero or one risk factor), intermediate-risk (two risk factors), and high-risk (three or more risk factors) categories-significant associations with overall survival were noted (81% [95% CI 75-87%], 55% (44-66), and 28% (18-40%), respectively). These results were validated and confirmed in an independent cohort, although the PINK-E model was only significantly associated with the high-risk group compared with the low-risk group. PINK and PINK-E are new prognostic models that can be used to develop risk-adapted treatment approaches for patients with ENKTL being treated in the contemporary era of non-anthracycline-based therapy. Samsung Biomedical Research Institute. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stage Separation Failure: Model Based Diagnostics and Prognostics

NASA Technical Reports Server (NTRS)

Luchinsky, Dmitry; Hafiychuk, Vasyl; Kulikov, Igor; Smelyanskiy, Vadim; Patterson-Hine, Ann; Hanson, John; Hill, Ashley

2010-01-01

Safety of the next-generation space flight vehicles requires development of an in-flight Failure Detection and Prognostic (FD&P) system. Development of such system is challenging task that involves analysis of many hard hitting engineering problems across the board. In this paper we report progress in the development of FD&P for the re-contact fault between upper stage nozzle and the inter-stage caused by the first stage and upper stage separation failure. A high-fidelity models and analytical estimations are applied to analyze the following sequence of events: (i) structural dynamics of the nozzle extension during the impact; (ii) structural stability of the deformed nozzle in the presence of the pressure and temperature loads induced by the hot gas flow during engine start up; and (iii) the fault induced thrust changes in the steady burning regime. The diagnostic is based on the measurements of the impact torque. The prognostic is based on the analysis of the correlation between the actuator signal and fault-induced changes in the nozzle structural stability and thrust.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Development and analysis of prognostic equations for mesoscale kinetic energy and mesoscale (subgrid scale) fluxes for large-scale atmospheric models

NASA Technical Reports Server (NTRS)

Avissar, Roni; Chen, Fei

1993-01-01

Generated by landscape discontinuities (e.g., sea breezes) mesoscale circulation processes are not represented in large-scale atmospheric models (e.g., general circulation models), which have an inappropiate grid-scale resolution. With the assumption that atmospheric variables can be separated into large scale, mesoscale, and turbulent scale, a set of prognostic equations applicable in large-scale atmospheric models for momentum, temperature, moisture, and any other gaseous or aerosol material, which includes both mesoscale and turbulent fluxes is developed. Prognostic equations are also developed for these mesoscale fluxes, which indicate a closure problem and, therefore, require a parameterization. For this purpose, the mean mesoscale kinetic energy (MKE) per unit of mass is used, defined as E-tilde = 0.5 (the mean value of u'(sub i exp 2), where u'(sub i) represents the three Cartesian components of a mesoscale circulation (the angle bracket symbol is the grid-scale, horizontal averaging operator in the large-scale model, and a tilde indicates a corresponding large-scale mean value). A prognostic equation is developed for E-tilde, and an analysis of the different terms of this equation indicates that the mesoscale vertical heat flux, the mesoscale pressure correlation, and the interaction between turbulence and mesoscale perturbations are the major terms that affect the time tendency of E-tilde. A-state-of-the-art mesoscale atmospheric model is used to investigate the relationship between MKE, landscape discontinuities (as characterized by the spatial distribution of heat fluxes at the earth's surface), and mesoscale sensible and latent heat fluxes in the atmosphere. MKE is compared with turbulence kinetic energy to illustrate the importance of mesoscale processes as compared to turbulent processes. This analysis emphasizes the potential use of MKE to bridge between landscape discontinuities and mesoscale fluxes and, therefore, to parameterize mesoscale fluxes generated by such subgrid-scale landscape discontinuities in large-scale atmospheric models.

Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

PubMed Central

Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

2014-01-01

Purpose The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT. PMID:24586971

Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of Xerostomia after intensity-modulated radiotherapy for head and neck cancer.

PubMed

Lee, Tsair-Fwu; Chao, Pei-Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

2014-01-01

The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3(+) xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R(2), chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R(2) was satisfactory and corresponded well with the expected values. Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.

Updated Prognostic Model for Predicting Overall Survival in First-Line Chemotherapy for Patients With Metastatic Castration-Resistant Prostate Cancer

PubMed Central

Halabi, Susan; Lin, Chen-Yen; Kelly, W. Kevin; Fizazi, Karim S.; Moul, Judd W.; Kaplan, Ellen B.; Morris, Michael J.; Small, Eric J.

2014-01-01

Purpose Prognostic models for overall survival (OS) for patients with metastatic castration-resistant prostate cancer (mCRPC) are dated and do not reflect significant advances in treatment options available for these patients. This work developed and validated an updated prognostic model to predict OS in patients receiving first-line chemotherapy. Methods Data from a phase III trial of 1,050 patients with mCRPC were used (Cancer and Leukemia Group B CALGB-90401 [Alliance]). The data were randomly split into training and testing sets. A separate phase III trial served as an independent validation set. Adaptive least absolute shrinkage and selection operator selected eight factors prognostic for OS. A predictive score was computed from the regression coefficients and used to classify patients into low- and high-risk groups. The model was assessed for its predictive accuracy using the time-dependent area under the curve (tAUC). Results The model included Eastern Cooperative Oncology Group performance status, disease site, lactate dehydrogenase, opioid analgesic use, albumin, hemoglobin, prostate-specific antigen, and alkaline phosphatase. Median OS values in the high- and low-risk groups, respectively, in the testing set were 17 and 30 months (hazard ratio [HR], 2.2; P < .001); in the validation set they were 14 and 26 months (HR, 2.9; P < .001). The tAUCs were 0.73 (95% CI, 0.70 to 0.73) and 0.76 (95% CI, 0.72 to 0.76) in the testing and validation sets, respectively. Conclusion An updated prognostic model for OS in patients with mCRPC receiving first-line chemotherapy was developed and validated on an external set. This model can be used to predict OS, as well as to better select patients to participate in trials on the basis of their prognosis. PMID:24449231

Local-Level Prognostics Health Management Systems Framework for Passive AdvSMR Components. Interim Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Roy, Surajit; Hirt, Evelyn H.

2014-09-12

This report describes research results to date in support of the integration and demonstration of diagnostics technologies for prototypical AdvSMR passive components (to establish condition indices for monitoring) with model-based prognostics methods. The focus of the PHM methodology and algorithm development in this study is at the localized scale. Multiple localized measurements of material condition (using advanced nondestructive measurement methods), along with available measurements of the stressor environment, enhance the performance of localized diagnostics and prognostics of passive AdvSMR components and systems.

Prognostic Prediction Model for Second Allogeneic Stem-Cell Transplantation in Patients With Relapsed Acute Myeloid Leukemia: Single-Center Report.

PubMed

Park, Sung-Soo; Kim, Hee-Je; Min, Kyoung Il; Min, Gi June; Jeon, Young-Woo; Yoon, Jae-Ho; Yahng, Seung-Ah; Shin, Seung-Hwan; Lee, Sung-Eun; Cho, Byung-Sik; Eom, Ki-Seong; Kim, Yoo-Jin; Lee, Seok; Min, Chang-Ki; Cho, Seok-Goo; Kim, Dong-Wook; Lee, Jong Wook; Min, Woo-Sung

2018-04-01

To identify factors affecting survival outcomes and to develop a prognostic model for second allogeneic stem-cell transplantation (allo-SCT2) for relapsed acute myeloid leukemia (AML) after the first autologous or allogeneic stem-cell transplantation. Seventy-eight consecutive adult AML patients who received allo-SCT2 were analyzed in this retrospective study. The 4-year overall survival (OS) rate was 28.7%. In multivariate analysis, poor cytogenetic risk at diagnosis, circulating blast ≥ 20% at relapse, duration from first transplantation to relapse < 9 months, and failure to achieve morphologic complete remission after allo-SCT2 were factors associated with poor OS. A prognostic model was developed with the following score system: intermediate and poor cytogenetic risk at diagnosis (0.5 and 1 point), peripheral blast ≥ 20% at relapse (1 point), duration from the first transplantation to relapse < 9 months (1 point), and failure to achieve morphologic complete remission after allo-SCT2 (1 point). The model identified 2 subgroups according to the 4-year OS rate: 51.3% in the low-risk group (score < 2) and 2.8% in the high-risk group (score ≥ 2) (P < .001). This prognostic model might be useful to make an appropriate decision for allo-SCT2 in relapsed AML after the first autologous or allogeneic stem-cell transplantation. Copyright © 2018 Elsevier Inc. All rights reserved.

An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2014-01-01

This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.

Development Of A Multivariate Prognostic Model For Pain And Activity Limitation In People With Low Back Disorders Receiving Physiotherapy.

PubMed

Ford, Jon J; Richards BPhysio, Matt C; Surkitt BPhysio, Luke D; Chan BPhysio, Alexander Yp; Slater, Sarah L; Taylor, Nicholas F; Hahne, Andrew J

2018-05-28

To identify predictors for back pain, leg pain and activity limitation in patients with early persistent low back disorders. Prospective inception cohort study; Setting: primary care private physiotherapy clinics in Melbourne, Australia. 300 adults aged 18-65 years with low back and/or referred leg pain of ≥6-weeks and ≤6-months duration. Not applicable. Numerical rating scales for back pain and leg pain as well as the Oswestry Disability Scale. Prognostic factors included sociodemographics, treatment related factors, subjective/physical examination, subgrouping factors and standardized questionnaires. Univariate analysis followed by generalized estimating equations were used to develop a multivariate prognostic model for back pain, leg pain and activity limitation. Fifty-eight prognostic factors progressed to the multivariate stage where 15 showed significant (p<0.05) associations with at least one of the three outcomes. There were five indicators of positive outcome (two types of low back disorder subgroups, paresthesia below waist, walking as an easing factor and low transversus abdominis tone) and 10 indicators of negative outcome (both parents born overseas, deep leg symptoms, longer sick leave duration, high multifidus tone, clinically determined inflammation, higher back and leg pain severity, lower lifting capacity, lower work capacity and higher pain drawing percentage coverage). The preliminary model identifying predictors of low back disorders explained up to 37% of the variance in outcome. This study evaluated a comprehensive range of prognostic factors reflective of both the biomedical and psychosocial domains of low back disorders. The preliminary multivariate model requires further validation before being considered for clinical use. Copyright © 2018. Published by Elsevier Inc.

Personalising the decision for prolonged dual antiplatelet therapy: development, validation and potential impact of prognostic models for cardiovascular events and bleeding in myocardial infarction survivors

PubMed Central

Pasea, Laura; Chung, Sheng-Chia; Pujades-Rodriguez, Mar; Moayyeri, Alireza; Denaxas, Spiros; Fox, Keith A.A.; Wallentin, Lars; Pocock, Stuart J.; Timmis, Adam; Banerjee, Amitava; Patel, Riyaz; Hemingway, Harry

2017-01-01

Aims The aim of this study is to develop models to aid the decision to prolong dual antiplatelet therapy (DAPT) that requires balancing an individual patient’s potential benefits and harms. Methods and results Using population-based electronic health records (EHRs) (CALIBER, England, 2000–10), of patients evaluated 1 year after acute myocardial infarction (MI), we developed (n = 12 694 patients) and validated (n = 5613) prognostic models for cardiovascular (cardiovascular death, MI or stroke) events and three different bleeding endpoints. We applied trial effect estimates to determine potential benefits and harms of DAPT and the net clinical benefit of individuals. Prognostic models for cardiovascular events (c-index: 0.75 (95% CI: 0.74, 0.77)) and bleeding (c index 0.72 (95% CI: 0.67, 0.77)) were well calibrated: 3-year risk of cardiovascular events was 16.5% overall (5.2% in the lowest- and 46.7% in the highest-risk individuals), while for major bleeding, it was 1.7% (0.3% in the lowest- and 5.4% in the highest-risk patients). For every 10 000 patients treated per year, we estimated 249 (95% CI: 228, 269) cardiovascular events prevented and 134 (95% CI: 87, 181) major bleeding events caused in the highest-risk patients, and 28 (95% CI: 19, 37) cardiovascular events prevented and 9 (95% CI: 0, 20) major bleeding events caused in the lowest-risk patients. There was a net clinical benefit of prolonged DAPT in 63–99% patients depending on how benefits and harms were weighted. Conclusion Prognostic models for cardiovascular events and bleeding using population-based EHRs may help to personalise decisions for prolonged DAPT 1-year following acute MI. PMID:28329300

Personalising the decision for prolonged dual antiplatelet therapy: development, validation and potential impact of prognostic models for cardiovascular events and bleeding in myocardial infarction survivors.

PubMed

Pasea, Laura; Chung, Sheng-Chia; Pujades-Rodriguez, Mar; Moayyeri, Alireza; Denaxas, Spiros; Fox, Keith A A; Wallentin, Lars; Pocock, Stuart J; Timmis, Adam; Banerjee, Amitava; Patel, Riyaz; Hemingway, Harry

2017-04-07

The aim of this study is to develop models to aid the decision to prolong dual antiplatelet therapy (DAPT) that requires balancing an individual patient's potential benefits and harms. Using population-based electronic health records (EHRs) (CALIBER, England, 2000-10), of patients evaluated 1 year after acute myocardial infarction (MI), we developed (n = 12 694 patients) and validated (n = 5613) prognostic models for cardiovascular (cardiovascular death, MI or stroke) events and three different bleeding endpoints. We applied trial effect estimates to determine potential benefits and harms of DAPT and the net clinical benefit of individuals. Prognostic models for cardiovascular events (c-index: 0.75 (95% CI: 0.74, 0.77)) and bleeding (c index 0.72 (95% CI: 0.67, 0.77)) were well calibrated: 3-year risk of cardiovascular events was 16.5% overall (5.2% in the lowest- and 46.7% in the highest-risk individuals), while for major bleeding, it was 1.7% (0.3% in the lowest- and 5.4% in the highest-risk patients). For every 10 000 patients treated per year, we estimated 249 (95% CI: 228, 269) cardiovascular events prevented and 134 (95% CI: 87, 181) major bleeding events caused in the highest-risk patients, and 28 (95% CI: 19, 37) cardiovascular events prevented and 9 (95% CI: 0, 20) major bleeding events caused in the lowest-risk patients. There was a net clinical benefit of prolonged DAPT in 63-99% patients depending on how benefits and harms were weighted. Prognostic models for cardiovascular events and bleeding using population-based EHRs may help to personalise decisions for prolonged DAPT 1-year following acute MI. © The Author 2017. Published on behalf of the European Society of Cardiology

Updating and prospective validation of a prognostic model for high sickness absence.

PubMed

Roelen, C A M; Heymans, M W; Twisk, J W R; van Rhenen, W; Pallesen, S; Bjorvatn, B; Moen, B E; Magerøy, N

2015-01-01

To further develop and validate a Dutch prognostic model for high sickness absence (SA). Three-wave longitudinal cohort study of 2,059 Norwegian nurses. The Dutch prognostic model was used to predict high SA among Norwegian nurses at wave 2. Subsequently, the model was updated by adding person-related (age, gender, marital status, children at home, and coping strategies), health-related (BMI, physical activity, smoking, and caffeine and alcohol intake), and work-related (job satisfaction, job demands, decision latitude, social support at work, and both work-to-family and family-to-work spillover) variables. The updated model was then prospectively validated for predictions at wave 3. 1,557 (77 %) nurses had complete data at wave 2 and 1,342 (65 %) at wave 3. The risk of high SA was under-estimated by the Dutch model, but discrimination between high-risk and low-risk nurses was fair after re-calibration to the Norwegian data. Gender, marital status, BMI, physical activity, smoking, alcohol intake, job satisfaction, job demands, decision latitude, support at the workplace, and work-to-family spillover were identified as potential predictors of high SA. However, these predictors did not improve the model's discriminative ability, which remained fair at wave 3. The prognostic model correctly identifies 73 % of Norwegian nurses at risk of high SA, although additional predictors are needed before the model can be used to screen working populations for risk of high SA.

PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

PubMed

Wishart, Gordon C; Azzato, Elizabeth M; Greenberg, David C; Rashbass, Jem; Kearins, Olive; Lawrence, Gill; Caldas, Carlos; Pharoah, Paul D P

2010-01-01

The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.

A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy.

PubMed

Jochems, Arthur; El-Naqa, Issam; Kessler, Marc; Mayo, Charles S; Jolly, Shruti; Matuszak, Martha; Faivre-Finn, Corinne; Price, Gareth; Holloway, Lois; Vinod, Shalini; Field, Matthew; Barakat, Mohamed Samir; Thwaites, David; de Ruysscher, Dirk; Dekker, Andre; Lambin, Philippe

2018-02-01

Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy. Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (N = 1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (N = 698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4 months of receiving the first radiotherapy fraction was used as the outcome. Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts. Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.

A Physics-Based Modeling Framework for Prognostic Studies

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2014-01-01

Prognostics and Health Management (PHM) methodologies have emerged as one of the key enablers for achieving efficient system level maintenance as part of a busy operations schedule, and lowering overall life cycle costs. PHM is also emerging as a high-priority issue in critical applications, where the focus is on conducting fundamental research in the field of integrated systems health management. The term diagnostics relates to the ability to detect and isolate faults or failures in a system. Prognostics on the other hand is the process of predicting health condition and remaining useful life based on current state, previous conditions and future operating conditions. PHM methods combine sensing, data collection, interpretation of environmental, operational, and performance related parameters to indicate systems health under its actual application conditions. The development of prognostics methodologies for the electronics field has become more important as more electrical systems are being used to replace traditional systems in several applications in the aeronautics, maritime, and automotive fields. The development of prognostics methods for electronics presents several challenges due to the great variety of components used in a system, a continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Similarly with electric unmanned aerial vehicles, electrichybrid cars, and commercial passenger aircraft, we are witnessing a drastic increase in the usage of batteries to power vehicles. However, for battery-powered vehicles to operate at maximum efficiency and reliability, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. We develop an electrochemistry-based model of Li-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles.

Development and Implementation of a Hardware In-the-Loop Test Bed for Unmanned Aerial Vehicle Control Algorithms

NASA Technical Reports Server (NTRS)

Nyangweso, Emmanuel; Bole, Brian

2014-01-01

Successful prediction and management of battery life using prognostic algorithms through ground and flight tests is important for performance evaluation of electrical systems. This paper details the design of test beds suitable for replicating loading profiles that would be encountered in deployed electrical systems. The test bed data will be used to develop and validate prognostic algorithms for predicting battery discharge time and battery failure time. Online battery prognostic algorithms will enable health management strategies. The platform used for algorithm demonstration is the EDGE 540T electric unmanned aerial vehicle (UAV). The fully designed test beds developed and detailed in this paper can be used to conduct battery life tests by controlling current and recording voltage and temperature to develop a model that makes a prediction of end-of-charge and end-of-life of the system based on rapid state of health (SOH) assessment.

Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

2014-01-01

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

NASA IVHM Technology Experiment for X-vehicles (NITEX)

NASA Technical Reports Server (NTRS)

Sandra, Hayden; Bajwa, Anupa

2001-01-01

The purpose of the NASA IVHM Technology Experiment for X-vehicles (NITEX) is to advance the development of selected IVHM technologies in a flight environment and to demonstrate the potential for reusable launch vehicle ground processing savings. The technologies to be developed and demonstrated include system-level and detailed diagnostics for real-time fault detection and isolation, prognostics for fault prediction, automated maintenance planning based on diagnostic and prognostic results, and a microelectronics hardware platform. Complete flight The Evolution of Flexible Insulation as IVHM consists of advanced sensors, distributed data acquisition, data processing that includes model-based diagnostics, prognostics and vehicle autonomy for control or suggested action, and advanced data storage. Complete ground IVHM consists of evolved control room architectures, advanced applications including automated maintenance planning and automated ground support equipment. This experiment will advance the development of a subset of complete IVHM.

Quantitative fibronectin to help decision-making in women with symptoms of preterm labour (QUIDS) part 1: Individual participant data meta-analysis and health economic analysis

PubMed Central

Wotherspoon, Lisa M; Boyd, Kathleen A; Morris, Rachel K; Jackson, Lesley; Chandiramani, Manju; David, Anna L; Khalil, Asma; Shennan, Andrew; Hodgetts Morton, Victoria; Lavender, Tina; Khan, Khalid; Harper-Clarke, Susan; Mol, Ben W; Riley, Richard D; Norrie, John; Norman, Jane E

2018-01-01

Introduction The aim of the QUIDS study is to develop a decision support tool for the management of women with symptoms and signs of preterm labour, based on a validated prognostic model using quantitative fetal fibronectin (qfFN) concentration, in combination with clinical risk factors. Methods and analysis The study will evaluate the Rapid fFN 10Q System (Hologic, Marlborough, Massachusetts) which quantifies fFN in a vaginal swab. In part 1 of the study, we will develop and internally validate a prognostic model using an individual participant data (IPD) meta-analysis of existing studies containing women with symptoms of preterm labour alongside fFN measurements and pregnancy outcome. An economic analysis will be undertaken to assess potential cost-effectiveness of the qfFN prognostic model. The primary endpoint will be the ability of the prognostic model to rule out spontaneous preterm birth within 7 days. Six eligible studies were identified by systematic review of the literature and five agreed to provide their IPD (n=5 studies, 1783 women and 139 events of preterm delivery within 7 days of testing). Ethics and dissemination The study is funded by the National Institute of Healthcare Research Health Technology Assessment (HTA 14/32/01). It has been approved by the West of Scotland Research Ethics Committee (16/WS/0068). PROSPERO registration number CRD42015027590. Version Protocol version 2, date 1 November 2016. PMID:29627817

Prognostic indices for early mortality in ischaemic stroke - meta-analysis.

PubMed

Mattishent, K; Kwok, C S; Mahtani, A; Pelpola, K; Myint, P K; Loke, Y K

2016-01-01

Several models have been developed to predict mortality in ischaemic stroke. We aimed to evaluate systematically the performance of published stroke prognostic scores. We searched MEDLINE and EMBASE in February 2014 for prognostic models (published between 2003 and 2014) used in predicting early mortality (<6 months) after ischaemic stroke. We evaluated discriminant ability of the tools through meta-analysis of the area under the curve receiver operating characteristic curve (AUROC) or c-statistic. We evaluated the following components of study validity: collection of prognostic variables, neuroimaging, treatment pathways and missing data. We identified 18 articles (involving 163 240 patients) reporting on the performance of prognostic models for mortality in ischaemic stroke, with 15 articles providing AUC for meta-analysis. Most studies were either retrospective, or post hoc analyses of prospectively collected data; all but three reported validation data. The iSCORE had the largest number of validation cohorts (five) within our systematic review and showed good performance in four different countries, pooled AUC 0.84 (95% CI 0.82-0.87). We identified other potentially useful prognostic tools that have yet to be as extensively validated as iSCORE - these include SOAR (2 studies, pooled AUC 0.79, 95% CI 0.78-0.80), GWTG (2 studies, pooled AUC 0.72, 95% CI 0.72-0.72) and PLAN (1 study, pooled AUC 0.85, 95% CI 0.84-0.87). Our meta-analysis has identified and summarized the performance of several prognostic scores with modest to good predictive accuracy for early mortality in ischaemic stroke, with the iSCORE having the broadest evidence base. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Accelerated Aging Experiments for Capacitor Health Monitoring and Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose Ramon; Biswas, Gautam; Goebel, Kai

2012-01-01

This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions. Electrolytic capacitors have higher failure rates than other components in electronic systems like power drives, power converters etc. Our current work focuses on developing first-principles-based degradation models for electrolytic capacitors under varying electrical and thermal stress conditions. Prognostics and health management for electronic systems aims to predict the onset of faults, study causes for system degradation, and accurately compute remaining useful life. Accelerated life test methods are often used in prognostics research as a way to model multiple causes and assess the effects of the degradation process through time. It also allows for the identification and study of different failure mechanisms and their relationships under different operating conditions. Experiments are designed for aging of the capacitors such that the degradation pattern induced by the aging can be monitored and analyzed. Experimental setups and data collection methods are presented to demonstrate this approach.

Factors Affecting Physicians' Intentions to Communicate Personalized Prognostic Information to Cancer Patients at the End of Life: An Experimental Vignette Study.

PubMed

Han, Paul K J; Dieckmann, Nathan F; Holt, Christina; Gutheil, Caitlin; Peters, Ellen

2016-08-01

To explore the effects of personalized prognostic information on physicians' intentions to communicate prognosis to cancer patients at the end of life, and to identify factors that moderate these effects. A factorial experiment was conducted in which 93 family medicine physicians were presented with a hypothetical vignette depicting an end-stage gastric cancer patient seeking prognostic information. Physicians' intentions to communicate prognosis were assessed before and after provision of personalized prognostic information, while emotional distress of the patient and ambiguity (imprecision) of the prognostic estimate were varied between subjects. General linear models were used to test the effects of personalized prognostic information, patient distress, and ambiguity on prognostic communication intentions, and potential moderating effects of 1) perceived patient distress, 2) perceived credibility of prognostic models, 3) physician numeracy (objective and subjective), and 4) physician aversion to risk and ambiguity. Provision of personalized prognostic information increased prognostic communication intentions (P < 0.001, η(2) = 0.38), although experimentally manipulated patient distress and prognostic ambiguity had no effects. Greater change in communication intentions was positively associated with higher perceived credibility of prognostic models (P = 0.007, η(2) = 0.10), higher objective numeracy (P = 0.01, η(2) = 0.09), female sex (P = 0.01, η(2) = 0.08), and lower perceived patient distress (P = 0.02, η(2) = 0.07). Intentions to communicate available personalized prognostic information were positively associated with higher perceived credibility of prognostic models (P = 0.02, η(2) = 0.09), higher subjective numeracy (P = 0.02, η(2) = 0.08), and lower ambiguity aversion (P = 0.06, η(2) = 0.04). Provision of personalized prognostic information increases physicians' prognostic communication intentions to a hypothetical end-stage cancer patient, and situational and physician characteristics moderate this effect. More research is needed to confirm these findings and elucidate the determinants of prognostic communication at the end of life. © The Author(s) 2016.

Cost-Utility of a Prognostic Test Guiding Adjuvant Chemotherapy Decisions in Early-Stage Non-Small Cell Lung Cancer.

PubMed

Stenehjem, David D; Bellows, Brandon K; Yager, Kraig M; Jones, Joshua; Kaldate, Rajesh; Siebert, Uwe; Brixner, Diana I

2016-02-01

A prognostic test was developed to guide adjuvant chemotherapy (ACT) decisions in early-stage non-small cell lung cancer (NSCLC) adenocarcinomas. The objective of this study was to compare the cost-utility of the prognostic test to the current standard of care (SoC) in patients with early-stage NSCLC. Lifetime costs (2014 U.S. dollars) and effectiveness (quality-adjusted life-years [QALYs]) of ACT treatment decisions were examined using a Markov microsimulation model from a U.S. third-party payer perspective. Cancer stage distribution and probability of receiving ACT with the SoC were based on data from an academic cancer center. The probability of receiving ACT with the prognostic test was estimated from a physician survey. Risk classification was based on the 5-year predicted NSCLC-related mortality. Treatment benefit with ACT was based on the prognostic score. Discounting at a 3% annual rate was applied to costs and QALYs. Deterministic one-way and probabilistic sensitivity analyses examined parameter uncertainty. Lifetime costs and effectiveness were $137,403 and 5.45 QALYs with the prognostic test and $127,359 and 5.17 QALYs with the SoC. The resulting incremental cost-effectiveness ratio for the prognostic test versus the SoC was $35,867/QALY gained. One-way sensitivity analyses indicated the model was most sensitive to the utility of patients without recurrence after ACT and the ACT treatment benefit. Probabilistic sensitivity analysis indicated the prognostic test was cost-effective in 65.5% of simulations at a willingness to pay of $50,000/QALY. The study suggests using a prognostic test to guide ACT decisions in early-stage NSCLC is potentially cost-effective compared with using the SoC based on globally accepted willingness-to-pay thresholds. Providing prognostic information to decision makers may help some patients with high-risk early stage non-small cell lung cancer receive appropriate adjuvant chemotherapy while avoiding the associated toxicities and costs in patients with low-risk disease. This study used an economic model to assess the effectiveness and costs associated with using a prognostic test to guide adjuvant chemotherapy decisions compared with the current standard of care in patients with non-small cell lung cancer. When compared with current standard care, the prognostic test was potentially cost effective at commonly accepted thresholds in the U.S. This study can be used to help inform decision makers who are considering using prognostic tests. ©AlphaMed Press.

A novel protein-based prognostic signature improves risk stratification to guide clinical management in early lung adenocarcinoma patients.

PubMed

Martínez-Terroba, Elena; Behrens, Carmen; de Miguel, Fernando J; Agorreta, Jackeline; Monsó, Eduard; Millares, Laura; Sainz, Cristina; Mesa-Guzman, Miguel; Pérez-Gracia, Jose Luis; Lozano, María Dolores; Zulueta, Javier J; Pio, Ruben; Wistuba, Ignacio I; Montuenga, Luis M; Pajares, María J

2018-05-13

Each of the pathological stages (I-IIIa) in which surgically resected non-small cell lung cancer patients are classified conceals hidden biological heterogeneity, manifested in heterogeneous outcomes within each stage. Thus, the finding of robust and precise molecular classifiers to assess individual patient risk is an unmet medical need. Here we identified and validated the clinical utility of a new prognostic signature based on three proteins (BRCA1, QKI and SLC2A1) to stratify early lung adenocarcinoma patients according to their risk of recurrence or death. Patients were staged following the new International Association for the Study of Lung Cancer (IASLC) staging criteria (8 th edition, 2018). A test cohort (n=239) was used to assess the value of this new prognostic index (PI) based on the three proteins. The prognostic signature was developed by Cox regression following stringent statistical criteria (TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). The model resulted in a highly significant predictor of five-year outcome for disease-free survival (P<0.001) and overall survival (P<0.001). The prognostic ability of the model was externally validated in an independent multi-institutional cohort of patients (n=114, P=0.021). We also demonstrated that this molecular classifier adds relevant information to the gold standard TNM-based pathological staging with a highly significant improvement of likelihood ratio. We subsequently developed a combined prognostic index (CPI) including both the molecular and the pathological data which improved the risk stratification in both cohorts (P≤0.001). Moreover, the signature may help to select stage I-IIA patients who might benefit from adjuvant chemotherapy. In summary, this protein-based signature accurately identifies those patients with high risk of recurrence and death, and adds further prognostic information to the TNM-based clinical staging, even applying the new IASLC 8 th edition staging criteria. More importantly, it may be a valuable tool for selecting patients for adjuvant therapy. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Generic Software Architecture for Prognostics (GSAP) User Guide

NASA Technical Reports Server (NTRS)

Teubert, Christopher Allen; Daigle, Matthew John; Watkins, Jason; Sankararaman, Shankar; Goebel, Kai

2016-01-01

The Generic Software Architecture for Prognostics (GSAP) is a framework for applying prognostics. It makes applying prognostics easier by implementing many of the common elements across prognostic applications. The standard interface enables reuse of prognostic algorithms and models across systems using the GSAP framework.

Nomograms Predicting Progression-Free Survival, Overall Survival, and Pelvic Recurrence in Locally Advanced Cervical Cancer Developed From an Analysis of Identifiable Prognostic Factors in Patients From NRG Oncology/Gynecologic Oncology Group Randomized Trials of Chemoradiotherapy

PubMed Central

Rose, Peter G.; Java, James; Whitney, Charles W.; Stehman, Frederick B.; Lanciano, Rachelle; Thomas, Gillian M.; DiSilvestro, Paul A.

2015-01-01

Purpose To evaluate the prognostic factors in locally advanced cervical cancer limited to the pelvis and develop nomograms for 2-year progression-free survival (PFS), 5-year overall survival (OS), and pelvic recurrence. Patients and Methods We retrospectively reviewed 2,042 patients with locally advanced cervical carcinoma enrolled onto Gynecologic Oncology Group clinical trials of concurrent cisplatin-based chemotherapy and radiotherapy. Nomograms for 2-year PFS, five-year OS, and pelvic recurrence were created as visualizations of Cox proportional hazards regression models. The models were validated by bootstrap-corrected, relatively unbiased estimates of discrimination and calibration. Results Multivariable analysis identified prognostic factors including histology, race/ethnicity, performance status, tumor size, International Federation of Gynecology and Obstetrics stage, tumor grade, pelvic node status, and treatment with concurrent cisplatin-based chemotherapy. PFS, OS, and pelvic recurrence nomograms had bootstrap-corrected concordance indices of 0.62, 0.64, and 0.73, respectively, and were well calibrated. Conclusion Prognostic factors were used to develop nomograms for 2-year PFS, 5-year OS, and pelvic recurrence for locally advanced cervical cancer clinically limited to the pelvis treated with concurrent cisplatin-based chemotherapy and radiotherapy. These nomograms can be used to better estimate individual and collective outcomes. PMID:25732170

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

PubMed

Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

2017-05-22

PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

[Problem of bioterrorism under modern conditions].

PubMed

Vorob'ev, A A; Boev, B V; Bondarenko, V M; Gintsburg, A L

2002-01-01

It is practically impossible to discuss the problem of bioterrorism (BT) and to develop effective programs of decreasing the losses and expenses suffered by the society from the BT acts without evaluation of the threat and prognosis of consequences based on research and empiric data. Stained international situation following the act of terrorism (attack on the USA) on September 11, 2001, makes the scenarios of the bacterial weapon use (the causative agents of plague, smallpox, anthrax, etc.) by international terrorists most probable. In this connection studies on the analysis and prognostication of the consequences of BT, including mathematical and computer modelling, are necessary. The authors present the results of initiative studies on the analysis and prognostication of the consequences of the hypothetical act of BT with the use of the smallpox causative agent in a city with the population of about 1,000,000 inhabitants. The analytical prognostic studies on the operative analysis and prognostication of the consequences of the BT act with the use of the smallpox causative agent has demonstrated that the mathematical (computer) model of the epidemic outbreak of smallpox is an effective instrument of calculation studies. Prognostic evaluations of the consequences of the act of BT under the conditions of different reaction of public health services (time of detection, interventions) have been obtained with the use of modelling. In addition, the computer model is necessary for training health specialists to react adequately to the acts of BT with the use of different kinds of bacteriological weapons.

Prognostic Modeling in Pathologic N1 Breast Cancer Without Elective Nodal Irradiation After Current Standard Systemic Management.

PubMed

Yu, Jeong Il; Park, Won; Choi, Doo Ho; Huh, Seung Jae; Nam, Seok Jin; Kim, Seok Won; Lee, Jeong Eon; Kil, Won Ho; Im, Young-Hyuck; Ahn, Jin Seok; Park, Yeon Hee; Cho, Eun Yoon

2015-08-01

This study was conducted to establish a prognostic model in patients with pathologic N1 (pN1) breast cancer who have not undergone elective nodal irradiation (ENI) under the current standard management and to suggest possible indications for ENI. We performed a retrospective study with patients with pN1 breast cancer who received the standard local and preferred adjuvant chemotherapy treatment without neoadjuvant chemotherapy and ENI from January 2005 to June 2011. Most of the indicated patients received endocrine and trastuzumab therapy. In 735 enrolled patients, the median follow-up period was 58.4 months (range, 7.2-111.3 months). Overall, 55 recurrences (7.4%) developed, and locoregional recurrence was present in 27 patients (3.8%). Recurrence-free survival was significantly related to lymphovascular invasion (P = .04, hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.03-2.88), histologic grade (P = .03, HR, 2.57; 95% CI, 1.05-6.26), and nonluminal A subtype (P = .02, HR, 3.04; 95% CI, 1.23-7.49) in multivariate analysis. The prognostic model was established by these 3 prognostic factors. Recurrence-free survival was less than 90% at 5 years in cases with 2 or 3 factors. The prognostic model has stratified risk groups in pN1 breast cancer without ENI. Patients with 2 or more factors should be considered for ENI. Copyright © 2015 Elsevier Inc. All rights reserved.

Moderate Traumatic Brain Injury: Clinical Characteristics and a Prognostic Model of 12-Month Outcome.

PubMed

Einarsen, Cathrine Elisabeth; van der Naalt, Joukje; Jacobs, Bram; Follestad, Turid; Moen, Kent Gøran; Vik, Anne; Håberg, Asta Kristine; Skandsen, Toril

2018-06-01

Patients with moderate traumatic brain injury (TBI) often are studied together with patients with severe TBI, even though the expected outcome of the former is better. Therefore, we aimed to describe patient characteristics and 12-month outcomes, and to develop a prognostic model based on admission data, specifically for patients with moderate TBI. Patients with Glasgow Coma Scale scores of 9-13 and age ≥16 years were prospectively enrolled in 2 level I trauma centers in Europe. Glasgow Outcome Scale Extended (GOSE) score was assessed at 12 months. A prognostic model predicting moderate disability or worse (GOSE score ≤6), as opposed to a good recovery, was fitted by penalized regression. Model performance was evaluated by area under the curve of the receiver operating characteristics curves. Of the 395 enrolled patients, 81% had intracranial lesions on head computed tomography, and 71% were admitted to an intensive care unit. At 12 months, 44% were moderately disabled or worse (GOSE score ≤6), whereas 8% were severely disabled and 6% died (GOSE score ≤4). Older age, lower Glasgow Coma Scale score, no day-of-injury alcohol intoxication, presence of a subdural hematoma, occurrence of hypoxia and/or hypotension, and preinjury disability were significant predictors of GOSE score ≤6 (area under the curve = 0.80). Patients with moderate TBI exhibit characteristics of significant brain injury. Although few patients died or experienced severe disability, 44% did not experience good recovery, indicating that follow-up is needed. The model is a first step in development of prognostic models for moderate TBI that are valid across centers. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

PubMed Central

Guinney, Justin; Wang, Tao; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J Christopher; Neto, Elias Chaibub; Khan, Suleiman A; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M; Shen, Liji; Abdallah, Kald; Norman, Thea; Friend, Stephen; Stolovitzky, Gustavo; Soule, Howard; Sweeney, Christopher J; Ryan, Charles J; Scher, Howard I; Sartor, Oliver; Xie, Yang; Aittokallio, Tero; Zhou, Fang Liz; Costello, James C

2016-01-01

Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. Funding Sanofi US Services, Project Data Sphere. PMID:27864015

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

PubMed

Guinney, Justin; Wang, Tao; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J Christopher; Neto, Elias Chaibub; Khan, Suleiman A; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M; Shen, Liji; Abdallah, Kald; Norman, Thea; Friend, Stephen; Stolovitzky, Gustavo; Soule, Howard; Sweeney, Christopher J; Ryan, Charles J; Scher, Howard I; Sartor, Oliver; Xie, Yang; Aittokallio, Tero; Zhou, Fang Liz; Costello, James C

2017-01-01

Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. Sanofi US Services, Project Data Sphere. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prognostic nomogram and score to predict overall survival in locally advanced untreated pancreatic cancer (PROLAP)

PubMed Central

Vernerey, Dewi; Huguet, Florence; Vienot, Angélique; Goldstein, David; Paget-Bailly, Sophie; Van Laethem, Jean-Luc; Glimelius, Bengt; Artru, Pascal; Moore, Malcolm J; André, Thierry; Mineur, Laurent; Chibaudel, Benoist; Benetkiewicz, Magdalena; Louvet, Christophe; Hammel, Pascal; Bonnetain, Franck

2016-01-01

Background: The management of locally advanced pancreatic cancer (LAPC) patients remains controversial. Better discrimination for overall survival (OS) at diagnosis is needed. We address this issue by developing and validating a prognostic nomogram and a score for OS in LAPC (PROLAP). Methods: Analyses were derived from 442 LAPC patients enrolled in the LAP07 trial. The prognostic ability of 30 baseline parameters was evaluated using univariate and multivariate Cox regression analyses. Performance assessment and internal validation of the final model were done with Harrell's C-index, calibration plot and bootstrap sample procedures. On the basis of the final model, a prognostic nomogram and a score were developed, and externally validated in 106 consecutive LAPC patients treated in Besançon Hospital, France. Results: Age, pain, tumour size, albumin and CA 19-9 were independent prognostic factors for OS. The final model had good calibration, acceptable discrimination (C-index=0.60) and robust internal validity. The PROLAP score has the potential to delineate three different prognosis groups with median OS of 15.4, 11.7 and 8.5 months (log-rank P<0.0001). The score ability to discriminate OS was externally confirmed in 63 (59%) patients with complete clinical data derived from a data set of 106 consecutive LAPC patients; median OS of 18.3, 14.1 and 7.6 months for the three groups (log-rank P<0.0001). Conclusions: The PROLAP nomogram and score can accurately predict OS before initiation of induction chemotherapy in LAPC-untreated patients. They may help to optimise clinical trials design and might offer the opportunity to define risk-adapted strategies for LAPC management in the future. PMID:27404456

Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

NASA Technical Reports Server (NTRS)

Cornhill, Dennis; Bharadwaj, Raj; Mylaraswamy, Dinkar

2013-01-01

This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer.

PubMed

Gagnon, B; Abrahamowicz, M; Xiao, Y; Beauchamp, M-E; MacDonald, N; Kasymjanova, G; Kreisman, H; Small, D

2010-03-30

C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption). We tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC). In the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03-1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months. Our analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies.

PubMed

Zhao, Lue Ping; Bolouri, Hamid

2016-04-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015). Copyright © 2016 Elsevier Inc. All rights reserved.

Object-Oriented Regression for Building Predictive Models with High Dimensional Omics Data from Translational Studies

PubMed Central

Zhao, Lue Ping; Bolouri, Hamid

2016-01-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and to make the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient’s similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient’s HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (p=0.015). PMID:26972839

Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks

NASA Astrophysics Data System (ADS)

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine; Hissel, Daniel

2016-08-01

Proton Exchange Membrane Fuel Cell (PEMFC) is considered the most versatile among available fuel cell technologies, which qualify for diverse applications. However, the large-scale industrial deployment of PEMFCs is limited due to their short life span and high exploitation costs. Therefore, ensuring fuel cell service for a long duration is of vital importance, which has led to Prognostics and Health Management of fuel cells. More precisely, prognostics of PEMFC is major area of focus nowadays, which aims at identifying degradation of PEMFC stack at early stages and estimating its Remaining Useful Life (RUL) for life cycle management. This paper presents a data-driven approach for prognostics of PEMFC stack using an ensemble of constraint based Summation Wavelet- Extreme Learning Machine (SW-ELM) models. This development aim at improving the robustness and applicability of prognostics of PEMFC for an online application, with limited learning data. The proposed approach is applied to real data from two different PEMFC stacks and compared with ensembles of well known connectionist algorithms. The results comparison on long-term prognostics of both PEMFC stacks validates our proposition.

Global Analysis, Interpretation and Modelling: An Earth Systems Modelling Program

NASA Technical Reports Server (NTRS)

Moore, Berrien, III; Sahagian, Dork

1997-01-01

The Goal of the GAIM is: To advance the study of the coupled dynamics of the Earth system using as tools both data and models; to develop a strategy for the rapid development, evaluation, and application of comprehensive prognostic models of the Global Biogeochemical Subsystem which could eventually be linked with models of the Physical-Climate Subsystem; to propose, promote, and facilitate experiments with existing models or by linking subcomponent models, especially those associated with IGBP Core Projects and with WCRP efforts. Such experiments would be focused upon resolving interface issues and questions associated with developing an understanding of the prognostic behavior of key processes; to clarify key scientific issues facing the development of Global Biogeochemical Models and the coupling of these models to General Circulation Models; to assist the Intergovernmental Panel on Climate Change (IPCC) process by conducting timely studies that focus upon elucidating important unresolved scientific issues associated with the changing biogeochemical cycles of the planet and upon the role of the biosphere in the physical-climate subsystem, particularly its role in the global hydrological cycle; and to advise the SC-IGBP on progress in developing comprehensive Global Biogeochemical Models and to maintain scientific liaison with the WCRP Steering Group on Global Climate Modelling.

NEW BIOGENIC VOC EMISSIONS MODEL

EPA Science Inventory

We intend to develop new prognostic models for the prediction of biogenic volatile organic compound emissions from forest ecosystems in the face of possible future changes in the climate and the concentration of carbon dioxide in the atmosphere. These models will b...

Quantitative fibronectin to help decision-making in women with symptoms of preterm labour (QUIDS) part 1: Individual participant data meta-analysis and health economic analysis.

PubMed

Stock, Sarah J; Wotherspoon, Lisa M; Boyd, Kathleen A; Morris, Rachel K; Dorling, Jon; Jackson, Lesley; Chandiramani, Manju; David, Anna L; Khalil, Asma; Shennan, Andrew; Hodgetts Morton, Victoria; Lavender, Tina; Khan, Khalid; Harper-Clarke, Susan; Mol, Ben W; Riley, Richard D; Norrie, John; Norman, Jane E

2018-04-07

The aim of the QUIDS study is to develop a decision support tool for the management of women with symptoms and signs of preterm labour, based on a validated prognostic model using quantitative fetal fibronectin (qfFN) concentration, in combination with clinical risk factors. The study will evaluate the Rapid fFN 10Q System (Hologic, Marlborough, Massachusetts) which quantifies fFN in a vaginal swab. In part 1 of the study, we will develop and internally validate a prognostic model using an individual participant data (IPD) meta-analysis of existing studies containing women with symptoms of preterm labour alongside fFN measurements and pregnancy outcome. An economic analysis will be undertaken to assess potential cost-effectiveness of the qfFN prognostic model. The primary endpoint will be the ability of the prognostic model to rule out spontaneous preterm birth within 7 days. Six eligible studies were identified by systematic review of the literature and five agreed to provide their IPD (n=5 studies, 1783 women and 139 events of preterm delivery within 7 days of testing). The study is funded by the National Institute of Healthcare Research Health Technology Assessment (HTA 14/32/01). It has been approved by the West of Scotland Research Ethics Committee (16/WS/0068). CRD42015027590. Protocol version 2, date 1 November 2016. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

DGKI methylation status modulates the prognostic value of MGMT in glioblastoma patients treated with combined radio-chemotherapy with temozolomide.

PubMed

Etcheverry, Amandine; Aubry, Marc; Idbaih, Ahmed; Vauleon, Elodie; Marie, Yannick; Menei, Philippe; Boniface, Rachel; Figarella-Branger, Dominique; Karayan-Tapon, Lucie; Quillien, Veronique; Sanson, Marc; de Tayrac, Marie; Delattre, Jean-Yves; Mosser, Jean

2014-01-01

Consistently reported prognostic factors for glioblastoma (GBM) are age, extent of surgery, performance status, IDH1 mutational status, and MGMT promoter methylation status. We aimed to integrate biological and clinical prognostic factors into a nomogram intended to predict the survival time of an individual GBM patient treated with a standard regimen. In a previous study we showed that the methylation status of the DGKI promoter identified patients with MGMT-methylated tumors that responded poorly to the standard regimen. We further evaluated the potential prognostic value of DGKI methylation status. 399 patients with newly diagnosed GBM and treated with a standard regimen were retrospectively included in this study. Survival modelling was performed on two patient populations: intention-to-treat population of all included patients (population 1) and MGMT-methylated patients (population 2). Cox proportional hazard models were fitted to identify the main prognostic factors. A nomogram was developed for population 1. The prognostic value of DGKI promoter methylation status was evaluated on population 1 and population 2. The nomogram-based stratification of the cohort identified two risk groups (high/low) with significantly different median survival. We validated the prognostic value of DGKI methylation status for MGMT-methylated patients. We also demonstrated that the DGKI methylation status identified 22% of poorly responding patients in the low-risk group defined by the nomogram. Our results improve the conventional MGMT stratification of GBM patients receiving standard treatment. These results could help the interpretation of published or ongoing clinical trial outcomes and refine patient recruitment in the future.

Statistical models of global Langmuir mixing

NASA Astrophysics Data System (ADS)

Li, Qing; Fox-Kemper, Baylor; Breivik, Øyvind; Webb, Adrean

2017-05-01

The effects of Langmuir mixing on the surface ocean mixing may be parameterized by applying an enhancement factor which depends on wave, wind, and ocean state to the turbulent velocity scale in the K-Profile Parameterization. Diagnosing the appropriate enhancement factor online in global climate simulations is readily achieved by coupling with a prognostic wave model, but with significant computational and code development expenses. In this paper, two alternatives that do not require a prognostic wave model, (i) a monthly mean enhancement factor climatology, and (ii) an approximation to the enhancement factor based on the empirical wave spectra, are explored and tested in a global climate model. Both appear to reproduce the Langmuir mixing effects as estimated using a prognostic wave model, with nearly identical and substantial improvements in the simulated mixed layer depth and intermediate water ventilation over control simulations, but significantly less computational cost. Simpler approaches, such as ignoring Langmuir mixing altogether or setting a globally constant Langmuir number, are found to be deficient. Thus, the consequences of Stokes depth and misaligned wind and waves are important.

ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts.

PubMed

Laajala, Teemu D; Murtojärvi, Mika; Virkki, Arho; Aittokallio, Tero

2018-06-15

Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data. We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients. The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage. Supplementary data are available at Bioinformatics online.

Accelerated Aging in Electrolytic Capacitors for Prognostics

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Kulkarni, Chetan; Saha, Sankalita; Biswas, Gautam; Goebel, Kai Frank

2012-01-01

The focus of this work is the analysis of different degradation phenomena based on thermal overstress and electrical overstress accelerated aging systems and the use of accelerated aging techniques for prognostics algorithm development. Results on thermal overstress and electrical overstress experiments are presented. In addition, preliminary results toward the development of physics-based degradation models are presented focusing on the electrolyte evaporation failure mechanism. An empirical degradation model based on percentage capacitance loss under electrical overstress is presented and used in: (i) a Bayesian-based implementation of model-based prognostics using a discrete Kalman filter for health state estimation, and (ii) a dynamic system representation of the degradation model for forecasting and remaining useful life (RUL) estimation. A leave-one-out validation methodology is used to assess the validity of the methodology under the small sample size constrain. The results observed on the RUL estimation are consistent through the validation tests comparing relative accuracy and prediction error. It has been observed that the inaccuracy of the model to represent the change in degradation behavior observed at the end of the test data is consistent throughout the validation tests, indicating the need of a more detailed degradation model or the use of an algorithm that could estimate model parameters on-line. Based on the observed degradation process under different stress intensity with rest periods, the need for more sophisticated degradation models is further supported. The current degradation model does not represent the capacitance recovery over rest periods following an accelerated aging stress period.

Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer

PubMed Central

Gagnon, B; Abrahamowicz, M; Xiao, Y; Beauchamp, M-E; MacDonald, N; Kasymjanova, G; Kreisman, H; Small, D

2010-01-01

Background: C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption). Methods: We tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC). Results: In the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03–1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months. Conclusion: Our analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP. PMID:20234363

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-06-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Match and mismatch - comparing plant phenological metrics from ground-observations and from a prognostic model

NASA Astrophysics Data System (ADS)

Rutishauser, This; Stöckli, Reto; Jeanneret, François; Peñuelas, Josep

2010-05-01

Changes in the seasonality of life cycles of plants as recorded in phenological observations have been widely analysed at the species level with data available for many decades back in time. At the same time, seasonality changes in satellite-based observations and prognostic phenology models comprise information at the pixel-size or landscape scale. Change analysis of satellite-based records is restricted due to relatively short satellite records that further include gaps while model-based analyses are biased due to current model deficiencies., At 30 selected sites across Europe, we analysed three different sources of plant seasonality during the 1971-2000 period. Data consisted of (1) species-specific development stages of flowering and leave-out with different species observed at each site. (2) We used a synthetic phenological metric that integrates the common interannual phenological signal across all species at one site. (3) We estimated daily Leaf Area Index with a prognostic phenology model. The prior uncertainties of the model's empirical parameter space are constrained by assimilating the Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS). We extracted the day of year when the 25%, 50% and 75% thresholds were passed each spring. The question arises how the three phenological signals compare and correlate across climate zones in Europe. Is there a match between single species observations, species-based ground-observed metrics and the landscape-scale prognostic model? Are there single key-species across Europe that best represent a landscape scale measure from the prognostic model? Can one source substitute another and serve as proxy-data? What can we learn from potential mismatches? Focusing on changes in spring this contribution presents first results of an ongoing comparison study from a number of European test sites that will be extended to the pan-European phenological database Cost725 and PEP725.

A Clinical Decision Support System for Breast Cancer Patients

NASA Astrophysics Data System (ADS)

Fernandes, Ana S.; Alves, Pedro; Jarman, Ian H.; Etchells, Terence A.; Fonseca, José M.; Lisboa, Paulo J. G.

This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prognostic index that can be analysed together in order to obtain a more accurate prognostic assessment of the patient. Missing data is incorporated in the mentioned models, a common issue in medical data that was overcome using multiple imputation techniques. Risk group assignments are also provided through a methodology based on regression trees, where Boolean rules can be obtained expressed with patient characteristics.

Expression of tNASP in Prostate Cancer: Opportunities for a Novel Diagnostic and Prognostic Biomarker Development

DTIC Science & Technology

2013-12-01

Cancer : Opportunities for a Novel Diagnostic and Prognostic Biomarker Development PRINCIPAL INVESTIGATOR: Oleg M. Alekseev CONTRACTING...Expression of tNASP in Prostate Cancer : Opportunities for a Novel Diagnostic and Prognostic Biomarker Development 5a. CONTRACT NUMBER...Expression of tNASP in Prostate Cancer : Opportunities for a Novel Diagnostic and Prognostic Biomarker Development 5b. GRANT NUMBER W81XWH-12-1-0361

A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

PubMed

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

2015-12-01

Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

[Prognostic model of the space station contamination stage].

PubMed

Zlotovol'skiĭ, V M; Smolenskaia, G S

1998-01-01

Forty two non-metallic materials, 8 human metabolites and a process liquid (ethylene glycol) were selected for development of a prognostic model of space station contamination by harmful trace admixtures (HTAs). Removal technologies made allowance for absorption by atmospheric condensate (AC) and filter adsorption. Calculations took in 18 HTAs representative of 8 classes of compounds. Simulation modeling allowed to determine HTA migration rates and percent ratio (1), calculate concentrations of contaminants in the atmosphere and atmospheric condensate (2), and to assess filter efficiency by comparison of loads on the filter and a refrigeration/drying set (3). Comparison of empirical and measured data permitted conclusions about adequacy of the model and its potentiality for predicting ramifications of nominal and contingency situations.

Prognostic factors for risk stratification of adult cancer patients with chemotherapy-induced febrile neutropenia: a systematic review and meta-analysis.

PubMed

Lee, Yee Mei; Lang, Dora; Lockwood, Craig

Increasing numbers of studies identify new prognostic factors for categorising chemotherapy-induced febrile neutropenia adult cancer patients into high- or low-risk groups for adverse outcomes. These groupings are used to tailor therapy according to level of risk. However many emerging factors with prognostic significance remain controversial, being based on single studies only. A systematic review was conducted to determine the strength of association of all identified factors associated with the outcomes of chemotherapy-induced febrile neutropenia patients. The participants included were adults of 15 years old and above, with a cancer diagnosis and who underwent cancer treatment.The review focused on clinical factors and their association with the outcomes of cancer patients with chemotherapy-induced febrile neutropenia at presentation of fever.All quantitative studies published in English which investigated clinical factors for risk stratification of adult cancer patients with chemotherapy-induced febrile neutropenia were considered.The primary outcome of interest was to identify the clinical factors for risk stratification of adult cancer patients with chemotherapy-induced febrile neutropenia. Electronic databases searched from their respective inception date up to December 2011 include MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Science-Direct, Scopus and Mednar. The quality of the included studies was subjected to assessment by two independent reviewers. The standardised critical appraisal tool from the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) was used to assess the following criteria: representativeness of study population; clearly defined prognostic factors and outcomes; whether potential confounders were addressed and appropriate statistical analysis was undertaken for the study design. Data extraction was performed using a modified version of the standardised extraction tool from the JBI-MAStARI. Prognostic factors and the accompanying odds ratio reported for the significance of these factors that were identified by multivariate regression, were extracted from each included study. Studies results were pooled in statistical meta-analysis using Review Manager 5.1. Where statistical pooling was not possible, the findings were presented in narrative form. Seven studies (four prospective cohort and three retrospective cohort) investigating 22 factors in total were included. Fixed effects meta-analysis showed: hypotension [OR=1.66, 95%CI, 1.14-2.41, p=0.008] and thrombocytopenia [OR=3.92, 95%CI, 2.19-7.01, p<0.00001)] were associated with high-risk of adverse outcomes for febrile neutropenia. Other factors that were statistically significant from single studies included: age of patients, clinical presentation at fever onset, presence or absence of co-morbidities, infections, duration and severity of neutropenia state. Five prognostic factors failed to demonstrate an association between the variables and the outcomes measured and they include: presence of pneumonia, total febrile days, median days to fever, recovery from neutropenia and presence of moderate clinical symptoms in association with Gram-negative bacteraemia. Despite the overall limitations identified in the included studies, this review has provided a synthesis of the best available evidence for the prognostic factors used in risk stratification of febrile neutropenia patients. However, the dynamic aspects of prognostic model development, validation and utilisation have not been addressed adequately thus far. Given the findings of this review, it is timely to address these issues and improve the utilisation of prognostic models in the management of febrile neutropenia patients. The identified factors are similar to the factors in current prognostic models. However, additional factors that were reported to be statistically significant in this review (thrombocytopenia, presence of central venous catheter, and duration and severity of neutropenia) have not previously been included in prognostic models. This review has found these factors may improve the performance of current models by adding or replacing some of the factors. The role of risk stratification of chemotherapy-induced febrile neutropenia patients continues to evolve as the practice of risk-based therapy has been demonstrated to be beneficial to patients, clinicians and health care organisations. Further research to identify new factors /markers is needed to develop a new model which is reliable and accurate for these patients, regardless of cancer types. A robust and well-validated prognostic model is the key to enhance patient safety in the risk-based management of cancer patients with chemotherapy-induced febrile neutropenia.

Joint System Prognostics For Increased Efficiency And Risk Mitigation In Advanced Nuclear Reactor Instrumentation and Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donald D. Dudenhoeffer; Tuan Q. Tran; Ronald L. Boring

2006-08-01

The science of prognostics is analogous to a doctor who, based on a set of symptoms and patient tests, assesses a probable cause, the risk to the patient, and a course of action for recovery. While traditional prognostics research has focused on the aspect of hydraulic and mechanical systems and associated failures, this project will take a joint view in focusing not only on the digital I&C aspect of reliability and risk, but also on the risks associated with the human element. Model development will not only include an approximation of the control system physical degradation but also on humanmore » performance degradation. Thus the goal of the prognostic system is to evaluate control room operation; to identify and potentially take action when performance degradation reduces plant efficiency, reliability or safety.« less

Study protocol: quantitative fibronectin to help decision-making in women with symptoms of preterm labour (QUIDS) part 2, UK Prospective Cohort Study

PubMed Central

Wotherspoon, Lisa M; Boyd, Kathleen Anne; Morris, Rachel K; Jackson, Lesley; Chandiramani, Manju; David, Anna L; Khalil, Asma; Shennan, Andrew; Hodgetts Morton, Victoria; Lavender, Tina; Khan, Khalid; Harper-Clarke, Susan; Mol, Ben; Riley, Richard D; Norrie, John; Norman, Jane

2018-01-01

Introduction The aim of the QUIDS study is to develop a decision support tool for the management of women with symptoms and signs of preterm labour, based on a validated prognostic model using quantitative fetal fibronectin (fFN) concentration, in combination with clinical risk factors. Methods and analysis The study will evaluate the Rapid fFN 10Q System (Hologic, Marlborough, Massachusetts, USA) which quantifies fFN in a vaginal swab. In QUIDS part 2, we will perform a prospective cohort study in at least eight UK consultant-led maternity units, in women with symptoms of preterm labour at 22+0 to 34+6 weeks gestation to externally validate a prognostic model developed in QUIDS part 1. The effects of quantitative fFN on anxiety will be assessed, and acceptability of the test and prognostic model will be evaluated in a subgroup of women and clinicians (n=30). The sample size is 1600 women (with estimated 96–192 events of preterm delivery within 7 days of testing). Clinicians will be informed of the qualitative fFN result (positive/negative) but be blinded to quantitative fFN result. Research midwives will collect outcome data from the maternal and neonatal clinical records. The final validated prognostic model will be presented as a mobile or web-based application. Ethics and dissemination The study is funded by the National Institute of Healthcare Research Health Technology Assessment (HTA 14/32/01). It has been approved by the West of Scotland Research Ethics Committee (16/WS/0068). Version Protocol V.2, Date 1 November 2016. Trial registration number ISRCTN41598423 and CPMS: 31277. PMID:29674373

Model-based prognostics for batteries which estimates useful life and uses a probability density function

NASA Technical Reports Server (NTRS)

Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor)

2012-01-01

This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.

A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis.

PubMed

Ji, Jun; Ling, Xuefeng B; Zhao, Yingzhen; Hu, Zhongkai; Zheng, Xiaolin; Xu, Zhening; Wen, Qiaojun; Kastenberg, Zachary J; Li, Ping; Abdullah, Fizan; Brandt, Mary L; Ehrenkranz, Richard A; Harris, Mary Catherine; Lee, Timothy C; Simpson, B Joyce; Bowers, Corinna; Moss, R Lawrence; Sylvester, Karl G

2014-01-01

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

A Prognostic Model for One-year Mortality in Patients Requiring Prolonged Mechanical Ventilation

PubMed Central

Carson, Shannon S.; Garrett, Joanne; Hanson, Laura C.; Lanier, Joyce; Govert, Joe; Brake, Mary C.; Landucci, Dante L.; Cox, Christopher E.; Carey, Timothy S.

2009-01-01

Objective A measure that identifies patients who are at high risk of mortality after prolonged ventilation will help physicians communicate prognosis to patients or surrogate decision-makers. Our objective was to develop and validate a prognostic model for 1-year mortality in patients ventilated for 21 days or more. Design Prospective cohort study. Setting University-based tertiary care hospital Patients 300 consecutive medical, surgical, and trauma patients requiring mechanical ventilation for at least 21 days were prospectively enrolled. Measurements and Main Results Predictive variables were measured on day 21 of ventilation for the first 200 patients and entered into logistic regression models with 1-year and 3-month mortality as outcomes. Final models were validated using data from 100 subsequent patients. One-year mortality was 51% in the development set and 58% in the validation set. Independent predictors of mortality included requirement for vasopressors, hemodialysis, platelet count ≤150 ×109/L, and age ≥50. Areas under the ROC curve for the development model and validation model were 0.82 (se 0.03) and 0.82 (se 0.05) respectively. The model had sensitivity of 0.42 (se 0.12) and specificity of 0.99 (se 0.01) for identifying patients who had ≥90% risk of death at 1 year. Observed mortality was highly consistent with both 3- and 12-month predicted mortality. These four predictive variables can be used in a simple prognostic score that clearly identifies low risk patients (no risk factors, 15% mortality) and high risk patients (3 or 4 risk factors, 97% mortality). Conclusions Simple clinical variables measured on day 21 of mechanical ventilation can identify patients at highest and lowest risk of death from prolonged ventilation. PMID:18552692

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real-world study.

PubMed

Hang, Junjie; Wu, Lixia; Zhu, Lina; Sun, Zhiqiang; Wang, Ge; Pan, Jingjing; Zheng, Suhua; Xu, Kequn; Du, Jiadi; Jiang, Hua

2018-06-01

It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C-index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19-9 (CA19-9) log-value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C-index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C-index: .699) and testing cohort (n = 133, C-index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3-risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Advanced Ground Systems Maintenance Prognostics Project

NASA Technical Reports Server (NTRS)

Perotti, Jose M.

2015-01-01

The project implements prognostics capabilities to predict when a component system or subsystem will no longer meet desired functional or performance criteria, called the end of life. The capability also provides an assessment of the remaining useful life of a hardware component. The project enables the delivery of system health advisories to ground system operators. This project will use modeling techniques and algorithms to assess components' health andpredict remaining life for such components. The prognostics capability being developed will beused:during the design phase and during pre/post operations to conduct planning and analysis ofsystem design, maintenance & logistics plans, and system/mission operations plansduring real-time operations to monitor changes to components' health and assess their impacton operations.This capability will be interfaced to Ground Operations' command and control system as a part ofthe AGSM project to help assure system availability and mission success. The initial modelingeffort for this capability will be developed for Liquid Oxygen ground loading applications.

A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes.

PubMed

Mushkudiani, Nino A; Hukkelhoven, Chantal W P M; Hernández, Adrián V; Murray, Gordon D; Choi, Sung C; Maas, Andrew I R; Steyerberg, Ewout W

2008-04-01

To describe the modeling techniques used for early prediction of outcome in traumatic brain injury (TBI) and to identify aspects for potential improvements. We reviewed key methodological aspects of studies published between 1970 and 2005 that proposed a prognostic model for the Glasgow Outcome Scale of TBI based on admission data. We included 31 papers. Twenty-four were single-center studies, and 22 reported on fewer than 500 patients. The median of the number of initially considered predictors was eight, and on average five of these were selected for the prognostic model, generally including age, Glasgow Coma Score (or only motor score), and pupillary reactivity. The most common statistical technique was logistic regression with stepwise selection of predictors. Model performance was often quantified by accuracy rate rather than by more appropriate measures such as the area under the receiver-operating characteristic curve. Model validity was addressed in 15 studies, but mostly used a simple split-sample approach, and external validation was performed in only four studies. Although most models agree on the three most important predictors, many were developed on small sample sizes within single centers and hence lack generalizability. Modeling strategies have to be improved, and include external validation.

Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Louie, Alexander V., E-mail: Dr.alexlouie@gmail.com; Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario; Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, Massachusetts

Purpose: A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). Methods and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogrammore » for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n=193) and SABR (n=543) datasets. Results: RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 2}=0.91). Conclusions: The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.« less

Fault Diagnostics and Prognostics for Large Segmented SRMs

NASA Technical Reports Server (NTRS)

Luchinsky, Dmitry; Osipov, Viatcheslav V.; Smelyanskiy, Vadim N.; Timucin, Dogan A.; Uckun, Serdar; Hayashida, Ben; Watson, Michael; McMillin, Joshua; Shook, David; Johnson, Mont;

Predictive and Prognostic Models: Implications for Healthcare Decision-Making in a Modern Recession

PubMed Central

Vogenberg, F. Randy

2009-01-01

Various modeling tools have been developed to address the lack of standardized processes that incorporate the perspectives of all healthcare stakeholders. Such models can assist in the decision-making process aimed at achieving specific clinical outcomes, as well as guide the allocation of healthcare resources and reduce costs. The current efforts in Congress to change the way healthcare is financed, reimbursed, and delivered have rendered the incorporation of modeling tools into the clinical decision-making all the more important. Prognostic and predictive models are particularly relevant to healthcare, particularly in the clinical decision-making, with implications for payers, patients, and providers. The use of these models is likely to increase, as providers and patients seek to improve their clinical decision process to achieve better outcomes, while reducing overall healthcare costs. PMID:25126292

Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method

NASA Astrophysics Data System (ADS)

Wang, Dong; Tse, Peter W.

2015-05-01

Slurry pumps are commonly used in oil-sand mining for pumping mixtures of abrasive liquids and solids. These operations cause constant wear of slurry pump impellers, which results in the breakdown of the slurry pumps. This paper develops a prognostic method for estimating remaining useful life of slurry pump impellers. First, a moving-average wear degradation index is proposed to assess the performance degradation of the slurry pump impeller. Secondly, the state space model of the proposed health index is constructed. A general sequential Monte Carlo method is employed to derive the parameters of the state space model. The remaining useful life of the slurry pump impeller is estimated by extrapolating the established state space model to a specified alert threshold. Data collected from an industrial oil sand pump were used to validate the developed method. The results show that the accuracy of the developed method improves as more data become available.

Assessment of published models and prognostic variables in epithelial ovarian cancer at Mayo Clinic

PubMed Central

Hendrickson, Andrea Wahner; Hawthorne, Kieran M.; Goode, Ellen L.; Kalli, Kimberly R.; Goergen, Krista M.; Bakkum-Gamez, Jamie N.; Cliby, William A.; Keeney, Gary L.; Visscher, Dan W.; Tarabishy, Yaman; Oberg, Ann L.; Hartmann, Lynn C.; Maurer, Matthew J.

2015-01-01

Objectives Epithelial ovarian cancer (EOC) is an aggressive disease in which first line therapy consists of a surgical staging/debulking procedure and platinum based chemotherapy. There is significant interest in clinically applicable, easy to use prognostic tools to estimate risk of recurrence and overall survival. In this study we used a large prospectively collected cohort of women with EOC to validate currently published models and assess prognostic variables. Methods Women with invasive ovarian, peritoneal, or fallopian tube cancer diagnosed between 2000-2011 and prospectively enrolled into the Mayo Clinic Ovarian Cancer registry were identified. Demographics and known prognostic markers as well as epidemiologic exposure variables were abstracted from the medical record and collected via questionnaire. Six previously published models of overall and recurrence-free survival were assessed for external validity. In addition, predictors of outcome were assessed in our dataset. Results Previously published models validated with a range of c-statistics (0.587-0.827), though application of models containing variables not part of routine practice were somewhat limited by missing data; utilization of all applicable models and comparison of results is suggested. Examination of prognostic variables identified only the presence of ascites and ASA score to be independent predictors of prognosis in our dataset, albeit with marginal gain in prognostic information, after accounting for stage and debulking. Conclusions Existing prognostic models for newly diagnosed EOC showed acceptable calibration in our cohort for clinical application. However, modeling of prospective variables in our dataset reiterates that stage and debulking remain the most important predictors of prognosis in this setting. PMID:25620544

Comparison of Prognostic and Diagnostic Approaches to Modeling Evapotranspiration in the Nile River Basin

NASA Astrophysics Data System (ADS)

Yilmaz, M.; Anderson, M. C.; Zaitchik, B. F.; Crow, W. T.; Hain, C.; Ozdogan, M.; Chun, J. A.

2012-12-01

Actual evapotranspiration (ET) can be estimated using both prognostic and diagnostic modeling approaches, providing independent yet complementary information for hydrologic applications. Both approaches have advantages and disadvantages. When provided with temporally continuous atmospheric forcing data, prognostic models offer continuous sub-daily ET information together with the full set of water and energy balance fluxes and states (i.e. soil moisture, runoff, sensible and latent heat). On the other hand, the diagnostic modeling approach provides ET estimates over regions where reliable information about available soil water is not known (e.g., due to irrigation practices or shallow ground water levels not included in the prognostic model structure, unknown soil texture or plant rooting depth, etc). Prognostic model-based ET estimates are of great interest whenever consistent and complete water budget information is required or when there is a need to project ET for climate or land use change scenarios. Diagnostic models establish a stronger link to remote sensing observations, can be applied in regions with limited or questionable atmospheric forcing data, and provide valuable observation-derived information about the current land-surface state. Analysis of independently obtained ET estimates is particularly important in data poor regions. Such comparisons can help to reduce the uncertainty in the modeled ET estimates and to exclude outliers based on physical considerations. The Nile river basin is home to tens of millions of people whose daily life depends on water extracted from the river Nile. Yet the complete basin scale water balance of the Nile has been studied only a few times, and the temporal and the spatial distribution of hydrological fluxes (particularly ET) are still a subject of active research. This is due in part to a scarcity of ground-based station data for validation. In such regions, comparison between prognostic and diagnostic model output may be a valuable model evaluation tool. Motivated by the complementary information that exists in prognostic and diagnostic energy balance modeling, as well as the need for evaluation of water consumption estimates over the Nile basin, the purpose of this study is to 1) better describe the conceptual differences between prognostic and diagnostic modeling, 2) present the potential for diagnostic models to capture important hydrologic features that are not explicitly represented in prognostic model, 3) explore the differences in these two approaches over the Nile Basin, where ground data are sparse and transnational data sharing is unreliable. More specifically, we will compare output from the Noah prognostic model and the Atmosphere-Land Exchange Inverse (ALEXI) diagnostic model generated over ground truth data-poor Nile basin. Preliminary results indicate spatially, temporally, and magnitude wise consistent flux estimates for ALEXI and NOAH over irrigated Delta region, while there are differences over river-fed wetlands.

On prognostic models, artificial intelligence and censored observations.

PubMed

Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

2001-03-01

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer

PubMed Central

Margolin, Adam A.; Bilal, Erhan; Huang, Erich; Norman, Thea C.; Ottestad, Lars; Mecham, Brigham H.; Sauerwine, Ben; Kellen, Michael R.; Mangravite, Lara M.; Furia, Matthew D.; Vollan, Hans Kristian Moen; Rueda, Oscar M.; Guinney, Justin; Deflaux, Nicole A.; Hoff, Bruce; Schildwachter, Xavier; Russnes, Hege G.; Park, Daehoon; Vang, Veronica O.; Pirtle, Tyler; Youseff, Lamia; Citro, Craig; Curtis, Christina; Kristensen, Vessela N.; Hellerstein, Joseph; Friend, Stephen H.; Stolovitzky, Gustavo; Aparicio, Samuel; Caldas, Carlos; Børresen-Dale, Anne-Lise

2013-01-01

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models. PMID:23596205

A Nonlinear Multigrid Solver for an Atmospheric General Circulation Model Based on Semi-Implicit Semi-Lagrangian Advection of Potential Vorticity

NASA Technical Reports Server (NTRS)

McCormick, S.; Ruge, John W.

1998-01-01

This work represents a part of a project to develop an atmospheric general circulation model based on the semi-Lagrangian advection of potential vorticity (PC) with divergence as the companion prognostic variable.

Prediction of Outcome after Moderate and Severe Traumatic Brain Injury: External Validation of the IMPACT and CRASH Prognostic Models

PubMed Central

Roozenbeek, Bob; Lingsma, Hester F.; Lecky, Fiona E.; Lu, Juan; Weir, James; Butcher, Isabella; McHugh, Gillian S.; Murray, Gordon D.; Perel, Pablo; Maas, Andrew I.R.; Steyerberg, Ewout W.

2012-01-01

Objective The International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models predict outcome after traumatic brain injury (TBI) but have not been compared in large datasets. The objective of this is study is to validate externally and compare the IMPACT and CRASH prognostic models for prediction of outcome after moderate or severe TBI. Design External validation study. Patients We considered 5 new datasets with a total of 9036 patients, comprising three randomized trials and two observational series, containing prospectively collected individual TBI patient data. Measurements Outcomes were mortality and unfavourable outcome, based on the Glasgow Outcome Score (GOS) at six months after injury. To assess performance, we studied the discrimination of the models (by AUCs), and calibration (by comparison of the mean observed to predicted outcomes and calibration slopes). Main Results The highest discrimination was found in the TARN trauma registry (AUCs between 0.83 and 0.87), and the lowest discrimination in the Pharmos trial (AUCs between 0.65 and 0.71). Although differences in predictor effects between development and validation populations were found (calibration slopes varying between 0.58 and 1.53), the differences in discrimination were largely explained by differences in case-mix in the validation studies. Calibration was good, the fraction of observed outcomes generally agreed well with the mean predicted outcome. No meaningful differences were noted in performance between the IMPACT and CRASH models. More complex models discriminated slightly better than simpler variants. Conclusions Since both the IMPACT and the CRASH prognostic models show good generalizability to more recent data, they are valid instruments to quantify prognosis in TBI. PMID:22511138

Prognostics and Health Management of Wind Turbines: Current Status and Future Opportunities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheng, Shuangwen

Prognostics and health management is not a new concept. It has been used in relatively mature industries, such as aviation and electronics, to help improve operation and maintenance (O&M) practices. In the wind industry, prognostics and health management is relatively new. The level for both wind industry applications and research and development (R&D) has increased in recent years because of its potential for reducing O&M cost of wind power, especially for turbines installed offshore. The majority of wind industry application efforts has been focused on diagnosis based on various sensing and feature extraction techniques. For R&D, activities are being conductedmore » in almost all areas of a typical prognostics and health management framework (i.e., sensing, data collection, feature extraction, diagnosis, prognosis, and maintenance scheduling). This presentation provides an overview of the current status of wind turbine prognostics and health management that focuses on drivetrain condition monitoring through vibration, oil debris, and oil condition analysis techniques. It also discusses turbine component health diagnosis through data mining and modeling based on supervisory control and data acquisition system data. Finally, it provides a brief survey of R&D activities for wind turbine prognostics and health management, along with future opportunities.« less

A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program.

PubMed

Schildcrout, Jonathan S; Shi, Yaping; Danciu, Ioana; Bowton, Erica; Field, Julie R; Pulley, Jill M; Basford, Melissa A; Gregg, William; Cowan, James D; Harrell, Frank E; Roden, Dan M; Peterson, Josh F; Denny, Joshua C

2016-04-01

We describe the development, implementation, and evaluation of a model to pre-emptively select patients for genotyping based on medication exposure risk. Using deidentified electronic health records, we derived a prognostic model for the prescription of statins, warfarin, or clopidogrel. The model was implemented into a clinical decision support (CDS) tool to recommend pre-emptive genotyping for patients exceeding a prescription risk threshold. We evaluated the rule on an independent validation cohort and on an implementation cohort, representing the population in which the CDS tool was deployed. The model exhibited moderate discrimination with area under the receiver operator characteristic curves ranging from 0.68 to 0.75 at 1 and 2 years after index dates. Risk estimates tended to underestimate true risk. The cumulative incidences of medication prescriptions at 1 and 2 years were 0.35 and 0.48, respectively, among 1,673 patients flagged by the model. The cumulative incidences in the same number of randomly sampled subjects were 0.12 and 0.19, and in patients over 50 years with the highest body mass indices, they were 0.22 and 0.34. We demonstrate that prognostic algorithms can guide pre-emptive pharmacogenetic testing toward those likely to benefit from it. Copyright © 2016 Elsevier Inc. All rights reserved.

A new extranodal scoring system based on the prognostically relevant extranodal sites in diffuse large B-cell lymphoma, not otherwise specified treated with chemoimmunotherapy.

PubMed

Hwang, Hee Sang; Yoon, Dok Hyun; Suh, Cheolwon; Huh, Jooryung

2016-08-01

Extranodal involvement is a well-known prognostic factor in patients with diffuse large B-cell lymphomas (DLBCL). Nevertheless, the prognostic impact of the extranodal scoring system included in the conventional international prognostic index (IPI) has been questioned in an era where rituximab treatment has become widespread. We investigated the prognostic impacts of individual sites of extranodal involvement in 761 patients with DLBCL who received rituximab-based chemoimmunotherapy. Subsequently, we established a new extranodal scoring system based on extranodal sites, showing significant prognostic correlation, and compared this system with conventional scoring systems, such as the IPI and the National Comprehensive Cancer Network-IPI (NCCN-IPI). An internal validation procedure, using bootstrapped samples, was also performed for both univariate and multivariate models. Using multivariate analysis with a backward variable selection, we found nine extranodal sites (the liver, lung, spleen, central nervous system, bone marrow, kidney, skin, adrenal glands, and peritoneum) that remained significant for use in the final model. Our newly established extranodal scoring system, based on these sites, was better correlated with patient survival than standard scoring systems, such as the IPI and the NCCN-IPI. Internal validation by bootstrapping demonstrated an improvement in model performance of our modified extranodal scoring system. Our new extranodal scoring system, based on the prognostically relevant sites, may improve the performance of conventional prognostic models of DLBCL in the rituximab era and warrants further external validation using large study populations.

Nomograms Predicting Platinum Sensitivity, Progression-Free Survival, and Overall Survival Using Pretreatment Complete Blood Cell Counts in Epithelial Ovarian Cancer

PubMed Central

Paik, E Sun; Sohn, Insuk; Baek, Sun-Young; Shim, Minhee; Choi, Hyun Jin; Kim, Tae-Joong; Choi, Chel Hun; Lee, Jeong-Won; Kim, Byoung-Gie; Lee, Yoo-Young; Bae, Duk-Soo

2017-01-01

Purpose This study was conducted to evaluate the prognostic significance of pre-treatment complete blood cell count (CBC), including white blood cell (WBC) differential, in epithelial ovarian cancer (EOC) patients with primary debulking surgery (PDS) and to develop nomograms for platinum sensitivity, progression-free survival (PFS), and overall survival (OS). Materials and Methods We retrospectively reviewed the records of 757 patients with EOC whose primary treatment consisted of surgical debulking and chemotherapy at Samsung Medical Center from 2002 to 2012. We subsequently created nomograms for platinum sensitivity, 3-year PFS, and 5-year OS as prediction models for prognostic variables including age, stage, grade, cancer antigen 125 level, residual disease after PDS, and pre-treatment WBC differential counts. The models were then validated by 10-fold cross-validation (CV). Results In addition to stage and residual disease after PDS, which are known predictors, lymphocyte and monocyte count were found to be significant prognostic factors for platinum-sensitivity, platelet count for PFS, and neutrophil count for OS on multivariate analysis. The area under the curves of platinum sensitivity, 3-year PFS, and 5-year OS calculated by the 10-fold CV procedure were 0.7405, 0.8159, and 0.815, respectively. Conclusion Prognostic factors including pre-treatment CBC were used to develop nomograms for platinum sensitivity, 3-year PFS, and 5-year OS of patients with EOC. These nomograms can be used to better estimate individual outcomes. PMID:27669704

Nomograms Predicting Platinum Sensitivity, Progression-Free Survival, and Overall Survival Using Pretreatment Complete Blood Cell Counts in Epithelial Ovarian Cancer.

PubMed

Paik, E Sun; Sohn, Insuk; Baek, Sun-Young; Shim, Minhee; Choi, Hyun Jin; Kim, Tae-Joong; Choi, Chel Hun; Lee, Jeong-Won; Kim, Byoung-Gie; Lee, Yoo-Young; Bae, Duk-Soo

2017-07-01

This study was conducted to evaluate the prognostic significance of pre-treatment complete blood cell count (CBC), including white blood cell (WBC) differential, in epithelial ovarian cancer (EOC) patients with primary debulking surgery (PDS) and to develop nomograms for platinum sensitivity, progression-free survival (PFS), and overall survival (OS). We retrospectively reviewed the records of 757 patients with EOC whose primary treatment consisted of surgical debulking and chemotherapy at Samsung Medical Center from 2002 to 2012. We subsequently created nomograms for platinum sensitivity, 3-year PFS, and 5-year OS as prediction models for prognostic variables including age, stage, grade, cancer antigen 125 level, residual disease after PDS, and pre-treatment WBC differential counts. The models were then validated by 10-fold cross-validation (CV). In addition to stage and residual disease after PDS, which are known predictors, lymphocyte and monocyte count were found to be significant prognostic factors for platinum-sensitivity, platelet count for PFS, and neutrophil count for OS on multivariate analysis. The area under the curves of platinum sensitivity, 3-year PFS, and 5-year OS calculated by the 10-fold CV procedure were 0.7405, 0.8159, and 0.815, respectively. Prognostic factors including pre-treatment CBC were used to develop nomograms for platinum sensitivity, 3-year PFS, and 5-year OS of patients with EOC. These nomograms can be used to better estimate individual outcomes.

Validation and Development of a Modified Breast Graded Prognostic Assessment As a Tool for Survival in Patients With Breast Cancer and Brain Metastases.

PubMed

Subbiah, Ishwaria M; Lei, Xiudong; Weinberg, Jeffrey S; Sulman, Erik P; Chavez-MacGregor, Mariana; Tripathy, Debu; Gupta, Rohan; Varma, Ankur; Chouhan, Jay; Guevarra, Richard P; Valero, Vicente; Gilbert, Mark R; Gonzalez-Angulo, Ana M

2015-07-10

Several indices have been developed to predict overall survival (OS) in patients with breast cancer with brain metastases, including the breast graded prognostic assessment (breast-GPA), comprising age, tumor subtype, and Karnofsky performance score. However, number of brain metastases-a highly relevant clinical variable-is less often incorporated into the final model. We sought to validate the existing breast-GPA in an independent larger cohort and refine it integrating number of brain metastases. Data were retrospectively gathered from a prospectively maintained institutional database. Patients with newly diagnosed brain metastases from 1996 to 2013 were identified. After validating the breast-GPA, multivariable Cox regression and recursive partitioning analysis led to the development of the modified breast-GPA. The performances of the breast-GPA and modified breast-GPA were compared using the concordance index. In our cohort of 1,552 patients, the breast-GPA was validated as a prognostic tool for OS (P < .001). In multivariable analysis of the breast-GPA and number of brain metastases (> three v ≤ three), both were independent predictors of OS. We therefore developed the modified breast-GPA integrating a fourth clinical parameter. Recursive partitioning analysis reinforced the prognostic significance of these four factors. Concordance indices were 0.78 (95% CI, 0.77 to 0.80) and 0.84 (95% CI, 0.83 to 0.85) for the breast-GPA and modified breast-GPA, respectively (P < .001). The modified breast-GPA incorporates four simple clinical parameters of high prognostic significance. This index has an immediate role in the clinic as a formative part of the clinician's discussion of prognosis and direction of care and as a potential patient selection tool for clinical trials. © 2015 by American Society of Clinical Oncology.

Exploring Stage I non-small-cell lung cancer: development of a prognostic model predicting 5-year survival after surgical resection†.

PubMed

Guerrera, Francesco; Errico, Luca; Evangelista, Andrea; Filosso, Pier Luigi; Ruffini, Enrico; Lisi, Elena; Bora, Giulia; Asteggiano, Elena; Olivetti, Stefania; Lausi, Paolo; Ardissone, Francesco; Oliaro, Alberto

2015-06-01

Despite impressive results in diagnosis and treatment of non-small-cell lung cancer (NSCLC), more than 30% of patients with Stage I NSCLC die within 5 years after surgical treatment. Identification of prognostic factors to select patients with a poor prognosis and development of tailored treatment strategies are then advisable. The aim of our study was to design a model able to define prognosis in patients with Stage I NSCLC, submitted to surgery with curative intent. A retrospective analysis of two surgical registries was performed. Predictors of survival were investigated using the Cox model with shared frailty (accounting for the within-centre correlation). Candidate predictors were: age, gender, smoking habit, morbidity, previous malignancy, Eastern Cooperative Oncology Group performance status, clinical N stage, maximum standardized uptake value (SUV(max)), forced expiratory volume in 1 s, carbon monoxide lung diffusion capacity (DLCO), extent of surgical resection, systematic lymphadenectomy, vascular invasion, pathological T stage, histology and histological grading. The final model included predictors with P < 0.20, after a backward selection. Missing data in evaluated predictors were multiple-imputed and combined estimates were obtained from 10 imputed data sets. Analysis was performed on 848 consecutive patients. The median follow-up was 48 months. Two hundred and nine patients died (25%), with a 5-year overall survival (OS) rate of 74%. The final Cox model demonstrated that mortality was significantly associated with age, male sex, presence of cardiac comorbidities, DLCO (%), SUV(max), systematic nodal dissection, presence of microscopic vascular invasion, pTNM stage and histological grading. The final model showed a fair discrimination ability (C-statistic = 0.69): the calibration of the model indicated a good agreement between observed and predicted survival. We designed an effective prognostic model based on clinical, pathological and surgical covariates. Our preliminary results need to be refined and validated in a larger patient population, in order to provide an easy-to-use prognostic tool for Stage I NSCLC patients. © The Author 2014. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

A new prognostic model for chemotherapy-induced febrile neutropenia.

PubMed

Ahn, Shin; Lee, Yoon-Seon; Lee, Jae-Lyun; Lim, Kyung Soo; Yoon, Sung-Cheol

2016-02-01

The objective of this study was to develop and validate a new prognostic model for febrile neutropenia (FN). This study comprised 1001 episodes of FN: 718 for the derivation set and 283 for the validation set. Multivariate logistic regression analysis was performed with unfavorable outcome as the primary endpoint and bacteremia as the secondary endpoint. In the derivation set, risk factors for adverse outcomes comprised age ≥ 60 years (2 points), procalcitonin ≥ 0.5 ng/mL (5 points), ECOG performance score ≥ 2 (2 points), oral mucositis grade ≥ 3 (3 points), systolic blood pressure <90 mmHg (3 points), and respiratory rate ≥ 24 breaths/min (3 points). The model stratified patients into three severity classes, with adverse event rates of 6.0 % in class I (score ≤ 2), 27.3 % in class II (score 3-8), and 67.9 % in class III (score ≥ 9). Bacteremia was present in 1.1, 11.5, and 29.8 % of patients in class I, II, and III, respectively. The outcomes of the validation set were similar in each risk class. When the derivation and validation sets were integrated, unfavorable outcomes occurred in 5.9 % of the low-risk group classified by the new prognostic model and in 12.2 % classified by the Multinational Association for Supportive Care in Cancer (MASCC) risk index. With the new prognostic model, we can classify patients with FN into three classes of increasing adverse outcomes and bacteremia. Early discharge would be possible for class I patients, short-term observation could safely manage class II patients, and inpatient admission is warranted for class III patients.

Design and validation of a model to predict early mortality in haemodialysis patients.

PubMed

Mauri, Joan M; Clèries, Montse; Vela, Emili

2008-05-01

Mortality and morbidity rates are higher in patients receiving haemodialysis therapy than in the general population. Detection of risk factors related to early death in these patients could be of aid for clinical and administrative decision making. Objectives. The aims of this study were (1) to identify risk factors (comorbidity and variables specific to haemodialysis) associated with death in the first year following the start of haemodialysis and (2) to design and validate a prognostic model to quantify the probability of death for each patient. An analysis was carried out on all patients starting haemodialysis treatment in Catalonia during the period 1997-2003 (n = 5738). The data source was the Renal Registry of Catalonia, a mandatory population registry. Patients were randomly divided into two samples: 60% (n = 3455) of the total were used to develop the prognostic model and the remaining 40% (n = 2283) to validate the model. Logistic regression analysis was used to construct the model. One-year mortality in the total study population was 16.5%. The predictive model included the following variables: age, sex, primary renal disease, grade of functional autonomy, chronic obstructive pulmonary disease, malignant processes, chronic liver disease, cardiovascular disease, initial vascular access and malnutrition. The analyses showed adequate calibration for both the sample to develop the model and the validation sample (Hosmer-Lemeshow statistic 0.97 and P = 0.49, respectively) as well as adequate discrimination (ROC curve 0.78 in both cases). Risk factors implicated in mortality at one year following the start of haemodialysis have been determined and a prognostic model designed. The validated, easy-to-apply model quantifies individual patient risk attributable to various factors, some of them amenable to correction by directed interventions.

Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients.

PubMed

Rapsomaniki, Eleni; Shah, Anoop; Perel, Pablo; Denaxas, Spiros; George, Julie; Nicholas, Owen; Udumyan, Ruzan; Feder, Gene Solomon; Hingorani, Aroon D; Timmis, Adam; Smeeth, Liam; Hemingway, Harry

2014-04-01

The population with stable coronary artery disease (SCAD) is growing but validated models to guide their clinical management are lacking. We developed and validated prognostic models for all-cause mortality and non-fatal myocardial infarction (MI) or coronary death in SCAD. Models were developed in a linked electronic health records cohort of 102 023 SCAD patients from the CALIBER programme, with mean follow-up of 4.4 (SD 2.8) years during which 20 817 deaths and 8856 coronary outcomes were observed. The Kaplan-Meier 5-year risk was 20.6% (95% CI, 20.3, 20.9) for mortality and 9.7% (95% CI, 9.4, 9.9) for non-fatal MI or coronary death. The predictors in the models were age, sex, CAD diagnosis, deprivation, smoking, hypertension, diabetes, lipids, heart failure, peripheral arterial disease, atrial fibrillation, stroke, chronic kidney disease, chronic pulmonary disease, liver disease, cancer, depression, anxiety, heart rate, creatinine, white cell count, and haemoglobin. The models had good calibration and discrimination in internal (external) validation with C-index 0.811 (0.735) for all-cause mortality and 0.778 (0.718) for non-fatal MI or coronary death. Using these models to identify patients at high risk (defined by guidelines as 3% annual mortality) and support a management decision associated with hazard ratio 0.8 could save an additional 13-16 life years or 15-18 coronary event-free years per 1000 patients screened, compared with models with just age, sex, and deprivation. These validated prognostic models could be used in clinical practice to support risk stratification as recommended in clinical guidelines.

Rotorcraft technology at Boeing Vertol: Recent advances

NASA Technical Reports Server (NTRS)

Shaw, John; Dadone, Leo; Wiesner, Robert

1988-01-01

An overview is presented of key accomplishments in the rotorcraft development at Boeing Vertol. Projects of particular significance: high speed rotor development and the Model 360 Advanced Technology Helicopter. Areas addressed in the overview are: advanced rotors with reduced noise and vibration, 3-D aerodynamic modeling, flight control and avionics, active control, automated diagnostics and prognostics, composite structures, and drive systems.

Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

PubMed Central

Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

2017-01-01

In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

One-Year Mortality in Older Patients with Cancer: Development and External Validation of an MNA-Based Prognostic Score.

PubMed

Bourdel-Marchasson, Isabelle; Diallo, Abou; Bellera, Carine; Blanc-Bisson, Christelle; Durrieu, Jessica; Germain, Christine; Mathoulin-Pélissier, Simone; Soubeyran, Pierre; Rainfray, Muriel; Fonck, Mariane; Doussau, Adelaïde

2016-01-01

The MNA (Mini Nutritional Assessment) is known as a prognosis factor in older population. We analyzed the prognostic value for one-year mortality of MNA items in older patients with cancer treated with chemotherapy as the basis of a simplified prognostic score. The prospective derivation cohort included 606 patients older than 70 years with an indication of chemotherapy for cancers. The endpoint to predict was one-year mortality. The 18 items of the Full MNA, age, gender, weight loss, cancer origin, TNM, performance status and lymphocyte count were considered to construct the prognostic model. MNA items were analyzed with a backward step-by-step multivariate logistic regression and other items were added in a forward step-by-step regression. External validation was performed on an independent cohort of 229 patients. At one year 266 deaths had occurred. Decreased dietary intake (p = 0.0002), decreased protein-rich food intake (p = 0.025), 3 or more prescribed drugs (p = 0.023), calf circumference <31 cm (p = 0.0002), tumor origin (p<0.0001), metastatic status (p = 0.0007) and lymphocyte count <1500/mm3 (0.029) were found to be associated with 1-year mortality in the final model and were used to construct a prognostic score. The area under curve (AUC) of the score was 0.793, which was higher than the Full MNA AUC (0.706). The AUC of the score in validation cohort (229 subjects, 137 deaths) was 0.698. Key predictors of one-year mortality included cancer cachexia clinical features, comorbidities, the origin and the advanced status of the tumor. The prognostic value of this model combining a subset of MNA items and cancer related items was better than the full MNA, thus providing a simple score to predict 1-year mortality in older patients with an indication of chemotherapy.

Prognostic value of baseline seric Syndecan-1 in initially unresectable metastatic colorectal cancer patients: a simple biological score.

PubMed

Jary, Marine; Lecomte, Thierry; Bouché, Olivier; Kim, Stefano; Dobi, Erion; Queiroz, Lise; Ghiringhelli, Francois; Etienne, Hélène; Léger, Julie; Godet, Yann; Balland, Jérémy; Lakkis, Zaher; Adotevi, Olivier; Bonnetain, Franck; Borg, Christophe; Vernerey, Dewi

2016-11-15

In first-line metastatic colorectal cancer (mCRC), baseline prognostic factors allowing death risk and treatment strategy stratification are lacking. Syndecan-1 (CD138) soluble form was never described as a prognostic biomarker in mCRC. We investigated its additional prognostic value for overall survival (OS). mCRC patients with unresectable disease at diagnosis were treated with bevacizumab-based chemotherapy in two independent prospective clinical trials (development set: n = 126, validation set: n = 51, study NCT00489697 and study NCT00544011, respectively). Serums were collected at baseline for CD138 measurement. OS determinants were assessed and, based on the final multivariate model, a prognostic score was proposed. Two independent OS prognostic factors were identified: Lactate Dehydrogenase (LDH) high level (p = 0.0066) and log-CD138 high level (p = 0.0190). The determination of CD138 binary information (cutoff: 75 ng/mL) allowed the assessment of a biological prognostic score with CD138 and LDH values, identifying three risk groups for death (median OS= 38.9, 30.1 and 19.8 months for the low, intermediate and high risk groups, respectively; p < 0.0001). This score had a good discrimination ability (C-index = 0.63). These results were externally confirmed in the validation set. Our study provides robust evidence in favor of the additional baseline soluble CD138 prognostic value for OS, in mCRC patients. A simple biological scoring system is proposed including LDH and CD138 binary status values. © 2016 UICC.

Development of a five-year mortality model in systemic sclerosis patients by different analytical approaches.

PubMed

Beretta, Lorenzo; Santaniello, Alessandro; Cappiello, Francesca; Chawla, Nitesh V; Vonk, Madelon C; Carreira, Patricia E; Allanore, Yannick; Popa-Diaconu, D A; Cossu, Marta; Bertolotti, Francesca; Ferraccioli, Gianfranco; Mazzone, Antonino; Scorza, Raffaella

2010-01-01

Systemic sclerosis (SSc) is a multiorgan disease with high mortality rates. Several clinical features have been associated with poor survival in different populations of SSc patients, but no clear and reproducible prognostic model to assess individual survival prediction in scleroderma patients has ever been developed. We used Cox regression and three data mining-based classifiers (Naïve Bayes Classifier [NBC], Random Forests [RND-F] and logistic regression [Log-Reg]) to develop a robust and reproducible 5-year prognostic model. All the models were built and internally validated by means of 5-fold cross-validation on a population of 558 Italian SSc patients. Their predictive ability and capability of generalisation was then tested on an independent population of 356 patients recruited from 5 external centres and finally compared to the predictions made by two SSc domain experts on the same population. The NBC outperformed the Cox-based classifier and the other data mining algorithms after internal cross-validation (area under receiving operator characteristic curve, AUROC: NBC=0.759; RND-F=0.736; Log-Reg=0.754 and Cox= 0.724). The NBC had also a remarkable and better trade-off between sensitivity and specificity (e.g. Balanced accuracy, BA) than the Cox-based classifier, when tested on an independent population of SSc patients (BA: NBC=0.769, Cox=0.622). The NBC was also superior to domain experts in predicting 5-year survival in this population (AUROC=0.829 vs. AUROC=0.788 and BA=0.769 vs. BA=0.67). We provide a model to make consistent 5-year prognostic predictions in SSc patients. Its internal validity, as well as capability of generalisation and reduced uncertainty compared to human experts support its use at bedside. Available at: http://www.nd.edu/~nchawla/survival.xls.

Modeling for Battery Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Goebel, Kai; Khasin, Michael; Hogge, Edward; Quach, Patrick

2017-01-01

For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.

Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework

NASA Astrophysics Data System (ADS)

Jha, Mayank Shekhar; Dauphin-Tanguy, G.; Ould-Bouamama, B.

2016-06-01

The paper's main objective is to address the problem of health monitoring of system parameters in Bond Graph (BG) modeling framework, by exploiting its structural and causal properties. The system in feedback control loop is considered uncertain globally. Parametric uncertainty is modeled in interval form. The system parameter is undergoing degradation (prognostic candidate) and its degradation model is assumed to be known a priori. The detection of degradation commencement is done in a passive manner which involves interval valued robust adaptive thresholds over the nominal part of the uncertain BG-derived interval valued analytical redundancy relations (I-ARRs). The latter forms an efficient diagnostic module. The prognostics problem is cast as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the system parameter (prognostic candidate). The observation equation is constructed from nominal part of the I-ARR. Using particle filter (PF) algorithms; the estimation of state of health (state of prognostic candidate) and associated hidden time-varying degradation progression parameters is achieved in probabilistic terms. A simplified variance adaptation scheme is proposed. Associated uncertainties which arise out of noisy measurements, parametric degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the remaining useful life of the prognostic candidate with suitable confidence bounds. The effectiveness of the novel methodology is demonstrated through simulations and experiments on a mechatronic system.

Clinical prognostic rules for severe acute respiratory syndrome in low- and high-resource settings.

PubMed

Cowling, Benjamin J; Muller, Matthew P; Wong, Irene O L; Ho, Lai-Ming; Lo, Su-Vui; Tsang, Thomas; Lam, Tai Hing; Louie, Marie; Leung, Gabriel M

2006-07-24

An accurate prognostic model for patients with severe acute respiratory syndrome (SARS) could provide a practical clinical decision aid. We developed and validated prognostic rules for both high- and low-resource settings based on data available at the time of admission. We analyzed data on all 1755 and 291 patients with SARS in Hong Kong (derivation cohort) and Toronto (validation cohort), respectively, using a multivariable logistic scoring method with internal and external validation. Scores were assigned on the basis of patient history in a basic model, and a full model additionally incorporated radiological and laboratory results. The main outcome measure was death. Predictors for mortality in the basic model included older age, male sex, and the presence of comorbid conditions. Additional predictors in the full model included haziness or infiltrates on chest radiography, less than 95% oxygen saturation on room air, high lactate dehydrogenase level, and high neutrophil and low platelet counts. The basic model had an area under the receiver operating characteristic (ROC) curve of 0.860 in the derivation cohort, which was maintained on external validation with an area under the ROC curve of 0.882. The full model improved discrimination with areas under the ROC curve of 0.877 and 0.892 in the derivation and validation cohorts, respectively. The model performs well and could be useful in assessing prognosis for patients who are infected with re-emergent SARS.

A Modified MELD Model for Chinese Pre-ACLF and ACLF Patients and It Reveals Poor Prognosis in Pre-ACLF Patients

PubMed Central

Zhang, Yimin; Guo, Yongzheng; Xu, Xiaowei; Yang, Qian; Du, Weibo; Liu, Xiaoli; Chen, Yuemei; Huang, Jianrong; Li, Lanjuan

2013-01-01

Background & Aims Acute-on-chronic liver failure (ACLF) is one of the most deadly, prevalent, and costly diseases in Asia. However, no prognostic model has been developed that is based specifically on data gathered from Asian patients with ACLF. The aim of the present study was to quantify the survival time of ACLF among Asians and to develop a prognostic model to estimate the probability of death related to ACLF. Methods We conducted a retrospective observational cohort study to analyze clinical data from 857 patients with ACLF/pre-ACLF who did not undergo liver transplantation. Kaplan–Meier and Cox proportional hazards regression model were used to estimate survival rates and survival affected factors. The area under the receiver operating characteristic curve (auROC) was used to evaluate the performance of the models for predicting early mortality. Results The mortality rates among patients with pre-ACLF at 12 weeks and 24 weeks after diagnosis were 30.5% and 33.2%, respectively. The mortality rates among patients with early-stage ACLF at 12 weeks and 24 weeks after diagnosis were 33.9% and 37.1%, respectively. The difference in survival between pre-ACLF patients and patients in the early stage of ACLF was not statistically significant. The prognostic model identified 5 independent factors significantly associated with survival among patients with ACLF and pre-ACLF: the model for end-stage liver disease (MELD) score; age, hepatic encephalopathy; triglyceride level and platelet count. Conclusion The findings of the present study suggest that the Chinese diagnostic criteria of ACLF might be broadened, thus enabling implementation of a novel model to predict ACLF-related death after comprehensive medical treatment. PMID:23755119

Forecasting of wet snow avalanche activity: Proof of concept and operational implementation

NASA Astrophysics Data System (ADS)

Gobiet, Andreas; Jöbstl, Lisa; Rieder, Hannes; Bellaire, Sascha; Mitterer, Christoph

2017-04-01

State-of-the-art tools for the operational assessment of avalanche danger include field observations, recordings from automatic weather stations, meteorological analyses and forecasts, and recently also indices derived from snowpack models. In particular, an index for identifying the onset of wet-snow avalanche cycles (LWCindex), has been demonstrated to be useful. However, its value for operational avalanche forecasting is currently limited, since detailed, physically based snowpack models are usually driven by meteorological data from automatic weather stations only and have therefore no prognostic ability. Since avalanche risk management heavily relies on timely information and early warnings, many avalanche services in Europe nowadays start issuing forecasts for the following days, instead of the traditional assessment of the current avalanche danger. In this context, the prognostic operation of detailed snowpack models has recently been objective of extensive research. In this study a new, observationally constrained setup for forecasting the onset of wet-snow avalanche cycles with the detailed snow cover model SNOWPACK is presented and evaluated. Based on data from weather stations and different numerical weather prediction models, we demonstrate that forecasts of the LWCindex as indicator for wet-snow avalanche cycles can be useful for operational warning services, but is so far not reliable enough to be used as single warning tool without considering other factors. Therefore, further development currently focuses on the improvement of the forecasts by applying ensemble techniques and suitable post processing approaches to the output of numerical weather prediction models. In parallel, the prognostic meteo-snow model chain is operationally used by two regional avalanche warning services in Austria since winter 2016/2017 for the first time. Experiences from the first operational season and first results from current model developments will be reported.

A new biologic prognostic model based on immunohistochemistry predicts survival in patients with diffuse large B-cell lymphoma.

PubMed

Perry, Anamarija M; Cardesa-Salzmann, Teresa M; Meyer, Paul N; Colomo, Luis; Smith, Lynette M; Fu, Kai; Greiner, Timothy C; Delabie, Jan; Gascoyne, Randy D; Rimsza, Lisa; Jaffe, Elaine S; Ott, German; Rosenwald, Andreas; Braziel, Rita M; Tubbs, Raymond; Cook, James R; Staudt, Louis M; Connors, Joseph M; Sehn, Laurie H; Vose, Julie M; López-Guillermo, Armando; Campo, Elias; Chan, Wing C; Weisenburger, Dennis D

2012-09-13

Biologic factors that predict the survival of patients with a diffuse large B-cell lymphoma, such as cell of origin and stromal signatures, have been discovered by gene expression profiling. We attempted to simulate these gene expression profiling findings and create a new biologic prognostic model based on immunohistochemistry. We studied 199 patients (125 in the training set, 74 in the validation set) with de novo diffuse large B-cell lymphoma treated with rituximab and CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) or CHOP-like therapies, and immunohistochemical stains were performed on paraffin-embedded tissue microarrays. In the model, 1 point was awarded for each adverse prognostic factor: nongerminal center B cell-like subtype, SPARC (secreted protein, acidic, and rich in cysteine) < 5%, and microvascular density quartile 4. The model using these 3 biologic markers was highly predictive of overall survival and event-free survival in multivariate analysis after adjusting for the International Prognostic Index in both the training and validation sets. This new model delineates 2 groups of patients, 1 with a low biologic score (0-1) and good survival and the other with a high score (2-3) and poor survival. This new biologic prognostic model could be used with the International Prognostic Index to stratify patients for novel or risk-adapted therapies.

Dynamic and Structural Gas Turbine Engine Modeling

NASA Technical Reports Server (NTRS)

Turso, James A.

2003-01-01

Model the interactions between the structural dynamics and the performance dynamics of a gas turbine engine. Generally these two aspects are considered separate, unrelated phenomena and are studied independently. For diagnostic purposes, it is desirable to bring together as much information as possible, and that involves understanding how performance is affected by structural dynamics (if it is) and vice versa. This can involve the relationship between thrust response and the excitation of structural modes, for instance. The job will involve investigating and characterizing these dynamical relationships, generating a model that incorporates them, and suggesting and/or developing diagnostic and prognostic techniques that can be incorporated in a data fusion system. If no coupling is found, at the least a vibration model should be generated that can be used for diagnostics and prognostics related to blade loss, for instance.

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

NASA Technical Reports Server (NTRS)

Ramasso, Emannuel; Saxena, Abhinav

2014-01-01

Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem several benchmarking datasets have been collected by NASA's prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement.

PubMed

Riley, Richard D; Elia, Eleni G; Malin, Gemma; Hemming, Karla; Price, Malcolm P

2015-07-30

A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta-analysis is difficult because primary studies often use different methods of measurement and/or different cut-points to dichotomise continuous factors into 'high' and 'low' groups; selective reporting is also common. We illustrate how multivariate random effects meta-analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut-points and/or methods of measurement. The models account for within-study and between-study correlations, which utilises more information and reduces the impact of unreported cut-points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut-point to reduce the number of unknown parameters. The models provide important inferential results for each cut-point and method of measurement, including the summary prognostic effect, the between-study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta-analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut-points than previously thought. In the second, a multivariate meta-analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut-points). © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Predictors of survival in patients with recurrent ovarian cancer undergoing secondary cytoreductive surgery based on the pooled analysis of an international collaborative cohort

PubMed Central

Zang, R Y; Harter, P; Chi, D S; Sehouli, J; Jiang, R; Tropé, C G; Ayhan, A; Cormio, G; Xing, Y; Wollschlaeger, K M; Braicu, E I; Rabbitt, C A; Oksefjell, H; Tian, W J; Fotopoulou, C; Pfisterer, J; du Bois, A; Berek, J S

2011-01-01

Background: This study aims to identify prognostic factors and to develop a risk model predicting survival in patients undergoing secondary cytoreductive surgery (SCR) for recurrent epithelial ovarian cancer. Methods: Individual data of 1100 patients with recurrent ovarian cancer of a progression-free interval at least 6 months who underwent SCR were pooled analysed. A simplified scoring system for each independent prognostic factor was developed according to its coefficient. Internal validation was performed to assess the discrimination of the model. Results: Complete SCR was strongly associated with the improvement of survival, with a median survival of 57.7 months, when compared with 27.0 months in those with residual disease of 0.1–1 cm and 15.6 months in those with residual disease of >1 cm, respectively (P<0.0001). Progression-free interval (⩽23.1 months vs >23.1 months, hazard ratio (HR): 1.72; score: 2), ascites at recurrence (present vs absent, HR: 1.27; score: 1), extent of recurrence (multiple vs localised disease, HR: 1.38; score: 1) as well as residual disease after SCR (R1 vs R0, HR: 1.90, score: 2; R2 vs R0, HR: 3.0, score: 4) entered into the risk model. Conclusion: This prognostic model may provide evidence to predict survival benefit from secondary cytoreduction in patients with recurrent ovarian cancer. PMID:21878937

THE VISUAL BEACH NUMERICAL MODEL: A DIAGNOSTIC AND PROGNOSTIC MODELING APPROACH TO ACHIEVING US BEACHES AESTHETIC AND PUBLIC HEALTH PROTECTION

EPA Science Inventory

Under the BEACH Act of 2000, EPA has committed to a program to monitor beach water quality and develop strategies, including modeling, for timely notification of the public when bacterial contamination poses a risk to bathers. EPA's goal is to manage 100% of significant public be...

An internally validated new clinical and inflammation-based prognostic score for patients with advanced hepatocellular carcinoma treated with sorafenib.

PubMed

Diaz-Beveridge, R; Bruixola, G; Lorente, D; Caballero, J; Rodrigo, E; Segura, Á; Akhoundova, D; Giménez, A; Aparicio, J

2018-03-01

Sorafenib is a standard treatment for patients (pts) with advanced hepatocellular carcinoma (aHCC), although the clinical benefit is heterogeneous between different pts groups. Among novel prognostic factors, a low baseline neutrophil-to-lymphocyte ratio (bNLR) and early-onset diarrhoea have been linked with a better prognosis. To identify prognostic factors in pts with aHCC treated with 1st-line sorafenib and to develop a new prognostic score to guide management. Retrospective review of 145 pts bNLR, overall toxicity, early toxicity rates and overall survival (OS) were assessed. Univariate and multivariate analysis of prognostic factors for OS was performed. The prognostic score was calculated from the coefficients found in the Cox analysis. ROC curves and pseudoR2 index were used for internal validation. Discrimination ability and calibration were tested by Harrel's c-index (HCI) and Akaike criteria (AIC). The optimal bNLR cut-off for the prediction of OS was 4 (AUC 0.62). Independent prognostic factors in multivariate analysis for OS were performance status (PS) (p < .0001), Child-Pugh (C-P) score (p = 0.005), early-onset diarrhoea (p = 0.006) and BNLR (0.011). The prognostic score based on these four variables was found efficient (HCI = 0.659; AIC = 1.180). Four risk groups for OS could be identified: a very low-risk (median OS = 48.6 months), a low-risk (median OS = 11.6 months), an intermediate-risk (median OS = 8.3 months) and a high-risk group (median OS = 4.4 months). PS and C-P score were the main prognostic factors for OS, followed by early-onset diarrhoea and bNLR. We identified four risk groups for OS depending on these parameters. This prognostic model could be useful for patient stratification, but an external validation is needed.

Cutaneous Lymphoma International Consortium Study of Outcome in Advanced Stages of Mycosis Fungoides and Sézary Syndrome: Effect of Specific Prognostic Markers on Survival and Development of a Prognostic Model

PubMed Central

Scarisbrick, Julia J.; Prince, H. Miles; Vermeer, Maarten H.; Quaglino, Pietro; Horwitz, Steven; Porcu, Pierluigi; Stadler, Rudolf; Wood, Gary S.; Beylot-Barry, Marie; Pham-Ledard, Anne; Foss, Francine; Girardi, Michael; Bagot, Martine; Michel, Laurence; Battistella, Maxime; Guitart, Joan; Kuzel, Timothy M.; Martinez-Escala, Maria Estela; Estrach, Teresa; Papadavid, Evangelia; Antoniou, Christina; Rigopoulos, Dimitis; Nikolaou, Vassilki; Sugaya, Makoto; Miyagaki, Tomomitsu; Gniadecki, Robert; Sanches, José Antonio; Cury-Martins, Jade; Miyashiro, Denis; Servitje, Octavio; Muniesa, Cristina; Berti, Emilio; Onida, Francesco; Corti, Laura; Hodak, Emilia; Amitay-Laish, Iris; Ortiz-Romero, Pablo L.; Rodríguez-Peralto, Jose L.; Knobler, Robert; Porkert, Stefanie; Bauer, Wolfgang; Pimpinelli, Nicola; Grandi, Vieri; Cowan, Richard; Rook, Alain; Kim, Ellen; Pileri, Alessandro; Patrizi, Annalisa; Pujol, Ramon M.; Wong, Henry; Tyler, Kelly; Stranzenbach, Rene; Querfeld, Christiane; Fava, Paolo; Maule, Milena; Willemze, Rein; Evison, Felicity; Morris, Stephen; Twigger, Robert; Talpur, Rakhshandra; Kim, Jinah; Ognibene, Grant; Li, Shufeng; Tavallaee, Mahkam; Hoppe, Richard T.; Duvic, Madeleine; Whittaker, Sean J.; Kim, Youn H.

2015-01-01

Purpose Advanced-stage mycosis fungoides (MF; stage IIB to IV) and Sézary syndrome (SS) are aggressive lymphomas with a median survival of 1 to 5 years. Clinical management is stage based; however, there is wide range of outcome within stages. Published prognostic studies in MF/SS have been single-center trials. Because of the rarity of MF/SS, only a large collaboration would power a study to identify independent prognostic markers. Patients and Methods Literature review identified the following 10 candidate markers: stage, age, sex, cutaneous histologic features of folliculotropism, CD30 positivity, proliferation index, large-cell transformation, WBC/lymphocyte count, serum lactate dehydrogenase, and identical T-cell clone in blood and skin. Data were collected at specialist centers on patients diagnosed with advanced-stage MF/SS from 2007. Each parameter recorded at diagnosis was tested against overall survival (OS). Results Staging data on 1,275 patients with advanced MF/SS from 29 international sites were included for survival analysis. The median OS was 63 months, with 2- and 5-year survival rates of 77% and 52%, respectively. The median OS for patients with stage IIB disease was 68 months, but patients diagnosed with stage III disease had slightly improved survival compared with patients with stage IIB, although patients diagnosed with stage IV disease had significantly worse survival (48 months for stage IVA and 33 months for stage IVB). Of the 10 variables tested, four (stage IV, age > 60 years, large-cell transformation, and increased lactate dehydrogenase) were independent prognostic markers for a worse survival. Combining these four factors in a prognostic index model identified the following three risk groups across stages with significantly different 5-year survival rates: low risk (68%), intermediate risk (44%), and high risk (28%). Conclusion To our knowledge, this study includes the largest cohort of patients with advanced-stage MF/SS and identifies markers with independent prognostic value, which, used together in a prognostic index, may be useful to stratify advanced-stage patients. PMID:26438120

Cutaneous Lymphoma International Consortium Study of Outcome in Advanced Stages of Mycosis Fungoides and Sézary Syndrome: Effect of Specific Prognostic Markers on Survival and Development of a Prognostic Model.

PubMed

Scarisbrick, Julia J; Prince, H Miles; Vermeer, Maarten H; Quaglino, Pietro; Horwitz, Steven; Porcu, Pierluigi; Stadler, Rudolf; Wood, Gary S; Beylot-Barry, Marie; Pham-Ledard, Anne; Foss, Francine; Girardi, Michael; Bagot, Martine; Michel, Laurence; Battistella, Maxime; Guitart, Joan; Kuzel, Timothy M; Martinez-Escala, Maria Estela; Estrach, Teresa; Papadavid, Evangelia; Antoniou, Christina; Rigopoulos, Dimitis; Nikolaou, Vassilki; Sugaya, Makoto; Miyagaki, Tomomitsu; Gniadecki, Robert; Sanches, José Antonio; Cury-Martins, Jade; Miyashiro, Denis; Servitje, Octavio; Muniesa, Cristina; Berti, Emilio; Onida, Francesco; Corti, Laura; Hodak, Emilia; Amitay-Laish, Iris; Ortiz-Romero, Pablo L; Rodríguez-Peralto, Jose L; Knobler, Robert; Porkert, Stefanie; Bauer, Wolfgang; Pimpinelli, Nicola; Grandi, Vieri; Cowan, Richard; Rook, Alain; Kim, Ellen; Pileri, Alessandro; Patrizi, Annalisa; Pujol, Ramon M; Wong, Henry; Tyler, Kelly; Stranzenbach, Rene; Querfeld, Christiane; Fava, Paolo; Maule, Milena; Willemze, Rein; Evison, Felicity; Morris, Stephen; Twigger, Robert; Talpur, Rakhshandra; Kim, Jinah; Ognibene, Grant; Li, Shufeng; Tavallaee, Mahkam; Hoppe, Richard T; Duvic, Madeleine; Whittaker, Sean J; Kim, Youn H

2015-11-10

Advanced-stage mycosis fungoides (MF; stage IIB to IV) and Sézary syndrome (SS) are aggressive lymphomas with a median survival of 1 to 5 years. Clinical management is stage based; however, there is wide range of outcome within stages. Published prognostic studies in MF/SS have been single-center trials. Because of the rarity of MF/SS, only a large collaboration would power a study to identify independent prognostic markers. Literature review identified the following 10 candidate markers: stage, age, sex, cutaneous histologic features of folliculotropism, CD30 positivity, proliferation index, large-cell transformation, WBC/lymphocyte count, serum lactate dehydrogenase, and identical T-cell clone in blood and skin. Data were collected at specialist centers on patients diagnosed with advanced-stage MF/SS from 2007. Each parameter recorded at diagnosis was tested against overall survival (OS). Staging data on 1,275 patients with advanced MF/SS from 29 international sites were included for survival analysis. The median OS was 63 months, with 2- and 5-year survival rates of 77% and 52%, respectively. The median OS for patients with stage IIB disease was 68 months, but patients diagnosed with stage III disease had slightly improved survival compared with patients with stage IIB, although patients diagnosed with stage IV disease had significantly worse survival (48 months for stage IVA and 33 months for stage IVB). Of the 10 variables tested, four (stage IV, age > 60 years, large-cell transformation, and increased lactate dehydrogenase) were independent prognostic markers for a worse survival. Combining these four factors in a prognostic index model identified the following three risk groups across stages with significantly different 5-year survival rates: low risk (68%), intermediate risk (44%), and high risk (28%). To our knowledge, this study includes the largest cohort of patients with advanced-stage MF/SS and identifies markers with independent prognostic value, which, used together in a prognostic index, may be useful to stratify advanced-stage patients. © 2015 by American Society of Clinical Oncology.

Proposal and validation of a new model to estimate survival for hepatocellular carcinoma patients.

PubMed

Liu, Po-Hong; Hsu, Chia-Yang; Hsia, Cheng-Yuan; Lee, Yun-Hsuan; Huang, Yi-Hsiang; Su, Chien-Wei; Lee, Fa-Yauh; Lin, Han-Chieh; Huo, Teh-Ia

2016-08-01

The survival of hepatocellular carcinoma (HCC) patients is heterogeneous. We aim to develop and validate a simple prognostic model to estimate survival for HCC patients (MESH score). A total of 3182 patients were randomised into derivation and validation cohort. Multivariate analysis was used to identify independent predictors of survival in the derivation cohort. The validation cohort was employed to examine the prognostic capabilities. The MESH score allocated 1 point for each of the following parameters: large tumour (beyond Milan criteria), presence of vascular invasion or metastasis, Child-Turcotte-Pugh score ≥6, performance status ≥2, serum alpha-fetoprotein level ≥20 ng/ml, and serum alkaline phosphatase ≥200 IU/L, with a maximal of 6 points. In the validation cohort, significant survival differences were found across all MESH scores from 0 to 6 (all p < 0.01). The MESH system was associated with the highest homogeneity and lowest corrected Akaike information criterion compared with Barcelona Clínic Liver Cancer, Hong Kong Liver Cancer (HKLC), Cancer of the Liver Italian Program, Taipei Integrated Scoring and model to estimate survival in ambulatory HCC Patients systems. The prognostic accuracy of the MESH scores remained constant in patients with hepatitis B- or hepatitis C-related HCC. The MESH score can also discriminate survival for patients from early to advanced stages of HCC. This newly proposed simple and accurate survival model provides enhanced prognostic accuracy for HCC. The MESH system is a useful supplement to the BCLC and HKLC classification schemes in refining treatment strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Embedded Diagnostic/Prognostic Reasoning and Information Continuity for Improved Avionics Maintenance

DTIC Science & Technology

2006-01-01

enabling technologies such as built-in-test, advanced health monitoring algorithms, reliability and component aging models, prognostics methods, and...deployment and acceptance. This framework and vision is consistent with the onboard PHM ( Prognostic and Health Management) as well as advanced... monitored . In addition to the prognostic forecasting capabilities provided by monitoring system power, multiple confounding errors by electronic

Model Adaptation for Prognostics in a Particle Filtering Framework

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, Kai Frank

2011-01-01

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Prognostic model for chronic hypertension in women with a history of hypertensive pregnancy disorders at term.

PubMed

Visser, V S; Hermes, W; Twisk, J; Franx, A; van Pampus, M G; Koopmans, C; Mol, B W J; de Groot, C J M

2017-10-01

The association between hypertensive pregnancy disorders and cardiovascular disease later in life is well described. In this study we aim to develop a prognostic model from patients characteristics known before, early in, during and after pregnancy to identify women at increased risk of cardiovascular disease e.g. chronic hypertension years after pregnancy complicated by hypertension at term. We included women with a history of singleton pregnancy complicated by hypertension at term. Women using antihypertensive medication before pregnancy were excluded. We measured hypertension in these women more than 2years postpartum. Different patients characteristics before, early in, during and after pregnancy were considered to develop a prognostic model of chronic hypertension at 2-years. These included amongst others maternal age, blood pressure at pregnancy intake and blood pressure six weeks post-partum. Univariable analyses followed by a multivariable logistic regression analysis was performed to determine which combination of predictors best predicted chronic hypertension. Model performance was assessed by calibration (graphical plot) and discrimination (area under the receiver operating characteristic (AUC)). Of the 305 women in who blood pressure 2.5years after pregnancy was assessed, 105 women (34%) had chronic hypertension. The following patient characteristics were significant associated with chronic hypertension: higher maternal age, lower education, negative family history on hypertensive pregnancy disorders, higher BMI at booking, higher diastolic blood pressure at pregnancy intake, higher systolic blood pressure during pregnancy and higher diastolic blood pressure at six weeks post-partum. These characteristics were included in the prognostic model for chronic hypertension. Model performance was good as indicated by good calibration and good discrimination (AUC; 0.83 (95% CI 0.75 - 0.92). Chronic hypertension can be expected from patient characteristics before, early in, during and after pregnancy. These data underline the importance and awareness of detectable risk factors both for increased risk of complicated pregnancy as well as increased risk of cardiovascular disease later in life. Copyright © 2017 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Prognostication in Philadelphia Chromosome Negative Myeloproliferative Neoplasms: a Review of the Recent Literature.

PubMed

Zhou, Amy; Afzal, Amber; Oh, Stephen T

2017-10-01

The prognosis for patients with Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is highly variable. All Ph-negative MPNs carry an increased risk for thrombotic complications, bleeding, and leukemic transformation. Several clinical, biological, and molecular prognostic factors have been identified in recent years, which provide important information in guiding management of patients with Ph-negative MPNs. In this review, we critically evaluate the recent published literature and discuss important new developments in clinical and molecular factors that impact survival, disease transformation, and thrombosis in patients with polycythemia vera, essential thrombocythemia, and primary myelofibrosis. Recent studies have identified several clinical factors and non-driver mutations to have prognostic impact on Ph-negative MPNs independent of conventional risk stratification and prognostic models. In polycythemia vera (PV), leukocytosis, abnormal karyotype, phlebotomy requirement on hydroxyurea, increased bone marrow fibrosis, and mutations in ASXL1, SRSF2, and IDH2 were identified as additional adverse prognostic factors. In essential thrombocythemia (ET), JAK2 V617F mutation, splenomegaly, and mutations in SH2B3, SF3B1, U2AF1, TP53, IDH2, and EZH2 were found to be additional negative prognostic factors. Bone marrow fibrosis and mutations in ASXL1, SRSF2, EZH2, and IDH1/2 have been found to be additional prognostic factors in primary myelofibrosis (PMF). CALR mutations appear to be a favorable prognostic factor in PMF, which has not been clearly demonstrated in ET. The prognosis for patients with PV, ET, and PMF is dependent upon the presence or absence of several clinical, biological, and molecular risk factors. The significance of additional risk factors identified in these recent studies will need further validation in prospective studies to determine how they may be best utilized in the management of these disorders.

Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

PubMed

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

2014-01-01

In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

An Assessment of Integrated Health Management (IHM) Frameworks

DOE Office of Scientific and Technical Information (OSTI.GOV)

N. Lybeck; M. Tawfik; L. Bond

In order to meet the ever increasing demand for energy, the United States nuclear industry is turning to life extension of existing nuclear power plants (NPPs). Economically ensuring the safe, secure, and reliable operation of aging nuclear power plants presents many challenges. The 2009 Light Water Reactor Sustainability Workshop identified online monitoring of active and structural components as essential to the better understanding and management of the challenges posed by aging nuclear power plants. Additionally, there is increasing adoption of condition-based maintenance (CBM) for active components in NPPs. These techniques provide a foundation upon which a variety of advanced onlinemore » surveillance, diagnostic, and prognostic techniques can be deployed to continuously monitor and assess the health of NPP systems and components. The next step in the development of advanced online monitoring is to move beyond CBM to estimating the remaining useful life of active components using prognostic tools. Deployment of prognostic health management (PHM) on the scale of a NPP requires the use of an integrated health management (IHM) framework - a software product (or suite of products) used to manage the necessary elements needed for a complete implementation of online monitoring and prognostics. This paper provides a thoughtful look at the desirable functions and features of IHM architectures. A full PHM system involves several modules, including data acquisition, system modeling, fault detection, fault diagnostics, system prognostics, and advisory generation (operations and maintenance planning). The standards applicable to PHM applications are indentified and summarized. A list of evaluation criteria for PHM software products, developed to ensure scalability of the toolset to an environment with the complexity of a NPP, is presented. Fourteen commercially available PHM software products are identified and classified into four groups: research tools, PHM system development tools, deployable architectures, and peripheral tools.« less

Development, validation and psychometric properties of a diagnostic/prognostic tool for breakthrough pain in mixed chronic-pain patients.

PubMed

Samolsky Dekel, Boaz Gedaliahu; Remondini, Francesca; Gori, Alberto; Vasarri, Alessio; Di Nino, GianFranco; Melotti, Rita Maria

2016-02-01

Breakthrough pain (BTP) shows variable prevalence in different clinical contexts of cancer and non-cancer patients. BTP diagnostic tools with demonstrated reliability, validation and prognostic capability are lacking. We report the development, psychometric and validation properties of a diagnostic/prognostic tool, the IQ-BTP, for BTP recognition, its likelihood and clinical features among chronic-pain (CP) patients. n=120 consecutive mixed cancer/non-cancer CP in/outpatients. Development, psychometric analyses and formal validation included: Face/Content validity (by 'experts' opinion and assessing the relationship between the IQ-BTP classes and criteria derived from BTP operational-case-definition); Construct validity, by Principle Component Analysis (PCA); and the strength of Spearman correlation between IQ-BTP classes and the Brief Pain Inventory (BPI) items; Reliability, by Cronbach's alpha statistics. Associations with clinical/demographic moderators were assessed applying χ(2) analysis. Potential-BTP was found in 36.7% of patients (38.4% of non-cancer and 32.4% of cancer patients). Among these the likelihood for BTP diagnosis was 'high' in 25%, 'intermediate' in 41% and, 'low' 34% of patients. Analyses showed significant differences between IQ-BTP classes and between the latter BPI pain-item scores. Correlation between IQ-BTP classes and BPI items was moderate. PCA and scree test identified 3 components accounting for 62.3% of the variance. Cronbach's alpha was 0.71. The IQ-BTP showed satisfactory psychometric and validation properties. With adequate feasibility it enabled the allocating of cancer/non-cancer CP patients in three prognostic classes. Results are sufficient to warrant a subsequent impact study of the IQ-BTP as prognostic model and screening tool for BTP in both CP populations. Copyright © 2016 Elsevier B.V. All rights reserved.

Prognostics of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven and Model-Based Methodologies

NASA Technical Reports Server (NTRS)

Celaya, Jose; Saxena, Abhinav; Saha, Sankalita; Goebel, Kai F.

2011-01-01

An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Dieattach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.

Statistical considerations on prognostic models for glioma

PubMed Central

Molinaro, Annette M.; Wrensch, Margaret R.; Jenkins, Robert B.; Eckel-Passow, Jeanette E.

2016-01-01

Given the lack of beneficial treatments in glioma, there is a need for prognostic models for therapeutic decision making and life planning. Recently several studies defining subtypes of glioma have been published. Here, we review the statistical considerations of how to build and validate prognostic models, explain the models presented in the current glioma literature, and discuss advantages and disadvantages of each model. The 3 statistical considerations to establishing clinically useful prognostic models are: study design, model building, and validation. Careful study design helps to ensure that the model is unbiased and generalizable to the population of interest. During model building, a discovery cohort of patients can be used to choose variables, construct models, and estimate prediction performance via internal validation. Via external validation, an independent dataset can assess how well the model performs. It is imperative that published models properly detail the study design and methods for both model building and validation. This provides readers the information necessary to assess the bias in a study, compare other published models, and determine the model's clinical usefulness. As editors, reviewers, and readers of the relevant literature, we should be cognizant of the needed statistical considerations and insist on their use. PMID:26657835

Associations between interarm differences in blood pressure and cardiovascular disease outcomes: protocol for an individual patient data meta-analysis and development of a prognostic algorithm.

PubMed

Clark, Christopher E; Boddy, Kate; Warren, Fiona C; Taylor, Rod S; Aboyans, Victor; Cloutier, Lyne; McManus, Richard J; Shore, Angela C; Campbell, John L

2017-07-02

Individual cohort studies in various populations and study-level meta-analyses have shown interarm differences (IAD) in blood pressure to be associated with increased cardiovascular and all-cause mortality. However, key questions remain, such as follows: (1) What is the additional contribution of IAD to prognostic risk estimation for cardiovascular and all-cause mortality? (2) What is the minimum cut-off value for IAD that defines elevated risk? (3) Is there a prognostic value of IAD and do different methods of IAD measurement impact on the prognostic value of IAD? We aim to address these questions by conducting an individual patient data (IPD) meta-analysis. This study will identify prospective cohort studies that measured blood pressure in both arms during recruitment, and invite authors to contribute IPD datasets to this collaboration. All patient data received will be combined into a single dataset. Using one-stage meta-analysis, we will undertake multivariable time-to-event regression modelling, with the aim of developing a new prognostic model for cardiovascular risk estimation that includes IAD. We will explore variations in risk contribution of IAD across predefined population subgroups (eg, hypertensives, diabetics), establish the lower limit of IAD that is associated with additional cardiovascular risk and assess the impact of different methods of IAD measurement on risk prediction. This study will not include any patient identifiable data. Included datasets will already have ethical approval and consent from their sponsors. Findings will be presented to international conferences and published in peer reviewed journals, and we have a comprehensive dissemination strategy in place with integrated patient and public involvement. CRD42015031227. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Simulations of horizontal roll vortex development above lines of extreme surface heating

Treesearch

W.E. Heilman; J.D. Fast

1992-01-01

A two-dimensional, nonhydrostatic, coupled, earth/atmospheric model has been used to simulate mean and turbulent atmospheric characteristics near lines of extreme surface heating. Prognostic equations are used to solve for the horizontal and vertical wind components, potential temperature, and turbulent kinetic energy (TKE). The model computes nonhydrostatic pressure...

Markov Modeling of Component Fault Growth over a Derived Domain of Feasible Output Control Effort Modifications

NASA Technical Reports Server (NTRS)

Bole, Brian; Goebel, Kai; Vachtsevanos, George

2012-01-01

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.

Toward IVHM Prognostics

NASA Technical Reports Server (NTRS)

Walsh, Kevin; Venti, Mike

2007-01-01

This viewgraph presentation reviews the prognostics of Integrated Vehicle Health Management. The contents include: 1) Aircraft Operations-Today's way of doing business; 2) Prognostics; 3) NASA's instrumentation data-system rack; 4) Data mining for IVHM; 5) NASA GRC's C-MAPSS generic engine model; and 6) Concluding thoughts.

Incidence and prognostic factors for postoperative frozen shoulder after shoulder surgery: a prospective cohort study.

PubMed

Koorevaar, Rinco C T; Van't Riet, Esther; Ipskamp, Marcel; Bulstra, Sjoerd K

2017-03-01

Frozen shoulder is a potential complication after shoulder surgery. It is a clinical condition that is often associated with marked disability and can have a profound effect on the patient's quality of life. The incidence, etiology, pathology and prognostic factors of postoperative frozen shoulder after shoulder surgery are not known. The purpose of this explorative study was to determine the incidence of postoperative frozen shoulder after various operative shoulder procedures. A second aim was to identify prognostic factors for postoperative frozen shoulder after shoulder surgery. 505 consecutive patients undergoing elective shoulder surgery were included in this prospective cohort study. Follow-up was 6 months after surgery. A prediction model was developed to identify prognostic factors for postoperative frozen shoulder after shoulder surgery using the TRIPOD guidelines. We nominated five potential predictors: gender, diabetes mellitus, type of physiotherapy, arthroscopic surgery and DASH score. Frozen shoulder was identified in 11% of the patients after shoulder surgery and was more common in females (15%) than in males (8%). Frozen shoulder was encountered after all types of operative procedures. A prediction model based on four variables (diabetes mellitus, specialized shoulder physiotherapy, arthroscopic surgery and DASH score) discriminated reasonably well with an AUC of 0.712. Postoperative frozen shoulder is a serious complication after shoulder surgery, with an incidence of 11%. Four prognostic factors were identified for postoperative frozen shoulder: diabetes mellitus, arthroscopic surgery, specialized shoulder physiotherapy and DASH score. The combination of these four variables provided a prediction rule for postoperative frozen shoulder with reasonable fit. Level II, prospective cohort study.

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing.

PubMed

McHenry, John N; Vukovich, Jeffery M; Hsu, N Christina

2015-12-01

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new "Deep Blue" retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented. Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin

USDA-ARS?s Scientific Manuscript database

Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance, or with spatially distributed prognostic models that simultaneously balance both the energy and water budgets over landscapes using predictive equations for land...

Numerical Modeling of the Global Atmosphere

NASA Technical Reports Server (NTRS)

Arakawa, Akio; Mechoso, Carlos R.

1996-01-01

Under this grant, we continued development and evaluation of the updraft downdraft model for cumulus parameterization. The model includes the mass, rainwater and vertical momentum budget equations for both updrafts and downdrafts. The rainwater generated in an updraft falls partly inside and partly outside the updraft. Two types of stationary solutions are identified for the coupled rainwater budget and vertical momentum equations: (1) solutions for small tilting angles, which are unstable; (2) solutions for large tilting angles, which are stable. In practical applications, we select the smallest stable tilting angle as an optimum value. The model has been incorporated into the Arakawa-Schubert (A-S) cumulus parameterization. The results of semi-prognostic and single-column prognostic tests of the revised A-S parameterization show drastic improvement in predicting the humidity field. Cheng and Arakawa presents the rationale and basic design of the updraft-downdraft model, together with these test results. Cheng and Arakawa, on the other hand gives technical details of the model as implemented in current version of the UCLA GCM.

Risk stratification personalised model for prediction of life-threatening ventricular tachyarrhythmias in patients with chronic heart failure.

PubMed

Frolov, Alexander Vladimirovich; Vaikhanskaya, Tatjana Gennadjevna; Melnikova, Olga Petrovna; Vorobiev, Anatoly Pavlovich; Guel, Ludmila Michajlovna

2017-01-01

The development of prognostic factors of life-threatening ventricular tachyarrhythmias (VTA) and sudden cardiac death (SCD) continues to maintain its priority and relevance in cardiology. The development of a method of personalised prognosis based on multifactorial analysis of the risk factors associated with life-threatening heart rhythm disturbances is considered a key research and clinical task. To design a prognostic and mathematical model to define personalised risk for life-threatening VTA in patients with chronic heart failure (CHF). The study included 240 patients with CHF (mean-age of 50.5 ± 12.1 years; left ventricular ejection fraction 32.8 ± 10.9%; follow-up period 36.8 ± 5.7 months). The participants received basic therapy for heart failure. The elec-trocardiogram (ECG) markers of myocardial electrical instability were assessed including microvolt T-wave alternans, heart rate turbulence, heart rate deceleration, and QT dispersion. Additionally, echocardiography and Holter monitoring (HM) were performed. The cardiovascular events were considered as primary endpoints, including SCD, paroxysmal ventricular tachycardia/ventricular fibrillation (VT/VF) based on HM-ECG data, and data obtained from implantable device interrogation (CRT-D, ICD) as well as appropriated shocks. During the follow-up period, 66 (27.5%) subjects with CHF showed adverse arrhythmic events, including nine SCD events and 57 VTAs. Data from a stepwise discriminant analysis of cumulative ECG-markers of myocardial electrical instability were used to make a mathematical model of preliminary VTA risk stratification. Uni- and multivariate Cox logistic regression analysis were performed to define an individualised risk stratification model of SCD/VTA. A binary logistic regression model demonstrated a high prognostic significance of discriminant function with a classification sensitivity of 80.8% and specificity of 99.1% (F = 31.2; c2 = 143.2; p < 0.0001). The method of personalised risk stratification using Cox logistic regression allows correct classification of more than 93.9% of CHF cases. A robust body of evidence concerning logistic regression prognostic significance to define VTA risk allows inclusion of this method into the algorithm of subsequent control and selection of the optimal treatment modality to treat patients with CHF.

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

PubMed

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

PubMed Central

Winzer, Klaus-Jürgen; Buchholz, Anika; Schumacher, Martin; Sauerbrei, Willi

2016-01-01

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. PMID:26938061

A prognostic role for Low tri-iodothyronine syndrome in acute stroke patients: A systematic review and meta-analysis.

PubMed

Lamba, Nayan; Liu, Chunming; Zaidi, Hasan; Broekman, M L D; Simjian, Thomas; Shi, Chen; Doucette, Joanne; Ren, Steven; Smith, Timothy R; Mekary, Rania A; Bunevicius, Adomas

2018-06-01

Low triiodothyronine (T3) syndrome could be a powerful prognostic factor for acute stroke; yet, a prognostic role for low T3 has not been given enough importance in stroke management. This meta-analysis aimed to evaluate whether low T3 among acute stroke patients could be used as a prognostic biomarker for stroke severity, functional outcome, and mortality. Studies that investigated low T3 prognostic roles in acute stroke patients were sought from PubMed/Medline, Embase, and Cochrane databases through 11/23/2016. Pooled estimates of baseline stroke severity, mortality, and functional outcomes were assessed from fixed-effect (FE) and random-effects (RE) models. Eighteen studies met the inclusion criteria. Six studies (1,203 patients) provided data for low-T3 and normal-T3 patients and were meta-analyzed. Using the FE model, pooled results revealed low-T3 patients exhibited a significantly higher stroke severity, as assessed by the National Institutes of Health Stroke Scale (NIHSS) score at admission (mean difference = 3.18; 95%CI = 2.74, 3.63; I 2  = 61.9%), had 57% higher risk of developing poor functional outcome (RR = 1.57; 95%CI = 1.33,1.8), and had 83% higher odds of mortality (Peto-OR = 1.83; 95%CI = 1.21, 1.99) compared to normal-T3 patients. In a univariate meta-regression analysis, the low-T3 and stroke severity association was reduced in studies with higher smokers% (slope = -0.11; P = 0.02), higher hypertension% (slope = -0.11; P = 0.047), older age (slope = -0.54; P = 0.02), or longer follow-up (slope = -0/17, P < 0.01). RE models yielded similar results. No significant publication bias was observed for either outcome using Begg's and Egger's tests. Low-T3 syndrome in acute stroke patients is an effective prognostic factor for predicting greater baseline stroke severity, poorer functional outcome, and higher overall mortality risk. Copyright © 2018 Elsevier B.V. All rights reserved.

A framework for quantifying net benefits of alternative prognostic models.

PubMed

Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

2012-01-30

New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd.

Outpatient management of pulmonary embolism in cancer: data on a prospective cohort of 138 consecutive patients.

PubMed

Font, Carme; Carmona-Bayonas, Alberto; Fernández-Martinez, Aranzazu; Beato, Carmen; Vargas, Andrés; Gascon, Pere; Otero, Remedios

2014-03-01

The purpose of this prospective cohort study was to assess the feasibility of outpatient treatment in patients with cancer and objectively confirmed pulmonary embolism (PE), and to compare the performance of the different prognostic scales available in this setting. Patients were selected for outpatient management according to a set of exclusion criteria. Outcomes at 30 and 90 days of follow-up included thromboembolic recurrences, major bleeding, and all-cause death. The performance of 4 prognostic scales (Pulmonary Embolism Severity Index, Geneva Prognostic Score, POMPE-C, and Registro Informatizado de Enfermedad Tromboembólica [RIETE registry]) was evaluated. Of 138 patients, 62 (45%) were managed as outpatients. Incidental PE constituted 47% of the sample. Most patients treated at home had an incidentally detected PE (89%). The rate of recurrence and major bleeding events was similar in both groups. Mortality rates were higher for patients admitted to the hospital compared with outpatients at 30 days (18% vs 3%; P=.06) and 90 days (34% vs 10%; P=.001) of follow-up. None of the patients selected for home treatment required further admission because of PE complications. None of the prognostic models developed for symptomatic PE was significantly associated with 30-day mortality. Improved survival outcomes were observed in incidentally detected PEs compared with acute symptomatic events (overall mortality rates, 3.2% vs 18.4%; P=.006). A large proportion of patients with cancer and PE may be safely treated as outpatients, especially those with incidental PE. Cancer-specific prognostic scales including incidental PE should be developed for the optimal management of PE in this setting.

Computational analysis identifies putative prognostic biomarkers of pathological scarring in skin wounds.

PubMed

Nagaraja, Sridevi; Chen, Lin; DiPietro, Luisa A; Reifman, Jaques; Mitrophanov, Alexander Y

2018-02-20

Pathological scarring in wounds is a prevalent clinical outcome with limited prognostic options. The objective of this study was to investigate whether cellular signaling proteins could be used as prognostic biomarkers of pathological scarring in traumatic skin wounds. We used our previously developed and validated computational model of injury-initiated wound healing to simulate the time courses for platelets, 6 cell types, and 21 proteins involved in the inflammatory and proliferative phases of wound healing. Next, we analysed thousands of simulated wound-healing scenarios to identify those that resulted in pathological (i.e., excessive) scarring. Then, we identified candidate proteins that were elevated (or decreased) at the early stages of wound healing in those simulations and could therefore serve as predictive biomarkers of pathological scarring outcomes. Finally, we performed logistic regression analysis and calculated the area under the receiver operating characteristic curve to quantitatively assess the predictive accuracy of the model-identified putative biomarkers. We identified three proteins (interleukin-10, tissue inhibitor of matrix metalloproteinase-1, and fibronectin) whose levels were elevated in pathological scars as early as 2 weeks post-wounding and could predict a pathological scarring outcome occurring 40 days after wounding with 80% accuracy. Our method for predicting putative prognostic wound-outcome biomarkers may serve as an effective means to guide the identification of proteins predictive of pathological scarring.

A review on prognostic techniques for non-stationary and non-linear rotating systems

NASA Astrophysics Data System (ADS)

Kan, Man Shan; Tan, Andy C. C.; Mathew, Joseph

2015-10-01

The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

Temporal Causal Diagrams for Diagnosing Failures in Cyber Physical Systems

DTIC Science & Technology

2014-10-02

11 P Open Close C Close none St Close Table 3. Transition Information for Distance Relay’s behavioral model. Rows 1-7 deal with the anomaly detection ... PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 238 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 fall into the Zone settings of...OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 239 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 event systems has

A review on prognostics and health monitoring of Li-ion battery

NASA Astrophysics Data System (ADS)

Zhang, Jingliang; Lee, Jay

2011-08-01

The functionality and reliability of Li-ion batteries as major energy storage devices have received more and more attention from a wide spectrum of stakeholders, including federal/state policymakers, business leaders, technical researchers, environmental groups and the general public. Failures of Li-ion battery not only result in serious inconvenience and enormous replacement/repair costs, but also risk catastrophic consequences such as explosion due to overheating and short circuiting. In order to prevent severe failures from occurring, and to optimize Li-ion battery maintenance schedules, breakthroughs in prognostics and health monitoring of Li-ion batteries, with an emphasis on fault detection, correction and remaining-useful-life prediction, must be achieved. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring, and summarizes the techniques, algorithms and models used for state-of-charge (SOC) estimation, current/voltage estimation, capacity estimation and remaining-useful-life (RUL) prediction.

GPU Accelerated Prognostics

NASA Technical Reports Server (NTRS)

Gorospe, George E., Jr.; Daigle, Matthew J.; Sankararaman, Shankar; Kulkarni, Chetan S.; Ng, Eley

2017-01-01

Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.

Comparison of prognostic and diagnostic approached to modeling evapotranspiration in the Nile river basin

USDA-ARS?s Scientific Manuscript database

Actual evapotranspiration (ET) can be estimated using both prognostic and diagnostic modeling approaches, providing independent yet complementary information for hydrologic applications. Both approaches have advantages and disadvantages. When provided with temporally continuous atmospheric forcing d...

Upper digestive bleeding in cirrhosis. Post-therapeutic outcome and prognostic indicators.

PubMed

D'Amico, Gennaro; De Franchis, Roberto

2003-09-01

Several treatments have been proven to be effective for variceal bleeding in patients with cirrhosis. The aim of this multicenter, prospective, cohort study was to assess how these treatments are used in clinical practice and what are the posttherapeutic prognosis and prognostic indicators of upper digestive bleeding in patients with cirrhosis. A training set of 291 and a test set of 174 bleeding cirrhotic patients were included. Treatment was according to the preferences of each center and the follow-up period was 6 weeks. Predictive rules for 5-day failure (uncontrolled bleeding, rebleeding, or death) and 6-week mortality were developed by the logistic model in the training set and validated in the test set. Initial treatment controlled bleeding in 90% of patients, including vasoactive drugs in 27%, endoscopic therapy in 10%, combined (endoscopic and vasoactive) in 45%, balloon tamponade alone in 1%, and none in 17%. The 5-day failure rate was 13%, 6-week rebleeding was 17%, and mortality was 20%. Corresponding findings for variceal versus nonvariceal bleeding were 15% versus 7% (P =.034), 19% versus 10% (P =.019), and 20% versus 15% (P =.22). Active bleeding on endoscopy, hematocrit levels, aminotransferase levels, Child-Pugh class, and portal vein thrombosis were significant predictors of 5-day failure; alcohol-induced etiology, bilirubin, albumin, encephalopathy, and hepatocarcinoma were predictors of 6-week mortality. Prognostic reassessment including blood transfusions improved the predictive accuracy. All the developed prognostic models were superior to the Child-Pugh score. In conclusion, prognosis of digestive bleeding in cirrhosis has much improved over the past 2 decades. Initial treatment stops bleeding in 90% of patients. Accurate predictive rules are provided for early recognition of high-risk patients.

Proposal and validation of prognostic scoring systems for IgG and IgA monoclonal gammopathies of undetermined significance.

PubMed

Rossi, Francesca; Petrucci, Maria Teresa; Guffanti, Andrea; Marcheselli, Luigi; Rossi, Davide; Callea, Vincenzo; Vincenzo, Federico; De Muro, Marianna; Baraldi, Alessandra; Villani, Oreste; Musto, Pellegrino; Bacigalupo, Andrea; Gaidano, Gianluca; Avvisati, Giuseppe; Goldaniga, Maria; Depaoli, Lorenzo; Baldini, Luca

2009-07-01

The presenting clinico-hematologic features of 1,283 patients with IgG and IgA monoclonal gammopathies of undetermined significance (MGUS) were correlated with the frequency of evolution into multiple myeloma (MM). Two IgG MGUS populations were evaluated: a training sample (553 patients) and a test sample (378 patients); the IgA MGUS population consisted of 352 patients. Forty-seven of the 553 training group patients and 22 of 378 test group IgG patients developed MM after a median follow-up of 6.7 and 3.6 years, respectively. Multivariate analysis showed that serum monoclonal component (MC) levels of < or =1.5 g/dL, the absence of light-chain proteinuria and normal serum polyclonal immunoglobulin levels defined a prognostically favorable subset of patients, and could be used to stratify the patients into three groups at different 10-year risk of evolution (hazard ratio, 1.0, 5.04, 11.2; P < 0.001). This scoring system was validated in the test sample. Thirty of the 352 IgA patients developed MM after a median follow-up of 4.8 years, and multivariate analysis showed that hemoglobin levels of <12.5 g/dL and reduced serum polyclonal immunoglobulin correlated with progression. A pooled statistical analysis of all of the patients confirmed the validity of Mayo Clinic risk model showing that IgA class, serum MC levels, and light-chain proteinuria are the most important variables correlated with disease progression. Using simple variables, we validated a prognostic model for IgG MGUS. Among the IgA cases, the possible prognostic role of hemoglobin emerged in addition to a decrease in normal immunoglobulin levels.

Prognosis Research Strategy (PROGRESS) 2: prognostic factor research.

PubMed

Riley, Richard D; Hayden, Jill A; Steyerberg, Ewout W; Moons, Karel G M; Abrams, Keith; Kyzas, Panayiotis A; Malats, Núria; Briggs, Andrew; Schroter, Sara; Altman, Douglas G; Hemingway, Harry

2013-01-01

Prognostic factor research aims to identify factors associated with subsequent clinical outcome in people with a particular disease or health condition. In this article, the second in the PROGRESS series, the authors discuss the role of prognostic factors in current clinical practice, randomised trials, and developing new interventions, and explain why and how prognostic factor research should be improved.

Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

PubMed Central

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G. M.; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M.

2014-01-01

Background In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. Materials and Methods We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). Results We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66). Conclusion The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making. PMID:24695692

Extensions to regret-based decision curve analysis: an application to hospice referral for terminal patients.

PubMed

Tsalatsanis, Athanasios; Barnes, Laura E; Hozo, Iztok; Djulbegovic, Benjamin

2011-12-23

Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed. We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care. The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available. We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned.

Extensions to Regret-based Decision Curve Analysis: An application to hospice referral for terminal patients

PubMed Central

2011-01-01

Background Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed. Methods We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care. Results The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available. Conclusions We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned. PMID:22196308

A Prognostic Gene Expression Profile That Predicts Circulating Tumor Cell Presence in Breast Cancer Patients

PubMed Central

Molloy, Timothy J.; Roepman, Paul; Naume, Bjørn; van't Veer, Laura J.

2012-01-01

The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into “good prognosis” or “poor prognosis” are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the “CTC profile” also provided prognostic information independent of the well-established and powerful ‘70-gene’ prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays. PMID:22384245

Two-protein signature of novel serological markers apolipoprotein-A2 and serum amyloid alpha predicts prognosis in patients with metastatic renal cell cancer and improves the currently used prognostic survival models.

PubMed

Vermaat, J S; van der Tweel, I; Mehra, N; Sleijfer, S; Haanen, J B; Roodhart, J M; Engwegen, J Y; Korse, C M; Langenberg, M H; Kruit, W; Groenewegen, G; Giles, R H; Schellens, J H; Beijnen, J H; Voest, E E

2010-07-01

In metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering Cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with overall survival (OS). Sera from 114 mRCC patients were screened by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS). Identified proteins were related to OS. Three proteins were subsequently validated with enzyme-linked immunosorbent assays and immunoturbidimetry. Prognostic models were statistically bootstrapped to correct for overestimation. SELDI-TOF MS detected 10 proteins associated with OS. Of these, apolipoprotein A2 (ApoA2), serum amyloid alpha (SAA) and transthyretin were validated for their association with OS (P = 5.5 x 10(-9), P = 1.1 x 10(-7) and P = 0.0004, respectively). Combining ApoA2 and SAA yielded a prognostic two-protein signature [Akaike's Information Criteria (AIC) = 732, P = 5.2 x 10(-7)]. Including previously identified prognostic factors, multivariable Cox regression analysis revealed ApoA2, SAA, lactate dehydrogenase, performance status and number of metastasis sites as independent factors for survival. Using these five factors, categorization of patients into three risk groups generated a novel protein-based model predicting patient prognosis (AIC = 713, P = 4.3 x 10(-11)) more robustly than the MSKCC model (AIC = 729, P = 1.3 x 10(-7)). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients. Proteomics and subsequent validation yielded two novel prognostic markers and survival models which improved prediction of OS in mRCC patients over commonly used risk models. Implementation of these models has the potential to improve current risk stratification, although prospective validation will still be necessary.

Prognostics Approach for Power MOSFET Under Thermal-Stress

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon Celaya; Saxena, Abhinav; Kulkarni, Chetan S.; Saha, Sankalita; Goebel, Kai

2012-01-01

The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes thermal and power cycling to accelerate the life of the devices. The major failure mechanism for the stress conditions is dieattachment degradation, typical for discrete devices with leadfree solder die attachment. It has been determined that dieattach degradation results in an increase in ON-state resistance due to its dependence on junction temperature. Increasing resistance, thus, can be used as a precursor of failure for the die-attach failure mechanism under thermal stress. A feature based on normalized ON-resistance is computed from in-situ measurements of the electro-thermal response. An Extended Kalman filter is used as a model-based prognostics techniques based on the Bayesian tracking framework. The proposed prognostics technique reports on preliminary work that serves as a case study on the prediction of remaining life of power MOSFETs and builds upon the work presented in [1]. The algorithm considered in this study had been used as prognostics algorithm in different applications and is regarded as suitable candidate for component level prognostics. This work attempts to further the validation of such algorithm by presenting it with real degradation data including measurements from real sensors, which include all the complications (noise, bias, etc.) that are regularly not captured on simulated degradation data. The algorithm is developed and tested on the accelerated aging test timescale. In real world operation, the timescale of the degradation process and therefore the RUL predictions will be considerable larger. It is hypothesized that even though the timescale will be larger, it remains constant through the degradation process and the algorithm and model would still apply under the slower degradation process. By using accelerated aging data with actual device measurements and real sensors (no simulated behavior), we are attempting to assess how such algorithm behaves under realistic conditions.

Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation.

PubMed

Zaccardi, Francesco; Webb, David R; Davies, Melanie J; Dhalwani, Nafeesa N; Gray, Laura J; Chatterjee, Sudesna; Housley, Gemma; Shaw, Dominick; Hatton, James W; Khunti, Kamlesh

2017-06-01

Hospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia. We used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score ('base' model). In the second model, we added to the 'base' model the 20 most common medical conditions and applied a stepwise backward selection of variables ('disease' model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics. In derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models. This simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia.

Plants and pixels: Comparing phenologies from the ground and from space (Invited)

NASA Astrophysics Data System (ADS)

Rutishauser, T.; Stoekli, R.; Jeanneret, F.; Peñuelas, J.

2010-12-01

Changes in the seasonality of life cycles of plants as recorded in phenological observations have been widely analysed at the species level with data available for many decades back in time. At the same time, seasonality changes in satellite-based observations and prognostic phenology models comprise information at the pixel-size or landscape scale. Change analysis of satellite-based records is restricted due to relatively short satellite records that further include gaps while model-based analyses are biased due to current model deficiencies. At 30 selected sites across Europe, we analysed three different sources of plant seasonality during the 1971-2000 period. Data consisted of (1) species-specific development stages of flowering and leave-out with different species observed at each site. (2) We used a synthetic phenological metric that integrates the common interannual phenological signal across all species at one site. (3) We estimated daily Leaf Area Index with a prognostic phenology model. The prior uncertainties of the model’s empirical parameter space are constrained by assimilating the Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS). We extracted the day of year when the 25%, 50% and 75% thresholds were passed each spring. The question arises how the three phenological signals compare and correlate across climate zones in Europe. Is there a match between single species observations, species-based ground-observed metrics and the landscape-scale prognostic model? Are there single key-species across Europe that best represent a landscape scale measure from the prognostic model? Can one source substitute another and serve as proxy-data? What can we learn from potential mismatches? Focusing on changes in spring this contribution presents first results of an ongoing comparison study from a number of European test sites that will be extended to the pan-European phenological database Cost725 and PEP725.

Remote sensing data assimilation for a prognostic phenology model

Treesearch

R. Stockli; T. Rutishauser; D. Dragoni; J. O' Keefe; P. E. Thornton; M. Jolly; L. Lu; A. S. Denning

2008-01-01

Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active...

On Statistical Modeling of Sequencing Noise in High Depth Data to Assess Tumor Evolution

NASA Astrophysics Data System (ADS)

Rabadan, Raul; Bhanot, Gyan; Marsilio, Sonia; Chiorazzi, Nicholas; Pasqualucci, Laura; Khiabanian, Hossein

2018-07-01

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

On Statistical Modeling of Sequencing Noise in High Depth Data to Assess Tumor Evolution

NASA Astrophysics Data System (ADS)

Rabadan, Raul; Bhanot, Gyan; Marsilio, Sonia; Chiorazzi, Nicholas; Pasqualucci, Laura; Khiabanian, Hossein

2017-12-01

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

A framework for quantifying net benefits of alternative prognostic models‡

PubMed Central

Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

2012-01-01

New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21905066

A Novel UAV Electric Propulsion Testbed for Diagnostics and Prognostics

NASA Technical Reports Server (NTRS)

Gorospe, George E., Jr.; Kulkarni, Chetan S.

2017-01-01

This paper presents a novel hardware-in-the-loop (HIL) testbed for systems level diagnostics and prognostics of an electric propulsion system used in UAVs (unmanned aerial vehicle). Referencing the all electric, Edge 540T aircraft used in science and research by NASA Langley Flight Research Center, the HIL testbed includes an identical propulsion system, consisting of motors, speed controllers and batteries. Isolated under a controlled laboratory environment, the propulsion system has been instrumented for advanced diagnostics and prognostics. To produce flight like loading on the system a slave motor is coupled to the motor under test (MUT) and provides variable mechanical resistance, and the capability of introducing nondestructive mechanical wear-like frictional loads on the system. This testbed enables the verification of mathematical models of each component of the propulsion system, the repeatable generation of flight-like loads on the system for fault analysis, test-to-failure scenarios, and the development of advanced system level diagnostics and prognostics methods. The capabilities of the testbed are extended through the integration of a LabVIEW-based client for the Live Virtual Constructive Distributed Environment (LVCDC) Gateway which enables both the publishing of generated data for remotely located observers and prognosers and the synchronization the testbed propulsion system with vehicles in the air. The developed HIL testbed gives researchers easy access to a scientifically relevant portion of the aircraft without the overhead and dangers encountered during actual flight.

Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest Intracranial Tumor Volume for Stereotactic Radiosurgery-Treated Brain Metastasis Patients.

PubMed

Hirshman, Brian R; Wilson, Bayard; Ali, Mir Amaan; Proudfoot, James A; Koiso, Takao; Nagano, Osamu; Carter, Bob S; Serizawa, Toru; Yamamoto, Masaaki; Chen, Clark C

2018-04-01

Two intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV). To determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components-LITV-with CITV. We compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients. In both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model. After accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients.

Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance.

PubMed

Sammut, Eva C; Villa, Adriana D M; Di Giovine, Gabriella; Dancy, Luke; Bosio, Filippo; Gibbs, Thomas; Jeyabraba, Swarna; Schwenke, Susanne; Williams, Steven E; Marber, Michael; Alfakih, Khaled; Ismail, Tevfik F; Razavi, Reza; Chiribiri, Amedeo

2018-05-01

This study sought to evaluate the prognostic usefulness of visual and quantitative perfusion cardiac magnetic resonance (CMR) ischemic burden in an unselected group of patients and to assess the validity of consensus-based ischemic burden thresholds extrapolated from nuclear studies. There are limited data on the prognostic value of assessing myocardial ischemic burden by CMR, and there are none using quantitative perfusion analysis. Patients with suspected coronary artery disease referred for adenosine-stress perfusion CMR were included (n = 395; 70% male; age 58 ± 13 years). The primary endpoint was a composite of cardiovascular death, nonfatal myocardial infarction, aborted sudden death, and revascularization after 90 days. Perfusion scans were assessed visually and with quantitative analysis. Cross-validated Cox regression analysis and net reclassification improvement were used to assess the incremental prognostic value of visual or quantitative perfusion analysis over a baseline clinical model, initially as continuous covariates, then using accepted thresholds of ≥2 segments or ≥10% myocardium. After a median 460 days (interquartile range: 190 to 869 days) follow-up, 52 patients reached the primary endpoint. At 2 years, the addition of ischemic burden was found to increase prognostic value over a baseline model of age, sex, and late gadolinium enhancement (baseline model area under the curve [AUC]: 0.75; visual AUC: 0.84; quantitative AUC: 0.85). Dichotomized quantitative ischemic burden performed better than visual assessment (net reclassification improvement 0.043 vs. 0.003 against baseline model). This study was the first to address the prognostic benefit of quantitative analysis of perfusion CMR and to support the use of consensus-based ischemic burden thresholds by perfusion CMR for prognostic evaluation of patients with suspected coronary artery disease. Quantitative analysis provided incremental prognostic value to visual assessment and established risk factors, potentially representing an important step forward in the translation of quantitative CMR perfusion analysis to the clinical setting. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

[PROGNOSTIC MODELS IN MODERN MANAGEMENT OF VULVAR CANCER].

PubMed

Tsvetkov, Ch; Gorchev, G; Tomov, S; Nikolova, M; Genchev, G

2016-01-01

The aim of the research was to evaluate and analyse prognosis and prognostic factors in patients with squamous cell vulvar carcinoma after primary surgery with individual approach applied during the course of treatment. In the period between January 2000 and July 2010, 113 patients with squamous cell carcinoma of the vulva were diagnosed and operated on at Gynecologic Oncology Clinic of Medical University, Pleven. All the patients were monitored at the same clinic. Individual approach was applied to each patient and whenever it was possible, more conservative operative techniques were applied. The probable clinicopathological characteristics influencing the overall survival and recurrence free survival were analyzed. Univariate statistical analysis and Cox regression analysis were made in order to evaluate the characteristics, which were statistically significant for overall survival and survival without recurrence. A multivariate logistic regression analysis (Forward Wald procedure) was applied to evaluate the combined influence of the significant factors. While performing the multivariate analysis, the synergic effect of the independent prognostic factors of both kinds of survivals was also evaluated. Approaching individually each patient, we applied the following operative techniques: 1. Deep total radical vulvectomy with separate incisions for lymph dissection (LD) or without dissection--68 (60.18 %) patients. 2. En-bloc vulvectomy with bilateral LD without vulva reconstruction--10 (8.85%) 3. Modified radical vulvactomy (hemivulvectomy, patial vulvactomy)--25 (22.02%). 4. wide-local excision--3 (2.65%). 5. Simple (total /partial) vulvectomy--5 (4.43%) patients. 6. En-bloc resection with reconstruction--2 (1.77%) After a thorough analysis of the overall survival and recurrence free survival, we made the conclusion that the relapse occurrence and clinical stage of FIGO were independent prognostic factors for overall survival and the independent prognostic factors for recurrence free survival were: metastatic inguinal nodes (unilateral or bilateral), tumor size (above or below 3 cm) and lymphovascular space invasion. On the basis of these results we created two prognostic models: 1. A prognostic model of overall survival 2. A prognostic model for survival without recurrence. Following the surgical staging of the disease, were able to gather and analyse important clinicopathological indexes, which gave us the opportunity to form prognostic groups for overall survival and recurrence-free survival.

Comprehensive Analysis of the Neutrophil-to-Lymphocyte Ratio for Preoperative Prognostic Prediction Nomogram in Gastric Cancer.

PubMed

Choi, Jong-Ho; Suh, Yun-Suhk; Choi, Yunhee; Han, Jiyeon; Kim, Tae Han; Park, Shin-Hoo; Kong, Seong-Ho; Lee, Hyuk-Joon; Yang, Han-Kwang

2018-02-01

The role of neutrophil-to-lymphocyte ratio (NLR) and preoperative prediction model in gastric cancer is controversial, while postoperative prognostic models are available. This study investigated NLR as a preoperative prognostic indicator in gastric cancer. We reviewed patients with primary gastric cancer who underwent surgery during 2007-2010. Preoperative clinicopathologic factors were analyzed with their interaction and used to develop a prognosis prediction nomogram. That preoperative prediction nomogram was compared to a nomogram using pTNM or a historical postoperative prediction nomogram. The contribution of NLR to a preoperative nomogram was evaluated with integrated discrimination improvement (IDI). Using 2539 records, multivariable analysis revealed that NLR was one of the independent prognostic factors and had a significant interaction with only age among other preoperative factors (especially significant in patients < 50 years old). NLR was constantly significant between 1.1 and 3.1 without any distinctive cutoff value. Preoperative prediction nomogram using NLR showed a Harrell's C-index of 0.79 and an R 2 of 25.2%, which was comparable to the C-index of 0.78 and 0.82 and R 2 of 26.6 and 25.8% from nomogram using pTNM and a historical postoperative prediction nomogram, respectively. IDI of NLR to nomogram in the overall population was 0.65%, and that of patients < 50 years old was 2.72%. NLR is an independent prognostic factor for gastric cancer, especially in patients < 50 years old. A preoperative prediction nomogram using NLR can predict prognosis of gastric cancer as effectively as pTNM and a historical postoperative prediction nomogram.

Evaluating biomarkers for prognostic enrichment of clinical trials.

PubMed

Kerr, Kathleen F; Roth, Jeremy; Zhu, Kehao; Thiessen-Philbrook, Heather; Meisner, Allison; Wilson, Francis Perry; Coca, Steven; Parikh, Chirag R

2017-12-01

A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions.

PubMed

Roelen, Corné A M; Bültmann, Ute; Groothoff, Johan W; Twisk, Jos W R; Heymans, Martijn W

2015-11-01

Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (≥ 30) SA days and high (≥ 3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic models. The purpose of this study was to investigate fatigue as new predictor in SA prognostic models by using risk reclassification methods and measures. This was a prospective cohort study with 1-year follow-up of 1,137 office workers. Fatigue was measured at baseline with the 20-item checklist individual strength and added to the existing SA prognostic models. SA days and episodes during 1-year follow-up were retrieved from an occupational health service register. The added value of fatigue was investigated with Net Reclassification Index (NRI) and integrated discrimination improvement (IDI) measures. In total, 579 (51 %) office workers had complete data for analysis. Fatigue was prospectively associated with both high SA days and episodes. The NRI revealed that adding fatigue to the SA days model correctly reclassified workers with high SA days, but incorrectly reclassified workers without high SA days. The IDI indicated no improvement in risk discrimination by the SA days model. Both NRI and IDI showed that the prognostic model predicting high SA episodes did not improve when fatigue was added as predictor variable. In the present study, fatigue increased false-positive rates which may reduce the cost-effectiveness of interventions for preventing SA.

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Newman, John A.; Warner, James E.; Leser, William P.; Hochhalter, Jacob D.; Yuan, Fuh-Gwo

2016-01-01

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.

A consensus prognostic gene expression classifier for ER positive breast cancer

PubMed Central

Teschendorff, Andrew E; Naderi, Ali; Barbosa-Morais, Nuno L; Pinder, Sarah E; Ellis, Ian O; Aparicio, Sam; Brenton, James D; Caldas, Carlos

2006-01-01

Background A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. Results Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. Conclusion The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors. PMID:17076897

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-20

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)

PubMed Central

Reitsma, Johannes B.; Altman, Douglas G.; Moons, Karel G.M.

2015-01-01

Background— Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. Methods— The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. Results— The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. Conclusions— To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25561516

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement

PubMed Central

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-01

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25562432

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Royal College of Obstetricians and Gynaecologists.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-13

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 The Authors.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.

PubMed

Pereira, Telma; Lemos, Luís; Cardoso, Sandra; Silva, Dina; Rodrigues, Ana; Santana, Isabel; de Mendonça, Alexandre; Guerreiro, Manuela; Madeira, Sara C

2017-07-19

Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.

Prognostic Impact of Indocyanine Green Plasma Disappearance Rate in Hepatocellular Carcinoma Patients after Radiofrequency Ablation: A Prognostic Nomogram Study

PubMed Central

Azumi, Motoi; Suda, Takeshi; Terai, Shuji; Akazawa, Kouhei

2017-01-01

Objective Radiofrequency ablation has been used widely for the local ablation of hepatocellular carcinoma, particularly in its early stages. The study aim was to identify significant prognostic factors and develop a predictive nomogram for patients with hepatocellular carcinoma who have undergone radiofrequency ablation. We also developed the formula to predict the probability of 3- and 5-year overall survival based on clinical variables. Methods We retrospectively studied 96 consecutive patients with hepatocellular carcinoma who had undergone radiofrequency ablation as a first-line treatment. Independent and significant factors affecting the overall survival were selected using a Cox proportional hazards model, and a prognostic nomogram was developed based on these factors. The predictive accuracy of the nomogram was determined by Harrell's concordance index and compared with the Cancer of the Liver Italian Program score and Japan Integrated Staging score. Results A multivariate analysis revealed that age, indocyanine green plasma disappearance rate, and log(des-gamma-carboxy prothrombin) level were independent and significant factors influencing the overall survival. The nomogram was based on these three factors. The mean concordance index of the nomogram was 0.74±0.08, which was significantly better than that of conventional staging systems using the Cancer of the Liver Italian Program score (0.54±0.03) and Japan Integrated Staging score (0.59±0.07). Conclusion This study suggested that the indocyanine green plasma disappearance rate and age at radiofrequency ablation (RFA) and des-gamma-carboxy-prothrombin (DCP) are good predictors of the prognosis in hepatocellular carcinoma patients after radiofrequency ablation. We successfully developed a nomogram using obtainable variables before treatment. PMID:28458303

Prognostic Impact of Indocyanine Green Plasma Disappearance Rate in Hepatocellular Carcinoma Patients after Radiofrequency Ablation: A Prognostic Nomogram Study.

PubMed

Azumi, Motoi; Suda, Takeshi; Terai, Shuji; Akazawa, Kouhei

2017-01-01

Objective Radiofrequency ablation has been used widely for the local ablation of hepatocellular carcinoma, particularly in its early stages. The study aim was to identify significant prognostic factors and develop a predictive nomogram for patients with hepatocellular carcinoma who have undergone radiofrequency ablation. We also developed the formula to predict the probability of 3- and 5-year overall survival based on clinical variables. Methods We retrospectively studied 96 consecutive patients with hepatocellular carcinoma who had undergone radiofrequency ablation as a first-line treatment. Independent and significant factors affecting the overall survival were selected using a Cox proportional hazards model, and a prognostic nomogram was developed based on these factors. The predictive accuracy of the nomogram was determined by Harrell's concordance index and compared with the Cancer of the Liver Italian Program score and Japan Integrated Staging score. Results A multivariate analysis revealed that age, indocyanine green plasma disappearance rate, and log (des-gamma-carboxy prothrombin) level were independent and significant factors influencing the overall survival. The nomogram was based on these three factors. The mean concordance index of the nomogram was 0.74±0.08, which was significantly better than that of conventional staging systems using the Cancer of the Liver Italian Program score (0.54±0.03) and Japan Integrated Staging score (0.59±0.07). Conclusion This study suggested that the indocyanine green plasma disappearance rate and age at radiofrequency ablation (RFA) and des-gamma-carboxy-prothrombin (DCP) are good predictors of the prognosis in hepatocellular carcinoma patients after radiofrequency ablation. We successfully developed a nomogram using obtainable variables before treatment.

Prognostic value of the new Grade Groups in Prostate Cancer: a multi-institutional European validation study.

PubMed

Mathieu, R; Moschini, M; Beyer, B; Gust, K M; Seisen, T; Briganti, A; Karakiewicz, P; Seitz, C; Salomon, L; de la Taille, A; Rouprêt, M; Graefen, M; Shariat, S F

2017-06-01

We aimed to assess the prognostic relevance of the new Grade Groups in Prostate Cancer (PCa) within a large cohort of European men treated with radical prostatectomy (RP). Data from 27 122 patients treated with RP at seven European centers were analyzed. We investigated the prognostic performance of the new Grade Groups (based on Gleason score 3+3, 3+4, 4+3, 8 and 9-10) on biopsy and RP specimen, adjusted for established clinical and pathological characteristics. Multivariable Cox proportional hazards regression models assessed the association of new Grade Groups with biochemical recurrence (BCR). Prognostic accuracies of the models were assessed using Harrell's C-index. Median follow-up was 29 months (interquartile range, 13-54). The 4-year estimated BCR-free survival (bRFS) for biopsy Grade Groups 1-5 were 91.3, 81.6, 69.8, 60.3 and 44.4%, respectively. The 4-year estimated bRFS for RP Grade Groups 1-5 were 96.1%, 86.7%, 67.0%, 63.1% and 41.0%, respectively. Compared with Grade Group 1, all other Grade Groups based both on biopsy and RP specimen were independently associated with a lower bRFS (all P<0.01). Adjusted pairwise comparisons revealed statistically differences between all Grade Groups, except for group 3 and 4 on RP specimen (P=0.10). The discriminations of the multivariable base prognostic models based on the current three-tier and the new five-tier systems were not clinically different (0.3 and 0.9% increase in discrimination for clinical and pathological model). We validated the independent prognostic value of the new Grade Groups on biopsy and RP specimen from European PCa men. However, it does not improve the accuracies of prognostic models by a clinically significant margin. Nevertheless, this new classification may help physicians and patients estimate disease aggressiveness with a user-friendly, clinically relevant and reproducible method.

An inflammation-based cumulative prognostic score system in patients with diffuse large B cell lymphoma in rituximab era.

PubMed

Sun, Feifei; Zhu, Jia; Lu, Suying; Zhen, Zijun; Wang, Juan; Huang, Junting; Ding, Zonghui; Zeng, Musheng; Sun, Xiaofei

2018-01-02

Systemic inflammatory parameters are associated with poor outcomes in malignant patients. Several inflammation-based cumulative prognostic score systems were established for various solid tumors. However, there is few inflammation based cumulative prognostic score system for patients with diffuse large B cell lymphoma (DLBCL). We retrospectively reviewed 564 adult DLBCL patients who had received rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone (R-CHOP) therapy between Nov 1 2006 and Dec 30 2013 and assessed the prognostic significance of six systemic inflammatory parameters evaluated in previous studies by univariate and multivariate analysis:C-reactive protein(CRP), albumin levels, the lymphocyte-monocyte ratio (LMR), the neutrophil-lymphocyte ratio(NLR), the platelet-lymphocyte ratio(PLR)and fibrinogen levels. Multivariate analysis identified CRP, albumin levels and the LMR are three independent prognostic parameters for overall survival (OS). Based on these three factors, we constructed a novel inflammation-based cumulative prognostic score (ICPS) system. Four risk groups were formed: group ICPS = 0, ICPS = 1, ICPS = 2 and ICPS = 3. Advanced multivariate analysis indicated that the ICPS model is a prognostic score system independent of International Prognostic Index (IPI) for both progression-free survival (PFS) (p < 0.001) and OS (p < 0.001). The 3-year OS for patients with ICPS =0, ICPS =1, ICPS =2 and ICPS =3 were 95.6, 88.2, 76.0 and 62.2%, respectively (p < 0.001). The 3-year PFS for patients with ICPS = 0-1, ICPS = 2 and ICPS = 3 were 84.8, 71.6 and 54.5%, respectively (p < 0.001). The prognostic value of the ICPS model indicated that the degree of systemic inflammatory status was associated with clinical outcomes of patients with DLBCL in rituximab era. The ICPS model was shown to classify risk groups more accurately than any single inflammatory prognostic parameters. These findings may be useful for identifying candidates for further inflammation-related mechanism research or novel anti-inflammation target therapies.

State Space Formulation of Nonlinear Vibration Responses Collected from a Dynamic Rotor-Bearing System: An Extension of Bearing Diagnostics to Bearing Prognostics

PubMed Central

Tse, Peter W.; Wang, Dong

2017-01-01

Bearings are widely used in various industries to support rotating shafts. Their failures accelerate failures of other adjacent components and may cause unexpected machine breakdowns. In recent years, nonlinear vibration responses collected from a dynamic rotor-bearing system have been widely analyzed for bearing diagnostics. Numerous methods have been proposed to identify different bearing faults. However, these methods are unable to predict the future health conditions of bearings. To extend bearing diagnostics to bearing prognostics, this paper reports the design of a state space formulation of nonlinear vibration responses collected from a dynamic rotor-bearing system in order to intelligently predict bearing remaining useful life (RUL). Firstly, analyses of nonlinear vibration responses were conducted to construct a bearing health indicator (BHI) so as to assess the current bearing health condition. Secondly, a state space model of the BHI was developed to mathematically track the health evolution of the BHI. Thirdly, unscented particle filtering was used to predict bearing RUL. Lastly, a new bearing acceleration life testing setup was designed to collect natural bearing degradation data, which were used to validate the effectiveness of the proposed bearing prognostic method. Results show that the prediction accuracy of the proposed bearing prognostic method is promising and the proposed bearing prognostic method is able to reflect future bearing health conditions. PMID:28216586

An 8-gene qRT-PCR-based gene expression score that has prognostic value in early breast cancer

PubMed Central

2010-01-01

Background Gene expression profiling may improve prognostic accuracy in patients with early breast cancer. Our objective was to demonstrate that it is possible to develop a simple molecular signature to predict distant relapse. Methods We included 153 patients with stage I-II hormonal receptor-positive breast cancer. RNA was isolated from formalin-fixed paraffin-embedded samples and qRT-PCR amplification of 83 genes was performed with gene expression assays. The genes we analyzed were those included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Index. The association among gene expression, clinical variables and distant metastasis-free survival was analyzed using Cox regression models. Results An 8-gene prognostic score was defined. Distant metastasis-free survival at 5 years was 97% for patients defined as low-risk by the prognostic score versus 60% for patients defined as high-risk. The 8-gene score remained a significant factor in multivariate analysis and its performance was similar to that of two validated gene profiles: the 70-Gene Signature and the Recurrence Score. The validity of the signature was verified in independent cohorts obtained from the GEO database. Conclusions This study identifies a simple gene expression score that complements histopathological prognostic factors in breast cancer, and can be determined in paraffin-embedded samples. PMID:20584321

State Space Formulation of Nonlinear Vibration Responses Collected from a Dynamic Rotor-Bearing System: An Extension of Bearing Diagnostics to Bearing Prognostics.

PubMed

Tse, Peter W; Wang, Dong

2017-02-14

Bearings are widely used in various industries to support rotating shafts. Their failures accelerate failures of other adjacent components and may cause unexpected machine breakdowns. In recent years, nonlinear vibration responses collected from a dynamic rotor-bearing system have been widely analyzed for bearing diagnostics. Numerous methods have been proposed to identify different bearing faults. However, these methods are unable to predict the future health conditions of bearings. To extend bearing diagnostics to bearing prognostics, this paper reports the design of a state space formulation of nonlinear vibration responses collected from a dynamic rotor-bearing system in order to intelligently predict bearing remaining useful life (RUL). Firstly, analyses of nonlinear vibration responses were conducted to construct a bearing health indicator (BHI) so as to assess the current bearing health condition. Secondly, a state space model of the BHI was developed to mathematically track the health evolution of the BHI. Thirdly, unscented particle filtering was used to predict bearing RUL. Lastly, a new bearing acceleration life testing setup was designed to collect natural bearing degradation data, which were used to validate the effectiveness of the proposed bearing prognostic method. Results show that the prediction accuracy of the proposed bearing prognostic method is promising and the proposed bearing prognostic method is able to reflect future bearing health conditions.

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod-statement.org. © 2015 American College of Physicians.

22nd Annual Logistics Conference and Exhibition

DTIC Science & Technology

2006-04-20

Prognostics & Health Management at GE Dr. Piero P.Bonissone Industrial AI Lab GE Global Research NCD Select detection model Anomaly detection results...Mode 213 x Failure mode histogram 2130014 Anomaly detection from event-log data Anomaly detection from event-log data Diagnostics/ Prognostics Using...Failure Monitoring & AssessmentTactical C4ISR Sense Respond 7 •Diagnostics, Prognostics and health management

Prognostic factors of non-functioning pancreatic neuroendocrine tumor revisited: The value of WHO 2010 classification.

PubMed

Bu, Jiyoung; Youn, Sangmin; Kwon, Wooil; Jang, Kee Taek; Han, Sanghyup; Han, Sunjong; You, Younghun; Heo, Jin Seok; Choi, Seong Ho; Choi, Dong Wook

2018-02-01

Various factors have been reported as prognostic factors of non-functional pancreatic neuroendocrine tumors (NF-pNETs). There remains some controversy as to the factors which might actually serve to successfully prognosticate future manifestation and diagnosis of NF-pNETs. As well, consensus regarding management strategy has never been achieved. The aim of this study is to further investigate potential prognostic factors using a large single-center cohort to help determine the management strategy of NF-pNETs. During the time period 1995 through 2013, 166 patients with NF-pNETs who underwent surgery in Samsung Medical Center were entered in a prospective database, and those factors thought to represent predictors of prognosis were tested in uni- and multivariate models. The median follow-up time was 46.5 months; there was a maximum follow-up period of 217 months. The five-year overall survival and disease-free survival rates were 88.5% and 77.0%, respectively. The 2010 WHO classification was found to be the only prognostic factor which affects overall survival and disease-free survival in multivariate analysis. Also, pathologic tumor size and preoperative image tumor size correlated strongly with the WHO grades ( p <0.001, and p <0.001). Our study demonstrates that 2010 WHO classification represents a valuable prognostic factor of NF-pNETs and tumor size on preoperative image correlated with WHO grade. In view of the foregoing, the preoperative image size is thought to represent a reasonable reference with regard to determination and development of treatment strategy of NF-pNETs.

Prognostics for Microgrid Components

NASA Technical Reports Server (NTRS)

Saxena, Abhinav

2012-01-01

Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

Development and validation of a prognostic model to predict death in patients with traumatic bleeding, and evaluation of the effect of tranexamic acid on mortality according to baseline risk: a secondary analysis of a randomised controlled trial.

PubMed

Perel, P; Prieto-Merino, D; Shakur, H; Roberts, I

2013-06-01

Severe bleeding accounts for about one-third of in-hospital trauma deaths. Patients with a high baseline risk of death have the most to gain from the use of life-saving treatments. An accurate and user-friendly prognostic model to predict mortality in bleeding trauma patients could assist doctors and paramedics in pre-hospital triage and could shorten the time to diagnostic and life-saving procedures such as surgery and tranexamic acid (TXA). The aim of the study was to develop and validate a prognostic model for early mortality in patients with traumatic bleeding and to examine whether or not the effect of TXA on the risk of death and thrombotic events in bleeding adult trauma patients varies according to baseline risk. Multivariable logistic regression and risk-stratified analysis of a large international cohort of trauma patients. Two hundred and seventy-four hospitals in 40 high-, medium- and low-income countries. We derived prognostic models in a large placebo-controlled trial of the effects of early administration of a short course of TXA [Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage (CRASH-2) trial]. The trial included 20,127 trauma patients with, or at risk of, significant bleeding, within 8 hours of injury. We externally validated the model on 14,220 selected trauma patients from the Trauma Audit and Research Network (TARN), which included mainly patients from the UK. We examined the effect of TXA on all-cause mortality, death due to bleeding and thrombotic events (fatal and non-fatal myocardial infarction, stroke, deep-vein thrombosis and pulmonary embolism) within risk strata in the CRASH-2 trial data set and we estimated the proportion of premature deaths averted by applying the odds ratio (OR) from the CRASH-2 trial to each of the risk strata in TARN. For the stratified analysis according baseline risk we considered the intervention TXA (1 g over 10 minutes followed by 1 g over 8 hours) or matching placebo. For the prognostic models we included predictors for death in hospital within 4 weeks of injury. For the stratified analysis we reported ORs for all causes of death, death due to bleeding, and fatal and non-fatal thrombotic events associated with the use of TXA according to baseline risk. A total of 3076 (15%) patients died in the CRASH-2 trial and 1705 (12%) in the TARN data set. Glasgow Coma Scale score, age and systolic blood pressure were the strongest predictors of mortality. Discrimination and calibration were satisfactory, with C-statistics > 0.80 in both CRASH-2 trial and TARN data sets. A simple chart was constructed to readily provide the probability of death at the point of care, while a web-based calculator is available for a more detailed risk assessment. TXA reduced all-cause mortality and death due to bleeding in each stratum of baseline risk. There was no evidence of heterogeneity in the effect of TXA on all-cause mortality (p-value for interaction = 0.96) or death due to bleeding (p= 0.98). There was a significant reduction in the odds of fatal and non-fatal thrombotic events with TXA (OR = 0.69, 95% confidence interval 0.53 to 0.89; p= 0.005). There was no evidence of heterogeneity in the effect of TXA on the risk of thrombotic events (p= 0.74). This prognostic model can be used to obtain valid predictions of mortality in patients with traumatic bleeding. TXA can be administered safely to a wide spectrum of bleeding trauma patients and should not be restricted to the most severely injured. Future research should evaluate whether or not the use of this prognostic model in clinical practice has an impact on the management and outcomes of trauma patients.

Investigation of p16(INK4a) as a prognostic biomarker in oral epithelial dysplasia.

PubMed

Nankivell, Paul; Williams, Hazel; Webster, Keith; Pearson, David; High, Alec; MacLennan, Kenneth; Senguven, Burcu; McConkey, Christopher; Rabbitts, Pamela; Mehanna, Hisham

2014-04-01

Human papilloma virus is a risk factor for oropharyngeal cancer. Evidence for a similar aetiological role in the development of oral dysplasia or its transformation to oral cancer is not as clear. Meta-analyses estimate the prevalence of high-risk human papilloma virus (HPV) serotypes to be three times higher in pre-malignant lesions and cancer than in normal oral mucosa. However, this does not imply a causal relationship. Conflicting results are reported from the few studies examining the prognostic significance of HPV positivity in the development of oral cancer. We aimed to examine the ability of p16(INK4a) protein expression, a surrogate marker of HPV infection, to predict malignant progression in a large cohort of oral dysplasia patients. One hundred forty eight oral dysplasia cases underwent immunohistochemical analysis using a monoclonal antibody against p16(INK4a) . Clinical factors were also collated on each case. Slides were double scored independently by two trained observers. Univariate analyses using both logistic and Cox regression models were performed. Thirty nine of 148 cases progressed to cancer. Ten of 148 cases (7%) were p16(INK4a) positive. High grade of dysplasia (P = 0.0002) and lesion morphology (P = 0.03) were found to be prognostic of malignant progression. p16(INK4a) score was not prognostic in this cohort (P = 0.29). This did not change with a time to event analysis (P = 0.24). Few studies have assessed the aetiological role of HPV in cancer development from dysplastic lesions. Our study, using one of the largest cohorts of oral dysplasia, demonstrated a low rate of p16(INK4a) positivity and was unable to confirm a prognostic ability for this biomarker. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Evaluating a 4-marker signature of aggressive prostate cancer using time-dependent AUC.

PubMed

Gerke, Travis A; Martin, Neil E; Ding, Zhihu; Nuttall, Elizabeth J; Stack, Edward C; Giovannucci, Edward; Lis, Rosina T; Stampfer, Meir J; Kantoff, Phillip W; Parmigiani, Giovanni; Loda, Massimo; Mucci, Lorelei A

2015-12-01

We previously identified a protein tumor signature of PTEN, SMAD4, SPP1, and CCND1 that, together with clinical features, was associated with lethal outcomes among prostate cancer patients. In the current study, we sought to validate the molecular model using time-dependent measures of AUC and predictive values for discriminating lethal from non-lethal prostate cancer. Using data from the initial study, we fit survival models for men with prostate cancer who were participants in the Physicians' Health Study (PHS; n = 276). Based on these models, we generated prognostic risk scores in an independent population, the Health Professionals Follow-up Study (HPFS; n = 347) to evaluate external validity. In each cohort, men were followed prospectively from cancer diagnosis through 2011 for development of distant metastasis or cancer mortality. We measured protein tumor expression of PTEN, SMAD4, SPP1, and CCND1 on tissue microarrays. During a median of 11.9 and 14.3 years follow-up in the PHS and HPFS cohorts, 24 and 32 men (9%) developed lethal disease. When used as a prognostic factor in a new population, addition of the four markers to clinical variables did not improve discriminatory accuracy through 15 years of follow-up. Although the four markers have been identified as key biological mediators in metastatic progression, they do not provide independent, long-term prognostic information beyond clinical factors when measured at diagnosis. This finding may underscore the broad heterogeneity in aggressive prostate tumors and highlight the challenges that may result from overfitting in discovery-based research. © 2015 Wiley Periodicals, Inc.

Benchmarking the performance of a land data assimilation system for agricultural drought monitoring

USDA-ARS?s Scientific Manuscript database

The application of land data assimilation systems to operational agricultural drought monitoring requires the development of (at least) three separate system sub-components: 1) a retrieval model to invert satellite-derived observations into soil moisture estimates, 2) a prognostic soil water balance...

Clinical variables associated with recovery in patients with chronic tension-type headache after treatment with manual therapy.

PubMed

Castien, René F; van der Windt, Daniëlle A W M; Blankenstein, Annette H; Heymans, Martijn W; Dekker, Joost

2012-04-01

The aims of this study were to describe the course of chronic tension-type headache (CTTH) in participants receiving manual therapy (MT), and to develop a prognostic model for predicting recovery in participants receiving MT. Outcomes in 145 adults with CTTH who received MT as participants in a previously published randomised clinical trial (n=41) or in a prospective cohort study (n=104) were evaluated. Assessments were made at baseline and at 8 and 26 weeks of follow-up. Recovery was defined as a 50% reduction in headache days in combination with a score of 'much improved' or 'very much improved' for global perceived improvement. Potential prognostic factors were analyzed by univariable and multivariable regression analysis. After 8 weeks 78% of the participants reported recovery after MT, and after 26 weeks the frequency of recovered participants was 73%. Prognostic factors related to recovery were co-existing migraine, absence of multiple-site pain, greater cervical range of motion and higher headache intensity. In participants classified as being likely to be recovered, the posterior probability for recovery at 8 weeks was 92%, whereas for those being classified at low probability of recovery this posterior probability was 61%. It is concluded that the course of CTTH is favourable in primary care patients receiving MT. The prognostic models provide additional information to improve prediction of outcome. Copyright © 2012 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging

NASA Technical Reports Server (NTRS)

Celaya, Jose; Kulkarni, Chetan; Biswas, Gautam; Saha, Sankalita; Goebel, Kai

2011-01-01

A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.

Towards A Model-Based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Kulkarni, Chetan S.; Biswas, Gautam; Goebel, Kai

2012-01-01

A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter D.; Dawson, Andrew

2017-03-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelization to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. In this paper, we present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform model simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13 % for the shallow water model.

Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

PubMed

Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

2002-12-30

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study.

PubMed

Gnanapragasam, V J; Bratt, O; Muir, K; Lee, L S; Huang, H H; Stattin, P; Lophatananon, A

2018-02-28

The purpose of this study is to validate a new five-tiered prognostic classification system to better discriminate cancer-specific mortality in men diagnosed with primary non-metastatic prostate cancer. We applied a recently described five-strata model, the Cambridge Prognostic Groups (CPGs 1-5), in two international cohorts and tested prognostic performance against the current standard three-strata classification of low-, intermediate- or high-risk disease. Diagnostic clinico-pathological data for men obtained from the Prostate Cancer data Base Sweden (PCBaSe) and the Singapore Health Study were used. The main outcome measure was prostate cancer mortality (PCM) stratified by age group and treatment modality. The PCBaSe cohort included 72,337 men, of whom 7162 died of prostate cancer. The CPG model successfully classified men with different risks of PCM with competing risk regression confirming significant intergroup distinction (p < 0.0001). The CPGs were significantly better at stratified prediction of PCM compared to the current three-tiered system (concordance index (C-index) 0.81 vs. 0.77, p < 0.0001). This superiority was maintained for every age group division (p < 0.0001). Also in the ethnically different Singapore cohort of 2550 men with 142 prostate cancer deaths, the CPG model outperformed the three strata categories (C-index 0.79 vs. 0.76, p < 0.0001). The model also retained superior prognostic discrimination in the treatment sub-groups: radical prostatectomy (n = 20,586), C-index 0.77 vs. 074; radiotherapy (n = 11,872), C-index 0.73 vs. 0.69; and conservative management (n = 14,950), C-index 0.74 vs. 0.73. The CPG groups that sub-divided the old intermediate-risk (CPG2 vs. CPG3) and high-risk categories (CPG4 vs. CPG5) significantly discriminated PCM outcomes after radical therapy or conservative management (p < 0.0001). This validation study of nearly 75,000 men confirms that the CPG five-tiered prognostic model has superior discrimination compared to the three-tiered model in predicting prostate cancer death across different age and treatment groups. Crucially, it identifies distinct sub-groups of men within the old intermediate-risk and high-risk criteria who have very different prognostic outcomes. We therefore propose adoption of the CPG model as a simple-to-use but more accurate prognostic stratification tool to help guide management for men with newly diagnosed prostate cancer.

Prognosis of patients with hepatocellular carcinoma treated with sorafenib: a comparison of five models in a large Canadian database.

PubMed

Samawi, Haider H; Sim, Hao-Wen; Chan, Kelvin K; Alghamdi, Mohammad A; Lee-Ying, Richard M; Knox, Jennifer J; Gill, Parneet; Romagnino, Adriana; Batuyong, Eugene; Ko, Yoo-Joung; Davies, Janine M; Lim, Howard J; Cheung, Winson Y; Tam, Vincent C

2018-05-15

Several systems (tumor-node-metastasis [TNM], Barcelona Clinic Liver Cancer [BCLC], Okuda, Cancer of the Liver Italian Program [CLIP], and albumin-bilirubin grade [ALBI]) were developed to estimate the prognosis of patients with hepatocellular carcinoma (HCC) mostly prior to the prevalent use of sorafenib. We aimed to compare the prognostic and discriminatory power of these models in predicting survival for HCC patients treated with sorafenib and to identify independent prognostic factors for survival in this population. Patients who received sorafenib for the treatment of HCC between 1 January 2008 and 30 June 2015 in the provinces of British Columbia and Alberta, and two large cancer centers in Toronto, Ontario, were included. Survival was assessed using the Kaplan-Meier method. Multivariate Cox regression was used to identify predictors of survival. The models were compared with respect to homogeneity, discriminatory ability, monotonicity of gradients, time-dependent area under the curve, and Akaike information criterion. A total of 681 patients were included. 80% were males, 86% had Child-Pugh class A, and 37% of patients were East Asians. The most common etiology for liver disease was hepatitis B (34%) and C (31%). In all model comparisons, CLIP performed better while BCLC and TNM7 performed less favorably but the differences were small. The utility of each system in allocating patients into different prognostic groups varied, for example, TNM poorly differentiated patients in advanced stages (8.7 months (m) (95% CI 6.5-11.5) versus 8.4 m (95% CI 7.0-9.6) for stages III and IV, respectively) while ALBI had excellent discrimination of early grades (15.6 m [95% CI 13.0-18.4] versus 8.3 m [95% CI 7.0-9.2] for grades 1 and 2, respectively). On multivariate analysis, hepatitis C, alcoholism, and prior hepatic resection were independently prognostic of better survival (P < 0.01). In conclusion, none of the prognostic systems was optimal in predicting survival in sorafenib-treated patients with HCC. Etiology of liver disease should be considered in future models and clinical trial designs. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Distilling the Verification Process for Prognostics Algorithms

NASA Technical Reports Server (NTRS)

Roychoudhury, Indranil; Saxena, Abhinav; Celaya, Jose R.; Goebel, Kai

2013-01-01

The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm depends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognostics algorithm is demonstrated as the prognostics algorithm moves up TRLs.

Systematic review of prediction models for delirium in the older adult inpatient.

PubMed

Lindroth, Heidi; Bratzke, Lisa; Purvis, Suzanne; Brown, Roger; Coburn, Mark; Mrkobrada, Marko; Chan, Matthew T V; Davis, Daniel H J; Pandharipande, Pratik; Carlsson, Cynthia M; Sanders, Robert D

2018-04-28

To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (≥60 years) acute hospital population. Systematic review. PubMed, CINAHL, PsychINFO, SocINFO, Cochrane, Web of Science and Embase were searched from 1 January 1990 to 31 December 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and CHARMS Statement guided protocol development. age >60 years, inpatient, developed/validated a prognostic delirium prediction model. alcohol-related delirium, sample size ≤50. The primary performance measures were calibration and discrimination statistics. Two authors independently conducted search and extracted data. The synthesis of data was done by the first author. Disagreement was resolved by the mentoring author. The initial search resulted in 7,502 studies. Following full-text review of 192 studies, 33 were excluded based on age criteria (<60 years) and 27 met the defined criteria. Twenty-three delirium prediction models were identified, 14 were externally validated and 3 were internally validated. The following populations were represented: 11 medical, 3 medical/surgical and 13 surgical. The assessment of delirium was often non-systematic, resulting in varied incidence. Fourteen models were externally validated with an area under the receiver operating curve range from 0.52 to 0.94. Limitations in design, data collection methods and model metric reporting statistics were identified. Delirium prediction models for older adults show variable and typically inadequate predictive capabilities. Our review highlights the need for development of robust models to predict delirium in older inpatients. We provide recommendations for the development of such models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter; Dawson, Andrew

2017-04-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.

Comprehensive analysis and validation of contemporary survival prognosticators in Korean patients with metastatic renal cell carcinoma treated with targeted therapy: prognostic impact of pretreatment neutrophil-to-lymphocyte ratio.

PubMed

Koo, Kyo Chul; Lee, Kwang Suk; Cho, Kang Su; Rha, Koon Ho; Hong, Sung Joon; Chung, Byung Ha

2016-06-01

In line with the era of targeted therapy (TT), an increasing number of prognosticators are becoming available for patients with metastatic renal cell carcinoma (mRCC). Here, potential prognosticators of cancer-specific survival (CSS) were identified based on the contemporary literature and were comprehensively validated in an independent cohort of patients treated for mRCC. Data were collected from 478 patients treated with TT for mRCC between January 1999 and July 2013 at a single institution. The analysis included 25 clinicopathological covariates that included both traditional and contemporary prognosticators. Multivariate Cox regression models were used to quantify the effect of covariates on CSS. Median survival from the initial diagnosis of metastasis was 24.5 (IQR, 11.5-55.7) months. There were 303 (63.4 %) cancer-specific deaths, yielding a 2-year CSS rate of 62.5 %. Low Karnofsky performance status (KPS), hypercalcemia, neutrophil-to-lymphocyte ratio (NLR), the number of metastatic sites (≥2), and the presence of brain metastases were independent adverse prognosticators of CSS. The C-index of the model was 0.78. Patients with at least one adverse prognosticator demonstrated lower 2-year CSS rates compared to those with no prognosticators (53.9 vs. 70.6 %; log rank p < 0.001). Together with traditional prognosticators such as KPS, hypercalcemia, and the number and location of metastases, the NLR was an independent predictor of CSS in patients with mRCC treated with TT. Our findings could be useful for guiding clinical decision making including stratification of patients for TT and inclusion in clinical trials.

Predicting remaining life by fusing the physics of failure modeling with diagnostics

NASA Astrophysics Data System (ADS)

Kacprzynski, G. J.; Sarlashkar, A.; Roemer, M. J.; Hess, A.; Hardman, B.

2004-03-01

Technology that enables failure prediction of critical machine components (prognostics) has the potential to significantly reduce maintenance costs and increase availability and safety. This article summarizes a research effort funded through the U.S. Defense Advanced Research Projects Agency and Naval Air System Command aimed at enhancing prognostic accuracy through more advanced physics-of-failure modeling and intelligent utilization of relevant diagnostic information. H-60 helicopter gear is used as a case study to introduce both stochastic sub-zone crack initiation and three-dimensional fracture mechanics lifing models along with adaptive model updating techniques for tuning key failure mode variables at a local material/damage site based on fused vibration features. The overall prognostic scheme is aimed at minimizing inherent modeling and operational uncertainties via sensed system measurements that evolve as damage progresses.

Microphysical Timescales in Clouds and their Application in Cloud-Resolving Modeling

NASA Technical Reports Server (NTRS)

Zeng, Xiping; Tao, Wei-Kuo; Simpson, Joanne

2007-01-01

Independent prognostic variables in cloud-resolving modeling are chosen on the basis of the analysis of microphysical timescales in clouds versus a time step for numerical integration. Two of them are the moist entropy and the total mixing ratio of airborne water with no contributions from precipitating particles. As a result, temperature can be diagnosed easily from those prognostic variables, and cloud microphysics be separated (or modularized) from moist thermodynamics. Numerical comparison experiments show that those prognostic variables can work well while a large time step (e.g., 10 s) is used for numerical integration.

[Prognostic value of JAK2, MPL and CALR mutations in Chinese patients with primary myelofibrosis].

PubMed

Xu, Z F; Li, B; Liu, J Q; Li, Y; Ai, X F; Zhang, P H; Qin, T J; Zhang, Y; Wang, J Y; Xu, J Q; Zhang, H L; Fang, L W; Pan, L J; Hu, N B; Qu, S Q; Xiao, Z J

2016-07-01

To evaluate the prognostic value of JAK2, MPL and CALR mutations in Chinese patients with primary myelofibrosis (PMF). Four hundred and two Chinese patients with PMF were retrospectively analyzed. The Kaplan-Meier method, the Log-rank test, the likelihood ratio test and the Cox proportional hazards regression model were used to evaluate the prognostic scoring system. This cohort of patients included 209 males and 193 females with a median age of 55 years (range: 15- 89). JAK2V617F mutations were detected in 189 subjects (47.0% ), MPLW515 mutations in 13 (3.2%) and CALR mutations in 81 (20.1%) [There were 30 (37.0%) type-1, 48 (59.3%) type-2 and 3 (3.7%) less common CALR mutations], respectively. 119 subjects (29.6%) had no detectable mutation in JAK2, MPL or CALR. Univariate analysis indicated that patients with CALR type-2 mutations or no detectable mutations had inferior survival compared to those with JAK2, MPL or CALR type- 1 or other less common CALR mutations (the median survival was 74vs 168 months, respectively [HR 2.990 (95% CI 1.935-4.619),P<0.001]. Therefore, patients were categorized into the high-risk with CALR type- 2 mutations or no detectable driver mutations and the low- risk without aforementioned mutations status. The DIPSS-Chinese molecular prognostic model was proposed by adopting mutation categories and DIPSS-Chinese risk group. The median survival of patients classified in low risk (132 subjects, 32.8% ), intermediate- 1 risk (143 subjects, 35.6%), intermediate- 2 risk (106 subjects, 26.4%) and high risk (21 subjects, 5.2%) were not reached, 156 (95% CI 117- 194), 60 (95% CI 28- 91) and 22 (95% CI 10- 33) months, respectively, and there was a statistically significant difference in overall survival among the four risk groups (P<0.001). There was significantly higher predictive power for survival according to the DIPSS-Chinese molecular prognostic model compared with the DIPSS-Chinese model (P=0.005, -2 log-likelihood ratios of 855.6 and 869.7, respectively). The impact of the CALR type- 2 mutations or no detectable driver mutation on survival was independent of current prognostic scoring systems. The DIPSS- Chinese molecular prognostic model based on the molecular features of Chinese patients was proposed and worked well for prognostic indication.

Time-dependent summary receiver operating characteristics for meta-analysis of prognostic studies.

PubMed

Hattori, Satoshi; Zhou, Xiao-Hua

2016-11-20

Prognostic studies are widely conducted to examine whether biomarkers are associated with patient's prognoses and play important roles in medical decisions. Because findings from one prognostic study may be very limited, meta-analyses may be useful to obtain sound evidence. However, prognostic studies are often analyzed by relying on a study-specific cut-off value, which can lead to difficulty in applying the standard meta-analysis techniques. In this paper, we propose two methods to estimate a time-dependent version of the summary receiver operating characteristics curve for meta-analyses of prognostic studies with a right-censored time-to-event outcome. We introduce a bivariate normal model for the pair of time-dependent sensitivity and specificity and propose a method to form inferences based on summary statistics reported in published papers. This method provides a valid inference asymptotically. In addition, we consider a bivariate binomial model. To draw inferences from this bivariate binomial model, we introduce a multiple imputation method. The multiple imputation is found to be approximately proper multiple imputation, and thus the standard Rubin's variance formula is justified from a Bayesian view point. Our simulation study and application to a real dataset revealed that both methods work well with a moderate or large number of studies and the bivariate binomial model coupled with the multiple imputation outperforms the bivariate normal model with a small number of studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Integrating Tenascin-C protein expression and 1q25 copy number status in pediatric intracranial ependymoma prognostication: A new model for risk stratification.

PubMed

Andreiuolo, Felipe; Le Teuff, Gwénaël; Bayar, Mohamed Amine; Kilday, John-Paul; Pietsch, Torsten; von Bueren, André O; Witt, Hendrik; Korshunov, Andrey; Modena, Piergiorgio; Pfister, Stefan M; Pagès, Mélanie; Castel, David; Giangaspero, Felice; Chimelli, Leila; Varlet, Pascale; Rutkowski, Stefan; Frappaz, Didier; Massimino, Maura; Grundy, Richard; Grill, Jacques

2017-01-01

Despite multimodal therapy, prognosis of pediatric intracranial ependymomas remains poor with a 5-year survival rate below 70% and frequent late deaths. This multicentric European study evaluated putative prognostic biomarkers. Tenascin-C (TNC) immunohistochemical expression and copy number status of 1q25 were retained for a pooled analysis of 5 independent cohorts. The prognostic value of TNC and 1q25 on the overall survival (OS) was assessed using a Cox model adjusted to age at diagnosis, tumor location, WHO grade, extent of resection, radiotherapy and stratified by cohort. Stratification on a predictor that did not satisfy the proportional hazards assumption was considered. Model performance was evaluated and an internal-external cross validation was performed. Among complete cases with 5-year median follow-up (n = 470; 131 deaths), TNC and 1q25 gain were significantly associated with age at diagnosis and posterior fossa tumor location. 1q25 status added independent prognostic value for death beyond the classical variables with a hazard ratio (HR) = 2.19 95%CI = [1.29; 3.76] (p = 0.004), while TNC prognostic relation was tumor location-dependent with HR = 2.19 95%CI = [1.29; 3.76] (p = 0.004) in posterior fossa and HR = 0.64 [0.28; 1.48] (p = 0.295) in supratentorial (interaction p value = 0.015). The derived prognostic score identified 3 different robust risk groups. The omission of upfront RT was not associated with OS for good and intermediate prognostic groups while the absence of upfront RT was negatively associated with OS in the poor risk group. Integrated TNC expression and 1q25 status are useful to better stratify patients and to eventually adapt treatment regimens in pediatric intracranial ependymoma.

Validation of the prognostic value of lymph node ratio in patients with cutaneous melanoma: a population-based study of 8,177 cases.

PubMed

Mocellin, Simone; Pasquali, Sandro; Rossi, Carlo Riccardo; Nitti, Donato

2011-07-01

The proportion of positive among examined lymph nodes (lymph node ratio [LNR]) has been recently proposed as an useful and easy-to-calculate prognostic factor for patients with cutaneous melanoma. However, its independence from the standard prognostic system TNM has not been formally proven in a large series of patients. Patients with histologically proven cutaneous melanoma were identified from the Surveillance Epidemiology End Results database. Disease-specific survival was the clinical outcome of interest. The prognostic ability of conventional factors and LNR was assessed by multivariable survival analysis using the Cox regression model. Eligible patients (n = 8,177) were diagnosed with melanoma between 1998 and 2006. Among lymph node-positive cases (n = 3,872), most LNR values ranged from 1% to 10% (n = 2,187). In the whole series (≥5 lymph nodes examined) LNR significantly contributed to the Cox model independently of the TNM effect on survival (hazard ratio, 1.28; 95% confidence interval, 1.23-1.32; P < .0001). On subgroup analysis, the significant and independent prognostic value of LNR was confirmed both in patients with ≥10 lymph nodes examined (n = 4,381) and in those with TNM stage III disease (n = 3,658). In all cases, LNR increased the prognostic accuracy of the survival model. In this large series of patients, the LNR independently predicted disease-specific survival, improving the prognostic accuracy of the TNM system. Accordingly, the LNR should be taken into account for the stratification of patients' risk, both in clinical and research settings. Copyright © 2011 Mosby, Inc. All rights reserved.

Developing a CD-CBM Anticipatory Approach for Cavitation - Defining a Model-Based Descriptor Consistent Across Processes, Phase 1 Final Report Context-Dependent Prognostics and Health Assessment: A New Paradigm for Condition-based Maintenance SBIR Topic No. N98-114

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allgood, G.O.; Dress, W.B.; Kercel, S.W.

1999-06-01

The objective of this research, and subsequent testing, was to identify specific features of cavitation that could be used as a model-based descriptor in a context-dependent condition-based maintenance (CD-CBM) anticipatory prognostic and health assessment model. This descriptor is based on the physics of the phenomena, capturing the salient features of the process dynamics. The test methodology and approach were developed to make the cavitation features the dominant effect in the process and collected signatures. This would allow the accurate characterization of the salient cavitation features at different operational states. By developing such an abstraction, these attributes can be used asmore » a general diagnostic for a system or any of its components. In this study, the particular focus will be pumps. As many as 90% of pump failures are catastrophic. They seem to be operating normally and fail abruptly without warning. This is true whether the failure is sudden hardware damage requiring repair, such as a gasket failure, or a transition into an undesired operating mode, such as cavitation. This means that conventional diagnostic methods fail to predict 90% of incipient failures and that in addressing this problem, model-based methods can add value where it is actually needed.« less

Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

PubMed

Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

2013-05-07

To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P < 0.05. The survival rate was 74% at 3 years and 68% at 5 years. The results of univariate analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P < 0.05). Lymph node ratio (LNR) was also a strong prognostic factor in stage III CRC (P < 0.0001). We divided 341 stage III patients into three groups according to LNR values (LNR1, LNR ≤ 0.33, n = 211; LNR2, LNR 0.34-0.66, n = 76; and LNR3, LNR ≥ 0.67, n = 54). Univariate analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P < 0.0001). The multivariate analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

Unavailability of thymidine kinase does not preclude the use of German comprehensive prognostic index: results of an external validation analysis in early chronic lymphocytic leukemia and comparison with MD Anderson Cancer Center model.

PubMed

Molica, Stefano; Giannarelli, Diana; Mirabelli, Rosanna; Levato, Luciano; Russo, Antonio; Linardi, Maria; Gentile, Massimo; Morabito, Fortunato

2016-01-01

A comprehensive prognostic index that includes clinical (i.e., age, sex, ECOG performance status), serum (i.e., ß2-microglobulin, thymidine kinase [TK]), and molecular (i.e., IGVH mutational status, del 17p, del 11q) markers developed by the German CLL Study Group (GCLLSG) was externally validated in a prospective, community-based cohort consisting of 338 patients with early chronic lymphocytic leukemia (CLL) using as endpoint the time to first treatment (TTFT). Because serum TK was not available, a slightly modified version of the model based on seven instead of eight prognostic variables was used. By German index, 62.9% of patients were scored as having low-risk CLL (score 0-2), whereas 37.1% had intermediate-risk CLL (score 3-5). This stratification translated into a significant difference in the TTFT [HR = 4.21; 95% C.I. (2.71-6.53); P < 0.0001]. Also the 2007 MD Anderson Cancer Center (MDACC) score, barely based on traditional clinical parameters, showed comparable reliability [HR = 2.73; 95% C.I. (1.79-4.17); P < 0.0001]. A comparative performance assessment between the two models revealed that prediction of the TTFT was more accurate with German score. The c-statistic of the MDACC model was 0.65 (range, 0.53-0.78) a level below that of the German index [0.71 (range, 0.60-0.82)] and below the accepted 0.7 threshold necessary to have value at the individual patient level. Results of this external comparative validation analysis strongly support the German score as the benchmark for comparison of any novel prognostic scheme aimed at evaluating the TTFT in patients with early CLL even when a modified version which does not include TK is utilized. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Identification of Patients Expected to Benefit from Electronic Alerts for Acute Kidney Injury.

PubMed

Biswas, Aditya; Parikh, Chirag R; Feldman, Harold I; Garg, Amit X; Latham, Stephen; Lin, Haiqun; Palevsky, Paul M; Ugwuowo, Ugochukwu; Wilson, F Perry

2018-06-07

Electronic alerts for heterogenous conditions such as AKI may not provide benefit for all eligible patients and can lead to alert fatigue, suggesting that personalized alert targeting may be useful. Uplift-based alert targeting may be superior to purely prognostic-targeting of interventions because uplift models assess marginal treatment effect rather than likelihood of outcome. This is a secondary analysis of a clinical trial of 2278 adult patients with AKI randomized to an automated, electronic alert system versus usual care. We used three uplift algorithms and one purely prognostic algorithm, trained in 70% of the data, and evaluated the effect of targeting alerts to patients with higher scores in the held-out 30% of the data. The performance of the targeting strategy was assessed as the interaction between the model prediction of likelihood to benefit from alerts and randomization status. The outcome of interest was maximum relative change in creatinine from the time of randomization to 3 days after randomization. The three uplift score algorithms all gave rise to a significant interaction term, suggesting that a strategy of targeting individuals with higher uplift scores would lead to a beneficial effect of AKI alerting, in contrast to the null effect seen in the overall study. The prognostic model did not successfully stratify patients with regards to benefit of the intervention. Among individuals in the high uplift group, alerting was associated with a median reduction in change in creatinine of -5.3% ( P =0.03). In the low uplift group, alerting was associated with a median increase in change in creatinine of +5.3% ( P =0.005). Older individuals, women, and those with a lower randomization creatinine were more likely to receive high uplift scores, suggesting that alerts may benefit those with more slowly developing AKI. Uplift modeling, which accounts for treatment effect, can successfully target electronic alerts for AKI to those most likely to benefit, whereas purely prognostic targeting cannot. Copyright © 2018 by the American Society of Nephrology.

Prognostic value of clinical variables and liver histology for development of fibrosis and cirrhosis in autoimmune hepatitis.

PubMed

Puustinen, Lauri; Boyd, Sonja; Mustonen, Harri; Arkkila, Perttu; Arola, Johanna; Färkkilä, Martti

2017-03-01

In autoimmune hepatitis, data on the prognostic value of baseline liver biopsy and the sequential histology is controversial. Our aim was to evaluate the prognostic value of clinical variables and biopsy at the time of diagnosis and during the disease course. All 98 patients in our hospital during 1995-2012 were included. Sequential biopsies were available in 66 patients. Analyses based on clinical and histological variables were performed to find parameters predicting the progression of fibrosis, and development of cirrhosis. At the time of diagnosis, 7% were cirrhotic. Fibrosis progressed in 28 (42%) patients, remained stable in 26 (39%) and resolved in 12 (18%) patients. Findings which predicted fibrosis progression, were baseline total inflammation (odds ratio 1.7, 95% CI 1.01-2.8), cumulative total inflammation (1.8, 95% CI 1.01-3.2, rosette formation (2.8, 95% CI 1.1-7.1), absence of pericholangitis (0.4, 95% CI 0.1-1.0) and necrosis (1.4, 95% CI 1.0-2.0). Risk factors for the development of cirrhosis were cholestasis (4.6, 95% CI 1.2-16.9), interphase inflammation (3.4, 95% CI 1.1-10.4), and necrosis (3.3, 95% CI 1.2-9.7). In a cumulative model, cumulative total inflammation (4.5, 95% CI 1.4-15.0), necrosis (6.7, 95% CI 1.3-34.6), or cumulative immunoglobulin G load (61.8, 95% CI 2.0-1954.3) were risk factors. None of the patients with histological pericholangitis or granulomas developed cirrhosis. The histology provides prognostic information regarding progression of fibrosis or the development of cirrhosis. The total cumulative inflammatory activity predicts the progression of fibrosis, whereas baseline fibrosis, interphase inflammation, cholestasis, necrosis, as well as the cumulative total inflammation and cumulative immunoglobulin G, are risk factors for cirrhosis.

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

PubMed Central

Mei, Wenjuan; Zeng, Xianping; Yang, Chenglin; Zhou, Xiuyun

2017-01-01

The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches. PMID:29099811

Prognostic stratification model for patients with stage I non-small cell lung cancer adenocarcinoma treated with surgical resection without adjuvant therapies using metabolic features measured on F-18 FDG PET and postoperative pathologic factors.

PubMed

Kang, Yeon-Koo; Song, Yoo Sung; Cho, Sukki; Jheon, Sanghoon; Lee, Won Woo; Kim, Kwhanmien; Kim, Sang Eun

2018-05-01

In the management of non-small cell lung cancer (NSCLC), the prognostic stratification of stage I tumors without indication of adjuvant therapy, remains to be elucidated in order to better select patients who can benefit from additional therapies. We aimed to stratify the prognosis of patients with stage I NSCLC adenocarcinoma using clinicopathologic factors and F-18 FDG PET. We retrospectively enrolled 128 patients with stage I NSCLC without any high-risk factors, who underwent curative surgical resection without adjuvant therapies. Preoperative clinical and postoperative pathologic factors were evaluated by medical record review. Standardized uptake value corrected with lean body mass (SUL max ) was measured on F-18 FDG PET. Among the factors, independent predictors for recurrence-free survival (RFS) were selected using univariate and stepwise multivariate survival analyses. A prognostic stratification model for RFS was designed using the selected factors. Tumors recurred in nineteen patients (14.8%). Among the investigated clinicopathologic and FDG PET factors, SUL max on PET and spread through air spaces (STAS) on pathologic review were determined to be independent prognostic factors for RFS. A prognostic model was designed using these two factors in the following manner: (1) Low-risk: SUL max  ≤ 1.9 and no STAS, (2) intermediate-risk: neither low-risk nor high-risk, (3) high-risk: SUL max> 1.9 and observed STAS. This model exhibited significant predictive power for RFS. We showed that FDG uptake and STAS are significant prognostic markers in stage I NSCLC adenocarcinoma treated with surgical resection without adjuvant therapies. Copyright © 2018 Elsevier B.V. All rights reserved.

Online Monitoring of Induction Motors

DOE Office of Scientific and Technical Information (OSTI.GOV)

McJunkin, Timothy R.; Agarwal, Vivek; Lybeck, Nancy Jean

2016-01-01

The online monitoring of active components project, under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program, researched diagnostic and prognostic models for alternating current induction motors (IM). Idaho National Laboratory (INL) worked with the Electric Power Research Institute (EPRI) to augment and revise the fault signatures previously implemented in the Asset Fault Signature Database of EPRI’s Fleet Wide Prognostic and Health Management (FW PHM) Suite software. Induction Motor diagnostic models were researched using the experimental data collected by Idaho State University. Prognostic models were explored in the set of literature and through amore » limited experiment with 40HP to seek the Remaining Useful Life Database of the FW PHM Suite.« less

Problems of Pedagogical Creativity Development

ERIC Educational Resources Information Center

Ibragimkyzy, Shynar; Slambekova, Tolkyn S.; Saylaubay, Yerlan E.; Albytova, Nazymgul

2016-01-01

This article provides analysis of research papers by different scholars, dedicated to topical issues of pedagogical creativity development in the educational process. The authors determined that pedagogical creativity could be considered at five levels: information-reproducing, adaptive-prognostic, innovative, research and creative-prognostic. In…

Clouds in GEOS-5

NASA Technical Reports Server (NTRS)

Bacmeister, Julio; Rienecker, Michele; Suarez, Max; Norris, Peter

2007-01-01

The GEOS-5 atmospheric model is being developed as a weather-and-climate capable model. It must perform well in assimilation mode as well as in weather and climate simulations and forecasts and in coupled chemistry-climate simulations. In developing GEOS-5, attention has focused on the representation of moist processes. The moist physics package uses a single phase prognostic condensate and a prognostic cloud fraction. Two separate cloud types are distinguished by their source: "anvil" cloud originates in detraining convection, and large-scale cloud originates in a PDF-based condensation calculation. Ice and liquid phases for each cloud type are considered. Once created, condensate and fraction from the anvil and statistical cloud types experience the same loss processes: evaporation of condensate and fraction, auto-conversion of liquid or mixed phase condensate, sedimentation of frozen condensate, and accretion of condensate by falling precipitation. The convective parameterization scheme is the Relaxed Arakawa-Schubert, or RAS, scheme. Satellite data are used to evaluate the performance of the moist physics packages and help in their tuning. In addition, analysis of and comparisons to cloud-resolving models such as the Goddard Cumulus Ensemble model are used to help improve the PDFs used in the moist physics. The presentation will show some of our evaluations including precipitation diagnostics.

A new prognostic model identifies patients aged 80 years and older with diffuse large B-cell lymphoma who may benefit from curative treatment: A multicenter, retrospective analysis by the Spanish GELTAMO group.

PubMed

Pardal, Emilia; Díez Baeza, Eva; Salas, Queralt; García, Tomás; Sancho, Juan M; Monzón, Encarna; Moraleda, José M; Córdoba, Raúl; de la Cruz, Fátima; Queizán, José A; Rodríguez, María J; Navarro, Belén; Hernández, José A; Díez, Rosana; Vahi, María; Viguria, María C; Canales, Miguel; Peñarrubia, María J; González-López, Tomás J; Montes-Moreno, Santiago; González-Barca, Eva; Caballero, Dolores; Martín, Alejandro

2018-04-15

The means of optimally managing very elderly patients with diffuse large B-cell lymphoma (DLBCL) has not been established. We retrospectively analyzed 252 patients aged 80-100 years, diagnosed with DLBCL or grade 3B follicular lymphoma, treated in 19 hospitals from the GELTAMO group. Primary objective was to analyze the influence of the type of treatment and comorbidity scales on progression-free survival (PFS) and overall survival (OS). One hundred sixty-three patients (63%) were treated with chemotherapy that included anthracyclines and/or rituximab, whereas 15% received no chemotherapeutic treatment. With a median follow-up of 44 months, median PFS and OS were 9.5 and 12.5 months, respectively. In an analysis restricted to the 205 patients treated with any kind of chemotherapy, comorbidity scales did not influence the choice of treatment type significantly. Independent factors associated with better PFS and OS were: age < 86 years, cumulative illness rating scale (CIRS) score < 6, intermediate risk (1-2) R-IPI, and treatment with R-CHOP at full or reduced doses. We developed a prognostic model based on the multivariate analysis of the 108 patients treated with R-CHOP-like: median OS was 45 vs. 12 months (P = .001), respectively, for patients with 0-1 vs. 2-3 risk factors (age > 85 years, R-IPI 3-5 or CIRS > 5). In conclusion, treatment with R-CHOP-like is associated with good survival in a significant proportion of patients. We have developed a simple prognostic model that may aid the selection patients who could benefit from a curative treatment, although it needs to be validated in larger series. © 2018 Wiley Periodicals, Inc.

Sub-classification of Advanced-Stage Hepatocellular Carcinoma: A Cohort Study Including 612 Patients Treated with Sorafenib.

PubMed

Yoo, Jeong-Ju; Chung, Goh Eun; Lee, Jeong-Hoon; Nam, Joon Yeul; Chang, Young; Lee, Jeong Min; Lee, Dong Ho; Kim, Hwi Young; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-04-01

Advanced hepatocellular carcinoma (HCC) is associated with various clinical conditions including major vessel invasion, metastasis, and poor performance status. The aim of this study was to establish a prognostic scoring system and to propose a sub-classification of the Barcelona-Clinic Liver Cancer (BCLC) stage C. This retrospective study included consecutive patientswho received sorafenib for BCLC stage C HCC at a single tertiary hospital in Korea. A Cox proportional hazard model was used to develop a scoring system, and internal validationwas performed by a 5-fold cross-validation. The performance of the model in predicting risk was assessed by the area under the curve and the Hosmer-Lemeshow test. A total of 612 BCLC stage C HCC patients were sub- classified into strata depending on their performance status. Five independent prognostic factors (Child-Pugh score, α-fetoprotein, tumor type, extrahepatic metastasis, and portal vein invasion) were identified and used in the prognostic scoring system. This scoring system showed good discrimination (area under the receiver operating characteristic curve, 0.734 to 0.818) and calibration functions (both p < 0.05 by the Hosmer-Lemeshow test at 1 month and 12 months, respectively). The differences in survival among the different risk groups classified by the total score were significant (p < 0.001 by the log-rank test in both the Eastern Cooperative Oncology Group 0 and 1 strata). The heterogeneity of patientswith BCLC stage C HCC requires sub-classification of advanced HCC. A prognostic scoring system with five independent factors is useful in predicting the survival of patients with BCLC stage C HCC.

[Predicting the outcome in severe injuries: an analysis of 2069 patients from the trauma register of the German Society of Traumatology (DGU)].

PubMed

Rixen, D; Raum, M; Bouillon, B; Schlosser, L E; Neugebauer, E

2001-03-01

On hospital admission numerous variables are documented from multiple trauma patients. The value of these variables to predict outcome are discussed controversially. The aim was the ability to initially determine the probability of death of multiple trauma patients. Thus, a multivariate probability model was developed based on data obtained from the trauma registry of the Deutsche Gesellschaft für Unfallchirurgie (DGU). On hospital admission the DGU trauma registry collects more than 30 variables prospectively. In the first step of analysis those variables were selected, that were assumed to be clinical predictors for outcome from literature. In a second step a univariate analysis of these variables was performed. For all primary variables with univariate significance in outcome prediction a multivariate logistic regression was performed in the third step and a multivariate prognostic model was developed. 2069 patients from 20 hospitals were prospectively included in the trauma registry from 01.01.1993-31.12.1997 (age 39 +/- 19 years; 70.0% males; ISS 22 +/- 13; 18.6% lethality). From more than 30 initially documented variables, the age, the GCS, the ISS, the base excess (BE) and the prothrombin time were the most important prognostic factors to predict the probability of death (P(death)). The following prognostic model was developed: P(death) = 1/1 + e(-[k + beta 1(age) + beta 2(GCS) + beta 3(ISS) + beta 4(BE) + beta 5(prothrombin time)]) where: k = -0.1551, beta 1 = 0.0438 with p < 0.0001, beta 2 = -0.2067 with p < 0.0001, beta 3 = 0.0252 with p = 0.0071, beta 4 = -0.0840 with p < 0.0001 and beta 5 = -0.0359 with p < 0.0001. Each of the five variables contributed significantly to the multifactorial model. These data show that the age, GCS, ISS, base excess and prothrombin time are potentially important predictors to initially identify multiple trauma patients with a high risk of lethality. With the base excess and prothrombin time value, as only variables of this multifactorial model that can be therapeutically influenced, it might be possible to better guide early and aggressive therapy.

Gene Expression Analysis Of Circulating Hormone Refractory Prostate Cancer Micrometastases

DTIC Science & Technology

2011-02-01

of prostate cancer. We hypothesized that the copy number changes could be prognostic and aid in future chemotherapy regimen selection. After...Task 1 will be analyzed over the next year to elicit statistically meaningful prognostic DNA based biomarkers. Two of the patients (#8 and #13) had...HRPC), and to determine whether CECs can be used to predict survival in these patients. PATIENTS AND METHODS Several prognostic models that

PROSPECT: Profiling of Resistance Patterns & Oncogenic Signaling Pathways in Evaluation of Cancers of the Thorax and Therapeutic Target Identification

DTIC Science & Technology

2012-06-01

neoadjuvant therapies on disease-free, progression-free, and overall survival will vary across prognostically distinct groups. 3. Specific molecular... prognostically distinct subpopulations of patients with resectable NSCLC, and to assess the extent to which these molecular profiles correlate with tumor...overall survival, and will use Cox proportional hazards models and recursive partitioning methods to identify important biomarkers and prognostically

A Distributed Approach to System-Level Prognostics

DTIC Science & Technology

2012-09-01

the end of (useful) life ( EOL ) and/or the remaining useful life (RUL) of components, subsystems, or systems. The prognostics problem itself can be...system state estimate, computes EOL and/or RUL. In this paper, we focus on a model-based prognostics approach (Orchard & Vachtse- vanos, 2009; Daigle...been focused on individual components, and determining their EOL and RUL, e.g., (Orchard & Vachtsevanos, 2009; Saha & Goebel, 2009; Daigle & Goebel

Markers of systemic inflammation predict survival in patients with advanced renal cell cancer.

PubMed

Fox, P; Hudson, M; Brown, C; Lord, S; Gebski, V; De Souza, P; Lee, C K

2013-07-09

The host inflammatory response has a vital role in carcinogenesis and tumour progression. We examined the prognostic value of inflammatory markers (albumin, white-cell count and its components, and platelets) in pre-treated patients with advanced renal cell carcinoma (RCC). Using data from a randomised trial, multivariable proportional hazards models were generated to examine the impact of inflammatory markers and established prognostic factors (performance status, calcium, and haemoglobin) on overall survival (OS). We evaluated a new prognostic classification incorporating additional information from inflammatory markers. Of the 416 patients, 362 were included in the analysis. Elevated neutrophil counts, elevated platelet counts, and a high neutrophil-lymphocyte ratio were significant independent predictors for shorter OS in a model with established prognostic factors. The addition of inflammatory markers improves the discriminatory value of the prognostic classification as compared with established factors alone (C-statistic 0.673 vs 0.654, P=0.002 for the difference), with 25.8% (P=0.004) of patients more appropriately classified using the new classification. Markers of systemic inflammation contribute significantly to prognostic classification in addition to established factors for pre-treated patients with advanced RCC. Upon validation of these data in independent studies, stratification of patients using these markers in future clinical trials is recommended.

The Role of the 21-Gene Recurrence Score in Breast Cancer Treatment.

PubMed

Ethier, Josee-Lyne; Amir, Eitan

2016-08-01

Several multi-gene assays have been developed to predict the risk of recurrence in patients with estrogen receptor-positive early breast cancer and in whom endocrine therapy is planned. The 21-gene assay is widely used and its prognostic value has been retrospectively validated, showing significant differences in the risk of distant recurrence for patients at high versus low risk. Its role in predicting chemotherapy benefit has also been established, showing a clear benefit for high-risk patients and minimal benefit in those at low risk. These findings have been prospectively investigated in TAILORx (Trial Assigning Individualized Options for Treatment), where available data from the low-risk cohort confirms the prognostic value of this diagnostic test. The prognostic utility of the 21-gene assay increases when combined with clinicopathologic variables, and data from integrated models suggest that its use should be limited to patients with tumor characteristics suggestive of potential chemotherapy benefit. Furthermore, the 21-gene assay has been shown to impact clinical decision making in a cost-effective manner, although direct evidence of benefit from modified treatment recommendations is yet to be proven. The prognostic value of this test has also been shown in populations with node-positive or locally advanced disease treated with neoadjuvant chemotherapy, and ongoing trials aim to prospectively validate these findings.

[The mathematical modelling of the possible morbidity from epidemic louse-borne typhus under current conditions].

PubMed

Lukin, E P; Mikhaĭlov, V V; Oleĭchik, V L; Solodiankin, A I

1996-01-01

On the basis of their earlier formula for modeling the possible development of the epidemic process of louse-borne exanthematous typhus the authors have calculated the probability of the development of such process for high indices (10 -- 12 % of convalescents with louse contamination rate among them reaching 20 -- 40 %) characterizing this process. The number of sources of this infection (primary patients), as well as the rate of increase and scale of louse contamination of the population, are of prime importance for the prognostication of the development of the epidemic.

Current status of accurate prognostic awareness in advanced/terminally ill cancer patients: Systematic review and meta-regression analysis.

PubMed

Chen, Chen Hsiu; Kuo, Su Ching; Tang, Siew Tzuh

2017-05-01

No systematic meta-analysis is available on the prevalence of cancer patients' accurate prognostic awareness and differences in accurate prognostic awareness by publication year, region, assessment method, and service received. To examine the prevalence of advanced/terminal cancer patients' accurate prognostic awareness and differences in accurate prognostic awareness by publication year, region, assessment method, and service received. Systematic review and meta-analysis. MEDLINE, Embase, The Cochrane Library, CINAHL, and PsycINFO were systematically searched on accurate prognostic awareness in adult patients with advanced/terminal cancer (1990-2014). Pooled prevalences were calculated for accurate prognostic awareness by a random-effects model. Differences in weighted estimates of accurate prognostic awareness were compared by meta-regression. In total, 34 articles were retrieved for systematic review and meta-analysis. At best, only about half of advanced/terminal cancer patients accurately understood their prognosis (49.1%; 95% confidence interval: 42.7%-55.5%; range: 5.4%-85.7%). Accurate prognostic awareness was independent of service received and publication year, but highest in Australia, followed by East Asia, North America, and southern Europe and the United Kingdom (67.7%, 60.7%, 52.8%, and 36.0%, respectively; p = 0.019). Accurate prognostic awareness was higher by clinician assessment than by patient report (63.2% vs 44.5%, p < 0.001). Less than half of advanced/terminal cancer patients accurately understood their prognosis, with significant variations by region and assessment method. Healthcare professionals should thoroughly assess advanced/terminal cancer patients' preferences for prognostic information and engage them in prognostic discussion early in the cancer trajectory, thus facilitating their accurate prognostic awareness and the quality of end-of-life care decision-making.

Latent class analysis derived subgroups of low back pain patients - do they have prognostic capacity?

PubMed

Molgaard Nielsen, Anne; Hestbaek, Lise; Vach, Werner; Kent, Peter; Kongsted, Alice

2017-08-09

Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the 'comparator variables'). This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above. The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and from 6.8%-20.3% for disability, and highest at the 2 weeks follow-up. Latent Class-derived subgroups provided additional prognostic information when compared to a range of variables, but the improvements were not substantial enough to warrant further development into a new prognostic tool. Further research could investigate if these novel subgrouping approaches may help to improve existing tools that subgroup low back pain patients.

Development and validation of a prognostic index for 4-year mortality in older adults.

PubMed

Lee, Sei J; Lindquist, Karla; Segal, Mark R; Covinsky, Kenneth E

2006-02-15

Both comorbid conditions and functional measures predict mortality in older adults, but few prognostic indexes combine both classes of predictors. Combining easily obtained measures into an accurate predictive model could be useful to clinicians advising patients, as well as policy makers and epidemiologists interested in risk adjustment. To develop and validate a prognostic index for 4-year mortality using information that can be obtained from patient report. Using the 1998 wave of the Health and Retirement Study (HRS), a population-based study of community-dwelling US adults older than 50 years, we developed the prognostic index from 11,701 individuals and validated the index with 8009. Individuals were asked about their demographic characteristics, whether they had specific diseases, and whether they had difficulty with a series of functional measures. We identified variables independently associated with mortality and weighted the variables to create a risk index. Death by December 31, 2002. The overall response rate was 81%. During the 4-year follow-up, there were 1361 deaths (12%) in the development cohort and 1072 deaths (13%) in the validation cohort. Twelve independent predictors of mortality were identified: 2 demographic variables (age: 60-64 years, 1 point; 65-69 years, 2 points; 70-74 years, 3 points; 75-79 years, 4 points; 80-84 years, 5 points, >85 years, 7 points and male sex, 2 points), 6 comorbid conditions (diabetes, 1 point; cancer, 2 points; lung disease, 2 points; heart failure, 2 points; current tobacco use, 2 points; and body mass index <25, 1 point), and difficulty with 4 functional variables (bathing, 2 points; walking several blocks, 2 points; managing money, 2 points, and pushing large objects, 1 point. Scores on the risk index were strongly associated with 4-year mortality in the validation cohort, with 0 to 5 points predicting a less than 4% risk, 6 to 9 points predicting a 15% risk, 10 to 13 points predicting a 42% risk, and 14 or more points predicting a 64% risk. The risk index showed excellent discrimination with a cstatistic of 0.84 in the development cohort and 0.82 in the validation cohort. This prognostic index, incorporating age, sex, self-reported comorbid conditions, and functional measures, accurately stratifies community-dwelling older adults into groups at varying risk of mortality.

Coronary artery calcium scoring does not add prognostic value to standard 64-section CT angiography protocol in low-risk patients suspected of having coronary artery disease.

PubMed

Kwon, Sung Woo; Kim, Young Jin; Shim, Jaemin; Sung, Ji Min; Han, Mi Eun; Kang, Dong Won; Kim, Ji-Ye; Choi, Byoung Wook; Chang, Hyuk-Jae

2011-04-01

To evaluate the prognostic outcome of cardiac computed tomography (CT) for prediction of major adverse cardiac events (MACEs) in low-risk patients suspected of having coronary artery disease (CAD) and to explore the differential prognostic values of coronary artery calcium (CAC) scoring and coronary CT angiography. Institutional review committee approval and informed consent were obtained. In 4338 patients who underwent 64-section CT for evaluation of suspected CAD, both CAC scoring and CT angiography were concurrently performed by using standard scanning protocols. Follow-up clinical outcome data regarding composite MACEs were procured. Multivariable Cox proportional hazards models were developed to predict MACEs. Risk-adjusted models incorporated traditional risk factors for CAC scoring and coronary CT angiography. During the mean follow-up of 828 days ± 380, there were 105 MACEs, for an event rate of 3%. The presence of obstructive CAD at coronary CT angiography had independent prognostic value, which escalated according to the number of stenosed vessels (P < .001). In the receiver operating characteristic curve (ROC) analysis, the superiority of coronary CT angiography to CAC scoring was demonstrated by a significantly greater area under the ROC curve (AUC) (0.892 vs 0.810, P < .001), whereas no significant incremental value for the addition of CAC scoring to coronary CT angiography was established (AUC = 0.892 for coronary CT angiography alone vs 0.902 with addition of CAC scoring, P = .198). Coronary CT angiography is better than CAC scoring in predicting MACEs in low-risk patients suspected of having CAD. Furthermore, the current standard multisection CT protocol (coronary CT angiography combined with CAC scoring) has no incremental prognostic value compared with coronary CT angiography alone. Therefore, in terms of determining prognosis, CAC scoring may no longer need to be incorporated in the cardiac CT protocol in this population. © RSNA, 2011.

Development and validation of a prognostic nomogram for terminally ill cancer patients.

PubMed

Feliu, Jaime; Jiménez-Gordo, Ana María; Madero, Rosario; Rodríguez-Aizcorbe, José Ramón; Espinosa, Enrique; Castro, Javier; Acedo, Jesús Domingo; Martínez, Beatriz; Alonso-Babarro, Alberto; Molina, Raquel; Cámara, Juan Carlos; García-Paredes, María Luisa; González-Barón, Manuel

2011-11-02

Determining life expectancy in terminally ill cancer patients is a difficult task. We aimed to develop and validate a nomogram to predict the length of survival in patients with terminal disease. From February 1, 2003, to December 31, 2005, 406 consecutive terminally ill patients were entered into the study. We analyzed 38 features prognostic of life expectancy among terminally ill patients by multivariable Cox regression and identified the most accurate and parsimonious model by backward variable elimination according to the Akaike information criterion. Five clinical and laboratory variables were built into a nomogram to estimate the probability of patient survival at 15, 30, and 60 days. We validated and calibrated the nomogram with an external validation cohort of 474 patients who were treated from June 1, 2006, through December 31, 2007. The median overall survival was 29.1 days for the training set and 18.3 days for the validation set. Eastern Cooperative Oncology Group performance status, lactate dehydrogenase levels, lymphocyte levels, albumin levels, and time from initial diagnosis to diagnosis of terminal disease were retained in the multivariable Cox proportional hazards model as independent prognostic factors of survival and formed the basis of the nomogram. The nomogram had high predictive performance, with a bootstrapped corrected concordance index of 0.70, and it showed good calibration. External independent validation revealed 68% predictive accuracy. We developed a highly accurate tool that uses basic clinical and analytical information to predict the probability of survival at 15, 30, and 60 days in terminally ill cancer patients. This tool can help physicians making decisions on clinical care at the end of life.

[Studies of prognostic factor and chemotherapeutic effect of epithelial ovarian cancer using Cox's proportional hazard model].

PubMed

Umesaki, N; Sugawa, T; Yajima, A; Satoh, S; Terashima, Y; Ochiai, K; Tomoda, Y; Kanoh, T; Noda, K; Yakushiji, M

1993-12-01

To make clear the prognostic factor and chemotherapeutic effect of epithelial ovarian cancer, a multiple-center study involving 22 hospitals in Japan was conducted using Cox's proportional hazard model. A total of 1,181 cases were reviewed. Clinical stage, histologic type, and residual tumor diameter were significant prognostic factors, but the degree of tissue differentiation was not. The effect of remission induction chemotherapy was assessed with or without CDDP, and a distinct prognostic difference was noted. Among the patients receiving CDDP + ADM + other chemotherapeutic agents (PA group), CDDP + other chemotherapeutic agents (PO group) and CDDP only (P group), the prognosis of the PO group was better than for the P group. The long-term prognosis improving effect of chemotherapy was assessed. Neither maintenance chemotherapy based on oral administration of pyrimidine fluoride nor immunotherapy had any long-term prognosis improving effect, while intermittent chemotherapy based on CDDP resulted in improved prognosis.

Towards Prognostics of Electrolytic Capacitors

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Kulkarni, Chetan; Biswas, Gautam; Goegel, Kai

2011-01-01

A remaining useful life prediction algorithm and degradation model for electrolytic capacitors is presented. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management research. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. In particular, experimental results of an accelerated aging test under electrical stresses are presented. The capacitors used in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors.

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, kai

2007-01-01

Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium

PubMed Central

Alfonso, J. C. L.; Schaadt, N. S.; Schönmeyer, R.; Brieu, N.; Forestier, G.; Wemmert, C.; Feuerhake, F.; Hatzikirou, H.

2016-01-01

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches. PMID:27659691

In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium

NASA Astrophysics Data System (ADS)

Alfonso, J. C. L.; Schaadt, N. S.; Schönmeyer, R.; Brieu, N.; Forestier, G.; Wemmert, C.; Feuerhake, F.; Hatzikirou, H.

2016-09-01

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches.

Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial.

PubMed

Bartlett, John M S; Christiansen, Jason; Gustavson, Mark; Rimm, David L; Piper, Tammy; van de Velde, Cornelis J H; Hasenburg, Annette; Kieback, Dirk G; Putter, Hein; Markopoulos, Christos J; Dirix, Luc Y; Seynaeve, Caroline; Rea, Daniel W

2016-01-01

Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

Low Expression of Mucin-4 Predicts Poor Prognosis in Patients With Clear-Cell Renal Cell Carcinoma

PubMed Central

Fu, Hangcheng; Liu, Yidong; Xu, Le; Chang, Yuan; Zhou, Lin; Zhang, Weijuan; Yang, Yuanfeng; Xu, Jiejie

2016-01-01

Abstract Mucin-4 (MUC4), a member of membrane-bound mucins, has been reported to exert a large variety of distinctive roles in tumorigenesis of different cancers. MUC4 is aberrantly expressed in clear-cell renal cell carcinoma (ccRCC) but its prognostic value is still unveiled. This study aims to assess the clinical significance of MUC4 expression in patients with ccRCC. The expression of MUC4 was assessed by immunohistochemistry in 198 patients with ccRCC who underwent nephrectomy retrospectively in 2003 and 2004. Sixty-seven patients died before the last follow-up in the cohort. Kaplan–Meier method with log-rank test was applied to compare survival curves. Univariate and multivariate Cox regression models were applied to evaluate the prognostic value of MUC4 expression in overall survival (OS). The predictive nomogram was constructed based on the independent prognostic factors. The calibration was built to evaluate the predictive accuracy of nomogram. In patients with ccRCC, MUC4 expression, which was determined to be an independent prognostic indicator for OS (hazard ratio [HR] 3.891; P < 0.001), was negatively associated with tumor size (P = 0.036), Fuhrman grade (P = 0.044), and OS (P < 0.001). The prognostic accuracy of TNM stage, UCLA Integrated Scoring System (UISS), and Mayo clinic stage, size, grade, and necrosis score (SSIGN) prognostic models was improved when MUC4 expression was added. The independent prognostic factors, pT stage, distant metastases, Fuhrman grade, sarcomatoid, and MUC4 expression were integrated to establish a predictive nomogram with high predictive accuracy. MUC4 expression is an independent prognostic factor for OS in patients with ccRCC. PMID:27124015

Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions

DTIC Science & Technology

2012-09-01

make end of life ( EOL ) and remaining useful life (RUL) estimations. Model-based prognostics approaches perform these tasks with the help of first...in parameters Degradation Modeling Parameter estimation Prediction Thermal / Electrical Stress Experimental Data State Space model RUL EOL ...distribution at given single time point kP , and use this for multi-step predictions to EOL . There are several methods which exits for selecting the sigma

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

PubMed Central

2018-01-01

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related networks. A deep learning based risk stratification model was constructed with representative genes of these networks. The model was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could predict patients' survival independent of clinicopathological variables. Five networks were significantly associated with patients' survival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for input of the model. The output of the model was significantly associated with patients' survival in two test sets and training set (p < 0.00001, p < 0.0001 and p = 0.02 for training and test sets 1 and 2, resp.). In multivariate analyses, the model was associated with patients' prognosis independent of other clinicopathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction. PMID:29581968

Development of a prognostic model for predicting spontaneous singleton preterm birth.

PubMed

Schaaf, Jelle M; Ravelli, Anita C J; Mol, Ben Willem J; Abu-Hanna, Ameen

2012-10-01

To develop and validate a prognostic model for prediction of spontaneous preterm birth. Prospective cohort study using data of the nationwide perinatal registry in The Netherlands. We studied 1,524,058 singleton pregnancies between 1999 and 2007. We developed a multiple logistic regression model to estimate the risk of spontaneous preterm birth based on maternal and pregnancy characteristics. We used bootstrapping techniques to internally validate our model. Discrimination (AUC), accuracy (Brier score) and calibration (calibration graphs and Hosmer-Lemeshow C-statistic) were used to assess the model's predictive performance. Our primary outcome measure was spontaneous preterm birth at <37 completed weeks. Spontaneous preterm birth occurred in 57,796 (3.8%) pregnancies. The final model included 13 variables for predicting preterm birth. The predicted probabilities ranged from 0.01 to 0.71 (IQR 0.02-0.04). The model had an area under the receiver operator characteristic curve (AUC) of 0.63 (95% CI 0.63-0.63), the Brier score was 0.04 (95% CI 0.04-0.04) and the Hosmer Lemeshow C-statistic was significant (p<0.0001). The calibration graph showed overprediction at higher values of predicted probability. The positive predictive value was 26% (95% CI 20-33%) for the 0.4 probability cut-off point. The model's discrimination was fair and it had modest calibration. Previous preterm birth, drug abuse and vaginal bleeding in the first half of pregnancy were the most important predictors for spontaneous preterm birth. Although not applicable in clinical practice yet, this model is a next step towards early prediction of spontaneous preterm birth that enables caregivers to start preventive therapy in women at higher risk. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Doubt and belief in physicians' ability to prognosticate during critical illness: The perspective of surrogate decision makers

PubMed Central

Zier, Lucas S.; Burack, Jeffrey H.; Micco, Guy; Chipman, Anne K.; Frank, James A.; Luce, John M.; White, Douglas B.

2009-01-01

Objectives: Although discussing a prognosis is a duty of physicians caring for critically ill patients, little is known about surrogate decision-makers' beliefs about physicians' ability to prognosticate. We sought to determine: 1) surrogates' beliefs about whether physicians can accurately prognosticate for critically ill patients; and 2) how individuals use prognostic information in their role as surrogate decision-makers. Design, Setting, and Patients: Multicenter study in intensive care units of a public hospital, a tertiary care hospital, and a veterans' hospital. We conducted semistructured interviews with 50 surrogate decision-makers of critically ill patients. We analyzed the interview transcripts using grounded theory methods to inductively develop a framework to describe surrogates' beliefs about physicians' ability to prognosticate. Validation methods included triangulation by multidisciplinary analysis and member checking. Measurements and Main Results: Overall, 88% (44 of 50) of surrogates expressed doubt about physicians' ability to prognosticate for critically ill patients. Four distinct themes emerged that explained surrogates' doubts about prognostic accuracy: a belief that God could alter the course of the illness, a belief that predicting the future is inherently uncertain, prior experiences where physicians' prognostications were inaccurate, and experiences with prognostication during the patient's intensive care unit stay. Participants also identified several factors that led to belief in physicians' prognostications, such as receiving similar prognostic estimates from multiple physicians and prior experiences with accurate prognostication. Surrogates' doubts about prognostic accuracy did not prevent them from wanting prognostic information. Instead, most surrogate decision-makers view physicians' prognostications as rough estimates that are valuable in informing decisions, but are not determinative. Surrogates identified the act of prognostic disclosure as a key step in preparing emotionally and practically for the possibility that a patient may not survive. Conclusions: Although many surrogate decision-makers harbor some doubt about the accuracy of physicians' prognostications, they highly value discussions about prognosis and use the information for multiple purposes. (Crit Care Med 2008; 36: 2341–2347) PMID:18596630

Prognostic value of coronary computed tomographic angiography findings in asymptomatic individuals: a 6-year follow-up from the prospective multicentre international CONFIRM study.

PubMed

Cho, Iksung; Al'Aref, Subhi J; Berger, Adam; Ó Hartaigh, Bríain; Gransar, Heidi; Valenti, Valentina; Lin, Fay Y; Achenbach, Stephan; Berman, Daniel S; Budoff, Matthew J; Callister, Tracy Q; Al-Mallah, Mouaz H; Cademartiri, Filippo; Chinnaiyan, Kavitha; Chow, Benjamin J W; DeLago, Augustin; Villines, Todd C; Hadamitzky, Martin; Hausleiter, Joerg; Leipsic, Jonathon; Shaw, Leslee J; Kaufmann, Philipp A; Feuchtner, Gudrun; Kim, Yong-Jin; Maffei, Erica; Raff, Gilbert; Pontone, Gianluca; Andreini, Daniele; Marques, Hugo; Rubinshtein, Ronen; Chang, Hyuk-Jae; Min, James K

2018-03-14

The long-term prognostic benefit of coronary computed tomographic angiography (CCTA) findings of coronary artery disease (CAD) in asymptomatic populations is unknown. From the prospective multicentre international CONFIRM long-term study, we evaluated asymptomatic subjects without known CAD who underwent both coronary artery calcium scoring (CACS) and CCTA (n = 1226). Coronary computed tomographic angiography findings included the severity of coronary artery stenosis, plaque composition, and coronary segment location. Using the C-statistic and likelihood ratio tests, we evaluated the incremental prognostic utility of CCTA findings over a base model that included a panel of traditional risk factors (RFs) as well as CACS to predict long-term all-cause mortality. During a mean follow-up of 5.9 ± 1.2 years, 78 deaths occurred. Compared with the traditional RF alone (C-statistic 0.64), CCTA findings including coronary stenosis severity, plaque composition, and coronary segment location demonstrated improved incremental prognostic utility beyond traditional RF alone (C-statistics range 0.71-0.73, all P < 0.05; incremental χ2 range 20.7-25.5, all P < 0.001). However, no added prognostic benefit was offered by CCTA findings when added to a base model containing both traditional RF and CACS (C-statistics P > 0.05, for all). Coronary computed tomographic angiography improved prognostication of 6-year all-cause mortality beyond a set of conventional RF alone, although, no further incremental value was offered by CCTA when CCTA findings were added to a model incorporating RF and CACS.

Cytogenetic prognostication within medulloblastoma subgroups.

PubMed

Shih, David J H; Northcott, Paul A; Remke, Marc; Korshunov, Andrey; Ramaswamy, Vijay; Kool, Marcel; Luu, Betty; Yao, Yuan; Wang, Xin; Dubuc, Adrian M; Garzia, Livia; Peacock, John; Mack, Stephen C; Wu, Xiaochong; Rolider, Adi; Morrissy, A Sorana; Cavalli, Florence M G; Jones, David T W; Zitterbart, Karel; Faria, Claudia C; Schüller, Ulrich; Kren, Leos; Kumabe, Toshihiro; Tominaga, Teiji; Shin Ra, Young; Garami, Miklós; Hauser, Peter; Chan, Jennifer A; Robinson, Shenandoah; Bognár, László; Klekner, Almos; Saad, Ali G; Liau, Linda M; Albrecht, Steffen; Fontebasso, Adam; Cinalli, Giuseppe; De Antonellis, Pasqualino; Zollo, Massimo; Cooper, Michael K; Thompson, Reid C; Bailey, Simon; Lindsey, Janet C; Di Rocco, Concezio; Massimi, Luca; Michiels, Erna M C; Scherer, Stephen W; Phillips, Joanna J; Gupta, Nalin; Fan, Xing; Muraszko, Karin M; Vibhakar, Rajeev; Eberhart, Charles G; Fouladi, Maryam; Lach, Boleslaw; Jung, Shin; Wechsler-Reya, Robert J; Fèvre-Montange, Michelle; Jouvet, Anne; Jabado, Nada; Pollack, Ian F; Weiss, William A; Lee, Ji-Yeoun; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Leonard, Jeffrey R; Rubin, Joshua B; de Torres, Carmen; Lavarino, Cinzia; Mora, Jaume; Cho, Yoon-Jae; Tabori, Uri; Olson, James M; Gajjar, Amar; Packer, Roger J; Rutkowski, Stefan; Pomeroy, Scott L; French, Pim J; Kloosterhof, Nanne K; Kros, Johan M; Van Meir, Erwin G; Clifford, Steven C; Bourdeaut, Franck; Delattre, Olivier; Doz, François F; Hawkins, Cynthia E; Malkin, David; Grajkowska, Wieslawa A; Perek-Polnik, Marta; Bouffet, Eric; Rutka, James T; Pfister, Stefan M; Taylor, Michael D

2014-03-20

Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.

Cytogenetic Prognostication Within Medulloblastoma Subgroups

PubMed Central

Shih, David J.H.; Northcott, Paul A.; Remke, Marc; Korshunov, Andrey; Ramaswamy, Vijay; Kool, Marcel; Luu, Betty; Yao, Yuan; Wang, Xin; Dubuc, Adrian M.; Garzia, Livia; Peacock, John; Mack, Stephen C.; Wu, Xiaochong; Rolider, Adi; Morrissy, A. Sorana; Cavalli, Florence M.G.; Jones, David T.W.; Zitterbart, Karel; Faria, Claudia C.; Schüller, Ulrich; Kren, Leos; Kumabe, Toshihiro; Tominaga, Teiji; Shin Ra, Young; Garami, Miklós; Hauser, Peter; Chan, Jennifer A.; Robinson, Shenandoah; Bognár, László; Klekner, Almos; Saad, Ali G.; Liau, Linda M.; Albrecht, Steffen; Fontebasso, Adam; Cinalli, Giuseppe; De Antonellis, Pasqualino; Zollo, Massimo; Cooper, Michael K.; Thompson, Reid C.; Bailey, Simon; Lindsey, Janet C.; Di Rocco, Concezio; Massimi, Luca; Michiels, Erna M.C.; Scherer, Stephen W.; Phillips, Joanna J.; Gupta, Nalin; Fan, Xing; Muraszko, Karin M.; Vibhakar, Rajeev; Eberhart, Charles G.; Fouladi, Maryam; Lach, Boleslaw; Jung, Shin; Wechsler-Reya, Robert J.; Fèvre-Montange, Michelle; Jouvet, Anne; Jabado, Nada; Pollack, Ian F.; Weiss, William A.; Lee, Ji-Yeoun; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Leonard, Jeffrey R.; Rubin, Joshua B.; de Torres, Carmen; Lavarino, Cinzia; Mora, Jaume; Cho, Yoon-Jae; Tabori, Uri; Olson, James M.; Gajjar, Amar; Packer, Roger J.; Rutkowski, Stefan; Pomeroy, Scott L.; French, Pim J.; Kloosterhof, Nanne K.; Kros, Johan M.; Van Meir, Erwin G.; Clifford, Steven C.; Bourdeaut, Franck; Delattre, Olivier; Doz, François F.; Hawkins, Cynthia E.; Malkin, David; Grajkowska, Wieslawa A.; Perek-Polnik, Marta; Bouffet, Eric; Rutka, James T.; Pfister, Stefan M.; Taylor, Michael D.

2014-01-01

Purpose Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. Patients and Methods Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. Results Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. Conclusion Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials. PMID:24493713

Contribution of vascular endothelial growth factor to the Nottingham prognostic index in node-negative breast cancer

PubMed Central

Coradini, D; Boracchi, P; Daidone, M Grazia; Pellizzaro, C; Miodini, P; Ammatuna, M; Tomasic, G; Biganzoli, E

2001-01-01

The prognostic contribution of intratumour VEGF, the most important factor in tumour-induced angiogenesis, to NPI was evaluated by using flexible modelling in a series of 226 N-primary breast cancer patients in which steroid receptors and cell proliferation were also accounted for. VEGF provided an additional prognostic contribution to NPI mainly within ER-poor tumours. © 2001 Cancer Research Campaignhttp://www.bjcancer.com PMID:11556826

Development and Validation of a Novel Platform-Independent Metastasis Signature in Human Breast Cancer

PubMed Central

Speers, Corey; Liu, Meilan; Wilder-Romans, Kari; Lawrence, Theodore S.; Pierce, Lori J.; Feng, Felix Y.

2015-01-01

Purpose The molecular drivers of metastasis in breast cancer are not well understood. Therefore, we sought to identify the biological processes underlying distant progression and define a prognostic signature for metastatic potential in breast cancer. Experimental design In vivo screening for metastases was performed using Chick Chorioallantoic Membrane assays in 21 preclinical breast cancer models. Expressed genes associated with metastatic potential were identified using high-throughput analysis. Correlations with biological function were determined using the Database for Annotation, Visualization and Integrated Discovery. Results We identified a broad range of metastatic potential that was independent of intrinsic breast cancer subtypes. 146 genes were significantly associated with metastasis progression and were linked to cancer-related biological functions, including cell migration/adhesion, Jak-STAT, TGF-beta, and Wnt signaling. These genes were used to develop a platform-independent gene expression signature (M-Sig), which was trained and subsequently validated on 5 independent cohorts totaling nearly 1800 breast cancer patients with all p-values < 0.005 and hazard ratios ranging from approximately 2.5 to 3. On multivariate analysis accounting for standard clinicopathologic prognostic variables, M-Sig remained the strongest prognostic factor for metastatic progression, with p-values < 0.001 and hazard ratios > 2 in three different cohorts. Conclusion M-Sig is strongly prognostic for metastatic progression, and may provide clinical utility in combination with treatment prediction tools to better guide patient care. In addition, the platform-independent nature of the signature makes it an excellent research tool as it can be directly applied onto existing, and future, datasets. PMID:25974184

A clinical prognostic model compared to the newly adopted UICC staging in an independent validation cohort of P16 negative/positive head and neck cancer patients.

PubMed

Rasmussen, Jacob H; Håkansson, Katrin; Rasmussen, Gregers B; Vogelius, Ivan R; Friborg, Jeppe; Fischer, Barbara M; Bentzen, Søren M; Specht, Lena

2018-06-01

A previously published prognostic model in patients with head and neck squamous cell carcinoma (HNSCC) was validated in both a p16-negative and a p16-positive independent patient cohort and the performance was compared with the newly adopted 8th edition of the UICC staging system. Consecutive patients with HNSCC treated at a single institution from 2005 to 2012 were included. The cohort was divided in three. 1.) Training cohort, patients treated from 2005 to 2009 excluding patients with p16-positive oropharyngeal squamous cell carcinomas (OPSCC); 2.) A p16-negative validation cohort and 3.) A p16-positive validation cohort. A previously published prognostic model (clinical model) with the significant covariates (smoking status, FDG uptake, and tumor volume) was refitted in the training cohort and validated in the two validation cohorts. The clinical model was used to generate four risk groups based on the predicted risk of disease recurrence after 2 years and the performance was compared with UICC staging 8th edition using concordance index. Overall 568 patients were included. Compared to UICC the clinical model had a significantly better concordance index in the p16-negative validation cohort (AUC = 0.63 for UICC and AUC = 0.73 for the clinical model; p = 0.003) and a borderline significantly better concordance index in the p16-positive cohort (AUC = 0.63 for UICC and 0.72 for the clinical model; p = 0.088). The validated clinical model provided a better prognostication of risk of disease recurrence than UICC stage in the p16-negative validation cohort, and similar prognostication as the newly adopted 8th edition of the UICC staging in the p16-positive patient cohort. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prognostic factors, predictive markers and cancer biology: the triad for successful oral cancer chemoprevention.

PubMed

Monteiro de Oliveira Novaes, Jose Augusto; William, William N

2016-10-01

Oral squamous cell carcinomas represent a significant cancer burden worldwide. Unfortunately, chemoprevention strategies investigated to date have failed to produce an agent considered standard of care to prevent oral cancers. Nonetheless, recent advances in clinical trial design may streamline drug development in this setting. In this manuscript, we review some of these improvements, including risk prediction tools based on molecular markers that help select patients most suitable for chemoprevention. We also discuss the opportunities that novel preclinical models and modern molecular profiling techniques will bring to the prevention field in the near future, and propose a clinical trials framework that incorporates molecular prognostic factors, predictive markers and cancer biology as a roadmap to improve chemoprevention strategies for oral cancers.

Impact of prognostic factors for postmastectomy radiation therapy of breast cancer patients

NASA Astrophysics Data System (ADS)

Simonov, K. A.; Startseva, Zh. A.; Slonimskaya, E. M.; Velikaya, V. V.

2017-09-01

The study included 196 breast cancer patients with stages T1-3N0-3M0. The comprehensive therapy for breast cancer included surgical operation, chemotherapy, and radiotherapy. Multivariate analysis showed that multifocality growth of tumor (p = 0.004), high grade III (p = 0.008), two metastatic lymph nodes (p = 0.02) were associated with an increased risk of regional node failure in the patients with one to three positive lymph nodes. The prognostic models describing the probability of local recurrences of breast cancer were developed for individualization of the radiation therapy tactics. Postmastectomy radiation therapy in the patients with high-risk breast cancer treated with modified radical mastectomy improves locoregional control, breast cancer-specific survival, does not increase late toxicity.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study, regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Joint copyright. The Authors and Annals of Internal Medicine. Diabetic Medicine published by John Wiley Ltd. on behalf of Diabetes UK.

Fear of knowledge: Clinical hypotheses in diagnostic and prognostic reasoning.

PubMed

Chiffi, Daniele; Zanotti, Renzo

2017-10-01

Patients are interested in receiving accurate diagnostic and prognostic information. Models and reasoning about diagnoses have been extensively investigated from a foundational perspective; however, for all its importance, prognosis has yet to receive a comparable degree of philosophical and methodological attention, and this may be due to the difficulties inherent in accurate prognostics. In the light of these considerations, we discuss a considerable body of critical thinking on the topic of prognostication and its strict relations with diagnostic reasoning, pointing out the distinction between nosographic and pathophysiological types of diagnosis and prognosis, underlying the importance of the explication and explanation processes. We then distinguish between various forms of hypothetical reasoning applied to reach diagnostic and prognostic judgments, comparing them with specific forms of abductive reasoning. The main thesis is that creative abduction regarding clinical hypotheses in diagnostic process is very unlikely to occur, whereas this seems to be often the case for prognostic judgments. The reasons behind this distinction are due to the different types of uncertainty involved in diagnostic and prognostic judgments. © 2016 John Wiley & Sons, Ltd.

Prognostic factors in multiple myeloma: selection using Cox's proportional hazard model.

PubMed

Pasqualetti, P; Collacciani, A; Maccarone, C; Casale, R

1996-01-01

The pretreatment characteristics of 210 patients with multiple myeloma, observed between 1980 and 1994, were evaluated as potential prognostic factors for survival. Multivariate analysis according to Cox's proportional hazard model identified in the 160 dead patients with myeloma, among 26 different single prognostic variables, the following factors in order of importance: beta 2-microglobulin; bone marrow plasma cell percentage, hemoglobinemia, degree of lytic bone lesions, serum creatinine, and serum albumin. By analysis of these variables a prognostic index (PI), that considers the regression coefficients derived by Cox's model of all significant factors, was obtained. Using this it was possible to separate the whole patient group into three stages: stage I (PI < 1.485, 67 patients), stage II (PI: 1.485-2.090, 76 patients), and stage III (PI > 2.090, 67 patients), with a median survivals of 68, 36 and 13 months (P < 0.0001), respectively. Also the responses to therapy (P < 0.0001) and the survival curves (P < 0.00001) presented significant differences among the three subgroups. Knowledge of these factors could be of value in predicting prognosis and in planning therapy in patients with multiple myeloma.

Comparison of prognostic nomograms based on different nodal staging systems in patients with resected gastric cancer.

PubMed

Wang, Zi-Xian; Qiu, Miao-Zhen; Jiang, Yu-Ming; Zhou, Zhi-Wei; Li, Guo-Xin; Xu, Rui-Hua

2017-01-01

Purpose: Previous studies addressing the optimal nodal staging system in patients with resected gastric cancer have shown inconsistent results, and the optimal system for development of prognostic nomograms remains unclear. In this study, we compared prognostic nomograms based on the metastatic lymph node (MLN) count, lymph node ratio (LNR), and log odds of metastatic lymph nodes (LODDS) to predict the 5-year overall survival in patients with resected gastric cancer. Methods: We analysed 15,320 patients with resected gastric cancer in the Surveillance, Epidemiology, and End Results (SEER) database between 1988 and 2010. Missing data were handled using multiple imputation. When assessed as a continuous covariate with restricted cubic splines, each MLN, LNR, and LODDS variable was incorporated into a nomogram with other significant prognosticators to predict the 5-year overall survival. A two-centre Chinese dataset (1,595 cases) was used as external validation data. Results: The discriminatory abilities of the MLN-, LNR-, and LODDS-based nomograms were comparable (concordance indices: 0.744, 0.741, and 0.744, respectively, in the SEER set, P > 0.152 for all pairwise comparisons; 0.715, 0.712, and 0.713, respectively, in the Chinese set, P > 0.445 for all pairwise comparisons). The discriminatory abilities of the three nomograms were all superior to the American Joint Committee on Cancer (AJCC) TNM classification (concordance indices: 0.713, P < 0.001 for all in the SEER set; and 0.693, P < 0.001 for all in the Chinese set). The discriminatory abilities of the nomograms were comparable regardless of the number of nodes examined. Moreover, decision curve analyses indicated similar net benefits of using the nomograms. Conclusion: MLN-, LNR-, and LODDS should be considered equally in the development of multivariate prognostic models and nomograms to refine the prediction of survival among patients with resected gastric cancer.

[Skeletal Mass Depletion Is a Negative Prognostic Factor in Gastrointestinal Cancer Patients in the Terminal Stage].

PubMed

Takahashi, Goro; Yamada, Takeshi; Kan, Hayato; Koizumi, Michihiro; Shinji, Seiichi; Yokoyama, Yasuyuki; Iwai, Takuma; Uchida, Eiji

2015-10-01

Skeletal mass depletion has been reported to be a prognostic factor for cancer patients. However, special and expensive devices are required to measure skeletal mass, and this is a major reason why skeletal mass is not used extensively for prognostic marker in clinical settings. We developed a new method to measure skeletal mass for use as a prognostic marker using CT images without special and expensive devices. In this study, we evaluated the usefulness of skeletal mass as measured by this new method as a prognostic marker for gastrointestinal cancer patients. Patients who died from gastrointestinal cancer between March 2010 and October 2013 were included. We measured the right-sided maximum psoas muscle cross sectional area (MPCA) by using CT images before surgery and after the patients developed a terminal condition. The maximum psoas muscle cross sectional area ratio (MPCA-R) was defined as follows: MPCA-R=MPCA before surgery/MPCA after developing a terminal condition. We evaluated the correlation between MPCA-R and survival. Fifty-nine patients were included. The median survival was 44 days, and MPCA-R was significantly correlated with survival (p=0.001). On receiver operating characteristic (ROC) analysis, the area under the curve (AUC) to predict 30-day and 90-day survival was 0.710 and 0.748, respectively. MPCA-R is a new and novel prognostic marker for gastrointestinal cancer patients in terminal condition.

Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma.

PubMed

Grinchuk, Oleg V; Yenamandra, Surya P; Iyer, Ramakrishnan; Singh, Malay; Lee, Hwee Kuan; Lim, Kiat Hon; Chow, Pierce Kah-Hoe; Kuznetsov, Vladamir A

2018-01-01

Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro-oncogenic pathways in primary tumors (PT) and adjacent non-malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome-wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24-ribosomal gene-based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10 -6 ) and cross-cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross-validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c-MYC in the pro-oncogenic pattern of ribosomal biogenesis co-regulation in PT and AT. Microarray, quantitative RT-PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co-transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor-like metabolic redirection/assimilation in non-malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for stratifying HCC patient risks. The adjacent non-malignant liver tissue alone, or in combination with HCC tissue biopsy, could be an important target for developing predictive and monitoring strategies, as well as evidence-based therapeutic interventions, that aim to reduce the risk of post-surgery relapse in HCC patients. © 2017 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

Predicting mortality in sick African children: the FEAST Paediatric Emergency Triage (PET) Score.

PubMed

George, Elizabeth C; Walker, A Sarah; Kiguli, Sarah; Olupot-Olupot, Peter; Opoka, Robert O; Engoru, Charles; Akech, Samuel O; Nyeko, Richard; Mtove, George; Reyburn, Hugh; Berkley, James A; Mpoya, Ayub; Levin, Michael; Crawley, Jane; Gibb, Diana M; Maitland, Kathryn; Babiker, Abdel G

2015-07-31

Mortality in paediatric emergency care units in Africa often occurs within the first 24 h of admission and remains high. Alongside effective triage systems, a practical clinical bedside risk score to identify those at greatest risk could contribute to reducing mortality. Data collected during the Fluid As Expansive Supportive Therapy (FEAST) trial, a multi-centre trial involving 3,170 severely ill African children, were analysed to identify clinical and laboratory prognostic factors for mortality. Multivariable Cox regression was used to build a model in this derivation dataset based on clinical parameters that could be quickly and easily assessed at the bedside. A score developed from the model coefficients was externally validated in two admissions datasets from Kilifi District Hospital, Kenya, and compared to published risk scores using Area Under the Receiver Operating Curve (AUROC) and Hosmer-Lemeshow tests. The Net Reclassification Index (NRI) was used to identify additional laboratory prognostic factors. A risk score using 8 clinical variables (temperature, heart rate, capillary refill time, conscious level, severe pallor, respiratory distress, lung crepitations, and weak pulse volume) was developed. The score ranged from 0-10 and had an AUROC of 0.82 (95 % CI, 0.77-0.87) in the FEAST trial derivation set. In the independent validation datasets, the score had an AUROC of 0.77 (95 % CI, 0.72-0.82) amongst admissions to a paediatric high dependency ward and 0.86 (95 % CI, 0.82-0.89) amongst general paediatric admissions. This discriminative ability was similar to, or better than other risk scores in the validation datasets. NRI identified lactate, blood urea nitrogen, and pH to be important prognostic laboratory variables that could add information to the clinical score. Eight clinical prognostic factors that could be rapidly assessed by healthcare staff for triage were combined to create the FEAST Paediatric Emergency Triage (PET) score and externally validated. The score discriminated those at highest risk of fatal outcome at the point of hospital admission and compared well to other published risk scores. Further laboratory tests were also identified as prognostic factors which could be added if resources were available or as indices of severity for comparison between centres in future research studies.

Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325).

PubMed

Dehing-Oberije, Cary; Aerts, Hugo; Yu, Shipeng; De Ruysscher, Dirk; Menheere, Paul; Hilvo, Mika; van der Weide, Hiska; Rao, Bharat; Lambin, Philippe

2011-10-01

Currently, prediction of survival for non-small-cell lung cancer patients treated with (chemo)radiotherapy is mainly based on clinical factors. The hypothesis of this prospective study was that blood biomarkers related to hypoxia, inflammation, and tumor load would have an added prognostic value for predicting survival. Clinical data and blood samples were collected prospectively (NCT00181519, NCT00573040, and NCT00572325) from 106 inoperable non-small-cell lung cancer patients (Stages I-IIIB), treated with curative intent with radiotherapy alone or combined with chemotherapy. Blood biomarkers, including lactate dehydrogenase, C-reactive protein, osteopontin, carbonic anhydrase IX, interleukin (IL) 6, IL-8, carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1, were measured. A multivariate model, built on a large patient population (N = 322) and externally validated, was used as a baseline model. An extended model was created by selecting additional biomarkers. The model's performance was expressed as the area under the curve (AUC) of the receiver operating characteristic and assessed by use of leave-one-out cross validation as well as a validation cohort (n = 52). The baseline model consisted of gender, World Health Organization performance status, forced expiratory volume, number of positive lymph node stations, and gross tumor volume and yielded an AUC of 0.72. The extended model included two additional blood biomarkers (CEA and IL-6) and resulted in a leave-one-out AUC of 0.81. The performance of the extended model was significantly better than the clinical model (p = 0.004). The AUC on the validation cohort was 0.66 and 0.76, respectively. The performance of the prognostic model for survival improved markedly by adding two blood biomarkers: CEA and IL-6. Copyright © 2011 Elsevier Inc. All rights reserved.

Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations.

PubMed

Wang, Dong-Yu; Done, Susan J; Mc Cready, David R; Leong, Wey L

2014-07-04

Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). The original training cohort reached a statistically significant difference (p < 0.05) in disease-free survivals between the three CMTC groups after an additional two years of follow-up (median = 55 months). The prognostic value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups. Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments.

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

DTIC Science & Technology

2014-12-23

publications for benchmarking prognostics algorithms. The turbofan degradation datasets have received over seven thousand unique downloads in the last five...approaches that researchers have taken to implement prognostics using these turbofan datasets. Some unique characteristics of these datasets are also...Description of the five turbofan degradation datasets available from NASA repository. Datasets #Fault Modes #Conditions #Train Units #Test Units

Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature.

PubMed

Mbeutcha, Aurélie; Mathieu, Romain; Rouprêt, Morgan; Gust, Kilian M; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2016-10-01

In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease.

A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis.

PubMed

Passamonti, F; Giorgino, T; Mora, B; Guglielmelli, P; Rumi, E; Maffioli, M; Rambaldi, A; Caramella, M; Komrokji, R; Gotlib, J; Kiladjian, J J; Cervantes, F; Devos, T; Palandri, F; De Stefano, V; Ruggeri, M; Silver, R T; Benevolo, G; Albano, F; Caramazza, D; Merli, M; Pietra, D; Casalone, R; Rotunno, G; Barbui, T; Cazzola, M; Vannucchi, A M

2017-12-01

Polycythemia vera (PV) and essential thrombocythemia (ET) are myeloproliferative neoplasms with variable risk of evolution into post-PV and post-ET myelofibrosis, from now on referred to as secondary myelofibrosis (SMF). No specific tools have been defined for risk stratification in SMF. To develop a prognostic model for predicting survival, we studied 685 JAK2, CALR, and MPL annotated patients with SMF. Median survival of the whole cohort was 9.3 years (95% CI: 8-not reached-NR-). Through penalized Cox regressions we identified negative predictors of survival and according to beta risk coefficients we assigned 2 points to hemoglobin level <11 g/dl, to circulating blasts ⩾3%, and to CALR-unmutated genotype, 1 point to platelet count <150 × 10 9 /l and to constitutional symptoms, and 0.15 points to any year of age. Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) allocated SMF patients into four risk categories with different survival (P<0.0001): low (median survival NR; 133 patients), intermediate-1 (9.3 years, 95% CI: 8.1-NR; 245 patients), intermediate-2 (4.4 years, 95% CI: 3.2-7.9; 126 patients), and high risk (2 years, 95% CI: 1.7-3.9; 75 patients). Finally, we found that the MYSEC-PM represents the most appropriate tool for SMF decision-making to be used in clinical and trial settings.

Prognostic and survival analysis of presbyopia: The healthy twin study

NASA Astrophysics Data System (ADS)

Lira, Adiyani; Sung, Joohon

2015-12-01

Presbyopia, a vision condition in which the eye loses its flexibility to focus on near objects, is part of ageing process which mostly perceptible in the early or mid 40s. It is well known that age is its major risk factor, while sex, alcohol, poor nutrition, ocular and systemic diseases are known as common risk factors. However, many other variables might influence the prognosis. Therefore in this paper we developed a prognostic model to estimate survival from presbyopia. 1645 participants which part of the Healthy Twin Study, a prospective cohort study that has recruited Korean adult twins and their family members based on a nation-wide registry at public health agencies since 2005, were collected and analyzed by univariate analysis as well as Cox proportional hazard model to reveal the prognostic factors for presbyopia while survival curves were calculated by Kaplan-Meier method. Besides age, sex, diabetes, and myopia; the proposed model shows that education level (especially engineering program) also contribute to the occurrence of presbyopia as well. Generally, at 47 years old, the chance of getting presbyopia becomes higher with the survival probability is less than 50%. Furthermore, our study shows that by stratifying the survival curve, MZ has shorter survival with average onset time about 45.8 compare to DZ and siblings with 47.5 years old. By providing factors that have more effects and mainly associate with presbyopia, we expect that we could help to design an intervention to control or delay its onset time.

Predicting Long-Term Global Outcome after Traumatic Brain Injury: Development of a Practical Prognostic Tool Using the Traumatic Brain Injury Model Systems National Database.

PubMed

Walker, William C; Stromberg, Katharine A; Marwitz, Jennifer H; Sima, Adam P; Agyemang, Amma A; Graham, Kristin M; Harrison-Felix, Cynthia; Hoffman, Jeanne M; Brown, Allen W; Kreutzer, Jeffrey S; Merchant, Randall

2018-05-16

For patients surviving serious traumatic brain injury (TBI), families and other stakeholders often desire information on long-term functional prognosis, but accurate and easy-to-use clinical tools are lacking. We aimed to build utilitarian decision trees from commonly collected clinical variables to predict Glasgow Outcome Scale (GOS) functional levels at 1, 2, and 5 years after moderate-to-severe closed TBI. Flexible classification tree statistical modeling was used on prospectively collected data from the TBI-Model Systems (TBIMS) inception cohort study. Enrollments occurred at 17 designated, or previously designated, TBIMS inpatient rehabilitation facilities. Analysis included all participants with nonpenetrating TBI injured between January 1997 and January 2017. Sample sizes were 10,125 (year-1), 8,821 (year-2), and 6,165 (year-5) after cross-sectional exclusions (death, vegetative state, insufficient post-injury time, and unavailable outcome). In our final models, post-traumatic amnesia (PTA) duration consistently dominated branching hierarchy and was the lone injury characteristic significantly contributing to GOS predictability. Lower-order variables that added predictability were age, pre-morbid education, productivity, and occupational category. Generally, patient outcomes improved with shorter PTA, younger age, greater pre-morbid productivity, and higher pre-morbid vocational or educational achievement. Across all prognostic groups, the best and worst good recovery rates were 65.7% and 10.9%, respectively, and the best and worst severe disability rates were 3.9% and 64.1%. Predictability in test data sets ranged from C-statistic of 0.691 (year-1; confidence interval [CI], 0.675, 0.711) to 0.731 (year-2; CI, 0.724, 0.738). In conclusion, we developed a clinically useful tool to provide prognostic information on long-term functional outcomes for adult survivors of moderate and severe closed TBI. Predictive accuracy for GOS level was demonstrated in an independent test sample. Length of PTA, a clinical marker of injury severity, was by far the most critical outcome determinant.

Prognostic role of tumor-infiltrating lymphocytes in gastric cancer: a meta-analysis

PubMed Central

Shao, Yingjie; Xu, Bin; Chen, Lujun; Zhou, Qi; Hu, Wenwei; Zhang, Dachuan; Wu, Changping; Tao, Min; Zhu, Yibei; Jiang, Jingting

2017-01-01

Background In patients with gastric cancer, the prognostic value of tumor-infiltrating lymphocytes (TILs) is still controversial. A meta-analysis was performed to evaluate the prognostic value of TILs in gastric cancer. Materials and methods We identify studies from PubMed, Embase and the Cochrane Library to assess the prognostic effect of TILs in patients with gastric cancer. Fixed-effects models or random-effects models were used estimate the pooled hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS), which depend on the heterogeneity. Results A total of 31 observational studies including 4,185 patients were enrolled. For TILs subsets, the amount of CD8+, FOXP3+, CD3+, CD57+, CD20+, CD45RO+, Granzyme B+ and T-bet+ lymphocytes was significantly associated with improved survival (P < 0.05); moreover, the amount of CD3+ TILs in intra-tumoral compartment (IT) was the most significant prognostic marker (pooled HR = 0.52; 95% CI = 0.43–0.63; P < 0.001). However, CD4+ TILs was not statistically associated with patients’ survival. FOXP3+ TILs showed bidirectional prognostic roles which had positive effect in IT (pooled HR = 1.57; 95% CI = 1.04–2.37; P = 0.033) and negative effect in extra-tumoral compartment (ET) (pooled HR = 0.76; 95% CI = 0.60–0.96; P = 0.022). Conclusions This meta-analysis suggests that some TIL subsets could serve as prognostic biomarkers in gastric cancer. High-quality randomized controlled trials are needed to decide if these TILs could serve as targets for immunotherapy in gastric cancer. PMID:28915679

Predicting cancer prognosis using interactive online tools: A systematic review and implications for cancer care providers

PubMed Central

Rabin, Borsika A.; Gaglio, Bridget; Sanders, Tristan; Nekhlyudov, Larissa; Dearing, James W.; Bull, Sheana; Glasgow, Russell E.; Marcus, Alfred

2013-01-01

Cancer prognosis is of keen interest for cancer patients, their caregivers and providers. Prognostic tools have been developed to guide patient-physician communication and decision-making. Given the proliferation of prognostic tools, it is timely to review existing online cancer prognostic tools and discuss implications for their use in clinical settings. Using a systematic approach, we searched the Internet, Medline, and consulted with experts to identify existing online prognostic tools. Each was reviewed for content and format. Twenty-two prognostic tools addressing 89 different cancers were identified. Tools primarily focused on prostate (n=11), colorectal (n=10), breast (n=8), and melanoma (n=6), though at least one tool was identified for most malignancies. The input variables for the tools included cancer characteristics (n=22), patient characteristics (n=18), and comorbidities (n=9). Effect of therapy on prognosis was included in 15 tools. The most common predicted outcome was cancer specific survival/mortality (n=17). Only a few tools (n=4) suggested patients as potential target users. A comprehensive repository of online prognostic tools was created to understand the state-of-the-art in prognostic tool availability and characteristics. Use of these tools may support communication and understanding about cancer prognosis. Dissemination, testing, refinement of existing, and development of new tools under different conditions are needed. PMID:23956026

Prognostic relevance and performance characteristics of serum IGFBP-2 and PAPP-A in women with breast cancer: a long-term Danish cohort study.

PubMed

Espelund, Ulrick; Renehan, Andrew G; Cold, Søren; Oxvig, Claus; Lancashire, Lee; Su, Zhenqiang; Flyvbjerg, Allan; Frystyk, Jan

2018-05-03

Measurement of circulating insulin-like growth factors (IGFs), in particular IGF-binding protein (IGFBP)-2, at the time of diagnosis, is independently prognostic in many cancers, but its clinical performance against other routinely determined prognosticators has not been examined. We measured IGF-I, IGF-II, pro-IGF-II, IGF bioactivity, IGFBP-2, -3, and pregnancy-associated plasma protein A (PAPP-A), an IGFBP regulator, in baseline samples of 301 women with breast cancer treated on four protocols (Odense, Denmark: 1993-1998). We evaluated performance characteristics (expressed as area under the curve, AUC) using Cox regression models to derive hazard ratios (HR) with 95% confidence intervals (CIs) for 10-year recurrence-free survival (RFS) and overall survival (OS), and compared those against the clinically used Nottingham Prognostic Index (NPI). We measured the same biomarkers in 531 noncancer individuals to assess multidimensional relationships (MDR), and evaluated additional prognostic models using survival artificial neural network (SANN) and survival support vector machines (SSVM), as these enhance capture of MDRs. For RFS, increasing concentrations of circulating IGFBP-2 and PAPP-A were independently prognostic [HR biomarker doubling : 1.474 (95% CIs: 1.160, 1.875, P = 0.002) and 1.952 (95% CIs: 1.364, 2.792, P < 0.001), respectively]. The AUC RFS for NPI was 0.626 (Cox model), improving to 0.694 (P = 0.012) with the addition of IGFBP-2 plus PAPP-A. Derived AUC RFS using SANN and SSVM did not perform superiorly. Similar patterns were observed for OS. These findings illustrate an important principle in biomarker qualification-measured circulating biomarkers may demonstrate independent prognostication, but this does not necessarily translate into substantial improvement in clinical performance. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

A novel H-FABP assay and a fast prognostic score for risk assessment of normotensive pulmonary embolism.

PubMed

Dellas, Claudia; Tschepe, Merle; Seeber, Valerie; Zwiener, Isabella; Kuhnert, Katherina; Schäfer, Katrin; Hasenfuß, Gerd; Konstantinides, Stavros; Lankeit, Mareike

2014-05-05

We tested whether heart-type fatty acid binding protein (H-FABP) measured by a fully-automated immunoturbidimetric assay in comparison to ELISA provides additive prognostic value in patients with pulmonary embolism (PE), and validated a fast prognostic score in comparison to the ESC risk prediction model and the simplified Pulmonary Embolism Severity Index (sPESI). We prospectively examined 271 normotensive patients with PE; of those, 20 (7%) had an adverse 30-day outcome. H-FABP levels determined by immunoturbidimetry were higher (median, 5.2 [IQR; 2.7-9.8] ng/ml) than those by ELISA (2.9 [1.1-5.4] ng/ml), but Bland-Altman plot demonstrated a good agreement of both assays. The area under the curve for H-FABP was greater for immunoturbidimetry than for ELISA (0.82 [0.74-0.91] vs 0.78 [0.68-0.89]; P=0.039). H-FABP measured by immunoturbidimetry (but not by ELISA) provided additive prognostic information to other predictors of 30-day outcome (OR, 12.4 [95% CI, 1.6-97.6]; P=0.017). When H-FABP determined by immunoturbidimetry was integrated into a novel prognostic score (H-FABP, Syncope, and Tachycardia; FAST score), the score provided additive prognostic information by multivariable analysis (OR, 14.2 [3.9-51.4]; p<0.001; c-index, 0.86) which were superior to information obtained by the ESC model (c-index, 0.62; net reclassification improvement (NRI), 0.39 [0.21-0.56]; P<0.001) or the sPESI (c-index, 0.68; NRI, 0.24 [0.05-0.43]; P=0.012). In conclusion, determination of H-FABP by immunoturbidimetry provides prognostic information superior to that of ELISA and, if integrated in the FAST score, appears more suitable to identify patients with an adverse 30-day outcome compared to the ESC model and sPESI.

Development and validation of prognostic models in metastatic breast cancer: a GOCS study.

PubMed

Rabinovich, M; Vallejo, C; Bianco, A; Perez, J; Machiavelli, M; Leone, B; Romero, A; Rodriguez, R; Cuevas, M; Dansky, C

1992-01-01

The significance of several prognostic factors and the magnitude of their influence on response rate and survival were assessed by means of uni- and multivariate analyses in 362 patients with stage IV (UICC) breast carcinoma receiving combination chemotherapy as first systemic treatment over an 8-year period. Univariate analyses identified performance status and prior adjuvant radiotherapy as predictors of objective regression (OR), whereas the performance status, prior chemotherapy and radiotherapy (adjuvants), white blood cells count, SGOT and SGPT levels, and metastatic pattern were significantly correlated to survival. In multivariate analyses favorable characteristics associated to OR were prior adjuvant radiotherapy, no prior chemotherapy and postmenopausal status. Regarding survival, the performance status and visceral involvement were selected by the Cox model. The predictive accuracy of the logistic and the proportional hazards models was retrospectively tested in the training sample, and prospectively in a new population of 126 patients also receiving combined chemotherapy as first treatment for metastatic breast cancer. A certain overfitting to data in the training sample was observed with the regression model for response. However, the discriminative ability of the Cox model for survival was clearly confirmed.

The UK-PBC risk scores: Derivation and validation of a scoring system for long-term prediction of end-stage liver disease in primary biliary cholangitis.

PubMed

Carbone, Marco; Sharp, Stephen J; Flack, Steve; Paximadas, Dimitrios; Spiess, Kelly; Adgey, Carolyn; Griffiths, Laura; Lim, Reyna; Trembling, Paul; Williamson, Kate; Wareham, Nick J; Aldersley, Mark; Bathgate, Andrew; Burroughs, Andrew K; Heneghan, Michael A; Neuberger, James M; Thorburn, Douglas; Hirschfield, Gideon M; Cordell, Heather J; Alexander, Graeme J; Jones, David E J; Sandford, Richard N; Mells, George F

2016-03-01

The biochemical response to ursodeoxycholic acid (UDCA)--so-called "treatment response"--strongly predicts long-term outcome in primary biliary cholangitis (PBC). Several long-term prognostic models based solely on the treatment response have been developed that are widely used to risk stratify PBC patients and guide their management. However, they do not take other prognostic variables into account, such as the stage of the liver disease. We sought to improve existing long-term prognostic models of PBC using data from the UK-PBC Research Cohort. We performed Cox's proportional hazards regression analysis of diverse explanatory variables in a derivation cohort of 1,916 UDCA-treated participants. We used nonautomatic backward selection to derive the best-fitting Cox model, from which we derived a multivariable fractional polynomial model. We combined linear predictors and baseline survivor functions in equations to score the risk of a liver transplant or liver-related death occurring within 5, 10, or 15 years. We validated these risk scores in an independent cohort of 1,249 UDCA-treated participants. The best-fitting model consisted of the baseline albumin and platelet count, as well as the bilirubin, transaminases, and alkaline phosphatase, after 12 months of UDCA. In the validation cohort, the 5-, 10-, and 15-year risk scores were highly accurate (areas under the curve: >0.90). The prognosis of PBC patients can be accurately evaluated using the UK-PBC risk scores. They may be used to identify high-risk patients for closer monitoring and second-line therapies, as well as low-risk patients who could potentially be followed up in primary care. © 2015 by the American Association for the Study of Liver Diseases.

Inflammation-based prognostic score is a useful predictor of postoperative outcome in patients with extrahepatic cholangiocarcinoma.

PubMed

Oshiro, Yukio; Sasaki, Ryoko; Fukunaga, Kiyoshi; Kondo, Tadashi; Oda, Tatsuya; Takahashi, Hideto; Ohkohchi, Nobuhiro

2013-03-01

Recent studies have revealed that the Glasgow prognostic score (GPS), an inflammation-based prognostic score, is useful for predicting outcome in a variety of cancers. This study sought to investigate the significance of GPS for prognostication of patients who underwent surgery with extrahepatic cholangiocarcinoma. We retrospectively analyzed a total of 62 patients who underwent resection for extrahepatic cholangiocarcinoma. We calculated the GPS as follows: patients with both an elevated C-reactive protein (>10 mg/L) and hypoalbuminemia (<35 g/L) were allocated a score of 2; patients with one or none of these abnormalities were allocated a s ore of 1 or 0, respectively. Prognostic significance was analyzed by the log-rank test and a Cox proportional hazards model. Overall survival rate was 25.5 % at 5 years for all 62 patients. Venous invasion (p = 0.01), pathological primary tumor category (p = 0.013), lymph node metastasis category (p < 0.001), TNM stage (p < 0.001), and GPS (p = 0.008) were significantly associated with survival by univariate analysis. A Cox model demonstrated that increased GPS was an independent predictive factor with poor prognosis. The preoperative GPS is a useful predictor of postoperative outcome in patients with extrahepatic cholangiocarcinoma.

Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Sankararaman, Shankar

2013-01-01

Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.

Clinical impact of sentinel lymph node biopsy in patients with thick (>4 mm) melanomas.

PubMed

White, Ian; Fortino, Jeanine; Curti, Brendan; Vetto, John

2014-05-01

The role of sentinel lymph node status (SLNS) in thick melanoma is evolving. The purpose of this study was to determine the prognostic value of SLNS in thick melanoma. A retrospective analysis of 120 prospectively collected clinically node-negative thick melanomas over 5 years was performed. Patient (age/sex) and tumor (thickness, ulceration, SLNS, mitoses, metastases, and recurrence) features were collected. Multivariate analysis was performed using Cox proportional hazard model. Factors predictive of positive SLN included male sex, ulceration, and high mitoses. Factors associated with positive SLN had higher local-regional recurrence and metastases than negative SLN. SLNS and tumor thickness impacted 5-year disease-free survival (DFS) and overall survival (OS). Positive SLN, ulceration, age, and mitoses were independent predictors of DFS/OS. Nonulcerated/lower mitoses thick melanomas had lower positive SLN rates. Positive SLN develop recurrence and metastases and have worse OS/DFS. SLNS is an important prognosticator for OS/DFS. Sentinel lymph node biopsy delineates prognostic groups in thick melanomas and can impact management. Copyright © 2014 Elsevier Inc. All rights reserved.

Proposal for Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients

DTIC Science & Technology

2010-10-01

Mathematics , Indiana University Northwest, Gary, IN 3Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, NY 4H...however, is mathematically more parsimonious. The original DCA formulation required several mathematical manipulations making the simplicity of regret...into treatment administration examples; IH developed the mathematical formulation of the model; AV is the author of DCA; BD proposed the regret theory

Subgrid-scale Condensation Modeling for Entropy-based Large Eddy Simulations of Clouds

NASA Astrophysics Data System (ADS)

Kaul, C. M.; Schneider, T.; Pressel, K. G.; Tan, Z.

2015-12-01

An entropy- and total water-based formulation of LES thermodynamics, such as that used by the recently developed code PyCLES, is advantageous from physical and numerical perspectives. However, existing closures for subgrid-scale thermodynamic fluctuations assume more traditional choices for prognostic thermodynamic variables, such as liquid potential temperature, and are not directly applicable to entropy-based modeling. Since entropy and total water are generally nonlinearly related to diagnosed quantities like temperature and condensate amounts, neglecting their small-scale variability can lead to bias in simulation results. Here we present the development of a subgrid-scale condensation model suitable for use with entropy-based thermodynamic formulations.

Prognostic model for psychological outcomes in ambulatory surgery patients: A prospective study using a structural equation modeling framework.

PubMed

Mijderwijk, Hendrik-Jan; Stolker, Robert Jan; Duivenvoorden, Hugo J; Klimek, Markus; Steyerberg, Ewout W

2018-01-01

Surgical procedures are increasingly carried out in a day-case setting. Along with this increase, psychological outcomes have become prominent. The objective was to evaluate prospectively the prognostic effects of sociodemographic, medical, and psychological variables assessed before day-case surgery on psychological outcomes after surgery. The study was carried out between October 2010 and September 2011. We analyzed 398 mixed patients, from a randomized controlled trial, undergoing day-case surgery at a university medical center. Structural equation modeling was used to jointly study presurgical prognostic variables relating to sociodemographics (age, sex, nationality, marital status, having children, religion, educational level, employment), medical status (BMI, heart rate), and psychological status associated with anxiety (State-Trait Anxiety Inventory (STAI), Hospital Anxiety and Depression Scale (HADS-A)), fatigue (Multidimensional Fatigue Inventory (MFI)), aggression (State-Trait Anger Scale (STAS)), depressive moods (HADS-D), self-esteem, and self-efficacy. We studied psychological outcomes on day 7 after surgery, including anxiety, fatigue, depressive moods, and aggression regulation. The final prognostic model comprised the following variables: anxiety (STAI, HADS-A), fatigue (MFI), depression (HADS-D), aggression (STAS), self-efficacy, sex, and having children. The corresponding psychological variables as assessed at baseline were prominent (i.e. standardized regression coefficients ≥ 0.20), with STAI-Trait score being the strongest predictor overall. STAI-State (adjusted R2 = 0.44), STAI-Trait (0.66), HADS-A (0.45) and STAS-Trait (0.54) were best predicted. We provide a prognostic model that adequately predicts multiple postoperative outcomes in day-case surgery. Consequently, this enables timely identification of vulnerable patients who may require additional medical or psychological preventive treatment or-in a worst-case scenario-could be unselected for day-case surgery.

Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose R.; Goebel, Kai; Biswas, Gautam

2012-01-01

Electrolytic capacitors are used in several applications ranging from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuator. Past experiences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress conditions during operations. This makes them good candidates for prognostics and health management. Model-based prognostics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of heal th and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation models for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.

A Testbed for Data Fusion for Engine Diagnostics and Prognostics1

DTIC Science & Technology

2002-03-01

detected ; too late to be useful for prognostics development. Table 1. Table of acronyms ACRONYM MEANING AD Anomaly detector...strictly defined points. Determining where we are on the engine health curve is the first step in prognostics . Fault detection / diagnostic reasoning... Detection As described above the ability of the monitoring system to detect an anomaly is especially important for knowledge-based systems, i.e.,

Prediction of clinical behaviour and treatment for cancers.

PubMed

Futschik, Matthias E; Sullivan, Mike; Reeve, Anthony; Kasabov, Nikola

2003-01-01

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Peripheral T cell lymphoma, not otherwise specified (PTCL-NOS). A new prognostic model developed by the International T cell Project Network.

PubMed

Federico, Massimo; Bellei, Monica; Marcheselli, Luigi; Schwartz, Marc; Manni, Martina; Tarantino, Vittoria; Pileri, Stefano; Ko, Young-Hyeh; Cabrera, Maria E; Horwitz, Steven; Kim, Won S; Shustov, Andrei; Foss, Francine M; Nagler, Arnon; Carson, Kenneth; Pinter-Brown, Lauren C; Montoto, Silvia; Spina, Michele; Feldman, Tatyana A; Lechowicz, Mary J; Smith, Sonali M; Lansigan, Frederick; Gabus, Raul; Vose, Julie M; Advani, Ranjana H

2018-06-01

Different models to investigate the prognosis of peripheral T cell lymphoma not otherwise specified (PTCL-NOS) have been developed by means of retrospective analyses. Here we report on a new model designed on data from the prospective T Cell Project. Twelve covariates collected by the T Cell Project were analysed and a new model (T cell score), based on four covariates (serum albumin, performance status, stage and absolute neutrophil count) that maintained their prognostic value in multiple Cox proportional hazards regression analysis was proposed. Among patients registered in the T Cell Project, 311 PTCL-NOS were retained for study. At a median follow-up of 46 months, the median overall survival (OS) and progression-free survival (PFS) was 20 and 10 months, respectively. Three groups were identified at low risk (LR, 48 patients, 15%, score 0), intermediate risk (IR, 189 patients, 61%, score 1-2), and high risk (HiR, 74 patients, 24%, score 3-4), having a 3-year OS of 76% [95% confidence interval 61-88], 43% [35-51], and 11% [4-21], respectively (P < 0·001). Comparing the performance of the T cell score on OS to that of each of the previously developed models, it emerged that the new score had the best discriminant power. The new T cell score, based on clinical variables, identifies a group with very unfavourable outcomes. © 2018 The Authors. British Journal of Haematology published by John Wiley & Sons Ltd.

Predicting Phenologic Response to Water Stress and Implications for Carbon Uptake across the Southeast U.S.

NASA Astrophysics Data System (ADS)

Lowman, L.; Barros, A. P.

2016-12-01

Representation of plant photosynthesis in modeling studies requires phenologic indicators to scale carbon assimilation by plants. These indicators are typically the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI) which represent plant responses to light and water availability, as well as temperature constraints. In this study, a prognostic phenology model based on the growing season index is adapted to determine the phenologic indicators of LAI and FPAR at the sub-daily scale based on meteorological and soil conditions. Specifically, we directly model vegetation green-up and die-off responses to temperature, vapor pressure deficit, soil water potential, and incoming solar radiation. The indices are based on the properties of individual plant functional types, driven by observational data and prior modeling applications. First, we describe and test the sensitivity of the carbon uptake response to predicted phenology for different vegetation types. Second, the prognostic phenology model is incorporated into a land-surface hydrology model, the Duke Coupled Hydrology Model with Prognostic Vegetation (DCHM-PV), to demonstrate the impact of dynamic phenology on modeled carbon assimilation rates and hydrologic feedbacks. Preliminary results show reduced carbon uptake rates when incorporating a prognostic phenology model that match well against the eddy-covariance flux tower observations. Additionally, grassland vegetation shows the most variability in LAI and FPAR tied to meteorological and soil conditions. These results highlight the need to incorporate vegetation-specific responses to water limitation in order to accurately estimate the terrestrial carbon storage component of the global carbon budget.

Prognostic Value of Molecular Markers and Implication for Molecular Targeted Therapies in Nasopharyngeal Carcinoma: An Update in an Era of New Targeted Molecules Development.

PubMed

Liu, Mu-Tai; Chen, Mu-Kuan; Huang, Chia-Chun; Huang, Chao-Yuan

2015-02-01

The aim of the study was to evaluate the prognostic significance of molecular biomarkers which could provide information for more accurate prognostication and development of novel therapeutic strategies for nasopharyngeal carcinoma (NPC). NPC is a unique malignant epithelial carcinoma of head and neck region, with an intimate association with the Epstein-Barr virus (EBV). Currently, the prediction of NPC prognosis is mainly based on the clinical TNM staging; however, NPC patients with the same clinical stage often present different clinical outcomes, suggesting that the TNM stage is insufficient to precisely predict the prognosis of this disease. In this review, we give an overview of the prognostic value of molecular markers in NPC and discuss potential strategies of targeted therapies for treatment of NPC. Molecular biomarkers, which play roles in abnormal proliferation signaling pathways (such as Wnt/β-catenin pathway), intracellular mitogenic signal aberration (such as hypoxia-inducible factor (HIF)-1α), receptor-mediated aberrations (such as vascular endothelial growth factor (VEGF)), tumor suppressors (such as p16 and p27 activity), cell cycle aberrations (such as cyclin D1 and cyclin E), cell adhesion aberrations (such as E-cadherin), apoptosis dysregualtion (such as survivin) and centromere aberration (centromere protein H), are prognostic markers for NPC. Plasma EBV DNA concentrations and EBV-encoded latent membrane proteins are also prognostic markers for NPC. Implication of molecular targeted therapies in NPC was discussed. Such therapies could have potential in combination with different cytotoxic agents to combat and eradicate tumor cells. In order to further improve overall survival for patients with loco-regionally advanced NPC, the development of innovative strategies, including prognostic molecular markers and molecular targeted agents is needed.

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

PubMed

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases. Copyright © 2016 Elsevier Inc. All rights reserved.

[A prognostic model of a cholera epidemic].

PubMed

Boev, B V; Bondarenko, V M; Prokop'eva, N V; San Román, R T; Raygoza-Anaya, M; García de Alba, R

1994-01-01

A new model for the prognostication of cholera epidemic on the territory of a large city is proposed. This model reflects the characteristic feature of contacting infection by sensitive individuals due to the preservation of Vibrio cholerae in their water habitat. The mathematical model of the epidemic quantitatively reflects the processes of the spread of infection by kinetic equations describing the interaction of the streams of infected persons, the causative agents and susceptible persons. The functions and parameters of the model are linked with the distribution of individuals according to the duration of the incubation period and infectious process, as well as the period of asymptomatic carrier state. The computer realization of the model by means of IBM PC/AT made it possible to study the cholera epidemic which took place in Mexico in 1833. The verified model of the cholera epidemic was used for the prognostication of the possible spread of this infection in Guadalajara, taking into account changes in the epidemiological situation and the size of the population, as well as improvements in sanitary and hygienic conditions, in the city.

Evaluation of Simulated Marine Aerosol Production Using the WaveWatchIII Prognostic Wave Model Coupled to the Community Atmosphere Model within the Community Earth System Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Long, M. S.; Keene, William C.; Zhang, J.

2016-11-08

Primary marine aerosol (PMA) is emitted into the atmosphere via breaking wind waves on the ocean surface. Most parameterizations of PMA emissions use 10-meter wind speed as a proxy for wave action. This investigation coupled the 3 rd generation prognostic WAVEWATCH-III wind-wave model within a coupled Earth system model (ESM) to drive PMA production using wave energy dissipation rate – analogous to whitecapping – in place of 10-meter wind speed. The wind speed parameterization did not capture basin-scale variability in relations between wind and wave fields. Overall, the wave parameterization did not improve comparison between simulated versus measured AOD ormore » Na +, thus highlighting large remaining uncertainties in model physics. Results confirm the efficacy of prognostic wind-wave models for air-sea exchange studies coupled with laboratory- and field-based characterizations of the primary physical drivers of PMA production. No discernible correlations were evident between simulated PMA fields and observed chlorophyll or sea surface temperature.« less

Use of Cox's Cure Model to Establish Clinical Determinants of Long-Term Disease-Free Survival in Neoadjuvant-Chemotherapy-Treated Breast Cancer Patients without Pathologic Complete Response.

PubMed

Asano, Junichi; Hirakawa, Akihiro; Hamada, Chikuma; Yonemori, Kan; Hirata, Taizo; Shimizu, Chikako; Tamura, Kenji; Fujiwara, Yasuhiro

2013-01-01

In prognostic studies for breast cancer patients treated with neoadjuvant chemotherapy (NAC), the ordinary Cox proportional-hazards (PH) model has been often used to identify prognostic factors for disease-free survival (DFS). This model assumes that all patients eventually experience relapse or death. However, a subset of NAC-treated breast cancer patients never experience these events during long-term follow-up (>10 years) and may be considered clinically "cured." Clinical factors associated with cure have not been studied adequately. Because the ordinary Cox PH model cannot be used to identify such clinical factors, we used the Cox PH cure model, a recently developed statistical method. This model includes both a logistic regression component for the cure rate and a Cox regression component for the hazard for uncured patients. The purpose of this study was to identify the clinical factors associated with cure and the variables associated with the time to recurrence or death in NAC-treated breast cancer patients without a pathologic complete response, by using the Cox PH cure model. We found that hormone receptor status, clinical response, human epidermal growth factor receptor 2 status, histological grade, and the number of lymph node metastases were associated with cure.

Resuspension and redistribution of radionuclides during grassland and forest fires in the Chernobyl exclusion zone: part II. Modeling the transport process.

PubMed

Yoschenko, V I; Kashparov, V A; Levchuk, S E; Glukhovskiy, A S; Khomutinin, Yu V; Protsak, V P; Lundin, S M; Tschiersch, J

2006-01-01

To predict parameters of radionuclide resuspension, transport and deposition during forest and grassland fires, several model modules were developed and adapted. Experimental data of controlled burning of prepared experimental plots in the Chernobyl exclusion zone have been used to evaluate the prognostic power of the models. The predicted trajectories and elevations of the plume match with those visually observed during the fire experiments in the grassland and forest sites. Experimentally determined parameters could be successfully used for the calculation of the initial plume parameters which provide the tools for the description of various fire scenarios and enable prognostic calculations. In summary, the model predicts a release of some per thousand from the radionuclide inventory of the fuel material by the grassland fires. During the forest fire, up to 4% of (137)Cs and (90)Sr and up to 1% of the Pu isotopes can be released from the forest litter according to the model calculations. However, these results depend on the parameters of the fire events. In general, the modeling results are in good accordance with the experimental data. Therefore, the considered models were successfully validated and can be recommended for the assessment of the resuspension and redistribution of radionuclides during grassland and forest fires in contaminated territories.

A self-cognizant dynamic system approach for prognostics and health management

NASA Astrophysics Data System (ADS)

Bai, Guangxing; Wang, Pingfeng; Hu, Chao

2015-03-01

Prognostics and health management (PHM) is an emerging engineering discipline that diagnoses and predicts how and when a system will degrade its performance and lose its partial or whole functionality. Due to the complexity and invisibility of rules and states of most dynamic systems, developing an effective approach to track evolving system states becomes a major challenge. This paper presents a new self-cognizant dynamic system (SCDS) approach that incorporates artificial intelligence into dynamic system modeling for PHM. A feed-forward neural network (FFNN) is selected to approximate a complex system response which is challenging task in general due to inaccessible system physics. The trained FFNN model is then embedded into a dual extended Kalman filter algorithm to track down system dynamics. A recursive computation technique used to update the FFNN model using online measurements is also derived. To validate the proposed SCDS approach, a battery dynamic system is considered as an experimental application. After modeling the battery system by a FFNN model and a state-space model, the state-of-charge (SoC) and state-of-health (SoH) are estimated by updating the FFNN model using the proposed approach. Experimental results suggest that the proposed approach improves the efficiency and accuracy for battery health management.

Model-Based Diagnosis and Prognosis of a Water Recycling System

NASA Technical Reports Server (NTRS)

Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

2013-01-01

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

Prognostic residual mean flow in an ocean general circulation model and its relation to prognostic Eulerian mean flow

DOE PAGES

Saenz, Juan A.; Chen, Qingshan; Ringler, Todd

2015-05-19

Recent work has shown that taking the thickness-weighted average (TWA) of the Boussinesq equations in buoyancy coordinates results in exact equations governing the prognostic residual mean flow where eddy–mean flow interactions appear in the horizontal momentum equations as the divergence of the Eliassen–Palm flux tensor (EPFT). It has been proposed that, given the mathematical tractability of the TWA equations, the physical interpretation of the EPFT, and its relation to potential vorticity fluxes, the TWA is an appropriate framework for modeling ocean circulation with parameterized eddies. The authors test the feasibility of this proposition and investigate the connections between the TWAmore » framework and the conventional framework used in models, where Eulerian mean flow prognostic variables are solved for. Using the TWA framework as a starting point, this study explores the well-known connections between vertical transfer of horizontal momentum by eddy form drag and eddy overturning by the bolus velocity, used by Greatbatch and Lamb and Gent and McWilliams to parameterize eddies. After implementing the TWA framework in an ocean general circulation model, we verify our analysis by comparing the flows in an idealized Southern Ocean configuration simulated using the TWA and conventional frameworks with the same mesoscale eddy parameterization.« less

Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems

NASA Technical Reports Server (NTRS)

Schumann, Johann; Roychoudhury, Indranil; Kulkarni, Chetan

2015-01-01

With increasing popularity of unmanned aircraft, continuous monitoring of their systems, software, and health status is becoming more and more important to ensure safe, correct, and efficient operation and fulfillment of missions. The paper presents integration of prognosis models and prognostic information with the R2U2 (REALIZABLE, RESPONSIVE, and UNOBTRUSIVE Unit) monitoring and diagnosis framework. This integration makes available statistically reliable health information predictions of the future at a much earlier time to enable autonomous decision making. The prognostic information can be used in the R2U2 model to improve diagnostic accuracy and enable decisions to be made at the present time to deal with events in the future. This will be an advancement over the current state of the art, where temporal logic observers can only do such valuation at the end of the time interval. Usefulness and effectiveness of this integrated diagnostics and prognostics framework was demonstrated using simulation experiments with the NASA Dragon Eye electric unmanned aircraft.

Prediction of two month modified Rankin Scale with an ordinal prediction model in patients with aneurysmal subarachnoid haemorrhage

PubMed Central

2010-01-01

Background Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with a frequently disabling outcome. Our aim was to develop a prognostic model to predict an ordinal clinical outcome at two months in patients with aSAH. Methods We studied patients enrolled in the International Subarachnoid Aneurysm Trial (ISAT), a randomized multicentre trial to compare coiling and clipping in aSAH patients. Several models were explored to estimate a patient's outcome according to the modified Rankin Scale (mRS) at two months after aSAH. Our final model was validated internally with bootstrapping techniques. Results The study population comprised of 2,128 patients of whom 159 patients died within 2 months (8%). Multivariable proportional odds analysis identified World Federation of Neurosurgical Societies (WFNS) grade as the most important predictor, followed by age, sex, lumen size of the aneurysm, Fisher grade, vasospasm on angiography, and treatment modality. The model discriminated moderately between those with poor and good mRS scores (c statistic = 0.65), with minor optimism according to bootstrap re-sampling (optimism corrected c statistic = 0.64). Conclusion We presented a calibrated and internally validated ordinal prognostic model to predict two month mRS in aSAH patients who survived the early stage up till a treatment decision. Although generalizability of the model is limited due to the selected population in which it was developed, this model could eventually be used to support clinical decision making after external validation. Trial Registration International Standard Randomised Controlled Trial, Number ISRCTN49866681 PMID:20920243

Systematic review of current prognostication systems for primary gastrointestinal stromal tumors.

PubMed

Khoo, Chun Yuet; Chai, Xun; Quek, Richard; Teo, Melissa C C; Goh, Brian K P

2018-04-01

The advent of tyrosine kinase inhibitors as adjuvant therapy has revolutionized the management of GIST and emphasized the need for accurate prognostication systems. Numerous prognostication systems have been proposed for GIST but at present it remains unknown which system is superior. The present systematic review aims to summarize current prognostication systems for primary treatment-naive GIST. A literature review of the Pubmed and Embase databases was performed to identify all published articles in English, from the 1st January 2002 to 28th Feb 2017, reporting on clinical prognostication systems of GIST. Twenty-three articles on GIST prognostication systems were included. These systems were classified as categorical systems, which stratify patients into risk groups, or continuous systems, which provide an individualized form of risk assessment. There were 16 categorical systems in total. There were 4 modifications of the National Institute of Health (NIH) system, 2 modifications of Armed Forces Institute of Pathology (AFIP) criteria and 3 modifications of Joensuu (modified NIH) criteria. Of the 7 continuous systems, there were 3 prognostic nomograms, 3 mathematical models and 1 prognostic heat/contour maps. Tumor size, location and mitotic count remain the main variables used in these systems. Numerous prognostication systems have been proposed for the risk stratification of GISTs. The most widely used systems today are the NIH, Joensuu modified NIH, AFIP and the Memorial Sloan Kettering Cancer Center nomogram. More validation and comparison studies are required to determine the optimal prognostication system for GIST. Copyright © 2018 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Prognostic factors and risk stratification in patients with castration-resistant prostate cancer receiving docetaxel-based chemotherapy.

PubMed

Yamashita, Shimpei; Kohjimoto, Yasuo; Iguchi, Takashi; Koike, Hiroyuki; Kusumoto, Hiroki; Iba, Akinori; Kikkawa, Kazuro; Kodama, Yoshiki; Matsumura, Nagahide; Hara, Isao

2016-03-22

While novel drugs have been developed, docetaxel remains one of the standard initial systemic therapies for castration-resistant prostate cancer (CRPC) patients. Despite the excellent anti-tumor effect of docetaxel, its severe adverse effects sometimes distress patients. Therefore, it would be very helpful to predict the efficacy of docetaxel before treatment. The aims of this study were to evaluate the potential value of patient characteristics in predicting overall survival (OS) and to develop a risk classification for CRPC patients treated with docetaxel-based chemotherapy. This study included 79 patients with CRPC treated with docetaxel. The variables, including patient characteristics at diagnosis and at the start of chemotherapy, were retrospectively collected. Prognostic factors predicting OS were analyzed using the Cox proportional hazard model. Risk stratification for overall survival was determined based on the results of multivariate analysis. PSA response ≥50 % was observed in 55 (69.6 %) of all patients, and the median OS was 22.5 months. The multivariate analysis showed that age, serum PSA level at the start of chemotherapy, and Hb were independent prognostic factors for OS. In addition, ECOG performance status (PS) and the CRP-to-albumin ratio were not significant but were considered possible predictors for OS. Risk stratification according to the number of these risk factors could effectively stratify CRPC patients treated with docetaxel in terms of OS. Age, serum PSA level at the start of chemotherapy, and Hb were identified as independent prognostic factors of OS. ECOG PS and the CRP-to-albumin ratio were not significant, but were considered possible predictors for OS in Japanese CRPC patients treated with docetaxel. Risk stratification based on these factors could be helpful for estimating overall survival.

MiR-148a functions to suppress metastasis and serves as a prognostic indicator in triple-negative breast cancer.

PubMed

Xu, Xin; Zhang, Yun; Jasper, Jeff; Lykken, Erik; Alexander, Peter B; Markowitz, Geoffrey J; McDonnell, Donald P; Li, Qi-Jing; Wang, Xiao-Fan

2016-04-12

Triple-negative breast cancer (TNBC) presents a major challenge in the clinic due to its lack of reliable prognostic markers and targeted therapies. Accumulating evidence strongly supports the notion that microRNAs (miRNAs) are involved in tumorigenesis and could serve as biomarkers for diagnostic purposes. To identify miRNAs that functionally suppress metastasis of TNBC, we employed a concerted approach with selecting miRNAs that display differential expression profiles from bioinformatic analyses of breast cancer patient databases and validating top candidates with functional assays using breast cancer cell lines and mouse models. We have found that miR-148a exhibits properties as a tumor suppressor as its expression is inversely correlated with the ability of both human and mouse breast cancer cells to colonize the lung in mouse xenograft tumor models. Mechanistically, miR-148a appears to suppress the extravasation process of cancer cells, likely by targeting two genes WNT1 and NRP1 in a cell non-autonomous manner. Importantly, lower expression of miR-148a is detected in higher-grade tumor samples and correlated with increased likelihood to develop metastases and poor prognosis in subsets of breast cancer patients, particularly those with TNBC. Thus, miR-148a is functionally defined as a suppressor of breast cancer metastasis and may serve as a prognostic biomarker for this disease.

Acute lymphoblastic leukemia in children and adolescents: prognostic factors and analysis of survival

PubMed Central

Lustosa de Sousa, Daniel Willian; de Almeida Ferreira, Francisco Valdeci; Cavalcante Félix, Francisco Helder; de Oliveira Lopes, Marcos Vinicios

2015-01-01

Objective To describe the clinical and laboratory features of children and adolescents with acute lymphoblastic leukemia treated at three referral centers in Ceará and evaluate prognostic factors for survival, including age, gender, presenting white blood cell count, immunophenotype, DNA index and early response to treatment. Methods Seventy-six under 19-year-old patients with newly diagnosed acute lymphoblastic leukemia treated with the Grupo Brasileiro de Tratamento de Leucemia da Infância – acute lymphoblastic leukemia-93 and -99 protocols between September 2007 and December 2009 were analyzed. The diagnosis was based on cytological, immunophenotypic and cytogenetic criteria. Associations between variables, prognostic factors and response to treatment were analyzed using the chi-square test and Fisher's exact test. Overall and event-free survival were estimated by Kaplan–Meier analysis and compared using the log-rank test. A Cox proportional hazards model was used to identify independent prognostic factors. Results The average age at diagnosis was 6.3 ± 0.5 years and males were predominant (65%). The most frequently observed clinical features were hepatomegaly, splenomegaly and lymphadenopathy. Central nervous system involvement and mediastinal enlargement occurred in 6.6% and 11.8%, respectively. B-acute lymphoblastic leukemia was more common (89.5%) than T-acute lymphoblastic leukemia. A DNA index >1.16 was found in 19% of patients and was associated with favorable prognosis. On Day 8 of induction therapy, 95% of the patients had lymphoblast counts <1000/μL and white blood cell counts <5.0 × 109/L. The remission induction rate was 95%, the induction mortality rate was 2.6% and overall survival was 72%. Conclusion The prognostic factors identified are compatible with the literature. The 5-year overall and event-free survival rates were lower than those reported for developed countries. As shown by the multivariate analysis, age and baseline white blood cell count were independent prognostic factors. PMID:26190424

Comparison of the Prognostic and Predictive Utilities of the 21-Gene Recurrence Score Assay and Adjuvant! for Women with Node-Negative, ER-Positive Breast Cancer: Results from NSABP B-14 and NSABP B-20

PubMed Central

Tang, Gong; Shak, Steven; Paik, Soonmyung; Anderson, Stewart J.; Costantino, Joseph P.; Geyer, Charles E.; Mamounas, Eleftherios P.; Wickerham, D. Lawrence; Wolmark, Norman

2012-01-01

The Oncotype DX® Recurrence Score® (RS) is a validated genomic predictor of outcome and response to adjuvant chemotherapy in ER-positive breast cancer. Adjuvant! was developed using SEER registry data and results from the Early Breast Cancer Clinical Trialists’ overview analyses to estimate outcome and benefit from adjuvant hormonal therapy and chemotherapy. In this report we compare the prognostic and predictive utility of these two tools in node-negative, ER-positive breast cancer. RS and Adjuvant! results were available from 668 tamoxifen-treated NSABP B-14 patients: 227 tamoxifen-treated NSABP B-20 patients, and 424 chemotherapy-plus-tamoxifen-treated B-20 patients. Adjuvant! results were also available from 1952 B-20 patients. The primary endpoint was distant recurrence-free interval (DRFI). Cox proportional hazards models were used to compare the prognostic and predictive utility of RS and Adjuvant!. Both RS (p<0.001) and Adjuvant! (p=0.002) provided strong independent prognostic information in tamoxifen-treated patients. Combining RS and individual clinicopathologic characteristics provided greater prognostic discrimination than combining RS and the composite Adjuvant!. In the B-20 cohort with RS results (n=651), RS was significantly predictive of chemotherapy benefit (interaction p=0.031 for DRFI, p=0.011 for overall survival [OS], p=0.082 for disease-free survival [DFS]), but Adjuvant! was not (interaction p=0.99, p=0.311 and p=0.357, respectively). However, in the larger B-20 sub-cohort (n=1952), Adjuvant! was significantly predictive of chemotherapy benefit for OS (interaction p=0.009) but not for DRFI (p=0.219) or DFS (p=0.099). Prognostic estimates can be optimized by combining RS and clinicopathologic information instead of simply combining RS and Adjuvant!. RS should be used for estimating relative chemotherapy benefit. PMID:21221771

Comparison of the prognostic and predictive utilities of the 21-gene Recurrence Score assay and Adjuvant! for women with node-negative, ER-positive breast cancer: results from NSABP B-14 and NSABP B-20.

PubMed

Tang, Gong; Shak, Steven; Paik, Soonmyung; Anderson, Stewart J; Costantino, Joseph P; Geyer, Charles E; Mamounas, Eleftherios P; Wickerham, D Lawrence; Wolmark, Norman

2011-05-01

The Oncotype DX Recurrence Score (RS) is a validated genomic predictor of outcome and response to adjuvant chemotherapy in ER-positive breast cancer. Adjuvant! was developed using SEER registry data and results from the Early Breast Cancer Clinical Trialists' overview analyses to estimate outcome and benefit from adjuvant hormonal therapy and chemotherapy. In this report we compare the prognostic and predictive utility of these two tools in node-negative, ER-positive breast cancer. RS and Adjuvant! results were available from 668 tamoxifen-treated NSABP B-14 patients, 227 tamoxifen-treated NSABP B-20 patients, and 424 chemotherapy plus tamoxifen-treated B-20 patients. Adjuvant! results were also available from 1952 B-20 patients. The primary endpoint was distant recurrence-free interval (DRFI). Cox proportional hazards models were used to compare the prognostic and predictive utility of RS and Adjuvant!. Both RS (P < 0.001) and Adjuvant! (P = 0.002) provided strong independent prognostic information in tamoxifen-treated patients. Combining RS and individual clinicopathologic characteristics provided greater prognostic discrimination than combining RS and the composite Adjuvant!. In the B-20 cohort with RS results (n = 651), RS was significantly predictive of chemotherapy benefit (interaction P = 0.031 for DRFI, P = 0.011 for overall survival [OS], P = 0.082 for disease-free survival [DFS]), but Adjuvant! was not (interaction P = 0.99, P = 0.311, and P = 0.357, respectively). However, in the larger B-20 sub-cohort (n = 1952), Adjuvant! was significantly predictive of chemotherapy benefit for OS (interaction P = 0.009) but not for DRFI (P = 0.219) or DFS (P = 0.099). Prognostic estimates can be optimized by combining RS and clinicopathologic information instead of simply combining RS and Adjuvant!. RS should be used for estimating relative chemotherapy benefit.

Physics-of-Failure Approach to Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Mayo Alliance Prognostic Model for Myelodysplastic Syndromes: Integration of Genetic and Clinical Information.

PubMed

Tefferi, Ayalew; Gangat, Naseema; Mudireddy, Mythri; Lasho, Terra L; Finke, Christy; Begna, Kebede H; Elliott, Michelle A; Al-Kali, Aref; Litzow, Mark R; Hook, C Christopher; Wolanskyj, Alexandra P; Hogan, William J; Patnaik, Mrinal M; Pardanani, Animesh; Zblewski, Darci L; He, Rong; Viswanatha, David; Hanson, Curtis A; Ketterling, Rhett P; Tang, Jih-Luh; Chou, Wen-Chien; Lin, Chien-Chin; Tsai, Cheng-Hong; Tien, Hwei-Fang; Hou, Hsin-An

2018-06-01

To develop a new risk model for primary myelodysplastic syndromes (MDS) that integrates information on mutations, karyotype, and clinical variables. Patients with World Health Organization-defined primary MDS seen at Mayo Clinic (MC) from December 28, 1994, through December 19, 2017, constituted the core study group. The National Taiwan University Hospital (NTUH) provided the validation cohort. Model performance, compared with the revised International Prognostic Scoring System, was assessed by Akaike information criterion and area under the curve estimates. The study group consisted of 685 molecularly annotated patients from MC (357) and NTUH (328). Multivariate analysis of the MC cohort identified monosomal karyotype (hazard ratio [HR], 5.2; 95% CI, 3.1-8.6), "non-MK abnormalities other than single/double del(5q)" (HR, 1.8; 95% CI, 1.3-2.6), RUNX1 (HR, 2.0; 95% CI, 1.2-3.1) and ASXL1 (HR, 1.7; 95% CI, 1.2-2.3) mutations, absence of SF3B1 mutations (HR, 1.6; 95% CI, 1.1-2.4), age greater than 70 years (HR, 2.2; 95% CI, 1.6-3.1), hemoglobin level less than 8 g/dL in women or less than 9 g/dL in men (HR, 2.3; 95% CI, 1.7-3.1), platelet count less than 75 × 10 9 /L (HR, 1.5; 95% CI, 1.1-2.1), and 10% or more bone marrow blasts (HR, 1.7; 95% CI, 1.1-2.8) as predictors of inferior overall survival. Based on HR-weighted risk scores, a 4-tiered Mayo alliance prognostic model for MDS was devised: low (89 patients), intermediate-1 (104), intermediate-2 (95), and high (69); respective median survivals (5-year overall survival rates) were 85 (73%), 42 (34%), 22 (7%), and 9 months (0%). The Mayo alliance model was subsequently validated by using the external NTUH cohort and, compared with the revised International Prognostic Scoring System, displayed favorable Akaike information criterion (1865 vs 1943) and area under the curve (0.87 vs 0.76) values. We propose a simple and contemporary risk model for MDS that is based on a limited set of genetic and clinical variables. Copyright © 2018. Published by Elsevier Inc.

Prognostic scores after surgical treatment for cervical intraepithelial neoplasia: a proposed model and possible implications for post-operative follow-up.

PubMed

Andrade, Carlos E M C; Scapulatempo-Neto, Cristovam; Longatto-Filho, Adhemar; Vieira, Marcelo A; Tsunoda, Audrey T; Da Silva, Ismael D C G; Fregnani, José Humberto T G

2014-09-01

To develop a prognostic model for women who underwent surgical treatment for cervical intraepithelial neoplasia. Cohort study. Patient inclusion and follow-up occurred retrospectively and prospectively. Barretos Cancer Hospital, Barretos, São Paulo, Brazil. Women (n = 242) diagnosed with cervical intraepithelial neoplasia who were submitted to conization. Immediately prior to surgical treatment, a cervical cytology sample was collected from each individual included in the study by endocervical brushing and stored in a preservative solution with methanol. A human papilloma virus-DNA test was conducted using an aliquot of the endocervical brushings. The surgical specimens were subjected to immunohistochemical analysis of p16 (immunohistochemical analysis 4a) protein expression. Two-year disease-free survival rates calculated for each study variable. Identified variables in the multivariate Cox model were used for elaboration of prognostic scores. Variables associated with outcome included age (p = 0.033), tobacco use (p < 0.001), final histopathological diagnosis (p = 0.007), surgical margins (p < 0.001), high-risk human papilloma virus status (p = 0.008), human papilloma virus-16 status (p < 0.001) and immunoexpression of p16 in the cytoplasm (p = 0.049). By the Cox model, independent risk factors for disease recurrence/persistence were: tobacco use (hazard risk = 3.0; 95% confidence interval 1.6-5.6), positive surgical margins (hazard risk = 3.2; 95% confidence interval 1.6-6.1), human papilloma virus-16 (hazard risk = 3.3; 95% confidence interval 1.6-6.9) and age over 45 years (hazard risk = 2.7; 95% confidence interval 1.1-6.6). Establishment of a prognostic score can represent a valuable tool for determining the risk of cervical intraepithelial neoplasia recurrence after conization. The use of clinical (age and tobacco use), pathological (surgical margins) and molecular (human papilloma virus-16 genotyping) factors can facilitate more appropriate patient follow up according to risk stratification. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

Aerosol Microphysics and Radiation Integration

DTIC Science & Technology

2007-09-30

http://www.nrlmry.navy.mil/ flambe / LONG-TERM GOALS This project works toward the development and support of real time global prognostic aerosol...Burning Emissions ( FLAMBE ) project were transition to the Fleet Numerical Oceanographic Center (FNMOC) Monterey in FY07. Meteorological guidance...Hyer, E. J. and J. S. Reid (2006), Evaluating the impact of improvements to the FLAMBE smoke source model on forecasts of aerosol distribution

Toward integrated management of cerebral aneurysms.

PubMed

Villa-Uriol, M C; Larrabide, I; Pozo, J M; Kim, M; Camara, O; De Craene, M; Zhang, C; Geers, A J; Morales, H; Bogunović, H; Cardenes, R; Frangi, A F

2010-06-28

In the last few years, some of the visionary concepts behind the virtual physiological human began to be demonstrated on various clinical domains, showing great promise for improving healthcare management. In the current work, we provide an overview of image- and biomechanics-based techniques that, when put together, provide a patient-specific pipeline for the management of intracranial aneurysms. The derivation and subsequent integration of morphological, morphodynamic, haemodynamic and structural analyses allow us to extract patient-specific models and information from which diagnostic and prognostic descriptors can be obtained. Linking such new indices with relevant clinical events should bring new insights into the processes behind aneurysm genesis, growth and rupture. The development of techniques for modelling endovascular devices such as stents and coils allows the evaluation of alternative treatment scenarios before the intervention takes place and could also contribute to the understanding and improved design of more effective devices. A key element to facilitate the clinical take-up of all these developments is their comprehensive validation. Although a number of previously published results have shown the accuracy and robustness of individual components, further efforts should be directed to demonstrate the diagnostic and prognostic efficacy of these advanced tools through large-scale clinical trials.

Development of a prognostic nomogram for cirrhotic patients with upper gastrointestinal bleeding.

PubMed

Zhou, Yu-Jie; Zheng, Ji-Na; Zhou, Yi-Fan; Han, Yi-Jing; Zou, Tian-Tian; Liu, Wen-Yue; Braddock, Martin; Shi, Ke-Qing; Wang, Xiao-Dong; Zheng, Ming-Hua

2017-10-01

Upper gastrointestinal bleeding (UGIB) is a complication with a high mortality rate in critically ill patients presenting with cirrhosis. Today, there exist few accurate scoring models specifically designed for mortality risk assessment in critically ill cirrhotic patients with upper gastrointestinal bleeding (CICGIB). Our aim was to develop and evaluate a novel nomogram-based model specific for CICGIB. Overall, 540 consecutive CICGIB patients were enrolled. On the basis of Cox regression analyses, the nomogram was constructed to estimate the probability of 30-day, 90-day, 270-day, and 1-year survival. An upper gastrointestinal bleeding-chronic liver failure-sequential organ failure assessment (UGIB-CLIF-SOFA) score was derived from the nomogram. Performance assessment and internal validation of the model were performed using Harrell's concordance index (C-index), calibration plot, and bootstrap sample procedures. UGIB-CLIF-SOFA was also compared with other prognostic models, such as CLIF-SOFA and model for end-stage liver disease, using C-indices. Eight independent factors derived from Cox analysis (including bilirubin, creatinine, international normalized ratio, sodium, albumin, mean artery pressure, vasopressin used, and hematocrit decrease>10%) were assembled into the nomogram and the UGIB-CLIF-SOFA score. The calibration plots showed optimal agreement between nomogram prediction and actual observation. The C-index of the nomogram using bootstrap (0.729; 95% confidence interval: 0.689-0.766) was higher than that of the other models for predicting survival of CICGIB. We have developed and internally validated a novel nomogram and an easy-to-use scoring system that accurately predicts the mortality probability of CICGIB on the basis of eight easy-to-obtain parameters. External validation is now warranted in future clinical studies.

Sarcopenia in the prognosis of cirrhosis: Going beyond the MELD score

PubMed Central

Kim, Hee Yeon; Jang, Jeong Won

2015-01-01

Estimating the prognosis of patients with cirrhosis remains challenging, because the natural history of cirrhosis varies according to the cause, presence of portal hypertension, liver synthetic function, and the reversibility of underlying disease. Conventional prognostic scoring systems, including the Child-Turcotte-Pugh score or model for end-stage liver diseases are widely used; however, revised models have been introduced to improve prognostic performance. Although sarcopenia is one of the most common complications related to survival of patients with cirrhosis, the newly proposed prognostic models lack a nutritional status evaluation of patients. This is reflected by the lack of an optimal index for sarcopenia in terms of objectivity, reproducibility, practicality, and prognostic performance, and of a consensus definition for sarcopenia in patients with cirrhosis in whom ascites and edema may interfere with body composition analysis. Quantifying skeletal muscle mass using cross-sectional abdominal imaging is a promising tool for assessing sarcopenia. As radiological imaging provides direct visualization of body composition, it is useful to evaluate sarcopenia in patients with cirrhosis whose body mass index, anthropometric measurements, or biochemical markers are inaccurate on a nutritional assessment. Sarcopenia defined by cross-sectional imaging-based muscular assessment is prevalent and predicts mortality in patients with cirrhosis. Sarcopenia alone or in combination with conventional prognostic systems shows promise for a cirrhosis prognosis. Including an objective assessment of sarcopenia with conventional scores to optimize the outcome prediction for patients with cirrhosis needs further research. PMID:26167066

Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

PubMed Central

Shedden, Kerby; Taylor, Jeremy M.G.; Enkemann, Steve A.; Tsao, Ming S.; Yeatman, Timothy J.; Gerald, William L.; Eschrich, Steve; Jurisica, Igor; Venkatraman, Seshan E.; Meyerson, Matthew; Kuick, Rork; Dobbin, Kevin K.; Lively, Tracy; Jacobson, James W.; Beer, David G.; Giordano, Thomas J.; Misek, David E.; Chang, Andrew C.; Zhu, Chang Qi; Strumpf, Dan; Hanash, Samir; Shepherd, Francis A.; Ding, Kuyue; Seymour, Lesley; Naoki, Katsuhiko; Pennell, Nathan; Weir, Barbara; Verhaak, Roel; Ladd-Acosta, Christine; Golub, Todd; Gruidl, Mike; Szoke, Janos; Zakowski, Maureen; Rusch, Valerie; Kris, Mark; Viale, Agnes; Motoi, Noriko; Travis, William; Sharma, Anupama

2009-01-01

Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. PMID:18641660

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

PubMed

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

A comparative analysis of prognostic factor models for follicular lymphoma based on a phase III trial of CHOP-rituximab versus CHOP + 131iodine--tositumomab.

PubMed

Press, Oliver W; Unger, Joseph M; Rimsza, Lisa M; Friedberg, Jonathan W; LeBlanc, Michael; Czuczman, Myron S; Kaminski, Mark; Braziel, Rita M; Spier, Catherine; Gopal, Ajay K; Maloney, David G; Cheson, Bruce D; Dakhil, Shaker R; Miller, Thomas P; Fisher, Richard I

2013-12-01

There is currently no consensus on optimal frontline therapy for patients with follicular lymphoma. We analyzed a phase III randomized intergroup trial comparing six cycles of CHOP-R (cyclophosphamide-Adriamycin-vincristine-prednisone (Oncovin)-rituximab) with six cycles of CHOP followed by iodine-131 tositumomab radioimmunotherapy (RIT) to assess whether any subsets benefited more from one treatment or the other, and to compare three prognostic models. We conducted univariate and multivariate Cox regression analyses of 532 patients enrolled on this trial and compared the prognostic value of the FLIPI (follicular lymphoma international prognostic index), FLIPI2, and LDH + β2M (lactate dehydrogenase + β2-microglobulin) models. Outcomes were excellent, but not statistically different between the two study arms [5-year progression-free survival (PFS) of 60% with CHOP-R and 66% with CHOP-RIT (P = 0.11); 5-year overall survival (OS) of 92% with CHOP-R and 86% with CHOP-RIT (P = 0.08); overall response rate of 84% for both arms]. The only factor found to potentially predict the impact of treatment was serum β2M; among patients with normal β2M, CHOP-RIT patients had better PFS compared with CHOP-R patients, whereas among patients with high serum β2M, PFS by arm was similar (interaction P value = 0.02). All three prognostic models (FLIPI, FLIPI2, and LDH + β2M) predicted both PFS and OS well, though the LDH + β2M model is easiest to apply and identified an especially poor risk subset. In an exploratory analysis using the latter model, there was a statistically significant trend suggesting that low-risk patients had superior observed PFS if treated with CHOP-RIT, whereas high-risk patients had a better PFS with CHOP-R. ©2013 AACR.

Thai venous stroke prognostic score: TV-SPSS.

PubMed

Poungvarin, Niphon; Prayoonwiwat, Naraporn; Ratanakorn, Disya; Towanabut, Somchai; Tantirittisak, Tassanee; Suwanwela, Nijasri; Phanthumchinda, Kamman; Tiamkoa, Somsak; Chankrachang, Siwaporn; Nidhinandana, Samart; Laptikultham, Somsak; Limsoontarakul, Sansern; Udomphanthuruk, Suthipol

2009-11-01

Prognosis of cerebral venous sinus thrombosis (CVST) has never been studied in Thailand. A simple prognostic score to predict poor prognosis of CVST has also never been reported. The authors are aiming to establish a simple and reliable prognostic score for this condition. The medical records of CVST patients from eight neurological training centers in Thailand who received between April 1993 and September 2005 were reviewed as part of this retrospective study. Clinical features included headache, seizure, stroke risk factors, Glasgow coma scale (GCS), blood pressure on arrival, papilledema, hemiparesis, meningeal irritation sign, location of occluded venous sinuses, hemorrhagic infarction, cerebrospinal fluid opening pressure, treatment options, length of stay, and other complications were analyzed to determine the outcome using modified Rankin scale (mRS). Poor prognosis (defined as mRS of 3-6) was determined on the discharge date. One hundred ninety four patients' records, 127 females (65.5%) and mean age of 36.6 +/- 14.4 years, were analyzed Fifty-one patients (26.3%) were in the poor outcome group (mRS 3-6). Overall mortality was 8.4%. Univariate analysis and then multivariate analysis using SPSS version 11.5 revealed only four statistically significant predictors influencing outcome of CVST They were underlying malignancy, low GCS, presence of hemorrhagic infarction (for poor outcome), and involvement of lateral sinus (for good outcome). Thai venous stroke prognostic score (TV-SPSS) was derived from these four factors using a multiple logistic model. A simple and pragmatic prognostic score for CVST outcome has been developed with high sensitivity (93%), yet low specificity (33%). The next study should focus on the validation of this score in other prospective populations.

Prognostic factors for specific lower extremity and spinal musculoskeletal injuries identified through medical screening and training load monitoring in professional football (soccer): a systematic review

PubMed Central

Sergeant, Jamie C; Parkes, Matthew J; Callaghan, Michael J

2017-01-01

Background Medical screening and load monitoring procedures are commonly used in professional football to assess factors perceived to be associated with injury. Objectives To identify prognostic factors (PFs) and models for lower extremity and spinal musculoskeletal injuries in professional/elite football players from medical screening and training load monitoring processes. Methods The MEDLINE, AMED, EMBASE, CINAHL Plus, SPORTDiscus and PubMed electronic bibliographic databases were searched (from inception to January 2017). Prospective and retrospective cohort studies of lower extremity and spinal musculoskeletal injury incidence in professional/elite football players aged between 16 and 40 years were included. The Quality in Prognostic Studies appraisal tool and the modified Grading of Recommendations Assessment, Development and Evaluation synthesis approach was used to assess the quality of the evidence. Results Fourteen studies were included. 16 specific lower extremity injury outcomes were identified. No spinal injury outcomes were identified. Meta-analysis was not possible due to heterogeneity and study quality. All evidence related to PFs and specific lower extremity injury outcomes was of very low to low quality. On the few occasions where multiple studies could be used to compare PFs and outcomes, only two factors demonstrated consensus. A history of previous hamstring injuries (HSI) and increasing age may be prognostic for future HSI in male players. Conclusions The assumed ability of medical screening tests to predict specific musculoskeletal injuries is not supported by the current evidence. Screening procedures should currently be considered as benchmarks of function or performance only. The prognostic value of load monitoring modalities is unknown. PMID:29177074

Current management and prognostic factors in physiotherapy practice for patients with shoulder pain: design of a prospective cohort study.

PubMed

Karel, Yasmaine H J M; Scholten-Peeters, Wendy G M; Thoomes-de Graaf, Marloes; Duijn, Edwin; Ottenheijm, Ramon P G; van den Borne, Maaike P J; Koes, Bart W; Verhagen, Arianne P; Dinant, Geert-Jan; Tetteroo, Eric; Beumer, Annechien; van Broekhoven, Joost B; Heijmans, Marcel

2013-02-11

Shoulder pain is disabling and has a considerable socio-economic impact. Over 50% of patients presenting in primary care still have symptoms after 6 months; moreover, prognostic factors such as pain intensity, age, disability level and duration of complaints are associated with poor outcome. Most shoulder complaints in this group are categorized as non-specific. Musculoskeletal ultrasound might be a useful imaging method to detect subgroups of patients with subacromial disorders.This article describes the design of a prospective cohort study evaluating the influence of known prognostic and possible prognostic factors, such as findings from musculoskeletal ultrasound outcome and working alliance, on the recovery of shoulder pain. Also, to assess the usual physiotherapy care for shoulder pain and examine the inter-rater reliability of musculoskeletal ultrasound between radiologists and physiotherapists for patients with shoulder pain. A prospective cohort study including an inter-rater reliability study. Patients presenting in primary care physiotherapy practice with shoulder pain are enrolled. At baseline validated questionnaires are used to measure patient characteristics, disease-specific characteristics and social factors. Physical examination is performed according to the expertise of the physiotherapists. Follow-up measurements will be performed 6, 12 and 26 weeks after inclusion. Primary outcome measure is perceived recovery, measured on a 7-point Likert scale. Logistic regression analysis will be used to evaluate the association between prognostic factors and recovery. The ShoCoDiP (Shoulder Complaints and using Diagnostic ultrasound in Physiotherapy practice) cohort study will provide information on current management of patients with shoulder pain in primary care, provide data to develop a prediction model for shoulder pain in primary care and to evaluate whether musculoskeletal ultrasound can improve prognosis.

Telangiectatic osteosarcoma: Outcome analyses and a diagnostic model for differentiation from aneurysmal bone cyst.

PubMed

Yin, Jun-Qiang; Fu, Yi-Wei; Xie, Xian-Biao; Cheng, Xiao-Yu; Yang, Xiao-Yu; Liu, Wei-Hai; Tu, Jian; Gao, Zhen-Hua; Shen, Jing-Nan

2018-06-01

Telangiectatic osteosarcoma (TOS), a rare variant of osteosarcoma, may be easily misdiagnosed as aneurysmal bone cyst (ABC). The aims of this study were to investigate the diagnostic and prognostic factors of TOS by reviewing our experience with TOS and to develop a diagnostic model that may distinguish TOS from ABC. We identified 51 cases of TOS treated at the First Affiliated Hospital of Sun Yat-Sen University from March 2001 to January 2016 and reviewed their records, imaging information and pathological studies. A diagnostic model was developed to differentiate TOS and ABC by Bayes discriminant analysis and was evaluated. The log-rank test was used to analyze the prognostic factors of TOS and to compare the outcome differences between TOS and other high-grade osteosarcoma subtypes. The multi-disciplinary diagnostic method employed that combined clinical, imaging, and pathological studies enhanced the diagnostic accuracy. Age 18 years or younger and pathologic fracture were more common among the TOS patients than among the ABC patients (P = .004 and .005, respectively). The average white blood cell (WBC), platelet, lactate dehydrogenase (LDH), and alkaline phosphatase (ALP) values of the TOS patients were higher than those of the ABC patients ( P = .002, .003, .007, and .007, respectively). Our diagnostic model, including the aforementioned factors, accurately predicted 62% and 78% of the TOS patients in the training and validation sets, respectively. The 5-year estimates of event-free survival and overall survival of the TOS patients were 52.5 ± 9.4% and 54.9 ± 8.8%, respectively, which were similar to those of patients with other osteosarcoma subtypes ( P = .950 and .615, respectively). Tumor volume and the LDH level were predictive prognostic factors ( P = .040 and .044) but not the presence of pathologic fracture or misdiagnosis ( P = .424 and .632, all respectively). The multi-disciplinary diagnostic method and diagnostic model based on predictive factors, i.e. , age, the presence of pathologic fracture, and platelet, LDH, ALP and WBC levels, aided the differentiation of TOS and ABC. Smaller tumors and normal LDH levels were associated with better outcomes.

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

DOE Office of Scientific and Technical Information (OSTI.GOV)

N. Lybeck; B. Pham; M. Tawfik

There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: (1) component/system/structure selection, (2) prognostic algorithms, and (3) prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure,more » and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: (1) research tools, (2) PHM system development tools, (3) deployable architectures, and (4) peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A. Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs.« less

Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations

PubMed Central

2014-01-01

Introduction Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. Methods An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). Results The original training cohort reached a statistically significant difference (p < 0.05) in disease-free survivals between the three CMTC groups after an additional two years of follow-up (median = 55 months). The prognostic value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups. Conclusions Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments. PMID:24996446

Physics Based Electrolytic Capacitor Degradation Models for Prognostic Studies under Thermal Overstress

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose R.; Goebel, Kai; Biswas, Gautam

2012-01-01

Electrolytic capacitors are used in several applications ranging from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining useful life of components or systems based on their current state of health and their anticipated future use and operational conditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress conditions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

Retrospective cohort study of prognostic factors in patients with oral cavity and oropharyngeal squamous cell carcinoma.

PubMed

Carrillo, José F; Carrillo, Liliana C; Cano, Ana; Ramirez-Ortega, Margarita C; Chanona, Jorge G; Avilés, Alejandro; Herrera-Goepfert, Roberto; Corona-Rivera, Jaime; Ochoa-Carrillo, Francisco J; Oñate-Ocaña, Luis F

2016-04-01

Prognostic factors in oral cavity and oropharyngeal squamous cell carcinoma (SCC) are debated. The purpose of this study was to investigate the association of prognostic factors with oncologic outcomes. Patients with oral cavity and oropharyngeal SCC treated from 1997 to 2012 were included in this retrospective cohort study. Associations of prognostic factors with locoregional recurrence (LRR) or overall survival (OS) were analyzed using the logistic regression and the Cox models. Six hundred thirty-four patients were included in this study; tumor size, surgical margins, and N classification were associated with LRR (p < .0001); considering histopathology: perineural invasion, lymphocytic infiltration, infiltrative borders, and N classification were significant determinants of LRR. Tumor size, N classification, alcoholism, and surgical margins were associated with OS (p < .0001); considering pathologic prognostic factors, perivascular invasion, islands borders, and surgical margins were independently associated with OS (p < .0001). Surgical margins, perineural and perivascular invasion, lymphocytic infiltration, and infiltrative patterns of tumor invasion are significant prognostic factors in oral cavity and oropharyngeal SCC. © 2015 Wiley Periodicals, Inc.

Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma.

PubMed

Giridhar, Karthik V; Sosa, Carlos P; Hillman, David W; Sanhueza, Cristobal; Dalpiaz, Candace L; Costello, Brian A; Quevedo, Fernando J; Pitot, Henry C; Dronca, Roxana S; Ertz, Donna; Cheville, John C; Donkena, Krishna Vanaja; Kohli, Manish

2017-11-03

The Memorial Sloan Kettering Cancer Center (MSKCC) prognostic score is based on clinical parameters. We analyzed whole blood mRNA expression in metastatic clear cell renal cell carcinoma (mCCRCC) patients and compared it to the MSKCC score for predicting overall survival. In a discovery set of 19 patients with mRCC, we performed whole transcriptome RNA sequencing and selected eighteen candidate genes for further evaluation based on associations with overall survival and statistical significance. In an independent validation of set of 47 patients with mCCRCC, transcript expression of the 18 candidate genes were quantified using a customized NanoString probeset. Cox regression multivariate analysis confirmed that two of the candidate genes were significantly associated with overall survival. Higher expression of BAG1 [hazard ratio (HR) of 0.14, p < 0.0001, 95% confidence interval (CI) 0.04-0.36] and NOP56 (HR 0.13, p < 0.0001, 95% CI 0.05-0.34) were associated with better prognosis. A prognostic model incorporating expression of BAG1 and NOP56 into the MSKCC score improved prognostication significantly over a model using the MSKCC prognostic score only ( p < 0.0001). Prognostic value of using whole blood mRNA gene profiling in mCCRCC is feasible and should be prospectively confirmed in larger studies.

Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma

PubMed Central

Sosa, Carlos P.; Hillman, David W.; Sanhueza, Cristobal; Dalpiaz, Candace L.; Costello, Brian A.; Quevedo, Fernando J.; Pitot, Henry C.; Dronca, Roxana S.; Ertz, Donna; Cheville, John C.; Donkena, Krishna Vanaja; Kohli, Manish

2017-01-01

The Memorial Sloan Kettering Cancer Center (MSKCC) prognostic score is based on clinical parameters. We analyzed whole blood mRNA expression in metastatic clear cell renal cell carcinoma (mCCRCC) patients and compared it to the MSKCC score for predicting overall survival. In a discovery set of 19 patients with mRCC, we performed whole transcriptome RNA sequencing and selected eighteen candidate genes for further evaluation based on associations with overall survival and statistical significance. In an independent validation of set of 47 patients with mCCRCC, transcript expression of the 18 candidate genes were quantified using a customized NanoString probeset. Cox regression multivariate analysis confirmed that two of the candidate genes were significantly associated with overall survival. Higher expression of BAG1 [hazard ratio (HR) of 0.14, p < 0.0001, 95% confidence interval (CI) 0.04–0.36] and NOP56 (HR 0.13, p < 0.0001, 95% CI 0.05–0.34) were associated with better prognosis. A prognostic model incorporating expression of BAG1 and NOP56 into the MSKCC score improved prognostication significantly over a model using the MSKCC prognostic score only (p < 0.0001). Prognostic value of using whole blood mRNA gene profiling in mCCRCC is feasible and should be prospectively confirmed in larger studies. PMID:29099775

Big genomics and clinical data analytics strategies for precision cancer prognosis.

PubMed

Ow, Ghim Siong; Kuznetsov, Vladimir A

2016-11-07

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

[Chronic lymphocytic leukaemia: current management].

PubMed

Aurran-Schleinitz, T; Arnoulet, C; Ivanov, V; Coso, D; Rey, J; Schiano, J-M; Stoppa, A-M; Bouabdallah, R; Gastaut, J-A

2008-05-01

Chronic lymphocytic leukemia (CLL) is the most common leukaemia in the Western world. Recent advancement in the aetiology, pathophysiology and the development of new therapeutics tools have significantly modified the current management of CLL. The cellular origin of CLL is still unknown. The current main hypothesis will be first briefly described. This review will then focus on the newly defined prognostic factors and the development and use of new drugs for the treatment of CLL. To describe the modern and practical management of CLL, we will compare classical and new prognostic markers. Then, we will discuss the various therapeutic options including chemotherapy and immunotherapy (monoclonal antibodies, allogenic transplantation), and define their current respective indications. These new diagnostic and prognostic markers will allow the characterization of new prognostic subgroups of patients. This will lead to a targeted and individualized therapeutic approach. We will present the first results of clinical trials and the on-going studies conducted in this disease.

Prognostic Stratification of Patients With Advanced Oral Cavity Squamous Cell Carcinoma.

PubMed

De Paz, Dante; Kao, Huang-Kai; Huang, Yenlin; Chang, Kai-Ping

2017-08-10

Prognosis of advanced oral squamous cell carcinoma remains a challenge for clinicians despite progress in its diagnosis and treatment over the past decades. In this review, we assessed clinicopathological factors and potential biomarkers along with their prognostic relevance in an attempt to develop optimal treatment strategies for these patients. In addition to several pathologic factors that have been proposed to improve prognostic stratification and treatment planning in the eighth edition of the American Joint Committee staging manual on cancer, we reviewed some other imaging and clinicopathological parameters demonstrated to be closely associated with patient prognosis, along with the biomarkers related to novel target or immune therapy. Evaluation of current literature regarding the prognostic stratification used in contemporary clinicopathological studies and progress in the development of targeted or immune therapy may help these patients benefit from tailored and personalized treatment and obtain better oncological results.

Combining early post-resuscitation EEG and HRV features improves the prognostic performance in cardiac arrest model of rats.

PubMed

Dai, Chenxi; Wang, Zhi; Wei, Liang; Chen, Gang; Chen, Bihua; Zuo, Feng; Li, Yongqin

2018-04-09

Early and reliable prediction of neurological outcome remains a challenge for comatose survivors of cardiac arrest (CA). The purpose of this study was to evaluate the predictive ability of EEG, heart rate variability (HRV) features and the combination of them for outcome prognostication in CA model of rats. Forty-eight male Sprague-Dawley rats were randomized into 6 groups (n=8 each) with different cause and duration of untreated arrest. Cardiopulmonary resuscitation was initiated after 5, 6 and 7min of ventricular fibrillation or 4, 6 and 8min of asphyxia. EEG and ECG were continuously recorded for 4h under normothermia after resuscitation. The relationships between features of early post-resuscitation EEG, HRV and 96-hour outcome were investigated. Prognostic performances were evaluated using the area under receiver operating characteristic curve (AUC). All of the animals were successfully resuscitated and 27 of them survived to 96h. Weighted-permutation entropy (WPE) and normalized high frequency (nHF) outperformed other EEG and HRV features for the prediction of survival. The AUC of WPE was markedly higher than that of nHF (0.892 vs. 0.759, p<0.001). The AUC was 0.954 when WPE and nHF were combined using a logistic regression model, which was significantly higher than the individual EEG (p=0.018) and HRV (p<0.001) features. Earlier post-resuscitation HRV provided prognostic information complementary to quantitative EEG in the CA model of rats. The combination of EEG and HRV features leads to improving performance of outcome prognostication compared to either EEG or HRV based features alone. Copyright © 2018. Published by Elsevier Inc.

Exploring the contribution of patient-reported and clinician based variables for the prediction of low back work status.

PubMed

Heymans, Martijn W; Ford, Jon J; McMeeken, Joan M; Chan, Alexander; de Vet, Henrica C W; van Mechelen, Willem

2007-09-01

Successful management of workers on sick leave due to low back pain by the general physician and physiotherapist depends on reliable prognostic information on the course of low back pain and work resumption. Retrospective cohort study in 194 patients who were compensated because of chronic low back pain and who were treated by a physiotherapy functional restoration program. Patient-reported and clinician based prognostic indicators were assessed at baseline before patients entered the functional restoration program. We investigated the predictive value of these indicators on work status at 6 months. Relationships were studied using logistic regression analysis in a 2-step bootstrap modelling approach and a nomogram was developed. Discrimination and calibration of the nomogram was evaluated internally and the explained variation of the nomogram calculated. Seventy percent of workers were back to work at 6 months. We found that including duration of complaints, functional disability, disc herniation and fear avoidance beliefs resulted in the "best" prognostic model. All these factors delayed work resumption. This model was used to construct a nomogram. The explained variation of the nomogram was 23.7%. Discrimination was estimated by the area under the receiver operating characteristic curve and was 0.76 and for calibration we used the slope estimate that was 0.91. The positive predictive values of the nomogram at different cut-off levels of predicted probability were good. Knowledge of the predictive value of these indicators by physicians and physiotherapists will help to identify subgroups of patients and will thus enhance clinical decision-making.

The Evolution of Prognostic Factors in Multiple Myeloma

PubMed Central

Hassanein, Mona; Rasheed, Walid; Aljurf, Mahmoud; Alsharif, Fahad

2017-01-01

Multiple myeloma (MM) is a heterogeneous hematologic malignancy involving the proliferation of plasma cells derived by different genetic events contributing to the development, progression, and prognosis of this disease. Despite improvement in treatment strategies of MM over the last decade, the disease remains incurable. All efforts are currently focused on understanding the prognostic markers of the disease hoping to incorporate the new therapeutic modalities to convert the disease into curable one. We present this comprehensive review to summarize the current standard prognostic markers used in MM along with novel techniques that are still in development and highlight their implications in current clinical practice. PMID:28321258

Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry.

PubMed

Canepa, Marco; Fonseca, Candida; Chioncel, Ovidiu; Laroche, Cécile; Crespo-Leiro, Maria G; Coats, Andrew J S; Mebazaa, Alexandre; Piepoli, Massimo F; Tavazzi, Luigi; Maggioni, Aldo P

2018-06-01

This study compared the performance of major heart failure (HF) risk models in predicting mortality and examined their utilization using data from a contemporary multinational registry. Several prognostic risk scores have been developed for ambulatory HF patients, but their precision is still inadequate and their use limited. This registry enrolled patients with HF seen in participating European centers between May 2011 and April 2013. The following scores designed to estimate 1- to 2-year all-cause mortality were calculated in each participant: CHARM (Candesartan in Heart Failure-Assessment of Reduction in Mortality), GISSI-HF (Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure), MAGGIC (Meta-analysis Global Group in Chronic Heart Failure), and SHFM (Seattle Heart Failure Model). Patients with hospitalized HF (n = 6,920) and ambulatory HF patients missing any variable needed to estimate each score (n = 3,267) were excluded, leaving a final sample of 6,161 patients. At 1-year follow-up, 5,653 of 6,161 patients (91.8%) were alive. The observed-to-predicted survival ratios (CHARM: 1.10, GISSI-HF: 1.08, MAGGIC: 1.03, and SHFM: 0.98) suggested some overestimation of mortality by all scores except the SHFM. Overprediction occurred steadily across levels of risk using both the CHARM and the GISSI-HF, whereas the SHFM underpredicted mortality in all risk groups except the highest. The MAGGIC showed the best overall accuracy (area under the curve [AUC] = 0.743), similar to the GISSI-HF (AUC = 0.739; p = 0.419) but better than the CHARM (AUC = 0.729; p = 0.068) and particularly better than the SHFM (AUC = 0.714; p = 0.018). Less than 1% of patients received a prognostic estimate from their enrolling physician. Performance of prognostic risk scores is still limited and physicians are reluctant to use them in daily practice. The need for contemporary, more precise prognostic tools should be considered. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Prognostic validation of a 17-segment score derived from a 20-segment score for myocardial perfusion SPECT interpretation.

PubMed

Berman, Daniel S; Abidov, Aiden; Kang, Xingping; Hayes, Sean W; Friedman, John D; Sciammarella, Maria G; Cohen, Ishac; Gerlach, James; Waechter, Parker B; Germano, Guido; Hachamovitch, Rory

2004-01-01

Recently, a 17-segment model of the left ventricle has been recommended as an optimally weighted approach for interpreting myocardial perfusion single photon emission computed tomography (SPECT). Methods to convert databases from previous 20- to new 17-segment data and criteria for abnormality for the 17-segment scores are needed. Initially, for derivation of the conversion algorithm, 65 patients were studied (algorithm population) (pilot group, n = 28; validation group, n = 37). Three conversion algorithms were derived: algorithm 1, which used mid, distal, and apical scores; algorithm 2, which used distal and apical scores alone; and algorithm 3, which used maximal scores of the distal septal, lateral, and apical segments in the 20-segment model for 3 corresponding segments of the 17-segment model. The prognosis population comprised 16,020 consecutive patients (mean age, 65 +/- 12 years; 41% women) who had exercise or vasodilator stress technetium 99m sestamibi myocardial perfusion SPECT and were followed up for 2.1 +/- 0.8 years. In this population, 17-segment scores were derived from 20-segment scores by use of algorithm 2, which demonstrated the best agreement with expert 17-segment reading in the algorithm population. The prognostic value of the 20- and 17-segment scores was compared by converting the respective summed scores into percent myocardium abnormal. Conversion algorithm 2 was found to be highly concordant with expert visual analysis by the 17-segment model (r = 0.982; kappa = 0.866) in the algorithm population. In the prognosis population, 456 cardiac deaths occurred during follow-up. When the conversion algorithm was applied, extent and severity of perfusion defects were nearly identical by 20- and derived 17-segment scores. The receiver operating characteristic curve areas by 20- and 17-segment perfusion scores were identical for predicting cardiac death (both 0.77 +/- 0.02, P = not significant). The optimal prognostic cutoff value for either 20- or derived 17-segment models was confirmed to be 5% myocardium abnormal, corresponding to a summed stress score greater than 3. Of note, the 17-segment model demonstrated a trend toward fewer mildly abnormal scans and more normal and severely abnormal scans. An algorithm for conversion of 20-segment perfusion scores to 17-segment scores has been developed that is highly concordant with expert visual analysis by the 17-segment model and provides nearly identical prognostic information. This conversion model may provide a mechanism for comparison of studies analyzed by the 17-segment system with previous studies analyzed by the 20-segment approach.

Damage tolerance modeling and validation of a wireless sensory composite panel for a structural health monitoring system

NASA Astrophysics Data System (ADS)

Talagani, Mohamad R.; Abdi, Frank; Saravanos, Dimitris; Chrysohoidis, Nikos; Nikbin, Kamran; Ragalini, Rose; Rodov, Irena

2013-05-01

The paper proposes the diagnostic and prognostic modeling and test validation of a Wireless Integrated Strain Monitoring and Simulation System (WISMOS). The effort verifies a hardware and web based software tool that is able to evaluate and optimize sensorized aerospace composite structures for the purpose of Structural Health Monitoring (SHM). The tool is an extension of an existing suite of an SHM system, based on a diagnostic-prognostic system (DPS) methodology. The goal of the extended SHM-DPS is to apply multi-scale nonlinear physics-based Progressive Failure analyses to the "as-is" structural configuration to determine residual strength, remaining service life, and future inspection intervals and maintenance procedures. The DPS solution meets the JTI Green Regional Aircraft (GRA) goals towards low weight, durable and reliable commercial aircraft. It will take advantage of the currently developed methodologies within the European Clean sky JTI project WISMOS, with the capability to transmit, store and process strain data from a network of wireless sensors (e.g. strain gages, FBGA) and utilize a DPS-based methodology, based on multi scale progressive failure analysis (MS-PFA), to determine structural health and to advice with respect to condition based inspection and maintenance. As part of the validation of the Diagnostic and prognostic system, Carbon/Epoxy ASTM coupons were fabricated and tested to extract the mechanical properties. Subsequently two composite stiffened panels were manufactured, instrumented and tested under compressive loading: 1) an undamaged stiffened buckling panel; and 2) a damaged stiffened buckling panel including an initial diamond cut. Next numerical Finite element models of the two panels were developed and analyzed under test conditions using Multi-Scale Progressive Failure Analysis (an extension of FEM) to evaluate the damage/fracture evolution process, as well as the identification of contributing failure modes. The comparisons between predictions and test results were within 10% accuracy.

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.

PubMed

Schoop, Rotraut; Beyersmann, Jan; Schumacher, Martin; Binder, Harald

2011-02-01

Prognostic models for time-to-event data play a prominent role in therapy assignment, risk stratification and inter-hospital quality assurance. The assessment of their prognostic value is vital not only for responsible resource allocation, but also for their widespread acceptance. The additional presence of competing risks to the event of interest requires proper handling not only on the model building side, but also during assessment. Research into methods for the evaluation of the prognostic potential of models accounting for competing risks is still needed, as most proposed methods measure either their discrimination or calibration, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (Statistics in Medicine 1999, 18, 2529–2545) and Gerds and Schumacher (Biometrical Journal 2006, 48, 1029–1040) to handle models with competing risks, i.e. more than one possible event type, and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows.

Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method.

PubMed

Takahashi, Hiro; Kobayashi, Takeshi; Honda, Hiroyuki

2005-01-15

For establishing prognostic predictors of various diseases using DNA microarray analysis technology, it is desired to find selectively significant genes for constructing the prognostic model and it is also necessary to eliminate non-specific genes or genes with error before constructing the model. We applied projective adaptive resonance theory (PART) to gene screening for DNA microarray data. Genes selected by PART were subjected to our FNN-SWEEP modeling method for the construction of a cancer class prediction model. The model performance was evaluated through comparison with a conventional screening signal-to-noise (S2N) method or nearest shrunken centroids (NSC) method. The FNN-SWEEP predictor with PART screening could discriminate classes of acute leukemia in blinded data with 97.1% accuracy and classes of lung cancer with 90.0% accuracy, while the predictor with S2N was only 85.3 and 70.0% or the predictor with NSC was 88.2 and 90.0%, respectively. The results have proven that PART was superior for gene screening. The software is available upon request from the authors. honda@nubio.nagoya-u.ac.jp

Application of molecular biology of differentiated thyroid cancer for clinical prognostication.

PubMed

Marotta, Vincenzo; Sciammarella, Concetta; Colao, Annamaria; Faggiano, Antongiulio

2016-11-01

Although cancer outcome results from the interplay between genetics and environment, researchers are making a great effort for applying molecular biology in the prognostication of differentiated thyroid cancer (DTC). Nevertheless, role of molecular characterisation in the prognostic setting of DTC is still nebulous. Among the most common and well-characterised genetic alterations related to DTC, including mutations of BRAF and RAS and RET rearrangements, BRAF V600E is the only mutation showing unequivocal association with clinical outcome. Unfortunately, its accuracy is strongly limited by low specificity. Recently, the introduction of next-generation sequencing techniques led to the identification of TERT promoter and TP53 mutations in DTC. These genetic abnormalities may identify a small subgroup of tumours with highly aggressive behaviour, thus improving specificity of molecular prognostication. Although knowledge of prognostic significance of TP53 mutations is still anecdotal, mutations of the TERT promoter have showed clear association with clinical outcome. Nevertheless, this genetic marker needs to be analysed according to a multigenetic model, as its prognostic effect becomes negligible when present in isolation. Given that any genetic alteration has demonstrated, taken alone, enough specificity, the co-occurrence of driving mutations is emerging as an independent genetic signature of aggressiveness, with possible future application in clinical practice. DTC prognostication may be empowered in the near future by non-tissue molecular prognosticators, including circulating BRAF V600E and miRNAs. Although promising, use of these markers needs to be refined by the technical sight, and the actual prognostic value is still yet to be validated. © 2016 Society for Endocrinology.

The Impact of ARM on Climate Modeling. Chapter 26

NASA Technical Reports Server (NTRS)

Randall, David A.; Del Genio, Anthony D.; Donner, Leo J.; Collins, William D.; Klein, Stephen A.

2016-01-01

Climate models are among humanity's most ambitious and elaborate creations. They are designed to simulate the interactions of the atmosphere, ocean, land surface, and cryosphere on time scales far beyond the limits of deterministic predictability, and including the effects of time-dependent external forcings. The processes involved include radiative transfer, fluid dynamics, microphysics, and some aspects of geochemistry, biology, and ecology. The models explicitly simulate processes on spatial scales ranging from the circumference of the Earth down to one hundred kilometers or smaller, and implicitly include the effects of processes on even smaller scales down to a micron or so. The atmospheric component of a climate model can be called an atmospheric global circulation model (AGCM). In an AGCM, calculations are done on a three-dimensional grid, which in some of today's climate models consists of several million grid cells. For each grid cell, about a dozen variables are time-stepped as the model integrates forward from its initial conditions. These so-called prognostic variables have special importance because they are the only things that a model remembers from one time step to the next; everything else is recreated on each time step by starting from the prognostic variables and the boundary conditions. The prognostic variables typically include information about the mass of dry air, the temperature, the wind components, water vapor, various condensed-water species, and at least a few chemical species such as ozone. A good way to understand how climate models work is to consider the lengthy and complex process used to develop one. Lets imagine that a new AGCM is to be created, starting from a blank piece of paper. The model may be intended for a particular class of applications, e.g., high-resolution simulations on time scales of a few decades. Before a single line of code is written, the conceptual foundation of the model must be designed through a creative envisioning that starts from the intended application and is based on current understanding of how the atmosphere works and the inventory of mathematical methods available.

Water-Exchange-Modified Kinetic Parameters from Dynamic Contrast-Enhanced MRI as Prognostic Biomarkers of Survival in Advanced Hepatocellular Carcinoma Treated with Antiangiogenic Monotherapy

PubMed Central

Lee, Sang Ho; Hayano, Koichi; Zhu, Andrew X.; Sahani, Dushyant V.; Yoshida, Hiroyuki

2015-01-01

Background To find prognostic biomarkers in pretreatment dynamic contrast-enhanced MRI (DCE-MRI) water-exchange-modified (WX) kinetic parameters for advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy. Methods Twenty patients with advanced HCC underwent DCE-MRI and were subsequently treated with sunitinib. Pretreatment DCE-MRI data on advanced HCC were analyzed using five different WX kinetic models: the Tofts-Kety (WX-TK), extended TK (WX-ETK), two compartment exchange, adiabatic approximation to tissue homogeneity (WX-AATH), and distributed parameter (WX-DP) models. The total hepatic blood flow, arterial flow fraction (γ), arterial blood flow (BF A), portal blood flow, blood volume, mean transit time, permeability-surface area product, fractional interstitial volume (v I), extraction fraction, mean intracellular water molecule lifetime (τ C), and fractional intracellular volume (v C) were calculated. After receiver operating characteristic analysis with leave-one-out cross-validation, individual parameters for each model were assessed in terms of 1-year-survival (1YS) discrimination using Kaplan-Meier analysis, and association with overall survival (OS) using univariate Cox regression analysis with permutation testing. Results The WX-TK-model-derived γ (P = 0.022) and v I (P = 0.010), and WX-ETK-model-derived τ C (P = 0.023) and v C (P = 0.042) were statistically significant prognostic biomarkers for 1YS. Increase in the WX-DP-model-derived BF A (P = 0.025) and decrease in the WX-TK, WX-ETK, WX-AATH, and WX-DP-model-derived v C (P = 0.034, P = 0.038, P = 0.028, P = 0.041, respectively) were significantly associated with an increase in OS. Conclusions The WX-ETK-model-derived v C was an effective prognostic biomarker for advanced HCC treated with sunitinib. PMID:26366997

Prognostic value of tumor-infiltrating lymphocytes on residual disease after primary chemotherapy for triple-negative breast cancer: a retrospective multicenter study

PubMed Central

Dieci, M. V.; Criscitiello, C.; Goubar, A.; Viale, G.; Conte, P.; Guarneri, V.; Ficarra, G.; Mathieu, M. C.; Delaloge, S.; Curigliano, G.; Andre, F.

2014-01-01

Background There is a need to develop surrogates for treatment efficacy in the neoadjuvant setting to speed-up drug development and stratify patients according to outcome. Preclinical studies showed that chemotherapy induces an antitumor immune response. In order to develop new surrogates for drug efficacy, we assessed the prognostic value of tumor-infiltrating lymphocytes (TIL) on residual disease after neoadjuvant chemotherapy (NACT) in patients with triple-negative breast cancer (TNBC). Patients and methods Three hundred four TNBC patients with residual disease after NACT were retrospectively identified in three different hospitals. Hematoxylin and eosin-stained slides from surgical postchemotherapy specimens were evaluated for intratumoral (It-TIL) and stromal (Str-TIL) TIL. Cases were classified as High-TIL if It-TIL and/or Str-TIL >60%. Results TIL were assessable for 278 cases. Continuous It-TIL and Str-TIL variables were strong prognostic factors in the multivariate model, both for metastasis-free [hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.77–0.96, P = 0.01 and HR 0.85, 95% CI 0.75–0.98, P = 0.02 for Str-TIL and It-TIL, respectively] and overall survival (HR 0.86, 95% CI 0.77–0.97, P = 0.01 and HR 0.86, 95% CI 0.75–0.99, P = 0.03 for Str-TIL and It-TIL, respectively). The 5-year overall survival rate was 91% (95% CI 68% to 97%) for High-TIL patients (n = 27) and 55% (95% CI 48% to 61%) for Low-TIL patients (HR 0.19, 95% CI 0.06–0.61, log-rank P = 0.0017). The major prognostic impact of TIL was seen for patients with large tumor burden following NACT (residual tumor >2 cm and/or node metastasis). In all but one High-TIL case, It-TIL and Str-TIL values were lower on the prechemotherapy sample. Conclusions The presence of TIL in residual disease after NACT is associated with better prognosis in TNBC patients. This parameter may represent a new surrogate of drug efficacy to test investigational agents in the neoadjuvant setting and a new prognostic marker to select patients at high risk of relapse. PMID:24401929

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Prognostic factors for patients undergoing vitrified-warmed human embryo transfer cycles: a retrospective cohort study.

PubMed

Takahashi, Toshifumi; Hasegawa, Ayumi; Igarashi, Hideki; Amita, Mitsuyoshi; Matsukawa, Jun; Takehara, Isao; Suzuki, Satoko; Nagase, Satoru

2017-06-01

We examined the prognostic factors for pregnancy in 210 vitrified-warmed embryo transfer (ET) cycles in 121 patients. The univariate analysis showed that age, gravida, the number of cycles associated with infertility caused by endometriosis, the number of previous assisted reproductive technology (ART) treatment cycles, and the number of ICSI procedures were significantly lower in pregnant cycles compared with non-pregnant cycles. The percentages of ET using at least one intact embryo and of ET using at least one embryo that had developed further after warming were significantly higher in pregnant cycles compared with non-pregnant cycles. Multivariate logistic regression analysis showed that previous ART treatment cycles, ET with at least one intact embryo, and ET using at least one embryo that had developed further were independent prognostic factors for pregnancy in vitrified-warmed ET cycles. We conclude that fewer previous ART treatment cycles, ET using at least one intact embryo, and ET with embryos that have developed further after warming might be favourable prognostic factors for pregnancy in vitrified-warmed ET cycles.

Evaluation of Liver Biomarkers as Prognostic Factors for Outcomes to Yttrium-90 Radioembolization of Primary and Secondary Liver Malignancies.

PubMed

Henrie, Adam M; Wittstrom, Kristina; Delu, Adam; Deming, Paulina

2015-09-01

The objective of this study was to examine indicators of liver function and inflammation for prognostic value in predicting outcomes to yttrium-90 radioembolization (RE). In a retrospective analysis, markers of liver function and inflammation, biomarkers required to stage liver function and inflammation, and data regarding survival, tumor response, and progression after RE were recorded. Univariate regression models were used to investigate the prognostic value of liver biomarkers in predicting outcome to RE as measured by survival, tumor progression, and radiographic and biochemical tumor response. Markers from all malignancy types were analyzed together. A subgroup analysis was performed on markers from patients with metastatic colorectal cancer. A total of 31 patients received RE from 2004 to 2014. Median survival after RE for all malignancies combined was 13.6 months (95% CI: 6.7-17.6 months). Results from an exploratory analysis of patient data suggest that liver biomarkers, including albumin concentrations, international normalized ratio, bilirubin concentrations, and the model for end-stage liver disease score, possess prognostic value in predicting outcomes to RE.

A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines.

PubMed

Sánchez Lasheras, Fernando; García Nieto, Paulino José; de Cos Juez, Francisco Javier; Mayo Bayón, Ricardo; González Suárez, Victor Manuel

2015-03-23

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

PubMed Central

Lasheras, Fernando Sánchez; Nieto, Paulino José García; de Cos Juez, Francisco Javier; Bayón, Ricardo Mayo; Suárez, Victor Manuel González

2015-01-01

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines. PMID:25806876

Prognostic value of inflammation-based scores in patients with osteosarcoma

PubMed Central

Liu, Bangjian; Huang, Yujing; Sun, Yuanjue; Zhang, Jianjun; Yao, Yang; Shen, Zan; Xiang, Dongxi; He, Aina

2016-01-01

Systemic inflammation responses have been associated with cancer development and progression. C-reactive protein (CRP), Glasgow prognostic score (GPS), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-platelet score (NPS) have been shown to be independent risk factors in various types of malignant tumors. This retrospective analysis of 162 osteosarcoma cases was performed to estimate their predictive value of survival in osteosarcoma. All statistical analyses were performed by SPSS statistical software. Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; area under the curve (AUC) was used to show the discriminatory abilities of inflammation-based scores; Kaplan-Meier analysis was performed to plot the survival curve; cox regression models were employed to determine the independent prognostic factors. The optimal cut-off points of NLR, PLR, and LMR were 2.57, 123.5 and 4.73, respectively. GPS and NLR had a markedly larger AUC than CRP, PLR and LMR. High levels of CRP, GPS, NLR, PLR, and low level of LMR were significantly associated with adverse prognosis (P < 0.05). Multivariate Cox regression analyses revealed that GPS, NLR, and occurrence of metastasis were top risk factors associated with death of osteosarcoma patients. PMID:28008988

Chronic lymphocytic leukemia: A prognostic model comprising only two biomarkers (IGHV mutational status and FISH cytogenetics) separates patients with different outcome and simplifies the CLL-IPI.

PubMed

Delgado, Julio; Doubek, Michael; Baumann, Tycho; Kotaskova, Jana; Molica, Stefano; Mozas, Pablo; Rivas-Delgado, Alfredo; Morabito, Fortunato; Pospisilova, Sarka; Montserrat, Emili

2017-04-01

Rai and Binet staging systems are important to predict the outcome of patients with chronic lymphocytic leukemia (CLL) but do not reflect the biologic diversity of the disease nor predict response to therapy, which ultimately shape patients' outcome. We devised a biomarkers-only CLL prognostic system based on the two most important prognostic parameters in CLL (i.e., IGHV mutational status and fluorescence in situ hybridization [FISH] cytogenetics), separating three different risk groups: (1) low-risk (mutated IGHV + no adverse FISH cytogenetics [del(17p), del(11q)]); (2) intermediate-risk (either unmutated IGHV or adverse FISH cytogenetics) and (3) high-risk (unmutated IGHV + adverse FISH cytogenetics). In 524 unselected subjects with CLL, the 10-year overall survival was 82% (95% CI 76%-88%), 52% (45%-62%), and 27% (17%-42%) for the low-, intermediate-, and high-risk groups, respectively. Patients with low-risk comprised around 50% of the series and had a life expectancy comparable to the general population. The prognostic model was fully validated in two independent cohorts, including 417 patients representative of general CLL population and 337 patients with Binet stage A CLL. The model had a similar discriminatory value as the CLL-IPI. Moreover, it applied to all patients with CLL independently of age, and separated patients with different risk within Rai or Binet clinical stages. The biomarkers-only CLL prognostic system presented here simplifies the CLL-IPI and could be useful in daily practice and to stratify patients in clinical trials. © 2017 Wiley Periodicals, Inc.

Prognostic models based on patient snapshots and time windows: Predicting disease progression to assisted ventilation in Amyotrophic Lateral Sclerosis.

PubMed

Carreiro, André V; Amaral, Pedro M T; Pinto, Susana; Tomás, Pedro; de Carvalho, Mamede; Madeira, Sara C

2015-12-01

Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient's condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient's current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians' experience. Copyright © 2015 Elsevier Inc. All rights reserved.

Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening.

PubMed

Trentham-Dietz, Amy; Ergun, Mehmet Ali; Alagoz, Oguzhan; Stout, Natasha K; Gangnon, Ronald E; Hampton, John M; Dittus, Kim; James, Ted A; Vacek, Pamela M; Herschorn, Sally D; Burnside, Elizabeth S; Tosteson, Anna N A; Weaver, Donald L; Sprague, Brian L

2018-02-01

Due to limitations in the ability to identify non-progressive disease, ductal carcinoma in situ (DCIS) is usually managed similarly to localized invasive breast cancer. We used simulation modeling to evaluate the potential impact of a hypothetical test that identifies non-progressive DCIS. A discrete-event model simulated a cohort of U.S. women undergoing digital screening mammography. All women diagnosed with DCIS underwent the hypothetical DCIS prognostic test. Women with test results indicating progressive DCIS received standard breast cancer treatment and a decrement to quality of life corresponding to the treatment. If the DCIS test indicated non-progressive DCIS, no treatment was received and women continued routine annual surveillance mammography. A range of test performance characteristics and prevalence of non-progressive disease were simulated. Analysis compared discounted quality-adjusted life years (QALYs) and costs for test scenarios to base-case scenarios without the test. Compared to the base case, a perfect prognostic test resulted in a 40% decrease in treatment costs, from $13,321 to $8005 USD per DCIS case. A perfect test produced 0.04 additional QALYs (16 days) for women diagnosed with DCIS, added to the base case of 5.88 QALYs per DCIS case. The results were sensitive to the performance characteristics of the prognostic test, the proportion of DCIS cases that were non-progressive in the model, and the frequency of mammography screening in the population. A prognostic test that identifies non-progressive DCIS would substantially reduce treatment costs but result in only modest improvements in quality of life when averaged over all DCIS cases.

On Applying the Prognostic Performance Metrics

NASA Technical Reports Server (NTRS)

Saxena, Abhinav; Celaya, Jose; Saha, Bhaskar; Saha, Sankalita; Goebel, Kai

2009-01-01

Prognostics performance evaluation has gained significant attention in the past few years. As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. Several shortcomings identified, while applying these metrics to a variety of real applications, are also summarized along with discussions that attempt to alleviate these problems. Further, these metrics have been enhanced to include the capability of incorporating probability distribution information from prognostic algorithms as opposed to evaluation based on point estimates only. Several methods have been suggested and guidelines have been provided to help choose one method over another based on probability distribution characteristics. These approaches also offer a convenient and intuitive visualization of algorithm performance with respect to some of these new metrics like prognostic horizon and alpha-lambda performance, and also quantify the corresponding performance while incorporating the uncertainty information.

Development of a turbojet engine gearbox test rig for prognostics and health management

NASA Astrophysics Data System (ADS)

Rezaei, Aida; Dadouche, Azzedine

2012-11-01

Aircraft engine gearboxes represent one of the many critical systems/elements that require special attention for longer and safer operation. Reactive maintenance strategies are unsuitable as they usually imply higher repair costs when compared to condition based maintenance. This paper discusses the main prognostics and health management (PHM) approaches, describes a newly designed gearbox experimental facility and analyses preliminary data for gear prognosis. The test rig is designed to provide full capabilities of performing controlled experiments suitable for developing a reliable diagnostic and prognostic system. The rig is based on the accessory gearbox of the GE J85 turbojet engine, which has been slightly modified and reconfigured to replicate real operating conditions such as speeds and loads. Defect to failure tests (DTFT) have been run to evaluate the performance of the rig as well as to assess prognostic metrics extracted from sensors installed on the gearbox casing (vibration and acoustic). The paper also details the main components of the rig and describes the various challenges encountered. Successful DTFT results were obtained during an idle engine performance test and prognostic metrics associated with the sensor suite were evaluated and discussed.

Prognostic nomogram for previously untreated adult patients with acute myeloid leukemia

PubMed Central

Zheng, Zhuojun; Li, Xiaodong; Zhu, Yuandong; Gu, Weiying; Xie, Xiaobao; Jiang, Jingting

2016-01-01

This study was designed to perform an acceptable prognostic nomogram for acute myeloid leukemia. The clinical data from 311 patients from our institution and 165 patients generated with Cancer Genome Atlas Research Network were reviewed. A prognostic nomogram was designed according to the Cox's proportional hazard model to predict overall survival (OS). To compare the capacity of the nomogram with that of the current prognostic system, the concordance index (C-index) was used to validate the accuracy as well as the calibration curve. The nomogram included 6 valuable variables: age, risk stratifications based on cytogenetic abnormalities, status of FLT3-ITD mutation, status of NPM1 mutation, expression of CD34, and expression of HLA-DR. The C-indexes were 0.71 and 0.68 in the primary and validation cohort respectively, which were superior to the predictive capacity of the current prognostic systems in both cohorts. The nomogram allowed both patients with acute myeloid leukemia and physicians to make prediction of OS individually prior to treatment. PMID:27689396

Neuroendocrine tumors of colon and rectum: validation of clinical and prognostic values of the World Health Organization 2010 grading classifications and European Neuroendocrine Tumor Society staging systems.

PubMed

Shen, Chaoyong; Yin, Yuan; Chen, Huijiao; Tang, Sumin; Yin, Xiaonan; Zhou, Zongguang; Zhang, Bo; Chen, Zhixin

2017-03-28

This study evaluated and compared the clinical and prognostic values of the grading criteria used by the World Health Organization (WHO) and the European Neuroendocrine Tumors Society (ENETS). Moreover, this work assessed the current best prognostic model for colorectal neuroendocrine tumors (CRNETs). The 2010 WHO classifications and the ENETS systems can both stratify the patients into prognostic groups, although the 2010 WHO criteria is more applicable to CRNET patients. Along with tumor location, the 2010 WHO criteria are important independent prognostic parameters for CRNETs in both univariate and multivariate analyses through Cox regression (P<0.05). Data from 192 consecutive patients histopathologically diagnosed with CRNETs and had undergone surgical resection from January 2009 to May 2016 in a single center were retrospectively analyzed. Findings suggest that the WHO classifications are superior over the ENETS classification system in predicting the prognosis of CRNETs. Additionally, the WHO classifications can be widely used in clinical practice.

Observational and Modeling Studies of Clouds and the Hydrological Cycle

NASA Technical Reports Server (NTRS)

Somerville, Richard C. J.

1997-01-01

Our approach involved validating parameterizations directly against measurements from field programs, and using this validation to tune existing parameterizations and to guide the development of new ones. We have used a single-column model (SCM) to make the link between observations and parameterizations of clouds, including explicit cloud microphysics (e.g., prognostic cloud liquid water used to determine cloud radiative properties). Surface and satellite radiation measurements were used to provide an initial evaluation of the performance of the different parameterizations. The results of this evaluation will then used to develop improved cloud and cloud-radiation schemes, which were tested in GCM experiments.

Research on prognostics and health management of underground pipeline

NASA Astrophysics Data System (ADS)

Zhang, Guangdi; Yang, Meng; Yang, Fan; Ni, Na

2018-04-01

With the development of the city, the construction of the underground pipeline is more and more complex, which has relation to the safety and normal operation of the city, known as "the lifeline of the city". First of all, this paper introduces the principle of PHM (Prognostics and Health Management) technology, then proposed for fault diagnosis, prognostics and health management in view of underground pipeline, make a diagnosis and prognostics for the faults appearing in the operation of the underground pipeline, and then make a health assessment of the whole underground pipe network in order to ensure the operation of the pipeline safely. Finally, summarize and prospect the future research direction.

A Comparison of Filter-based Approaches for Model-based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Saha, Bhaskar; Goebel, Kai

2012-01-01

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

Diagnostic and prognostic epigenetic biomarkers in cancer.

PubMed

Costa-Pinheiro, Pedro; Montezuma, Diana; Henrique, Rui; Jerónimo, Carmen

2015-01-01

Growing cancer incidence and mortality worldwide demands development of accurate biomarkers to perfect detection, diagnosis, prognostication and monitoring. Urologic (prostate, bladder, kidney), lung, breast and colorectal cancers are the most common and despite major advances in their characterization, this has seldom translated into biomarkers amenable for clinical practice. Epigenetic alterations are innovative cancer biomarkers owing to stability, frequency, reversibility and accessibility in body fluids, entailing great potential of assay development to assist in patient management. Several studies identified putative epigenetic cancer biomarkers, some of which have been commercialized. However, large multicenter validation studies are required to foster translation to the clinics. Herein we review the most promising epigenetic detection, diagnostic, prognostic and predictive biomarkers for the most common cancers.

Enhanced Self Tuning On-Board Real-Time Model (eSTORM) for Aircraft Engine Performance Health Tracking

NASA Technical Reports Server (NTRS)

Volponi, Al; Simon, Donald L. (Technical Monitor)

2008-01-01

A key technological concept for producing reliable engine diagnostics and prognostics exploits the benefits of fusing sensor data, information, and/or processing algorithms. This report describes the development of a hybrid engine model for a propulsion gas turbine engine, which is the result of fusing two diverse modeling methodologies: a physics-based model approach and an empirical model approach. The report describes the process and methods involved in deriving and implementing a hybrid model configuration for a commercial turbofan engine. Among the intended uses for such a model is to enable real-time, on-board tracking of engine module performance changes and engine parameter synthesis for fault detection and accommodation.

Keeping data continuous when analyzing the prognostic impact of a tumor marker: an example with cathepsin D in breast cancer.

PubMed

Bossard, N; Descotes, F; Bremond, A G; Bobin, Y; De Saint Hilaire, P; Golfier, F; Awada, A; Mathevet, P M; Berrerd, L; Barbier, Y; Estève, J

2003-11-01

The prognostic value of cathepsin D has been recently recognized, but as many quantitative tumor markers, its clinical use remains unclear partly because of methodological issues in defining cut-off values. Guidelines have been proposed for analyzing quantitative prognostic factors, underlining the need for keeping data continuous, instead of categorizing them. Flexible approaches, parametric and non-parametric, have been proposed in order to improve the knowledge of the functional form relating a continuous factor to the risk. We studied the prognostic value of cathepsin D in a retrospective hospital cohort of 771 patients with breast cancer, and focused our overall survival analysis, based on the Cox regression, on two flexible approaches: smoothing splines and fractional polynomials. We also determined a cut-off value from the maximum likelihood estimate of a threshold model. These different approaches complemented each other for (1) identifying the functional form relating cathepsin D to the risk, and obtaining a cut-off value and (2) optimizing the adjustment for complex covariate like age at diagnosis in the final multivariate Cox model. We found a significant increase in the death rate, reaching 70% with a doubling of the level of cathepsin D, after the threshold of 37.5 pmol mg(-1). The proper prognostic impact of this marker could be confirmed and a methodology providing appropriate ways to use markers in clinical practice was proposed.

A Virtual Laboratory for Aviation and Airspace Prognostics Research

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan; Gorospe, George; Teubert, Christ; Quach, Cuong C.; Hogge, Edward; Darafsheh, Kaveh

2017-01-01

Integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, and other aviation technologies, in the airspace is becoming more and more complicated, and will continue to do so in the future. Inclusion of new technology and complexity into the airspace increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems and systems of systems can be challenging, expensive, and at times unsafe when implementing real life scenarios. The application of prognostics to aviation and airspace management may produce new tools and insight into these problems. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. In our research, we develop a live, distributed, hardware- in-the-loop Prognostics Virtual Laboratory testbed for aviation and airspace prognostics. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. In our earlier work1 we discussed the initial Prognostics Virtual Laboratory testbed development work and related results for milestones 1 & 2. This paper describes the design, development, and testing of the integrated tested which are part of milestone 3, along with our next steps for validation of this work. Through a framework consisting of software/hardware modules and associated interface clients, the distributed testbed enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. The testbed modules can be used cohesively to construct complex and relevant airspace scenarios for research. Four modules are key to this research: the virtual aircraft module which uses the X-Plane simulator and X-PlaneConnect toolbox, the live aircraft module which connects fielded aircraft using onboard cellular communications devices, the hardware in the loop (HITL) module which connects laboratory based bench-top hardware testbeds and the research module which contains diagnostics and prognostics tools for analysis of live air traffic situations and vehicle health conditions. The testbed also features other modules for data recording and playback, information visualization, and air traffic generation. Software reliability, safety, and latency are some of the critical design considerations in development of the testbed.

Comparison of the prognostic utility of the revised International Prognostic Scoring System and the French Prognostic Scoring System in azacitidine-treated patients with myelodysplastic syndromes.

PubMed

Zeidan, Amer M; Lee, Ju-Whei; Prebet, Thomas; Greenberg, Peter; Sun, Zhuoxin; Juckett, Mark; Smith, Mitchell R; Paietta, Elisabeth; Gabrilove, Janice; Erba, Harry P; Tallman, Martin S; Gore, Steven D

2014-08-01

The revised International Prognostic Scoring System (IPSS-R) was developed in a cohort of untreated myelodysplastic syndromes (MDS) patients. A French Prognostic Scoring System (FPSS) was recently reported to identify differential survival among azacitidine-treated patients with high-risk MDS. We applied the FPSS and IPSS-R to 150 patients previously randomized to azacitidine monotherapy or a combination of azacitidine with entinostat (a histone deacetylase inhibitor). Neither score predicted response but both discriminated patients with different overall survival (OS; median OS, FPSS: 9·7, 14·7, and 25·3 months, P = 0·018; IPSS-R: 12·5, 11·3, 20·8, and 36 months, P = 0·005). Statistical analysis suggested no improvement in OS prediction for the FPSS over the IPSS-R in azacitidine-treated patients. © 2014 John Wiley & Sons Ltd.

Online Monitoring Technical Basis and Analysis Framework for Emergency Diesel Generators - Interim Report for FY 2013

DOE Office of Scientific and Technical Information (OSTI.GOV)

Binh T. Pham; Nancy J. Lybeck; Vivek Agarwal

The Light Water Reactor Sustainability program at Idaho National Laboratory is actively conducting research to develop and demonstrate online monitoring capabilities for active components in existing nuclear power plants. Idaho National Laboratory and the Electric Power Research Institute are working jointly to implement a pilot project to apply these capabilities to emergency diesel generators and generator step-up transformers. The Electric Power Research Institute Fleet-Wide Prognostic and Health Management Software Suite will be used to implement monitoring in conjunction with utility partners: Braidwood Generating Station (owned by Exelon Corporation) for emergency diesel generators, and Shearon Harris Nuclear Generating Station (owned bymore » Duke Energy Progress) for generator step-up transformers. This report presents monitoring techniques, fault signatures, and diagnostic and prognostic models for emergency diesel generators. Emergency diesel generators provide backup power to the nuclear power plant, allowing operation of essential equipment such as pumps in the emergency core coolant system during catastrophic events, including loss of offsite power. Technical experts from Braidwood are assisting Idaho National Laboratory and Electric Power Research Institute in identifying critical faults and defining fault signatures associated with each fault. The resulting diagnostic models will be implemented in the Fleet-Wide Prognostic and Health Management Software Suite and tested using data from Braidwood. Parallel research on generator step-up transformers was summarized in an interim report during the fourth quarter of fiscal year 2012.« less

Clinical and Cost Impact Analysis of a Novel Prognostic Test for Early Detection of Preterm Birth

PubMed Central

Caughey, Aaron B.; Zupancic, John A. F.; Greenberg, James M.; Garfield, Susan S.; Thung, Stephen F.; Iams, Jay D.

2016-01-01

Objective The objective of this study was to evaluate the potential impact to the U.S. health care system by adopting a novel test that identifies women at risk for spontaneous preterm birth. Methods A decision-analytic model was developed to assess clinical and cost outcomes over a 1-year period. The use of a prognostic test to predict spontaneous preterm birth in a hypothetical population of women reflective of the U.S. population (predictive arm) was compared with the current baseline rate of spontaneous preterm birth and associated infant morbidity and mortality (baseline care arm). Results In a population of 3,528,593 births, our model predicts a 23.5% reduction in infant mortality (8,300 vs. 6,343 deaths) with use of the novel test. The rate of acute conditions at birth decreased from 11.2 to 8.1%; similarly, the rate of developmental disabilities decreased from 13.2 to 11.5%. The rate of spontaneous preterm birth decreased from 9.8 to 9.1%, a reduction of 23,430 preterm births. Direct medical costs savings was $511.7M (− 2.1%) in the first year of life. Discussion The use of a prognostic test for reducing spontaneous preterm birth is a dominant strategy that could reduce costs and improve outcomes. More research is needed once such a test is available to determine if these results are borne out upon real-world use. PMID:27917307

Preoperative prognostic factors for mortality in peptic ulcer perforation: a systematic review.

PubMed

Møller, Morten Hylander; Adamsen, Sven; Thomsen, Reimar Wernich; Møller, Ann Merete

2010-08-01

Mortality and morbidity following perforated peptic ulcer (PPU) is substantial and probably related to the development of sepsis. During the last three decades a large number of preoperative prognostic factors in patients with PPU have been examined. The aim of this systematic review was to summarize available evidence on these prognostic factors. MEDLINE (January 1966 to June 2009), EMBASE (January 1980 to June 2009), and the Cochrane Library (Issue 3, 2009) were screened for studies reporting preoperative prognostic factors for mortality in patients with PPU. The methodological quality of the included studies was assessed. Summary relative risks with 95% confidence intervals for the identified prognostic factors were calculated and presented as Forest plots. Fifty prognostic studies with 37 prognostic factors comprising a total of 29,782 patients were included in the review. The overall methodological quality was acceptable, yet only two-thirds of the studies provided confounder adjusted estimates. The studies provided strong evidence for an association of older age, comorbidity, and use of NSAIDs or steroids with mortality. Shock upon admission, preoperative metabolic acidosis, tachycardia, acute renal failure, low serum albumin level, high American Society of Anaesthesiologists score, and preoperative delay >24 h were associated with poor prognosis. In patients with PPU, a number of negative prognostic factors can be identified prior to surgery, and many of these seem to be related to presence of the sepsis syndrome.

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

PubMed

Goos, Jeroen A C M; Coupé, Veerle M H; van de Wiel, Mark A; Diosdado, Begoña; Delis-Van Diemen, Pien M; Hiemstra, Annemieke C; de Cuba, Erienne M V; Beliën, Jeroen A M; Menke-van der Houven van Oordt, C Willemien; Geldof, Albert A; Meijer, Gerrit A; Hoekstra, Otto S; Fijneman, Remond J A

2016-01-12

Prognosis of patients with colorectal cancer liver metastasis (CRCLM) is estimated based on clinicopathological models. Stratifying patients based on tumor biology may have additional value. Tissue micro-arrays (TMAs), containing resected CRCLM and corresponding primary tumors from a multi-institutional cohort of 507 patients, were immunohistochemically stained for 18 candidate biomarkers. Cross-validated hazard rate ratios (HRRs) for overall survival (OS) and the proportion of HRRs with opposite effect (P(HRR < 1) or P(HRR > 1)) were calculated. A classifier was constructed by classification and regression tree (CART) analysis and its prognostic value determined by permutation analysis. Correlations between protein expression in primary tumor-CRCLM pairs were calculated. Based on their putative prognostic value, EGFR (P(HRR < 1) = .02), AURKA (P(HRR < 1) = .02), VEGFA (P(HRR < 1) = .02), PTGS2 (P(HRR < 1) = .01), SLC2A1 (P(HRR > 1) < 01), HIF1α (P(HRR > 1) = .06), KCNQ1 (P(HRR > 1) = .09), CEA (P (HRR > 1) = .05) and MMP9 (P(HRR < 1) = .07) were included in the CART analysis (n = 201). The resulting classifier was based on AURKA, PTGS2 and MMP9 expression and was associated with OS (HRR 2.79, p < .001), also after multivariate analysis (HRR 3.57, p < .001). The prognostic value of the biomarker-based classifier was superior to the clinicopathological model (p = .001). Prognostic value was highest for colon cancer patients (HRR 5.71, p < .001) and patients not treated with systemic therapy (HRR 3.48, p < .01). Classification based on protein expression in primary tumors could be based on AURKA expression only (HRR 2.59, p = .04). A classifier was generated for patients with CRCLM with improved prognostic value compared to the standard clinicopathological prognostic parameters, which may aid selection of patients who may benefit from adjuvant systemic therapy.

Prognostic significance of blood-brain barrier disruption in patients with severe nonpenetrating traumatic brain injury requiring decompressive craniectomy.

PubMed

Ho, Kwok M; Honeybul, Stephen; Yip, Cheng B; Silbert, Benjamin I

2014-09-01

The authors assessed the risk factors and outcomes associated with blood-brain barrier (BBB) disruption in patients with severe, nonpenetrating, traumatic brain injury (TBI) requiring decompressive craniectomy. At 2 major neurotrauma centers in Western Australia, a retrospective cohort study was conducted among 97 adult neurotrauma patients who required an external ventricular drain (EVD) and decompressive craniectomy during 2004-2012. Glasgow Outcome Scale scores were used to assess neurological outcomes. Logistic regression was used to identify factors associated with BBB disruption, defined by a ratio of total CSF protein concentrations to total plasma protein concentration > 0.007 in the earliest CSF specimen collected after TBI. Of the 252 patients who required decompressive craniectomy, 97 (39%) required an EVD to control intracranial pressure, and biochemical evidence of BBB disruption was observed in 43 (44%). Presence of disruption was associated with more severe TBI (median predicted risk for unfavorable outcome 75% vs 63%, respectively; p = 0.001) and with worse outcomes at 6, 12, and 18 months than was absence of BBB disruption (72% vs 37% unfavorable outcomes, respectively; p = 0.015). The only risk factor significantly associated with increased risk for BBB disruption was presence of nonevacuated intracerebral hematoma (> 1 cm diameter) (OR 3.03, 95% CI 1.23-7.50; p = 0.016). Although BBB disruption was associated with more severe TBI and worse long-term outcomes, when combined with the prognostic information contained in the Corticosteroid Randomization after Significant Head Injury (CRASH) prognostic model, it did not seem to add significant prognostic value (area under the receiver operating characteristic curve 0.855 vs 0.864, respectively; p = 0.453). Biochemical evidence of BBB disruption after severe nonpenetrating TBI was common, especially among patients with large intracerebral hematomas. Disruption of the BBB was associated with more severe TBI and worse long-term outcomes, but when combined with the prognostic information contained in the CRASH prognostic model, this information did not add significant prognostic value.

Prognostic value of interleukin-6 and interleukin-6 receptor in organ-confined clear-cell renal cell carcinoma: a 5-year conditional cancer-specific survival analysis.

PubMed

Fu, Qiang; Chang, Yuan; An, Huimin; Fu, Hangcheng; Zhu, Yu; Xu, Le; Zhang, Weijuan; Xu, Jiejie

2015-12-01

Interleukin-6 (IL-6) is the major cytokine that induces transcriptional acute and chronic inflammation responses, and was recently incorporated as a recurrence prognostication signature for localised clear-cell renal cell carcinoma (ccRCC). As the prognostic efficacy of initial risk factors may ebb during long-term practice, we aim to report conditional cancer-specific survival (CCSS) of RCC patients and evaluate the impact of IL-6 as well as its receptor (IL-6R) to offer more relevant prognostic information accounting for elapsing time. We enrolled 180 histologically proven localised ccRCC patients who underwent nephrectomy between 2001 and 2004 with available pathologic information. Five-year CCSS was determined and stratified by future prognostic factors. Constant Cox regression analysis and Harrell's concordance index were used to indicate the predictive accuracy of established models. The 5-year CCSS of organ-confined ccRCC patients with both IL-6- and IL-6R-positive expression was 52% at year 2 after surgery, which was close to locally advanced patients (48%, P=0.564) and was significantly poorer than organ-confined patients with IL-6- or IL-6R-negative expression (89%, P<0.001). Multivariate analyses proved IL-6 and IL-6R as independent predictors after adjusting for demographic factors. Concordance index of pT-IL-6-IL-6R risk stratification was markedly higher compared with the stage, size, grade and necrosis prognostic model (0.724 vs 0.669, P=0.002) or UCLA Integrated Staging System (0.724 vs 0.642, P=0.007) in organ-confined ccRCC population during the first 5 years. Combined IL-6 and IL-6R coexpression emerges as an independent early-stage immunologic prognostic factor for organ-confined ccRCC patients.

GCM Simulation of the Large-scale North American Monsoon Including Water Vapor Tracer Diagnostics

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Schubert, Siegfried D.; Sud, Yogesh; Walker, Gregory K.

2002-01-01

In this study, we have applied GCM water vapor tracers (WVT) to simulate the North American water cycle. WVTs allow quantitative computation of the geographical source of water for precipitation that occurs anywhere in the model simulation. This can be used to isolate the impact that local surface evaporation has on precipitation, compared to advection and convection. A 15 year 1 deg, 1.25 deg. simulation has been performed with 11 global and 11 North American regional WVTs. Figure 1 shows the source regions of the North American WVTs. When water evaporates from one of these predefined regions, its mass is used as the source for a distinct prognostic variable in the model. This prognostic variable allows the water to be transported and removed (precipitated) from the system in an identical way that occurs to the prognostic specific humidity. Details of the model are outlined by Bosilovich and Schubert (2002) and Bosilovich (2002). Here, we present results pertaining to the onset of the simulated North American monsoon.

Visit-to-visit blood pressure variability as a prognostic marker in patients with cardiovascular and cerebrovascular diseases--relationships and comparisons with vascular markers of atherosclerosis.

PubMed

Lau, Kui Kai; Wong, Yuen Kwun; Chan, Yap Hang; Teo, Kay Cheong; Chan, Koon Ho; Wai Li, Leonard Sheung; Cheung, Raymond Tak Fai; Siu, Chung Wah; Ho, Shu Leong; Tse, Hung Fat

2014-07-01

Visit-to-visit blood pressure variability (BPV) is a simple surrogate marker for the development of atherosclerotic diseases, cardiovascular and all-cause mortality. Nevertheless, the relative prognostic value of BPV in comparison with other established vascular assessments remain uncertain. We prospectively followed-up 656 high-risk patients with diabetes or established cardiovascular or cerebrovascular diseases for the occurrence of major adverse cardiovascular events (MACEs). Baseline brachial endothelial function, carotid intima-media thickness (IMT) and plaque burden, ankle-brachial index and arterial stiffness were determined. Visit-to-visit BPV were recorded during a mean 18 ± 9 outpatient clinic visits. After a mean 81 ± 12 month's follow-up, 123 patients (19%) developed MACEs. Patients who developed a MACE had significantly higher systolic BPV, more severe endothelial function, arterial stiffness and systemic atherosclerotic burden compared to patients who did not develop a MACE (all P<0.01). BPV significantly correlated with all of the vascular assessments (P<0.01). A high carotid IMT had the greatest prognostic value in predicting development of a MACE (area under receiver operating characteristic curve (AUC) 0.69 ± 0.03, P<0.01). A high BPV also had moderate prognostic value in prediction of MACE (AUC 0.65 ± 0.03, P<0.01). After adjustment of confounding factors, a high BPV remained a significant independent predictor of MACE (hazards ratio 1.67, 95% confidence interval 1.14-2.43, P<0.01). Compared with established surrogate markers of atherosclerosis, visit-to-visit BPV provides similar prognostic information and may represent a new and simple marker for adverse outcomes in patients with vascular diseases. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Lack of prognostic significance of conventional peritoneal cytology in colorectal and gastric cancers: results of EVOCAPE 2 multicentre prospective study.

PubMed

Cotte, E; Peyrat, P; Piaton, E; Chapuis, F; Rivoire, M; Glehen, O; Arvieux, C; Mabrut, J-Y; Chipponi, J; Gilly, F-N

2013-07-01

In digestive cancers, the prognostic significance of intraperitoneal free cancer cells remains unclear (IPCC). The main objective of this study was to assess the prognostic significance of IPCC in colorectal and gastric adenocarcinoma. The secondary objectives were to evaluate the predictive significance of IPCC for the development of peritoneal carcinomatosis (PC) and to evaluate the prevalence of synchronous PC and IPCC. This was a prospective multicentre study. All patients undergoing surgery for a digestive tract cancer had peritoneal cytology taken. Patients with gastric and colorectal cancer with no residual tumour after surgery and no evidence of PC were followed-up for 2 years. The primary end point was overall survival. Between 2002 and 2007, 1364 patients were enrolled and 956 were followed-up over 2 years. Prevalence of IPCC was 5.7% in colon cancer, 0.6% in rectal cancer and 19.5% in gastric cancer. The overall 2-year survival rate for patients with IPCC was 34.7% versus 86.8% for patients with negative cytology (p<0.0001). By multivariate analysis, IPCC was not an independent prognostic factor. No relationship between cytology and recurrence was found. The presence of IPCC was not an independent prognostic and didn't add any additional prognostic information to the usual prognostic factors related to the tumour (pTNM and differentiation). Moreover the presence of IPCC detected with this method didn't appear to predict development of PC. Peritoneal cytology using conventional staining doesn't seem to be a useful tool for the staging of colorectal and gastric cancers. Copyright © 2013 Elsevier Ltd. All rights reserved.

Symptomatic spinal metastasis: A systematic literature review of the preoperative prognostic factors for survival, neurological, functional and quality of life in surgically treated patients and methodological recommendations for prognostic studies

PubMed Central

Nater, Anick; Martin, Allan R.; Sahgal, Arjun; Choi, David

2017-01-01

Purpose While several clinical prediction rules (CPRs) of survival exist for patients with symptomatic spinal metastasis (SSM), these have variable prognostic ability and there is no recognized CPR for health related quality of life (HRQoL). We undertook a critical appraisal of the literature to identify key preoperative prognostic factors of clinical outcomes in patients with SSM who were treated surgically. The results of this study could be used to modify existing or develop new CPRs. Methods Seven electronic databases were searched (1990–2015), without language restriction, to identify studies that performed multivariate analysis of preoperative predictors of survival, neurological, functional and HRQoL outcomes in surgical patients with SSM. Individual studies were assessed for class of evidence. The strength of the overall body of evidence was evaluated using GRADE for each predictor. Results Among 4,818 unique citations, 17 were included; all were in English, rated Class III and focused on survival, revealing a total of 46 predictors. The strength of the overall body of evidence was very low for 39 and low for 7 predictors. Due to considerable heterogeneity in patient samples and prognostic factors investigated as well as several methodological issues, our results had a moderately high risk of bias and were difficult to interpret. Conclusions The quality of evidence for predictors of survival was, at best, low. We failed to identify studies that evaluated preoperative prognostic factors for neurological, functional, or HRQoL outcomes in surgical patients with SSM. We formulated methodological recommendations for prognostic studies to promote acquiring high-quality evidence to better estimate predictor effect sizes to improve patient education, surgical decision-making and development of CPRs. PMID:28225772

Risk factors for in-hospital post-hip fracture mortality.

PubMed

Frost, Steven A; Nguyen, Nguyen D; Black, Deborah A; Eisman, John A; Nguyen, Tuan V

2011-09-01

Approximately 10% of hip fracture patients die during hospitalization; however, it is not clear what risk factors contribute to the excess mortality. This study sought to examine risk factors of, and to develop prognostic model for, predicting in-hospital mortality among hip fracture patients. We studied outcomes among 410 men and 1094 women with a hip fracture who were admitted to a major-teaching-hospital in Sydney (Australia) between 1997 and 2007. Clinical data, including concomitant illnesses, were obtained from inpatient data. The primary outcome of the study was in-hospital mortality regardless of length of stay. A Log-binomial regression model was used to identify risk factors for in-hospital mortality. Using the identified risk factors, prognostic nomograms were developed for predicting short term risk of mortality for an individual. The median duration of hospitalization was 9 days. During hospitalization, the risk of mortality was higher in men (9%) than in women (4%). After adjusting for multiple risk factors, increased risk of in-hospital mortality was associated with advancing age (rate ratio [RR] for each 10-year increase in age: 1.91 95% confidence interval [CI]: 1.47 to 2.49), in men (RR 2.13; 95% CI 1.41 to 3.22), and the presence of comorbid conditions on admission (RR for one or more comorbid conditions vs. none: 2.30; 95% CI 1.52 to 3.48). Specifically, the risk of mortality was increased in patients with a pre-existing congestive heart failure (RR 3.02; 95% CI: 1.65 to 5.54), and liver disease (RR 4.75; 95% CI: 1.87 to 12.1). These factors collectively accounted for 69% of the risk for in-hospital mortality. A nomogram was developed from these risk factors to individualize the risk of in-hospital death following a hip fracture. The area under the receiver operating characteristic curve of the final model containing age, sex and comorbid conditions was 0.76. These data suggest that among hip fracture patients, advancing age, gender (men), and pre-existing concomitant diseases such as congestive heart failure and liver disease were the main risk factors for in-hospital mortality. The nomogram developed from this study can be used to convey useful prognostic information to help guide treatment decisions. Copyright © 2011 Elsevier Inc. All rights reserved.

Intelligent approach to prognostic enhancements of diagnostic systems

NASA Astrophysics Data System (ADS)

Vachtsevanos, George; Wang, Peng; Khiripet, Noppadon; Thakker, Ash; Galie, Thomas R.

2001-07-01

This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

The extension of total gain (TG) statistic in survival models: properties and applications.

PubMed

Choodari-Oskooei, Babak; Royston, Patrick; Parmar, Mahesh K B

2015-07-01

The results of multivariable regression models are usually summarized in the form of parameter estimates for the covariates, goodness-of-fit statistics, and the relevant p-values. These statistics do not inform us about whether covariate information will lead to any substantial improvement in prediction. Predictive ability measures can be used for this purpose since they provide important information about the practical significance of prognostic factors. R (2)-type indices are the most familiar forms of such measures in survival models, but they all have limitations and none is widely used. In this paper, we extend the total gain (TG) measure, proposed for a logistic regression model, to survival models and explore its properties using simulations and real data. TG is based on the binary regression quantile plot, otherwise known as the predictiveness curve. Standardised TG ranges from 0 (no explanatory power) to 1 ('perfect' explanatory power). The results of our simulations show that unlike many of the other R (2)-type predictive ability measures, TG is independent of random censoring. It increases as the effect of a covariate increases and can be applied to different types of survival models, including models with time-dependent covariate effects. We also apply TG to quantify the predictive ability of multivariable prognostic models developed in several disease areas. Overall, TG performs well in our simulation studies and can be recommended as a measure to quantify the predictive ability in survival models.

Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

PubMed

Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

2014-05-01

Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

Prediction of new brain metastases after radiosurgery: validation and analysis of performance of a multi-institutional nomogram.

PubMed

Ayala-Peacock, Diandra N; Attia, Albert; Braunstein, Steve E; Ahluwalia, Manmeet S; Hepel, Jaroslaw; Chung, Caroline; Contessa, Joseph; McTyre, Emory; Peiffer, Ann M; Lucas, John T; Isom, Scott; Pajewski, Nicholas M; Kotecha, Rupesh; Stavas, Mark J; Page, Brandi R; Kleinberg, Lawrence; Shen, Colette; Taylor, Robert B; Onyeuku, Nasarachi E; Hyde, Andrew T; Gorovets, Daniel; Chao, Samuel T; Corso, Christopher; Ruiz, Jimmy; Watabe, Kounosuke; Tatter, Stephen B; Zadeh, Gelareh; Chiang, Veronica L S; Fiveash, John B; Chan, Michael D

2017-11-01

Stereotactic radiosurgery (SRS) without whole brain radiotherapy (WBRT) for brain metastases can avoid WBRT toxicities, but with risk of subsequent distant brain failure (DBF). Sole use of number of metastases to triage patients may be an unrefined method. Data on 1354 patients treated with SRS monotherapy from 2000 to 2013 for new brain metastases was collected across eight academic centers. The cohort was divided into training and validation datasets and a prognostic model was developed for time to DBF. We then evaluated the discrimination and calibration of the model within the validation dataset, and confirmed its performance with an independent contemporary cohort. Number of metastases (≥8, HR 3.53 p = 0.0001), minimum margin dose (HR 1.07 p = 0.0033), and melanoma histology (HR 1.45, p = 0.0187) were associated with DBF. A prognostic index derived from the training dataset exhibited ability to discriminate patients' DBF risk within the validation dataset (c-index = 0.631) and Heller's explained relative risk (HERR) = 0.173 (SE = 0.048). Absolute number of metastases was evaluated for its ability to predict DBF in the derivation and validation datasets, and was inferior to the nomogram. A nomogram high-risk threshold yielding a 2.1-fold increased need for early WBRT was identified. Nomogram values also correlated to number of brain metastases at time of failure (r = 0.38, p < 0.0001). We present a multi-institutionally validated prognostic model and nomogram to predict risk of DBF and guide risk-stratification of patients who are appropriate candidates for radiosurgery versus upfront WBRT.

A Prognostic Indicator for Patients Hospitalized with Heart Failure.

PubMed

Snow, Richard; Vogel, Karen; Vanderhoff, Bruce; Kelch, Benjamin P; Ferris, Frank D

2016-12-01

Current methods for identifying patients at risk of dying within six months suffer from clinician biases resulting in underestimation of this risk. As a result, patients who are potentially eligible for hospice and palliative care services frequently do not benefit from these services until they are very close to the end of their lives. To develop a prospective prognostic indicator based on actual survival within Centers for Medicare and Medicaid Services (CMS) claims data that identifies patients with congestive heart failure (CHF) who are at risk of six-month mortality. CMS claims data from January 1, 2008 to June 30, 2009 were reviewed to find the first hospitalization for CHF patients with episode of care diagnosis-related groups (DRGs) 291, 292, and 293. Univariate and multivariable analyses were used to determine the associations between demographic and clinical factors and six-month mortality. The resulting model was evaluated for discrimination and calibration. The resulting prospective prognostic model demonstrated fair discrimination with an ROC of 0.71 and good calibration with a Hosmer-Lemshow statistic of 0.98. Across all DRGs, 5% of discharged patients had a six-month mortality risk of greater than 50%. This prospective approach appears to provide a method to identify patients with CHF who would potentially benefit from a clinical evaluation for referral to hospice care or for a palliative care consult due to high predicted risk of dying within 180 days after discharge from a hospital. This approach can provide a model to match at-risk patients with evidenced-based care in a more consistent manner. This method of identifying patients at risk needs further prospective evaluation to see if it has value for clinicians, increases referrals to hospice and palliative care services, and benefits patients and families.

Validation of the International Metastatic Renal-Cell Carcinoma Database Consortium (IMDC) prognostic model for first-line pazopanib in metastatic renal carcinoma: the Spanish Oncologic Genitourinary Group (SOGUG) SPAZO study.

PubMed

Pérez-Valderrama, B; Arranz Arija, J A; Rodríguez Sánchez, A; Pinto Marín, A; Borrega García, P; Castellano Gaunas, D E; Rubio Romero, G; Maximiano Alonso, C; Villa Guzmán, J C; Puertas Álvarez, J L; Chirivella González, I; Méndez Vidal, M J; Juan Fita, M J; León-Mateos, L; Lázaro Quintela, M; García Domínguez, R; Jurado García, J M; Vélez de Mendizábal, E; Lambea Sorrosal, J J; García Carbonero, I; González del Alba, A; Suárez Rodríguez, C; Jiménez Gallego, P; Meana García, J A; García Marrero, R D; Gajate Borau, P; Santander Lobera, C; Molins Palau, C; López Brea, M; Fernández Parra, E M; Reig Torras, O; Basterretxea Badiola, L; Vázquez Estévez, S; González Larriba, J L

2016-04-01

Patients with metastatic renal carcinoma (mRCC) treated with first-line pazopanib were not included in the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) prognostic model. SPAZO (NCT02282579) was a nation-wide retrospective observational study designed to assess the effectiveness and validate the IMDC prognostic model in patients treated with first-line pazopanib in clinical practice. Data of 278 patients, treated with first-line pazopanib for mRCC in 34 centres in Spain, were locally recorded and externally validated. Mean age was 66 years, there were 68.3% male, 93.5% clear-cell type, 74.8% nephrectomized, and 81.3% had ECOG 0-1. Metastatic sites were: lung 70.9%, lymph node 43.9%, bone 26.3%, soft tissue/skin 20.1%, liver 15.1%, CNS 7.2%, adrenal gland 6.5%, pleura/peritoneum 5.8%, pancreas 5%, and kidney 2.2%. After median follow-up of 23 months, 76.4% had discontinued pazopanib (57.2% due to progression), 47.9% had received second-line targeted therapy, and 48.9% had died. According to IMDC prognostic model, 19.4% had favourable risk (FR), 57.2% intermediate risk (IR), and 23.4% poor risk (PR). No unexpected toxicities were recorded. Response rate was 30.3% (FR: 44%, IR: 30% PR: 17.3%). Median progression-free survival (whole population) was 11 months (32 in FR, 11 in IR, 4 in PR). Median and 2-year overall survival (whole population) were 22 months and 48.1%, respectively (FR: not reached and 81.6%, IR: 22 and 48.7%, PR: 7 and 18.8%). These estimations and their 95% confidence intervals are fully consistent with the outcomes predicted by the IMDC prognostic model. Our results validate the IMDC model for first-line pazopanib in mRCC and confirm the effectiveness and safety of this treatment. © The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

On the influence of biomass burning on the seasonal CO2 signal as observed at monitoring stations

USGS Publications Warehouse

Wittenberg, U.; Heimann, Martin; Esse, G.; McGuire, A.D.; Sauf, W.

1998-01-01

We investigated the role of biomass burning in simulating the seasonal signal in both prognostic and diagnostic analyses. The prognostic anaysis involved the High-Resolution Biosphere Model, a prognostic terrestrial biosphere model, and the coupled vegetation fire module, which together produce a prognostic data set of biomass burning. The diagnostic analysis invovled the Simple Diagnostic Biosphere Model (SDBM) and the Hao and Liu [1994] diagnostic data set of bimass burning, which have been scaled to global 2 and 4 Pg C yr-1, respectively. The monthly carbon exchange fields between the atmosphere and the biosphere with a spatial resolution of 0.5?? ?? 0.5??, the seasonal atmosphere-ocean exchange fields, and the emissions from fossil fuels have been coupled to the three-dimensional atmospheric transport model TM2. We have chosen eight monitoring stations of the National Oceanic and Atmospheric Administration network to compare the predicted seasonal atmospheric CO2 signals with those deduced from atmosphere-biosphere carbon exchange fluxes without any contribution from biomass burning. The prognostic analysis and the diagnostic analysis with global burning emissions of 4 Pg C yr-1 agree with respect to the change in the amplitude of the seasonal CO2 concentration introduced through biomass burning. We find that the seasonal CO2 signal at stations in higher northern latitudes (north of 30??N) is marginally influenced by biomass burning. For stations in tropical regions an increase in the CO2 amplitude of more an 1 oppmv (up to 50% with respect to the observed trough to peak amplitude) has been calculated. Biomass burning at stations farther south accounts for an increase in the CO2 amplitude of up to 59% (0.6 ppmv). A change in the phase of the seasonal CO2 signal at tropical and southern stations has been shown to be strongly influenced by the onset of biomass burning in southern tropical Africa and America. Comparing simulated and observed seasonal CO2 signals, we find higher discrepancies at southern troical stations if biomass burning emissions are included. This is caused by the additional increase in the amplitude in the prognostic analysis and a phase shift in a diagnostic analysis. In contrast, at the northern tropical stations biomass burning tends to improve the estimates of the seasonal CO2 signal in the prognostic analysis because of strengthening of the amplitude. Since the SDCM predicts the seasonal CO2 signal resonably well for the northern hemisphere tropical stations, no general improvement of the fit occurs if biomass burning emissions are considered.

Prognostic Factors for Persistent Leg-Pain in Patients Hospitalized With Acute Sciatica.

PubMed

Fjeld, Olaf; Grotle, Margreth; Siewers, Vibeke; Pedersen, Linda M; Nilsen, Kristian Bernhard; Zwart, John-Anker

2017-03-01

Prospective cohort study. To identify potential prognostic factors for persistent leg-pain at 12 months among patients hospitalized with acute severe sciatica. The long-term outcome for patients admitted to hospital with sciatica is generally unfavorable. Results concerning prognostic factors for persistent sciatica are limited and conflicting. A total of 210 patients acutely admitted to hospital for either surgical or nonsurgical treatment of sciatica were consecutively recruited and received a thorough clinical and radiographic examination in addition to responding to a comprehensive questionnaire. Follow-up assessments were done at 6 weeks, 6 months, and 12 months. Potential prognostic factors were measured at baseline and at 6 weeks. The impact of these factors on leg-pain was analyzed by multiple linear regression modeling. A total of 151 patients completed the entire study, 93 receiving nonrandomized surgical treatment. The final multivariate models showed that the following factors were significantly associated with leg-pain at 12 months: high psychosocial risk according to the Örebro Musculosceletal Pain Questionnaire (unstandardized beta coefficient 1.55, 95% confidence interval [CI] 0.72-2.38, P < 0.001), not receiving surgical treatment (1.11, 95% CI 0.29-1.93, P = 0.01), not actively employed upon admission (1.47, 95% CI 0.63-2.31, P < 0.01), and self-reported leg-pain recorded 6 weeks posthospital admission (0.49, 95% CI 0.34-0.63, P < 0.001). Interaction analysis showed that the Örebro Musculosceletal Pain Questionnaire had significant prognostic value only on the nonsurgically treated patients (3.26, 95% CI 1.89-4.63, P < 0.001). The results suggest that a psychosocial screening tool and the implementation of a 6-week postadmission follow-up has prognostic value in the hospital management of severe sciatica. 2.

Heterogeneity of (18)F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma.

PubMed

Wang, Hung-Ming; Cheng, Nai-Ming; Lee, Li-Yu; Fang, Yu-Hua Dean; Chang, Joseph Tung-Chieh; Tsan, Din-Li; Ng, Shu-Hang; Liao, Chun-Ta; Yang, Lan-Yan; Yen, Tzu-Chen

2016-02-01

The Ang's risk profile (based on p16, smoking and cancer stage) is a well-known prognostic factor in oropharyngeal squamous cell carcinoma (OPSCC). Whether heterogeneity in (18)F-fluorodeoxyglucose (FDG) positron emission tomographic (PET) images and epidermal growth factor receptor (EGFR) expression could provide additional information on clinical outcomes in advanced-stage OPSCC was investigated. Patients with stage III-IV OPSCC who completed primary therapy were eligible. Zone-size nonuniformity (ZSNU) extracted from pretreatment FDG PET scans was used as an index of image heterogeneity. EGFR and p16 expression were examined by immunohistochemistry. Disease-specific survival (DSS) and overall survival (OS) served as outcome measures. Kaplan-Meier estimates and Cox proportional hazards regression models were used for survival analysis. A bootstrap resampling technique was applied to investigate the stability of outcomes. Finally, a recursive partitioning analysis (RPA)-based model was constructed. A total of 113 patients were included, of which 28 were p16-positive. Multivariate analysis identified the Ang's profile, EGFR and ZSNU as independent predictors of both DSS and OS. Using RPA, the three risk factors were used to devise a prognostic scoring system that successfully predicted DSS in both p16-positive and -negative cases. The c-statistic of the prognostic index for DSS was 0.81, a value which was significantly superior to both AJCC stage (0.60) and the Ang's risk profile (0.68). In patients showing an Ang's high-risk profile (N = 77), the use of our scoring system clearly identified three distinct prognostic subgroups. It was concluded that a novel index may improve the prognostic stratification of patients with advanced-stage OPSCC. © 2015 UICC.

Immunoscore encompassing CD3+ and CD8+ T cell densities in distant metastasis is a robust prognostic marker for advanced colorectal cancer

PubMed Central

Kwak, Yoonjin; Koh, Jiwon; Kim, Duck-Woo; Kang, Sung-Bum; Kim, Woo Ho; Lee, Hye Seung

2016-01-01

Background The immunoscore (IS), an index based on the density of CD3+ and CD8+ tumor-infiltrating lymphocytes (TILs) in the tumor center (CT) and invasive margin (IM), has gained considerable attention as a prognostic marker. Tumor-associated macrophages (TAMs) have also been reported to have prognostic value. However, its clinical significance has not been fully clarified in patients with advanced CRC who present with distant metastases. Methods The density of CD3+, CD4+, CD8+, FOXP3+, CD68+, and CD163+ immune cells within CRC tissue procured from three sites–the primary CT, IM, and distant metastasis (DM)–was determined using immunohistochemistry and digital image analyzer (n=196). The IS was obtained by quantifying the densities of CD3+ and CD8+ TILs in the CT and IM. IS-metastatic and IS-macrophage–additional IS models designed in this study–were obtained by adding the score of CD3 and CD8 in DM and the score of CD163 in primary tumors (CT and IM), respectively, to the IS. Result Higher IS, IS-metastatic, and IS-macrophage values were significantly correlated with better prognosis (p=0.020, p≤0.001, and p=0.005, respectively). Multivariate analysis revealed that only IS-metastatic was an independent prognostic marker (p=0.012). No significant correlation was observed between KRAS mutation and three IS models. However, in the subgroup analysis, IS-metastatic showed a prognostic association regardless of the KRAS mutational status. Conclusion IS is a reproducible method for predicting the survival of patients with advanced CRC. Additionally, an IS including the CD3+ and CD8+ TIL densities at DM could be a strong prognostic marker for advanced CRC. PMID:27835889

Comparison of the prognostic value of pretreatment measurements of systemic inflammatory response in patients undergoing curative resection of clear cell renal cell carcinoma.

PubMed

Lucca, Ilaria; de Martino, Michela; Hofbauer, Sebastian L; Zamani, Nura; Shariat, Shahrokh F; Klatte, Tobias

2015-12-01

Pretreatment measurements of systemic inflammatory response, including the Glasgow prognostic score (GPS), the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), the platelet-to-lymphocyte ratio (PLR) and the prognostic nutritional index (PNI) have been recognized as prognostic factors in clear cell renal cell carcinoma (CCRCC), but there is at present no study that compared these markers. We evaluated the pretreatment GPS, NLR, MLR, PLR and PNI in 430 patients, who underwent surgery for clinically localized CCRCC (pT1-3N0M0). Associations with disease-free survival were assessed with Cox models. Discrimination was measured with the C-index, and a decision curve analysis was used to evaluate the clinical net benefit. On multivariable analyses, all measures of systemic inflammatory response were significant prognostic factors. The increase in discrimination compared with the stage, size, grade and necrosis (SSIGN) score alone was 5.8 % for the GPS, 1.1-1.4 % for the NLR, 2.9-3.4 % for the MLR, 2.0-3.3 % for the PLR and 1.4-3.0 % for the PNI. On the simultaneous multivariable analysis of all candidate measures, the final multivariable model contained the SSIGN score (HR 1.40, P < 0.001), the GPS (HR 2.32, P < 0.001) and the MLR (HR 5.78, P = 0.003) as significant variables. Adding both the GPS and the MLR increased the discrimination of the SSIGN score by 6.2 % and improved the clinical net benefit. In patients with clinically localized CCRCC, the GPS and the MLR appear to be the most relevant prognostic measures of systemic inflammatory response. They may be used as an adjunct for patient counseling, tailoring management and clinical trial design.

Hilar fat infiltration: A new prognostic factor in metastatic clear cell renal cell carcinoma with first-line sunitinib treatment.

PubMed

Kammerer-Jacquet, Solène-Florence; Brunot, Angelique; Bensalah, Karim; Campillo-Gimenez, Boris; Lefort, Mathilde; Bayat, Sahar; Ravaud, Alain; Dupuis, Frantz; Yacoub, Mokrane; Verhoest, Gregory; Peyronnet, Benoit; Mathieu, Romain; Lespagnol, Alexandra; Mosser, Jean; Edeline, Julien; Laguerre, Brigitte; Bernhard, Jean-Christophe; Rioux-Leclercq, Nathalie

2017-10-01

The selection of patients with metastatic clear cell renal cell carcinoma (ccRCC) who may benefit from targeted tyrosine kinase inhibitors has been a challenge, even more so now with the advent of new therapies. Hilar fat infiltration (HFI) is a validated prognostic factor in nonmetastatic ccRCC (TNM 2009 staging system) but has never been studied in metastatic patients. We aimed to assess its phenotype and prognostic effect in patients with metastatic ccRCC treated with first-line sunitinib. In a multicentric study, we retrospectively included 90 patients and studied the corresponding ccRCC at the pathological, immunohistochemical, and molecular levels. Patient and tumor characteristics were compared using univariate and multivariate analysis. All the features were then studied by Cox models for prognostic effect. HFI was found in 42 patients (46.7%), who had worse prognosis (Heng criteria) (P = 0.003), liver metastases (P = 0.036), and progressive diseases at first radiological evaluation (P = 0.024). The corresponding ccRCC was associated with poor pathological prognostic factors that are well known in nonmetastatic ccRCC. For these patients, median progression-free survival was 4 months vs. 13 months (P = 0.02), and median overall survival was 14 months vs. 29 months (P = 0.006). In a multivariate Cox model integrating all the variables, only poor prognosis, according to the Heng criteria and HFI, remained independently associated with both progression-free survival and overall survival. HFI was demonstrated for the first time to be an independent poor prognostic factor. Its potential role in predicting resistance to antiangiogenic therapy warrants further investigation. Copyright © 2017 Elsevier Inc. All rights reserved.

Serum prognostic biomarkers in head and neck cancer patients.

PubMed

Lin, Ho-Sheng; Siddiq, Fauzia; Talwar, Harvinder S; Chen, Wei; Voichita, Calin; Draghici, Sorin; Jeyapalan, Gerald; Chatterjee, Madhumita; Fribley, Andrew; Yoo, George H; Sethi, Seema; Kim, Harold; Sukari, Ammar; Folbe, Adam J; Tainsky, Michael A

2014-08-01

A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC). Prospective cohort study. A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient's serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models. Poor overall survival was associated with African Americans (hazard ratio [HR] for death = 2.61; 95% confidence interval [CI]: 1.58-4.33; P = .000), advanced stage (HR = 2.79; 95% CI: 1.40-5.57; P = .004), and recurrent disease (HR = 6.66; 95% CI: 2.54-17.44; P = .000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival. The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Serum Prognostic Biomarkers in Head and Neck Cancer Patients

PubMed Central

Lin, Ho-Sheng; Siddiq, Fauzia; Talwar, Harvinder S.; Chen, Wei; Voichita, Calin; Draghici, Sorin; Jeyapalan, Gerald; Chatterjee, Madhumita; Fribley, Andrew; Yoo, George H.; Sethi, Seema; Kim, Harold; Sukari, Ammar; Folbe, Adam J.; Tainsky, Michael A.

2014-01-01

Objectives/Hypothesis A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC). Study Design Prospective cohort study. Methods A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient’s serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models. Results Poor overall survival was associated with African Americans (hazard ratio [HR] for death =2.61; 95% confidence interval [CI]: 1.58–4.33; P =.000), advanced stage (HR =2.79; 95% CI: 1.40–5.57; P =.004), and recurrent disease (HR =6.66; 95% CI: 2.54–17.44; P =.000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival. Conclusions The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted. PMID:24347532

Prognostic Disclosures to Children: A Historical Perspective.

PubMed

Sisk, Bryan A; Bluebond-Langner, Myra; Wiener, Lori; Mack, Jennifer; Wolfe, Joanne

2016-09-01

Prognostic disclosure to children has perpetually challenged clinicians and parents. In this article, we review the historical literature on prognostic disclosure to children in the United States using cancer as an illness model. Before 1948, there was virtually no literature focused on prognostic disclosure to children. As articles began to be published in the 1950s and 1960s, many clinicians and researchers initially recommended a "protective" approach to disclosure, where children were shielded from the harms of bad news. We identified 4 main arguments in the literature at this time supporting this "protective" approach. By the late 1960s, however, a growing number of clinicians and researchers were recommending a more "open" approach, where children were included in discussions of diagnosis, which at the time was often synonymous with a terminal prognosis. Four different arguments in the literature were used at this time supporting this "open" approach. Then, by the late 1980s, the recommended approach to prognostic disclosure in pediatrics shifted largely from "never tell" to "always tell." In recent years, however, there has been a growing appreciation for the complexity of prognostic disclosure in pediatrics. Current understanding of pediatric disclosure does not lead to simple "black-and-white" recommendations for disclosure practices. As with most difficult questions, we are left to balance competing factors on a case-by-case basis. We highlight 4 categories of current considerations related to prognostic disclosure in pediatrics, and we offer several approaches to prognostic disclosure for clinicians who care for these young patients and their families. Copyright © 2016 by the American Academy of Pediatrics.

lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia

PubMed Central

Pan, Jia-Qi; Zhang, Yan-Qing; Wang, Jing-Hua; Xu, Ping; Wang, Wei

2017-01-01

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of lncRNAs as prognostic biomarkers in AML and their detailed mechanisms of action in this disease have not yet been well characterized. For this purpose, in the present study, the expression levels of lncRNAs and mRNAs were calculated using the RNA-seq V2 data for AML, following which a lncRNA-lncRNA co-expression network (LLCN) was constructed. This revealed a total of 8 AML prognosis-related lncRNA modules were identified, which displayed a significant correlation with patient survival (p≤0.05). Subsequently, a prognosis-related lncRNA module pathway network was constructed to interpret the functional mechanism of the prognostic modules in AML. The results indicated that these prognostic modules were involved in the AML pathway, chemokine signaling pathway and WNT signaling pathway, all of which play important roles in AML. Furthermore, the investigation of lncRNAs in these prognostic modules suggested that an lncRNA (ZNF571-AS1) may be involved in AML via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway by regulating KIT and STAT5. The results of the present study not only provide potential lncRNA modules as prognostic biomarkers, but also provide further insight into the molecular mechanisms of action of lncRNAs. PMID:28204819

Predicting heart failure mortality in frail seniors: comparing the NYHA functional classification with the Resident Assessment Instrument (RAI) 2.0.

PubMed

Tjam, Erin Y; Heckman, George A; Smith, Stuart; Arai, Bruce; Hirdes, John; Poss, Jeff; McKelvie, Robert S

2012-02-23

Though the NYHA functional classification is recommended in clinical settings, concerns have been raised about its reliability particularly among older patients. The RAI 2.0 is a comprehensive assessment system specifically developed for frail seniors. We hypothesized that a prognostic model for heart failure (HF) developed from the RAI 2.0 would be superior to the NYHA classification. The purpose of this study was to determine whether a HF-specific prognostic model based on the RAI 2.0 is superior to the NYHA functional classification in predicting mortality in frail older HF patients. Secondary analysis of data from a prospective cohort study of a HF education program for care providers in long-term care and retirement homes. Univariate analyses identified RAI 2.0 variables predicting death at 6 months. These and the NYHA classification were used to develop logistic models. Two RAI 2.0 models were derived. The first includes six items: "weight gain of 5% or more of total body weight over 30 days", "leaving 25% or more food uneaten", "unable to lie flat", "unstable cognitive, ADL, moods, or behavioural patterns", "change in cognitive function" and "needing help to walk in room"; the C statistic was 0.866. The second includes the CHESS health instability scale and the item "requiring help walking in room"; the C statistic was 0.838. The C statistic for the NYHA scale was 0.686. These results suggest that data from the RAI 2.0, an instrument for comprehensive assessment of frail seniors, can better predict mortality than the NYHA classification. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics

PubMed Central

Qiao, Guixiu; Weiss, Brian A.

2016-01-01

Unexpected equipment downtime is a ‘pain point’ for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system. PMID:28058172

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics.

PubMed

Qiao, Guixiu; Weiss, Brian A

2016-01-01

Unexpected equipment downtime is a 'pain point' for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

Development and external validation of nomograms to predict the risk of skeletal metastasis at the time of diagnosis and skeletal metastasis-free survival in nasopharyngeal carcinoma.

PubMed

Yang, Lin; Xia, Liangping; Wang, Yan; He, Shasha; Chen, Haiyang; Liang, Shaobo; Peng, Peijian; Hong, Shaodong; Chen, Yong

2017-09-06

The skeletal system is the most common site of distant metastasis in nasopharyngeal carcinoma (NPC); various prognostic factors have been reported for skeletal metastasis, though most studies have focused on a single factor. We aimed to establish nomograms to effectively predict skeletal metastasis at initial diagnosis (SMAD) and skeletal metastasis-free survival (SMFS) in NPC. A total of 2685 patients with NPC who received bone scintigraphy (BS) and/or 18F-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and 2496 patients without skeletal metastasis were retrospectively assessed to develop individual nomograms for SMAD and SMFS. The models were validated externally using separate cohorts of 1329 and 1231 patients treated at two other institutions. Five independent prognostic factors were included in each nomogram. The SMAD nomogram had a significantly higher c-index than the TNM staging system (training cohort, P = 0.005; validation cohort, P < 0.001). The SMFS nomogram had significantly higher c-index values in the training and validation sets than the TNM staging system (P < 0.001 and P = 0.005, respectively). Three proposed risk stratification groups were created using the nomograms, and enabled significant discrimination of SMFS for each risk group. The prognostic nomograms established in this study enable accurate stratification of distinct risk groups for skeletal metastasis, which may improve counseling and facilitate individualized management of patients with NPC.

Retrospective evaluation of prognostic score performances in cirrhotic patients admitted to an intermediate care unit.

PubMed

Dupont, Benoît; Delvincourt, Maxime; Koné, Mamadou; du Cheyron, Damien; Ollivier-Hourmand, Isabelle; Piquet, Marie-Astrid; Terzi, Nicolas; Dao, Thông

2015-08-01

The prognosis of cirrhotic patients in the Intensive Care Unit requires the development of predictive tools for mortality. We aimed to evaluate the ability of different prognostic scores to predict hospital mortality in these patients. A single-centre retrospective analysis was conducted of 281 hospital stays of cirrhotic patients at an Intermediate Care Unit between June 2009 and December 2010. The performance of the Simplified Acute Physiology Score (SOFA), the Simplified Acute Physiology Score (SAPS) II or III, Child-Pugh, Model for End-Stage Liver Disease (MELD), MELD-Na and the Chronic Liver Failure-Consortium Acute-on-Chronic Liver Failure score (CLIF-C ACLF) in predicting hospital mortality were compared. Mean age was 58.2±12.1 years; 77% were male. The main cause of admission was acute gastrointestinal bleeding (47%). The in-hospital mortality rate was 25.3%. Receiver operating characteristic curve analyses demonstrated that SOFA (0.82) MELD-Na (0.82) or MELD (0.81) scores at admission predicted in-hospital mortality better than Child-Pugh (0.76), SAPS II (0.77), SAPS III (0.75) or CLIF-C ACLF (0.75). We then developed the cirrhosis prognostic score (Ci-Pro), which performed better (0.89) than SOFA. SOFA, MELD and especially the Ci-Pro score show the best performance in predicting hospital mortality of cirrhotic patients admitted to an Intermediate Care Unit. Copyright © 2015 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Prognostic factors in patients with spinal metastasis: a systematic review and meta-analysis.

PubMed

Luksanapruksa, Panya; Buchowski, Jacob M; Hotchkiss, William; Tongsai, Sasima; Wilartratsami, Sirichai; Chotivichit, Areesak

2017-05-01

Incidence of symptomatic spinal metastasis has increased owing to improvement in treatment of the disease. One of the key factors that influences decision-making is expected patient survival. To our knowledge, no systematic reviews or meta-analysis have been conducted that review independent prognostic factors in spinal metastases. This study aimed to determine independent prognostic factors that affect outcome in patients with metastatic spine disease. This is a systematic literature review and meta-analysis of publications for prognostic factors in spinal metastatic disease. Pooled patient results from cohort and observational studies. Meta-analysis for poor prognostic factors as determined by hazard ratio (HR) and 95% confidential interval (95% CI). We systematically searched relevant publications in PubMed and Embase. The following search terms were used: ("'spinal metastases'" OR "'vertebral metastases'" OR "spinal metastasis" OR 'vertebral metastases') AND ('"prognostic factors"' OR "'survival'"). Inclusion criteria were prospective and retrospective cohort series that report HR and 95% CI of independent prognostic factors from multivariate analysis. Two reviewers independently assessed all papers. The quality of included papers was assessed by using Newcastle-Ottawa Scale for cohort studies and publication bias was assessed by using funnel plot, Begg test, and Egger test. The prognostic factors that were mentioned in at least three publications were pooled. Meta-analysis was performed using HR and 95% CI as the primary outcomes of interest. Heterogeneity was assessed using the I 2 method. A total of 3,959 abstracts (1,382 from PubMed and 2,577 from Embase) were identified through database search and 40 publications were identified through review of cited publications. The reviewers selected a total of 51 studies for qualitative synthesis and 43 studies for meta-analysis. Seventeen poor prognostic factors were identified. These included presence of a neurologic deficit before surgery, non-ambulatory status before radiotherapy (RT), non-ambulatory status before surgery, presence of bone metastases, presence of multiple bone metastases (>2 sites), presence of multiple spinal metastases (>3 sites), development of motor deficit in <7 days before initiating RT, development of motor deficit in <14 days before initiating RT, time interval from cancer diagnosis to RT <15 months, Karnofsky Performance Score (KPS) 10-40, KPS 50-70, KPS<70, Eastern Cooperative Oncology Group (ECOG) grade 3-4, male gender, presence of visceral metastases, moderate growth tumor on Tomita score (TS) classification, and rapid growth tumor on TS classification. Seventeen independent poor prognostic factors were identified in this study. These can be categorized into cancer-specific and nonspecific prognostic factors. A tumor-based prognostic scoring system that combines all specific and general factors may enhance the accuracy of survival prediction in patients with metastatic spine disease. Copyright © 2016 Elsevier Inc. All rights reserved.

Circulating Tumor Cells in Breast Cancer Patients Treated by Neoadjuvant Chemotherapy: A Meta-analysis.

PubMed

Bidard, François-Clément; Michiels, Stefan; Riethdorf, Sabine; Mueller, Volkmar; Esserman, Laura J; Lucci, Anthony; Naume, Bjørn; Horiguchi, Jun; Gisbert-Criado, Rafael; Sleijfer, Stefan; Toi, Masakazu; Garcia-Saenz, Jose A; Hartkopf, Andreas; Generali, Daniele; Rothé, Françoise; Smerage, Jeffrey; Muinelo-Romay, Laura; Stebbing, Justin; Viens, Patrice; Magbanua, Mark Jesus M; Hall, Carolyn S; Engebraaten, Olav; Takata, Daisuke; Vidal-Martínez, José; Onstenk, Wendy; Fujisawa, Noriyoshi; Diaz-Rubio, Eduardo; Taran, Florin-Andrei; Cappelletti, Maria Rosa; Ignatiadis, Michail; Proudhon, Charlotte; Wolf, Denise M; Bauldry, Jessica B; Borgen, Elin; Nagaoka, Rin; Carañana, Vicente; Kraan, Jaco; Maestro, Marisa; Brucker, Sara Yvonne; Weber, Karsten; Reyal, Fabien; Amara, Dominic; Karhade, Mandar G; Mathiesen, Randi R; Tokiniwa, Hideaki; Llombart-Cussac, Antonio; Meddis, Alessandra; Blanche, Paul; d'Hollander, Koenraad; Cottu, Paul; Park, John W; Loibl, Sibylle; Latouche, Aurélien; Pierga, Jean-Yves; Pantel, Klaus

2018-04-12

We conducted a meta-analysis in nonmetastatic breast cancer patients treated by neoadjuvant chemotherapy (NCT) to assess the clinical validity of circulating tumor cell (CTC) detection as a prognostic marker. We collected individual patient data from 21 studies in which CTC detection by CellSearch was performed in early breast cancer patients treated with NCT. The primary end point was overall survival, analyzed according to CTC detection, using Cox regression models stratified by study. Secondary end points included distant disease-free survival, locoregional relapse-free interval, and pathological complete response. All statistical tests were two-sided. Data from patients were collected before NCT (n = 1574) and before surgery (n = 1200). CTC detection revealed one or more CTCs in 25.2% of patients before NCT; this was associated with tumor size (P < .001). The number of CTCs detected had a detrimental and decremental impact on overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P < .001), but not on pathological complete response. Patients with one, two, three to four, and five or more CTCs before NCT displayed hazard ratios of death of 1.09 (95% confidence interval [CI] = 0.65 to 1.69), 2.63 (95% CI = 1.42 to 4.54), 3.83 (95% CI = 2.08 to 6.66), and 6.25 (95% CI = 4.34 to 9.09), respectively. In 861 patients with full data available, adding CTC detection before NCT increased the prognostic ability of multivariable prognostic models for overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P = .008). CTC count is an independent and quantitative prognostic factor in early breast cancer patients treated by NCT. It complements current prognostic models based on tumor characteristics and response to therapy.

Metrics for Offline Evaluation of Prognostic Performance

NASA Technical Reports Server (NTRS)

Saxena, Abhinav; Celaya, Jose; Saha, Bhaskar; Saha, Sankalita; Goebel, Kai

2010-01-01

Prognostic performance evaluation has gained significant attention in the past few years. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.

The evolving field of prognostication and risk stratification in MDS: Recent developments and future directions.

PubMed

Lee, Eun-Ju; Podoltsev, Nikolai; Gore, Steven D; Zeidan, Amer M

2016-01-01

The clinical course of patients with myelodysplastic syndromes (MDS) is characterized by wide variability reflecting the underlying genetic and biological heterogeneity of the disease. Accurate prediction of outcomes for individual patients is an integral part of the evidence-based risk/benefit calculations that are necessary for tailoring the aggressiveness of therapeutic interventions. While several prognostication tools have been developed and validated for risk stratification, each of these systems has limitations. The recent progress in genomic sequencing techniques has led to discoveries of recurrent molecular mutations in MDS patients with independent impact on relevant clinical outcomes. Reliable assays of these mutations have already entered the clinic and efforts are currently ongoing to formally incorporate mutational analysis into the existing clinicopathologic risk stratification tools. Additionally, mutational analysis holds promise for going beyond prognostication to therapeutic selection and individualized treatment-specific prediction of outcomes; abilities that would revolutionize MDS patient care. Despite these exciting developments, the best way of incorporating molecular testing for use in prognostication and prediction of outcomes in clinical practice remains undefined and further research is warranted. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reporting and Methodology of Multivariable Analyses in Prognostic Observational Studies Published in 4 Anesthesiology Journals: A Methodological Descriptive Review.

PubMed

Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric

2015-10-01

Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate <36% (i.e., the 25th percentile), with some of them not identified in the STROBE checklist: blinded evaluation of the outcome (11.9%), reason for sample size (15.1%), handling of missing data (36.0%), assessment of colinearity (17.4%), assessment of interactions (13.9%), and calibration (34.9%). When reported, a few methodological shortcomings were observed, both in explanatory and predictive studies, such as an insufficient number of events of the outcome (44.6%), exclusion of cases with missing data (93.6%), or categorization of continuous variables (65.1%.). The reporting of multivariable analysis was fairly good and could be further improved by checking reporting guidelines and EQUATOR Network website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.

Adoption of multivariate copulae in prognostication of economic growth by means of interest rate

NASA Astrophysics Data System (ADS)

Saputra, Dewi Tanasia; Indratno, Sapto Wahyu, Dr.

2015-12-01

Inflation, at a healthy rate, is a sign of growing economy. Nonetheless, when inflation rate grows uncontrollably, it will negatively influence economic growth. Many tackle this problem by increasing interest rate to help protecting the value of money which is detained by inflation. There are few, however, who study the effects of interest rate in economic growth. The main purposes of this paper are to find how the change of interest rate affects economic growth and to use the relationship in prognostication of economic growth. By using expenditure model, a linear relationship between economic growth and interest rate is developed. The result is then used for prediction by normal copula and Vine Archimedean copula. It is shown that increasing interest rate to tackle inflation is a poor solution. Whereas implementation of copula in predicting economic growth yields an accurate result, with not more than 0.5% difference.

IGFBP6 Regulates Cell Apoptosis and Migration in Glioma.

PubMed

Bei, Yuanqi; Huang, Qingfeng; Shen, Jianhong; Shi, Jinlong; Shen, Chaoyan; Xu, Peng; Chang, Hao; Xia, Xiaojie; Xu, Li; Ji, Bin; Chen, JianGuo

2017-07-01

The insulin-like growth factor binding protein 6 (IGFBP6), as an inhibitor of IGF-II actions, plays an important role in inhibiting survival and migration of tumor cells. In our study, we intended to demonstrate the biological function of IGFBP6 in the development of glioma and its clinical significance. Firstly, Western blot and immunohistochemistry revealed that the expression of IGFBP6 inversely correlated with glioma grade. Secondly, multivariate analysis with the Cox proportional hazards model and Kaplan-Meier analysis indicated that IGFBP6 could be an independent prognostic factor for the survival of glioma patients. In addition, overexpression of IGFBP6 induced glioma cell apoptosis, and depletion of IGFBP6 had the opposite action. Finally, overexpression of IGFBP6 inhibited migration of glioma cells, and depletion of IGFBP6 had the opposite action. Together our findings suggest that IGFBP6 might be an important regulator and prognostic factor for glioma.

Development and validation of a prognostic scoring system for patients with chronic myelomonocytic leukemia.

PubMed

Such, Esperanza; Germing, Ulrich; Malcovati, Luca; Cervera, José; Kuendgen, Andrea; Della Porta, Matteo G; Nomdedeu, Benet; Arenillas, Leonor; Luño, Elisa; Xicoy, Blanca; Amigo, Mari L; Valcarcel, David; Nachtkamp, Kathrin; Ambaglio, Ilaria; Hildebrandt, Barbara; Lorenzo, Ignacio; Cazzola, Mario; Sanz, Guillermo

2013-04-11

The natural course of chronic myelomonocytic leukemia (CMML) is highly variable but a widely accepted prognostic scoring system for patients with CMML is not available. The main aim of this study was to develop a new CMML-specific prognostic scoring system (CPSS) in a large series of 558 patients with CMML (training cohort, Spanish Group of Myelodysplastic Syndromes) and to validate it in an independent series of 274 patients (validation cohort, Heinrich Heine University Hospital, Düsseldorf, Germany, and San Matteo Hospital, Pavia, Italy). The most relevant variables for overall survival (OS) and evolution to acute myeloblastic leukemia (AML) were FAB and WHO CMML subtypes, CMML-specific cytogenetic risk classification, and red blood cell (RBC) transfusion dependency. CPSS was able to segregate patients into 4 clearly different risk groups for OS (P < .001) and risk of AML evolution (P < .001) and its predictive capability was confirmed in the validation cohort. An alternative CPSS with hemoglobin instead of RBC transfusion dependency offered almost identical prognostic capability. This study confirms the prognostic impact of FAB and WHO subtypes, recognizes the importance of RBC transfusion dependency and cytogenetics, and offers a simple and powerful CPSS for accurately assessing prognosis and planning therapy in patients with CMML.

Establishment of an Adjusted Prognosis Analysis Model for Initially Diagnosed Non-Small-Cell Lung Cancer With Brain Metastases From Sun Yat-Sen University Cancer Center.

PubMed

Dinglin, Xiao-Xiao; Ma, Shu-Xiang; Wang, Fang; Li, De-Lan; Liang, Jian-Zhong; Chen, Xin-Ru; Liu, Qing; Zeng, Yin-Duo; Chen, Li-Kun

2017-05-01

The current published prognosis models for brain metastases (BMs) from cancer have not addressed the issue of either newly diagnosed non-small-cell lung cancer (NSCLC) with BMs or the lung cancer genotype. We sought to build an adjusted prognosis analysis (APA) model, a new prognosis model specifically for NSCLC patients with BMs at the initial diagnosis using adjusted prognosis analysis (APA). The model was derived using data from 1158 consecutive patients, with 837 in the derivation cohort and 321 in the validation cohort. The patients had initially received a diagnosis of BMs from NSCLC at Sun Yat-Sen University Cancer Center from 1994 to 2015. The prognostic factors analyzed included patient characteristics, disease characteristics, and treatments. The APA model was built according to the numerical score derived from the hazard ratio of each independent prognostic variable. The predictive accuracy of the APA model was determined using a concordance index and was compared with current prognosis models. The results were validated using bootstrap resampling and a validation cohort. We established 2 prognostic models (APA 1 and 2) for the whole group of patients and for those with known epidermal growth factor receptor (EGFR) genotype, respectively. Six factors were independently associated with survival time: Karnofsky performance status, age, smoking history (replaced by EGFR mutation in APA 2), local treatment of intracranial metastases, EGFR-tyrosine kinase inhibitor treatment, and chemotherapy. Patients in the derivation cohort were stratified into low- (score, 0-2), moderate- (score, 3-5), and high-risk (score 6-7) groups according to the median survival time (16.6, 10.3, and 5.2 months, respectively; P < .001). The results were further confirmed in the validation cohort. Compared with recursive partition analysis and graded prognostic assessment, APA seems to be more suitable for initially diagnosed NSCLC with BMs. Copyright © 2017 Elsevier Inc. All rights reserved.

Overcoming intratumoural heterogeneity for reproducible molecular risk stratification: a case study in advanced kidney cancer.

PubMed

Lubbock, Alexander L R; Stewart, Grant D; O'Mahony, Fiach C; Laird, Alexander; Mullen, Peter; O'Donnell, Marie; Powles, Thomas; Harrison, David J; Overton, Ian M

2017-06-26

Metastatic clear cell renal cell cancer (mccRCC) portends a poor prognosis and urgently requires better clinical tools for prognostication as well as for prediction of response to treatment. Considerable investment in molecular risk stratification has sought to overcome the performance ceiling encountered by methods restricted to traditional clinical parameters. However, replication of results has proven challenging, and intratumoural heterogeneity (ITH) may confound attempts at tissue-based stratification. We investigated the influence of confounding ITH on the performance of a novel molecular prognostic model, enabled by pathologist-guided multiregion sampling (n = 183) of geographically separated mccRCC cohorts from the SuMR trial (development, n = 22) and the SCOTRRCC study (validation, n = 22). Tumour protein levels quantified by reverse phase protein array (RPPA) were investigated alongside clinical variables. Regularised wrapper selection identified features for Cox multivariate analysis with overall survival as the primary endpoint. The optimal subset of variables in the final stratification model consisted of N-cadherin, EPCAM, Age, mTOR (NEAT). Risk groups from NEAT had a markedly different prognosis in the validation cohort (log-rank p = 7.62 × 10 -7 ; hazard ratio (HR) 37.9, 95% confidence interval 4.1-353.8) and 2-year survival rates (accuracy = 82%, Matthews correlation coefficient = 0.62). Comparisons with established clinico-pathological scores suggest favourable performance for NEAT (Net reclassification improvement 7.1% vs International Metastatic Database Consortium score, 25.4% vs Memorial Sloan Kettering Cancer Center score). Limitations include the relatively small cohorts and associated wide confidence intervals on predictive performance. Our multiregion sampling approach enabled investigation of NEAT validation when limiting the number of samples analysed per tumour, which significantly degraded performance. Indeed, sample selection could change risk group assignment for 64% of patients, and prognostication with one sample per patient performed only slightly better than random expectation (median logHR = 0.109). Low grade tissue was associated with 3.5-fold greater variation in predicted risk than high grade (p = 0.044). This case study in mccRCC quantitatively demonstrates the critical importance of tumour sampling for the success of molecular biomarker studies research where ITH is a factor. The NEAT model shows promise for mccRCC prognostication and warrants follow-up in larger cohorts. Our work evidences actionable parameters to guide sample collection (tumour coverage, size, grade) to inform the development of reproducible molecular risk stratification methods.

Prognostic durability of liver fibrosis tests and improvement in predictive performance for mortality by combining tests.

PubMed

Bertrais, Sandrine; Boursier, Jérôme; Ducancelle, Alexandra; Oberti, Frédéric; Fouchard-Hubert, Isabelle; Moal, Valérie; Calès, Paul

2017-06-01

There is currently no recommended time interval between noninvasive fibrosis measurements for monitoring chronic liver diseases. We determined how long a single liver fibrosis evaluation may accurately predict mortality, and assessed whether combining tests improves prognostic performance. We included 1559 patients with chronic liver disease and available baseline liver stiffness measurement (LSM) by Fibroscan, aspartate aminotransferase to platelet ratio index (APRI), FIB-4, Hepascore, and FibroMeter V2G . Median follow-up was 2.8 years during which 262 (16.8%) patients died, with 115 liver-related deaths. All fibrosis tests were able to predict mortality, although APRI (and FIB-4 for liver-related mortality) showed lower overall discriminative ability than the other tests (differences in Harrell's C-index: P < 0.050). According to time-dependent AUROCs, the time period with optimal predictive performance was 2-3 years in patients with no/mild fibrosis, 1 year in patients with significant fibrosis, and <6 months in cirrhotic patients even in those with a model of end-stage liver disease (MELD) score <15. Patients were then randomly split in training/testing sets. In the training set, blood tests and LSM were independent predictors of all-cause mortality. The best-fit multivariate model included age, sex, LSM, and FibroMeter V2G with C-index = 0.834 (95% confidence interval, 0.803-0.862). The prognostic model for liver-related mortality included the same covariates with C-index = 0.868 (0.831-0.902). In the testing set, the multivariate models had higher prognostic accuracy than FibroMeter V2G or LSM alone for all-cause mortality and FibroMeter V2G alone for liver-related mortality. The prognostic durability of a single baseline fibrosis evaluation depends on the liver fibrosis level. Combining LSM with a blood fibrosis test improves mortality risk assessment. © 2016 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reed, Sasha C.; Yang, Xiaojuan; Thornton, Peter E.

2015-06-25

Myriad field, laboratory, and modeling studies show that nutrient availability plays a fundamental role in regulating CO 2 exchange between the Earth's biosphere and atmosphere, and in determining how carbon pools and fluxes respond to climatic change. Accordingly, global models that incorporate coupled climate-carbon cycle feedbacks made a significant advance with the introduction of a prognostic nitrogen cycle. Here we propose that incorporating phosphorus cycling represents an important next step in coupled climate-carbon cycling model development, particularly for lowland tropical forests where phosphorus availability is often presumed to limit primary production. We highlight challenges to including phosphorus in modeling effortsmore » and provide suggestions for how to move forward.« less

Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor

USGS Publications Warehouse

Reed, Sasha C.; Yang, Xiaojuan; Thornton, Peter E.

2015-01-01

Myriad field, laboratory, and modeling studies show that nutrient availability plays a fundamental role in regulating CO2 exchange between the Earth's biosphere and atmosphere, and in determining how carbon pools and fluxes respond to climatic change. Accordingly, global models that incorporate coupled climate–carbon cycle feedbacks made a significant advance with the introduction of a prognostic nitrogen cycle. Here we propose that incorporating phosphorus cycling represents an important next step in coupled climate–carbon cycling model development, particularly for lowland tropical forests where phosphorus availability is often presumed to limit primary production. We highlight challenges to including phosphorus in modeling efforts and provide suggestions for how to move forward.

Wind laws for shockless initialization. [numerical forecasting model

NASA Technical Reports Server (NTRS)

Ghil, M.; Shkoller, B.

1976-01-01

A system of diagnostic equations for the velocity field, or wind laws, was derived for each of a number of models of large-scale atmospheric flow. The derivation in each case is mathematically exact and does not involve any physical assumptions not already present in the prognostic equations, such as nondivergence or vanishing of derivatives of the divergence. Therefore, initial states computed by solving these diagnostic equations should be compatible with the type of motion described by the prognostic equations of the model and should not generate initialization shocks when inserted into the model. Numerical solutions of the diagnostic system corresponding to a barotropic model are exhibited. Some problems concerning the possibility of implementing such a system in operational numerical weather prediction are discussed.

Profiling of Resistance Patterns & Oncogenic Signaling Pathways in Evaluation of Cancers of the Thorax and Therapeutic Target Identification

DTIC Science & Technology

2010-06-01

mutation si gnature i s prognostic in EGFR wild-type l ung adenocarcinomas and identifies Metastasis associated in colon cancer 1 (MACC1) as an EGFR...T790M mutation (N=7, blue curve) (AUC: area under the curve). Figure 3. EGFR dependency signature is a favorable prognostic factor. EGFR index...developed. T he si gnature w as shown t o b e prognostic regardless of EGFR status. T he results also suggest MACC1 to be a regulator of MET in NSCLC

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

PubMed

Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included. Copyright © 2011 Elsevier B.V. All rights reserved.

A Cryogenic Fluid System Simulation in Support of Integrated Systems Health Management

NASA Technical Reports Server (NTRS)

Barber, John P.; Johnston, Kyle B.; Daigle, Matthew

2013-01-01

Simulations serve as important tools throughout the design and operation of engineering systems. In the context of sys-tems health management, simulations serve many uses. For one, the underlying physical models can be used by model-based health management tools to develop diagnostic and prognostic models. These simulations should incorporate both nominal and faulty behavior with the ability to inject various faults into the system. Such simulations can there-fore be used for operator training, for both nominal and faulty situations, as well as for developing and prototyping health management algorithms. In this paper, we describe a methodology for building such simulations. We discuss the design decisions and tools used to build a simulation of a cryogenic fluid test bed, and how it serves as a core technology for systems health management development and maturation.

A prognostic pollen emissions model for climate models (PECM1.0)

NASA Astrophysics Data System (ADS)

Wozniak, Matthew C.; Steiner, Allison L.

2017-11-01

We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

Advanced Atmospheric Modeling for Emergency Response.

NASA Astrophysics Data System (ADS)

Fast, Jerome D.; O'Steen, B. Lance; Addis, Robert P.

1995-03-01

Atmospheric transport and diffusion models are an important part of emergency response systems for industrial facilities that have the potential to release significant quantities of toxic or radioactive material into the atmosphere. An advanced atmospheric transport and diffusion modeling system for emergency response and environmental applications, based upon a three-dimensional mesoscale model, has been developed for the U.S. Department of Energy's Savannah River Site so that complex, time-dependent flow fields not explicitly measured can be routinely simulated. To overcome some of the current computational demands of mesoscale models, two operational procedures for the advanced atmospheric transport and diffusion modeling system are described including 1) a semiprognostic calculation to produce high-resolution wind fields for local pollutant transport in the vicinity of the Savannah River Site and 2) a fully prognostic calculation to produce a regional wind field encompassing the southeastern United States for larger-scale pollutant problems. Local and regional observations and large-scale model output are used by the mesoscale model for the initial conditions, lateral boundary conditions, and four-dimensional data assimilation procedure. This paper describes the current status of the modeling system and presents two case studies demonstrating the capabilities of both modes of operation. While the results from the case studies shown in this paper are preliminary and certainly not definitive, they do suggest that the mesoscale model has the potential for improving the prognostic capabilities of atmospheric modeling for emergency response at the Savannah River Site. Long-term model evaluation will be required to determine under what conditions significant forecast errors exist.

Prognostics of Power MOSFET

NASA Technical Reports Server (NTRS)

Celaya, Jose Ramon; Saxena, Abhinav; Vashchenko, Vladislay; Saha, Sankalita; Goebel, Kai Frank

2011-01-01

This paper demonstrates how to apply prognostics to power MOSFETs (metal oxide field effect transistor). The methodology uses thermal cycling to age devices and Gaussian process regression to perform prognostics. The approach is validated with experiments on 100V power MOSFETs. The failure mechanism for the stress conditions is determined to be die-attachment degradation. Change in ON-state resistance is used as a precursor of failure due to its dependence on junction temperature. The experimental data is augmented with a finite element analysis simulation that is based on a two-transistor model. The simulation assists in the interpretation of the degradation phenomena and SOA (safe operation area) change.

Hyperactive gp130/STAT3‐driven gastric tumourigenesis promotes submucosal tertiary lymphoid structure development

PubMed Central

Hill, David G.; Yu, Liang; Gao, Hugh; Balic, Jesse J.; West, Alison; Oshima, Hiroko; McLeod, Louise; Oshima, Masanobu; Gallimore, Awen; D'Costa, Kimberley; Bhathal, Prithi S.; Sievert, William; Ferrero, Richard L.

2018-01-01

Tertiary lymphoid structures (TLSs) display phenotypic and functional characteristics of secondary lymphoid organs, and often develop in tissues affected by chronic inflammation, as well as in certain inflammation‐associated cancers where they are prognostic of improved patient survival. However, the mechanisms that govern the development of tumour‐associated TLSs remain ill‐defined. Here, we observed tumour‐associated TLSs in a preclinical mouse model (gp130 F/F) of gastric cancer, where tumourigenesis is dependent on hyperactive STAT3 signalling through the common IL‐6 family signalling receptor, gp130. Gastric tumourigenesis was associated with the development of B and T cell‐rich submucosal lymphoid aggregates, containing CD21+ cellular networks and high endothelial venules. Temporally, TLS formation coincided with the development of gastric adenomas and induction of homeostatic chemokines including Cxcl13, Ccl19 and Ccl21. Reflecting the requirement of gp130‐driven STAT3 signalling for gastric tumourigenesis, submucosal TLS development was also STAT3‐dependent, but independent of the cytokine IL‐17 which has been linked with lymphoid neogenesis in chronic inflammation and autoimmunity. Interestingly, upregulated lymphoid chemokine expression and TLS formation were also observed in a chronic gastritis model induced by Helicobacter felis infection. Tumour‐associated TLSs were also observed in patients with intestinal‐type gastric cancer, and a gene signature linked with TLS development in gp130 F/F mice was associated with advanced clinical disease, but was not prognostic of patient survival. Collectively, our in vivo data reveal that hyperactive gp130‐STAT3 signalling closely links gastric tumourigenesis with lymphoid neogenesis, and while a TLS gene signature was associated with advanced gastric cancer in patients, it did not indicate a favourable prognosis. PMID:29417587

Extended Survival and Prognostic Factors for Patients With ALK-Rearranged Non–Small-Cell Lung Cancer and Brain Metastasis

PubMed Central

Johung, Kimberly L.; Yeh, Norman; Desai, Neil B.; Williams, Terence M.; Lautenschlaeger, Tim; Arvold, Nils D.; Ning, Matthew S.; Attia, Albert; Lovly, Christine M.; Goldberg, Sarah; Beal, Kathryn; Yu, James B.; Kavanagh, Brian D.; Chiang, Veronica L.; Camidge, D. Ross

2016-01-01

Purpose We performed a multi-institutional study to identify prognostic factors and determine outcomes for patients with ALK-rearranged non–small-cell lung cancer (NSCLC) and brain metastasis. Patients and Methods A total of 90 patients with brain metastases from ALK-rearranged NSCLC were identified from six institutions; 84 of 90 patients received radiotherapy to the brain (stereotactic radiosurgery [SRS] or whole-brain radiotherapy [WBRT]), and 86 of 90 received tyrosine kinase inhibitor (TKI) therapy. Estimates for overall (OS) and intracranial progression-free survival were determined and clinical prognostic factors were identified by Cox proportional hazards modeling. Results Median OS after development of brain metastases was 49.5 months (95% CI, 29.0 months to not reached), and median intracranial progression-free survival was 11.9 months (95% CI, 10.1 to 18.2 months). Forty-five percent of patients with follow-up had progressive brain metastases at death, and repeated interventions for brain metastases were common. Absence of extracranial metastases, Karnofsky performance score ≥ 90, and no history of TKIs before development of brain metastases were associated with improved survival (P = .003, < .001, and < .001, respectively), whereas a single brain metastasis or initial treatment with SRS versus WBRT were not (P = .633 and .666, respectively). Prognostic factors significant by multivariable analysis were used to describe four patient groups with 2-year OS estimates of 33%, 59%, 76%, and 100%, respectively (P < .001). Conclusion Patients with brain metastases from ALK-rearranged NSCLC treated with radiotherapy (SRS and/or WBRT) and TKIs have prolonged survival, suggesting that interventions to control intracranial disease are critical. The refinement of prognosis for this molecular subtype of NSCLC identifies a population of patients likely to benefit from first-line SRS, close CNS observation, and treatment of emergent CNS disease. PMID:26438117

System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing.

PubMed

Malinowski, M L; Beling, P A; Haimes, Y Y; LaViers, A; Marvel, J A; Weiss, B A

2015-01-01

The fields of risk analysis and prognostics and health management (PHM) have developed in a largely independent fashion. However, both fields share a common core goal. They aspire to manage future adverse consequences associated with prospective dysfunctions of the systems under consideration due to internal or external forces. This paper describes how two prominent risk analysis theories and methodologies - Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) - can be adapted to support the design of PHM systems in the context of smart manufacturing processes. Specifically, the proposed methodologies will be used to identify targets - components, subsystems, or systems - that would most benefit from a PHM system in regards to achieving the following objectives: minimizing cost, minimizing production/maintenance time, maximizing system remaining usable life (RUL), maximizing product quality, and maximizing product output. HHM is a comprehensive modeling theory and methodology that is grounded on the premise that no system can be modeled effectively from a single perspective. It can also be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system. EFCs connote trends in external or internal sources of risk to a system that may adversely affect specific states of the system. An important aspect of proactive risk management includes bolstering the resilience of the system for specific EFCs by appropriately controlling the states. Risk scenarios for specific EFCs can be the basis for the design of prognostic and diagnostic systems that provide real-time predictions and recognition of scenario changes. The HHM methodology includes visual modeling techniques that can enhance stakeholders' understanding of shared states, resources, objectives and constraints among the interdependent and interconnected subsystems of smart manufacturing systems. In risk analysis, HHM is often paired with Risk Filtering, Ranking, and Management (RFRM). The RFRM process provides the users, (e.g., technology developers, original equipment manufacturers (OEMs), technology integrators, manufacturers), with the most critical risks to the objectives, which can be used to identify the most critical components and subsystems that would most benefit from a PHM system. A case study is presented in which HHM and RFRM are adapted for PHM in the context of an active manufacturing facility located in the United States. The methodologies help to identify the critical risks to the manufacturing process, and the major components and subsystems that would most benefit from a developed PHM system.

System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing

PubMed Central

Malinowski, M.L.; Beling, P.A.; Haimes, Y.Y.; LaViers, A.; Marvel, J.A.; Weiss, B.A.

2017-01-01

The fields of risk analysis and prognostics and health management (PHM) have developed in a largely independent fashion. However, both fields share a common core goal. They aspire to manage future adverse consequences associated with prospective dysfunctions of the systems under consideration due to internal or external forces. This paper describes how two prominent risk analysis theories and methodologies – Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) – can be adapted to support the design of PHM systems in the context of smart manufacturing processes. Specifically, the proposed methodologies will be used to identify targets – components, subsystems, or systems – that would most benefit from a PHM system in regards to achieving the following objectives: minimizing cost, minimizing production/maintenance time, maximizing system remaining usable life (RUL), maximizing product quality, and maximizing product output. HHM is a comprehensive modeling theory and methodology that is grounded on the premise that no system can be modeled effectively from a single perspective. It can also be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system. EFCs connote trends in external or internal sources of risk to a system that may adversely affect specific states of the system. An important aspect of proactive risk management includes bolstering the resilience of the system for specific EFCs by appropriately controlling the states. Risk scenarios for specific EFCs can be the basis for the design of prognostic and diagnostic systems that provide real-time predictions and recognition of scenario changes. The HHM methodology includes visual modeling techniques that can enhance stakeholders’ understanding of shared states, resources, objectives and constraints among the interdependent and interconnected subsystems of smart manufacturing systems. In risk analysis, HHM is often paired with Risk Filtering, Ranking, and Management (RFRM). The RFRM process provides the users, (e.g., technology developers, original equipment manufacturers (OEMs), technology integrators, manufacturers), with the most critical risks to the objectives, which can be used to identify the most critical components and subsystems that would most benefit from a PHM system. A case study is presented in which HHM and RFRM are adapted for PHM in the context of an active manufacturing facility located in the United States. The methodologies help to identify the critical risks to the manufacturing process, and the major components and subsystems that would most benefit from a developed PHM system. PMID:28664162

A Novel Independent Survival Predictor in Pulmonary Embolism: Prognostic Nutritional Index.

PubMed

Hayıroğlu, Mert İlker; Keskin, Muhammed; Keskin, Taha; Uzun, Ahmet Okan; Altay, Servet; Kaya, Adnan; Öz, Ahmet; Çinier, Göksel; Güvenç, Tolga Sinan; Kozan, Ömer

2018-05-01

The prognostic impact of nutritional status in patients with pulmonary embolism (PE) is poorly understood. A well-accepted nutritional status parameter, prognostic nutritional index (PNI), which was first demonstrated to be valuable in patients with cancer and gastrointestinal surgery, was introduced to patients with PE. Our aim was to evaluate the predictive value of PNI in outcomes of patients with PE. We evaluated the in-hospital and long-term (53.8 ± 5.4 months) prognostic impact of PNI on 251 patients with PE. During a median follow-up of 53.8 ± 5.4 months, 27 (11.6%) patients died in hospital course and 31 (13.4%) died in out-of-hospital course. The patients with lower PNI had significantly higher in-hospital and long-term mortality. The Cox proportional hazard analyses showed that PNI was associated with an increased risk of all-cause death for both unadjusted model and adjusted for all covariates. Our study demonstrated that PNI, calculated based on serum albumin level and lymphocyte count, is an independent prognostic factor for mortality in patients with PE.

Prognostic factors and genes associated with endometrial cancer based on gene expression profiling by bioinformatics analysis.

PubMed

Zhang, Ying; Zhang, Wei; Li, Xinglan; Li, Dapeng; Zhang, Xiaoling; Yin, Yajie; Deng, Xiangyun; Sheng, Xiugui

2016-06-01

Endometrial cancer (EC) is the most prevalent malignancy worldwide. Although several efforts had been made to explore the molecular mechanism responsible for EC progression, it is still not fully understood. To evaluate the clinical characteristics and prognostic factors of patients with EC, and further to search for novel genes associated with EC progression. We recruited 328 patients with EC and analyzed prognostic factors using Cox proportional hazard regression model. Further, a gene expression profile of EC was used to identify the differentially expressed genes (DEGs) between normal samples and tumor samples. Subsequently, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis ( http://www.genome.jp/kegg/ ) for DEGs were performed, and then protein-protein interaction (PPI) network of DEGs as well as the subnetwork of PPI were constructed with plug-in, MCODE by mapping DEGs into the Search Tool for the Retrieval of Interacting Genes database. Our results showed that body mass index (BMI), hypertension, myometrial invasion, pathological type, and Glut4 positive expression were prognostic factors in EC (P < 0.05). Bioinformatics analysis showed that upregulated DEGs were associated with cell cycle, and downregulated DEGs were related to MAPK pathway. Meanwhile, PPI network analysis revealed that upregulated CDK1 and CCNA2 as well as downregulated JUN and FOS were listed in top two nodes with high degrees. Patients with EC should be given more focused attentions in respect of pathological type, BMI, hypertension, and Glut4-positive expression. In addition, CDK1, CCNA2, JUN, and FOS might play important roles in EC development.

State of the Art: Blood Biomarkers for Risk Stratification in Patients with Stable Ischemic Heart Disease.

PubMed

Omland, Torbjørn; White, Harvey D

2017-01-01

Multiple circulating biomarkers have been associated with the incidence of cardiovascular events and proposed as potential tools for risk stratification in stable ischemic heart disease (IHD), yet current guidelines do not make any firm recommendations concerning the use of biomarkers for risk stratification in this setting. This state-of-the-art review provides an overview of biomarkers for risk stratification in stable IHD. Circulating biomarkers associated with the risk of cardiovascular events in patients with stable IHD reflect different pathophysiological processes, including myocardial injury, myocardial stress and remodeling, metabolic status, vascular inflammation, and oxidative stress. Compared to the primary prevention setting, biomarkers reflecting end-organ damage and future risk of heart failure development and cardiovascular death may play more important roles in the stable IHD setting. Accordingly, biomarkers that reflect chronic, low-grade myocardial injury, and stress, i.e., high-sensitivity cardiac troponins and natriuretic peptides, provide graded and incremental prognostic information to conventional risk markers. In contrast, in stable IHD patients the prognostic value of traditional metabolic biomarkers, including serum lipids, is limited. Among several novel biomarkers, growth-differentiation factor-15 may provide the most robust prognostic information, whereas most inflammatory markers provide limited incremental prognostic information to risk factor models that include conventional risk factors, natriuretic peptides, and high-sensitivity troponins. Circulating biomarkers hold promise as useful tools for risk stratification in stable IHD, but their future incorporation into clinically useful risk scores will depend on prospective, rigorously performed clinical trials that document enhanced risk prediction. © 2016 American Association for Clinical Chemistry.

Carboplatin–paclitaxel-induced leukopenia and neuropathy predict progression-free survival in recurrent ovarian cancer

PubMed Central

Lee, C K; Gurney, H; Brown, C; Sorio, R; Donadello, N; Tulunay, G; Meier, W; Bacon, M; Maenpaa, J; Petru, E; Reed, N; Gebski, V; Pujade-Lauraine, E; Lord, S; Simes, R J; Friedlander, M

2011-01-01

Background: We assess the prognostic value of chemotherapy-induced leukopenia and sensory neuropathy in the CALYPSO trial patients treated with carboplatin–paclitaxel (CP) or carboplatin–liposomal doxorubicin (CPLD). Methods: We performed a landmark analysis at first month after randomisation to correlate leukopenia (nadir white blood cell <4.0 × 109 per litre or severe infection) during cycle 1 of chemotherapy with progression-free survival (PFS). Using time-dependent proportional-hazards models, we also investigated the association between neuropathy and PFS. Results: Of 608 patients with nadir blood and did not receive growth factors, 72% (CP=70%, CPLD=73%) had leukopenia. Leukopenia was prognostic for PFS in those receiving CP (adjusted hazard ratio (aHR) 0.66, P=0.01). Carboplatin–liposomal doxorubicin was more effective than CP in patients without leukopenia (aHR 0.51, P=0.001), but not those experiencing leukopenia (aHR 0.93, P=0.54; interaction P=0.008). Of 949 patients, 32% (CP=62%, CPLD=28%) reported neuropathy during landmark. Neuropathy was prognostic for PFS in the CP group only (aHR 0.77, P=0.02). Carboplatin–liposomal doxorubicin appeared to be more effective than CP among patients without neuropathy (aHR 0.70, P<0.0001), but not those with neuropathy (aHR 0.96, P=0.81; interaction P=0.15). Conclusion: First-cycle leukopenia and neuropathy were prognostic for patients treated with CP. Efficacy of CP treatment was similar to CPLD in patients who developed leukopenia. These findings support further research to understand the mechanisms of treatment-related toxicity. PMID:21750553

Prognostic score–based balance measures for propensity score methods in comparative effectiveness research

PubMed Central

Stuart, Elizabeth A.; Lee, Brian K.; Leacy, Finbarr P.

2013-01-01

Objective Examining covariate balance is the prescribed method for determining when propensity score methods are successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (also known as the disease-risk score), to determine which balance measures best correlate with bias in the treatment effect estimate. Study Design and Setting The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only. Results The standardized mean difference in prognostic scores, the mean standardized mean difference, and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification and performed well under a variety of scenarios. Conclusion Researchers should consider using prognostic score–based balance measures for assessing the performance of propensity score methods for reducing bias in non-experimental studies. PMID:23849158

Prognostic, predictive and pharmacogenomic assessments of CDX2 refine stratification of colorectal cancer.

PubMed

Bruun, Jarle; Sveen, Anita; Barros, Rita; Eide, Peter W; Eilertsen, Ina; Kolberg, Matthias; Pellinen, Teijo; David, Leonor; Svindland, Aud; Kallioniemi, Olli; Guren, Marianne G; Nesbakken, Arild; Almeida, Raquel; Lothe, Ragnhild A

2018-06-14

We aimed to refine the value of CDX2 as an independent prognostic and predictive biomarker in colorectal cancer (CRC) according to disease stage and chemotherapy sensitivity in preclinical models. CDX2 expression was evaluated in 1045 stage I-IV primary CRCs by gene expression (n=403) or immunohistochemistry (n=642) and in relation to 5-year relapse-free survival (RFS), overall survival (OS), and chemotherapy. Pharmacogenomic associations between CDX2 expression and 69 chemotherapeutics were assessed by drug screening of 35 CRC cell lines. CDX2 expression was lost in 11.6% of cases and showed independent poor prognostic value in multivariable models. For individual stages, CDX2 was prognostic only in stage IV, independent of chemotherapy. Among stage I-III patients not treated in an adjuvant setting, CDX2 loss was associated with a particularly poor survival in the BRAF-mutated subgroup, but prognostic value was independent of microsatellite instability status and the consensus molecular subtypes In stage III, the 5-year RFS rate was higher among patients with loss of CDX2 who received adjuvant chemotherapy than among patients who did not. The CDX2-negative cell lines were significantly more sensitive to chemotherapeutics than CDX2-positive cells, and the multidrug resistance genes MDR1 and CFTR were significantly downregulated both in CDX2-negative cells and patient tumors. Molecular Oncology (2018) © 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

[Essential thrombocythemia: baseline characteristics and risk factors for survival and thrombosis in a series of 214 patients].

PubMed

Angona, Anna; Alvarez-Larrán, Alberto; Bellosillo, Beatriz; Martínez-Avilés, Luz; Garcia-Pallarols, Francesc; Longarón, Raquel; Ancochea, Àgueda; Besses, Carles

2015-03-15

Two prognostic models to predict overall survival and thrombosis-free survival have been proposed: International Prognostic Score for Essential Thrombocythemia (IPSET) and IPSET-Thrombosis, respectively, based on age, leukocytes count, history of previous thrombosis, the presence of cardiovascular risk factors and the JAK2 mutational status. The aim of the present study was to assess the clinical and biological characteristics at diagnosis and during evolution in essential thrombocythemia (ET) patients as well as the factors associated with survival and thrombosis and the usefulness of these new prognostic models. We have evaluated the clinical data and the mutation status of JAK2, MPL and calreticulin of 214 ET patients diagnosed in a single center between 1985 and 2012, classified according to classical risk stratification, IPSET and IPSET-Thrombosis. With a median follow-up of 6.9 years, overall survival was not associated with any variable by multivariate analysis. Thrombotic history and leukocytes>10×10(9)/l were associated with thrombosis-free survival (TFS). In our series, IPSET prognostic systems of survival and thrombosis did not provide more clinically relevant information regarding the classic risk of thrombosis stratification. Thrombotic history and leukocytosis>10×10(9)/l were significantly associated with lower TFS, while the prognostic IPSET-Thrombosis system did not provide more information than classical thrombotic risk assessment. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Do online prognostication tools represent a valid alternative to genomic profiling in the context of adjuvant treatment of early breast cancer? A systematic review of the literature.

PubMed

El Hage Chehade, Hiba; Wazir, Umar; Mokbel, Kinan; Kasem, Abdul; Mokbel, Kefah

2018-01-01

Decision-making regarding adjuvant chemotherapy has been based on clinical and pathological features. However, such decisions are seldom consistent. Web-based predictive models have been developed using data from cancer registries to help determine the need for adjuvant therapy. More recently, with the recognition of the heterogenous nature of breast cancer, genomic assays have been developed to aid in the therapeutic decision-making. We have carried out a comprehensive literature review regarding online prognostication tools and genomic assays to assess whether online tools could be used as valid alternatives to genomic profiling in decision-making regarding adjuvant therapy in early breast cancer. Breast cancer has been recently recognized as a heterogenous disease based on variations in molecular characteristics. Online tools are valuable in guiding adjuvant treatment, especially in resource constrained countries. However, in the era of personalized therapy, molecular profiling appears to be superior in predicting clinical outcome and guiding therapy. Copyright © 2017 Elsevier Inc. All rights reserved.

Prognostic indicators of poor short-term outcome of physiotherapy intervention in women with stress urinary incontinence.

PubMed

Hendriks, Erik J M; Kessels, Alfons G H; de Vet, Henrica C W; Bernards, Arnold T M; de Bie, Rob A

2010-03-01

To identify prognostic indicators independently associated with poor outcome of physiotherapy intervention in women with primary or recurrent stress urinary incontinence (stress UI). A prospective cohort study was performed in physiotherapy practices in primary care to identify prognostic indicators 12 weeks after initiation of physiotherapy intervention. Patients were referred by general practitioners or urogynecologists. Risk factors for stress UI were examined as potential prognostic indicators of poor outcome. The primary outcomes were defined as poor outcome on the binary Leakage Severity scale (LS scale) and the binary global perceived effectiveness (GPE) score. Two hundred sixty-seven women, with a mean age of 47.7 (SD = 8.3), with stress UI for at least 6 months were included. At 12 weeks, 43% and 59% of the women were considered recovered on the binary LS scale and the binary GPE score, respectively. Prognostic indicators associated with poor outcome included 11 indicators based on the binary LS scale and 8 based on the binary GPE score. The prognostic indicators shared by both models show that poor recovery was associated with women with severe stress UI, POP-Q stage > II, poor outcome of physiotherapy intervention for a previous UI episode, prolonged second stage of labor, BMI > 30, high psychological distress, and poor physical health. This study provides robust evidence of clinically meaningful prognostic indicators of poor short-term outcome. These findings need to be confirmed by replication studies. (c) 2009 Wiley-Liss, Inc.

Traditional and emerging molecular markers in neuroblastoma prognosis: the good, the bad and the ugly.

PubMed

Poremba, C; Hero, B; Goertz, H G; Scheel, C; Wai, D; Schaefer, K L; Christiansen, H; Berthold, F; Juergens, H; Boecker, W; Dockhorn-Dworniczak, B

2001-01-01

Neuroblastomas (NB) are a heterogeneous group of childhood tumours with a wide range of likelihood for tumour progression. As traditional parameters do not ensure completely accurate prognostic grouping, new molecular markers are needed for assessing the individual patient's prognosis more precisely. 133 NB of all stages were analysed in blind-trial fashion for telomerase activity (TA), expression of surviving, and MYCN status. These data were correlated with other traditional prognostic indicators and disease outcome. TA is a powerful independent prognostic marker for all stages and is capable of differentiating between good and poor outcome in putative "favourable" clinical or biological subgroups of NB patients. High surviving expression is associated with an adverse outcome, but is more difficult to interprete than TA because survivin expression needs to be accurately quantified to be of predictive value. We propose an extended progression model for NB including emerging prognostic markers, with emphasis on telomerase activity.

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

PubMed Central

Korolev, Igor O.; Symonds, Laura L.; Bozoki, Andrea C.

2016-01-01

Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment. PMID:26901338

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study.

PubMed

Hamilton, C A; Miller, A; Casablanca, Y; Horowitz, N S; Rungruang, B; Krivak, T C; Richard, S D; Rodriguez, N; Birrer, M J; Backes, F J; Geller, M A; Quinn, M; Goodheart, M J; Mutch, D G; Kavanagh, J J; Maxwell, G L; Bookman, M A

2018-02-01

To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). The analysis dataset included 3010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. Published by Elsevier Inc.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study

PubMed Central

Hamilton, C. A.; Miller, A.; Casablanca, Y.; Horowitz, N. S.; Rungruang, B.; Krivak, T. C.; Richard, S. D.; Rodriguez, N.; Birrer, M.J.; Backes, F.J.; Geller, M.A.; Quinn, M.; Goodheart, M.J.; Mutch, D.G.; Kavanagh, J.J.; Maxwell, G. L.; Bookman, M. A.

2018-01-01

Objective To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Methods Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10 years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). Results The analysis dataset included 3,010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. Conclusions The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. PMID:29195926

Distinguishing prognostic and predictive biomarkers: An information theoretic approach.

PubMed

Sechidis, Konstantinos; Papangelou, Konstantinos; Metcalfe, Paul D; Svensson, David; Weatherall, James; Brown, Gavin

2018-05-02

The identification of biomarkers to support decision-making is central to personalised medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies; and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification. Mistakenly assuming a biomarker to be predictive, when it is in fact largely prognostic (and vice-versa) is highly undesirable, and can result in financial, ethical and personal consequences. We present a framework for data-driven ranking of biomarkers on their prognostic/predictive strength, using a novel information theoretic method. This approach provides a natural algebra to discuss and quantify the individual predictive and prognostic strength, in a self-consistent mathematical framework. Our contribution is a novel procedure, INFO+, which naturally distinguishes the prognostic vs predictive role of each biomarker and handles higher order interactions. In a comprehensive empirical evaluation INFO+ outperforms more complex methods, most notably when noise factors dominate, and biomarkers are likely to be falsely identified as predictive, when in fact they are just strongly prognostic. Furthermore, we show that our methods can be 1-3 orders of magnitude faster than competitors, making it useful for biomarker discovery in 'big data' scenarios. Finally, we apply our methods to identify predictive biomarkers on two real clinical trials, and introduce a new graphical representation that provides greater insight into the prognostic and predictive strength of each biomarker. R implementations of the suggested methods are available at https://github.com/sechidis. konstantinos.sechidis@manchester.ac.uk. Supplementary data are available at Bioinformatics online.

Clinical implications of six inflammatory biomarkers as prognostic indicators in Ewing sarcoma

PubMed Central

Li, Yong-Jiang; Yang, Xi; Zhang, Wen-Biao; Yi, Cheng; Wang, Feng; Li, Ping

2017-01-01

Cancer-related systemic inflammation responses have been correlated with cancer development and progression. The prognostic significance of several inflammatory indicators, including neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), Glasgow Prognostic Score (GPS), C-reactive protein to albumin ratio (CRP/Alb ratio), lymphocyte–monocyte ratio (LMR), and neutrophil–platelet score (NPS), were found to be correlated with prognosis in several cancers. However, the prognostic role of these inflammatory biomarkers in Ewing sarcoma has not been evaluated. This study enrolled 122 Ewing patients. Receiver operating characteristic (ROC) analysis was generated to determine optimal cutoff values; areas under the curves (AUCs) were assessed to show the discriminatory ability of the biomarkers; Kaplan–Meier analysis was conducted to plot the survival curves; and Cox multivariate survival analysis was performed to identify independent prognostic factors. The optimal cutoff values of CRP/Alb ratio, NLR, PLR, and LMR were 0.225, 2.38, 131, and 4.41, respectively. CRP/Alb ratio had a significantly larger AUC than NLR, PLR, LMR, and NPS. Higher levels of CRP/Alb ratio (hazard ratio [HR] 2.41, P=0.005), GPS (HR 2.27, P=0.006), NLR (HR 2.07, P=0.013), and PLR (HR 1.85, P=0.032) were significantly correlated with poor prognosis. As the biomarkers had internal correlations, only the CRP/Alb ratio was involved in the multivariate Cox analysis and remained an independent prognostic indicator. The study demonstrated that CRP/Alb ratio, GPS, and NLR were effective prognostic indicators for patients with Ewing sarcoma, and the CRP/Alb ratio was the most robust prognostic indicator with a discriminatory ability superior to that of the other indicators; however, PLR, LMR, and NPS may not be suitable as prognostic indicators in Ewing sarcoma. PMID:29033609

A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma

PubMed Central

Wei, Jin-Huan; Haddad, Ahmed; Wu, Kai-Jie; Zhao, Hong-Wei; Kapur, Payal; Zhang, Zhi-Ling; Zhao, Liang-Yun; Chen, Zhen-Hua; Zhou, Yun-Yun; Zhou, Jian-Cheng; Wang, Bin; Yu, Yan-Hong; Cai, Mu-Yan; Xie, Dan; Liao, Bing; Li, Cai-Xia; Li, Pei-Xing; Wang, Zong-Ren; Zhou, Fang-Jian; Shi, Lei; Liu, Qing-Zuo; Gao, Zhen-Li; He, Da-Lin; Chen, Wei; Hsieh, Jer-Tsong; Li, Quan-Zhen; Margulis, Vitaly; Luo, Jun-Hang

2015-01-01

Clear cell renal cell carcinomas (ccRCCs) display divergent clinical behaviours. Molecular markers might improve risk stratification of ccRCC. Here we use, based on genome-wide CpG methylation profiling, a LASSO model to develop a five-CpG-based assay for ccRCC prognosis that can be used with formalin-fixed paraffin-embedded specimens. The five-CpG-based classifier was validated in three independent sets from China, United States and the Cancer Genome Atlas data set. The classifier predicts the overall survival of ccRCC patients (hazard ratio=2.96−4.82; P=3.9 × 10−6−2.2 × 10−9), independent of standard clinical prognostic factors. The five-CpG-based classifier successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome in respective clinical stages and individual ‘stage, size, grade and necrosis' scores. Moreover, methylation at the five CpGs correlates with expression of five genes: PITX1, FOXE3, TWF2, EHBP1L1 and RIN1. Our five-CpG-based classifier is a practical and reliable prognostic tool for ccRCC that can add prognostic value to the staging system. PMID:26515236

Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks

NASA Astrophysics Data System (ADS)

Sbarufatti, Claudio; Corbetta, Matteo; Giglio, Marco; Cadini, Francesco

2017-03-01

Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.

Prognostic significance of IDH 1 mutation in patients with glioblastoma multiforme.

PubMed

Khan, Inamullah; Waqas, Muhammad; Shamim, Muhammad Shahzad

2017-05-01

Focus of brain tumour research is shifting towards tumour genesis and genetics, and possible development of individualized treatment plans. Genetic analysis shows recurrent mutation in isocitrate dehydrogenase (IDH1) gene in most Glioblastoma multiforme (GBM) cells. In this review we evaluated the prognostic significance of IDH 1 mutation on the basis of published evidence. Multiple retrospective clinical analyses correlate the presence of IDH1 mutation in GBM with good prognostic outcomes compared to wild-type IDH1. A systematic review reported similar results. Based on the review of current literature IDH1 mutation is an independent factor for longer overall survival (OS) and progression free survival (PFS) in GBM patients when compared to wild-type IDH1. The prognostic significance opens up new avenues for treatment.

Added prognostic value of CT characteristics and IASLC/ATS/ERS histologic subtype in surgically resected lung adenocarcinomas.

PubMed

Suh, Young Joo; Lee, Hyun-Ju; Kim, Young Tae; Kang, Chang Hyun; Park, In Kyu; Jeon, Yoon Kyung; Chung, Doo Hyun

2018-06-01

Our study investigates the added value of computed tomography (CT) characteristics, histologic subtype classification of the International Association for the Study of Lung Cancer (IASLC)/the American Thoracic Society (ATS)/the European Respiratory Society (ERS), and genetic mutation for predicting postoperative prognoses of patients who received curative surgical resections for lung adenocarcinoma. We retrospectively enrolled 988 patients who underwent curative resection for invasive lung adenocarcinoma between October 2007 and December 2013. Cox's proportional hazard model was used to explore the risk of recurrence-free survival, based on the combination of conventional prognostic factors, CT characteristics, IASLC/ATS/ERS histologic subtype, and epidermal growth factor receptor (EGFR) mutations. Incremental prognostic values of CT characteristics, histologic subtype, and EGFR mutations over conventional risk factors were measured by C-statistics. During median follow-up period of 44.7 months (25th to 75th percentile 24.6-59.7 months), postoperative recurrence occurred in 248 patients (25.1%). In univariate Cox proportion hazard model, female sex, tumor size and stage, CT characteristics, and predominant histologic subtype were associated with tumor recurrence (P < 0.05). In multivariate Cox regression model adjusted for tumor size and stage, both CT characteristics and histologic subtype were independent tumor recurrence predictors (P < 0.05). Cox proportion hazard models combining CT characteristics or histologic subtype with size and tumor stage showed higher C-indices (0.763 and 0.767, respectively) than size and stage-only models (C-index 0.759, P > 0.05). CT characteristics and histologic subtype have relatively limited added prognostic values over tumor size and stage in surgically resected lung adenocarcinomas. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of a nomogram incorporating serum C-reactive protein level to predict overall survival of patients with advanced urothelial carcinoma and its evaluation by decision curve analysis.

PubMed

Ishioka, J; Saito, K; Sakura, M; Yokoyama, M; Matsuoka, Y; Numao, N; Koga, F; Masuda, H; Fujii, Y; Kawakami, S; Kihara, K

2012-09-25

The purpose of this study is to investigate the prognostic impact of C-reactive protein (CRP) on patients with advanced urothelial carcinoma and to develop a novel nomogram predicting survival. A total of 223 consecutive patients were treated at Tokyo Medical and Dental Hospital. A nomogram incorporating V was developed based on the result of a Cox proportional hazards model. Its efficacy and clinical usefulness was evaluated by concordance index (c-index) and decision curve analysis. Of the 223 patients, 184 (83%) died of cancer. Median follow-up periods of patients who died and those who remained alive were 5 and 11 months, respectively. We developed a novel nomogram incorporating Eastern Cooperative Oncology Group Performance Status, presence of visceral metastasis, haemoglobin and age. The c-index of the nomogram predicting survival probability 6 and 12 months after diagnosis was 0.788 and 0.765, respectively. Decision curve analyses revealed that the novel nomogram incorporating CRP had a superior net benefit than that without CRP for most of the examined probabilities. We demonstrated the prognostic impact of CRP that improved the predictive accuracy of a nomogram for survival probability in patients with advanced urothelial carcinoma.

Establishing a Traumatic Brain Injury Program of Care: Benchmarking Outcomes after Institutional Adoption of Evidence-Based Guidelines.

PubMed

Tarapore, Phiroz E; Vassar, Mary J; Cooper, Shelly; Lay, Twyila; Galletly, Julia; Manley, Geoffrey T; Huang, Michael C

2016-11-15

Traumatic brain injury (TBI) is a widespread global disease, often with widely varying outcomes. Standardization of care and adherence to established guidelines are central to the effort to improve outcomes. At our level I urban trauma center, we developed and implemented a Joint Commission-certified TBI Program of Care in 2011 and compared our post-implementation patient data set with historical controls, using the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic model. Historical controls were drawn from the San Francisco General Hospital Traumatic Coma Data Bank (SFGH/TCDB) from 1987 to 1996. Recent era patients were drawn from the NeuroTracker database, a customized electronic medical record used in our clinical practice. Descriptive statistics were calculated. Adherence to four quality-of-care metrics on the clinical service was tracked for 2011-2013. The IMPACT prognostic model was used to calculate expected versus observed mortality for current and historical patient groups. In the historical control group, 832 patients were identified and 6-month mortality was available for 592. Observed 6-month mortality was 49%. In the recent era patient group, 211 patients were identified and 6-month mortality was 38%. The IMPACT prognostic model was applied to each patient group. Areas under the curve for each analysis were >0.85 and goodness of fit was satisfactory, indicating good performance of the IMPACT model. Comparison of observed versus expected deaths in the recent versus the control patient sets revealed a drop of 59% in early mortality. The greatest reductions in mortality were observed in the group of patients with IMPACT-predicted mortality ≤50%. Significant progress has been made in reducing the percentage of unexpected deaths in TBI patients. It is likely that major factors include more aggressive management and tracking of compliance with the implementation of guidelines for the management of TBI patients.

A Framework for Model-Based Diagnostics and Prognostics of Switched-Mode Power Supplies

DTIC Science & Technology

2014-10-02

system. Some highlights of the work are included but not only limited to the following aspects: first, the methodology is based on electronic ... electronic health management, with the goal of expanding the realm of electronic diagnostics and prognostics. 1. INTRODUCTION Electronic systems such...as electronic controls, onboard computers, communications, navigation and radar perform many critical functions in onboard military and commercial

Army Logistician. Volume 39, Issue 1, January-February 2007

DTIC Science & Technology

2007-02-01

of electronic systems using statistical methods. P& C , however, requires advanced prognostic capabilities not only to detect the early onset of...patterns. Entities operating in a P& C -enabled environment will sense and understand contextual meaning , communicate their state and mission, and act to...accessing of historical and simulation patterns; on- board prognostics capabilities; physics of failure analyses; and predictive modeling. P& C also

[Mathematical calculation of strength of the vertebral column in surgical treatment of unstable fractures of the spine].

PubMed

Orlov, S V; Kanykin, A Iu; Moskalev, V P; Shchedrenok, V V; Sedov, R L

2009-01-01

A mathematical model of a three-vertebra complex was developed in order to make an exact calculation of loss of supporting ability of the vertebral column in trauma. Mathematical description of the dynamic processes was based on Lagrange differential equation of the second order. The degree of compression and instability of the three-vertebra complex, established using mathematical modeling, determines the decision on the surgical treatment and might be considered as a prognostic criterion of the course of the compression trauma of the spine. The method of mathematical modeling of supporting ability of the vertebral column was used in 72 patients.

Cancer Evolution: Mathematical Models and Computational Inference

PubMed Central

Beerenwinkel, Niko; Schwarz, Roland F.; Gerstung, Moritz; Markowetz, Florian

2015-01-01

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. PMID:25293804

Transarterial chemoembolization in patients with hepatocellular carcinoma and renal insufficiency.

PubMed

Hsu, Chia-Yang; Huang, Yi-Hsiang; Su, Chien-Wei; Chiang, Jen-Huey; Lin, Han-Chieh; Lee, Pui-Ching; Lee, Fa-Yauh; Huo, Teh-Ia; Lee, Shou-Dong

2010-09-01

Renal dysfunction is often present in patients with cirrhosis and hepatocellular carcinoma (HCC). Acute renal failure (ARF) may occur after transarterial chemoembolization (TACE) owing to radiocontrast agent. This study investigated the incidence and risk factors of ARF and prognostic predictors in HCC patients with preexisting renal insufficiency undergoing TACE. A total of 566 HCC patients undergoing TACE were enrolled. Renal insufficiency was defined as an estimated glomerular filtration rate less than 60 mL/min/1.73 m. In a mean follow-up duration of 18+/-16 months, 231 (40.8%) patients undergoing TACE died. Renal insufficiency that was present in 134 (23.7%) patients at baseline, independently predicted a poor prognosis in the Cox proportional hazards model [risk ratio (RR): 1.47, P=0.012]. Of them, 13 (10%) and 6 (5%) patients had transient and prolonged ARF after TACE, respectively. Post-TACE gastrointestinal bleeding [odds ratio (OR): 16.54, P=0.001] and higher Cancer of the Liver Italian Program (CLIP) scores (> or =2; OR: 4.22, P=0.02) were independent risk factors for ARF in the multivariate logistic regression analysis. In the Cox model, prolonged ARF (RR: 3.28, P<0.001) and higher CLIP scores (> or =2; RR: 2.13, P<0.001) were independent poor prognostic predictors for HCC patients with renal insufficiency receiving TACE. Gastrointestinal bleeding and higher CLIP scores are associated with the development of ARF in patients with HCC and renal insufficiency undergoing TACE. Higher CLIP scores and renal insufficiency, either preexisting before TACE or as a complication of TACE, are poor prognostic predictors in HCC patients receiving TACE.

Design and Validation of a Straight-Copy Typewriting Prognostic Test Using Kinesthetic Sensitivity.

ERIC Educational Resources Information Center

Olson, Norma Jean

1979-01-01

Describes the development and application of a kinesthetic sensitivity test to determine whether it is a valid and reliable measure of straight-copy typing speed and accuracy. The author states that this kinesthetic sensitivity instrument may be used as a prognostic aptitude test and recommends administration methods. (MF)

Prognostic Significance of Modified Advanced Lung Cancer Inflammation Index (ALI) in Patients with Small Cell Lung Cancer_ Comparison with Original ALI.

PubMed

Kim, Eun Young; Kim, Nambeom; Kim, Young Saing; Seo, Ja-Young; Park, Inkeun; Ahn, Hee Kyung; Jeong, Yu Mi; Kim, Jeong Ho

2016-01-01

Advanced lung cancer inflammation index (ALI, body mass index [BMI] x serum albumin/neutrophil-lymphocyte ratio [NLR]) has been shown to predict overall survival (OS) in small cell lung cancer (SCLC). CT enables skeletal muscle to be quantified, whereas BMI cannot accurately reflect body composition. The purpose was to evaluate prognostic value of modified ALI (mALI) using CT-determined L3 muscle index (L3MI, muscle area at L3/height2) beyond original ALI. L3MIs were calculated using the CT images of 186 consecutive patients with SCLC taken at diagnosis, and mALI was defined as L3MI x serum albumin/NLR. Using chi-squared test determined maximum cut-offs for low ALI and low mALI, the prognostic values of low ALI and low mALI were tested using Kaplan-Meier method and Cox proportional hazards analysis. Finally, deviance statistics was used to test whether the goodness of fit of the prognostic model is improved by adding mALI as an extra variable. Patients with low ALI (cut-off, 31.1, n = 94) had shorter OS than patients with high ALI (median, 6.8 months vs. 15.8 months; p < 0.001), and patients with low mALI (cut-off 67.7, n = 94) had shorter OS than patients with high mALI (median, 6.8 months vs. 16.5 months; p < 0.001). There was no significant difference in estimates of median survival time between low ALI and low mALI (z = 0.000, p = 1.000) and between high ALI and high mALI (z = 0.330, p = 0.740). Multivariable analysis showed that low ALI was an independent prognostic factor for shorter OS (HR, 1.67, p = 0.004), along with advanced age (HR, 1.49, p = 0.045), extensive disease (HR, 2.27, p < 0.001), supportive care only (HR, 7.86, p < 0.001), and elevated LDH (HR, 1.45, p = 0.037). Furthermore, goodness of fit of this prognostic model was not significantly increased by adding mALI as an extra variable (LR difference = 2.220, p = 0.136). The present study confirms mALI using CT-determined L3MI has no additional prognostic value beyond original ALI using BMI. ALI is a simple and useful prognostic indicator in SCLC.

Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa; Jackson, Tom

2017-09-01

This paper investigates the polymer electrolyte membrane (PEM) fuel cell internal behaviour variation at different operating condition, with characterization test data taken at predefined inspection times, and uses the determined internal behaviour evolution to predict the future PEM fuel cell performance. For this purpose, a PEM fuel cell behaviour model is used, which can be related to various fuel cell losses. By matching the model to the collected polarization curves from the PEM fuel cell system, the variation of fuel cell internal behaviour can be obtained through the determined model parameters. From the results, the source of PEM fuel cell degradation during its lifetime at different conditions can be better understood. Moreover, with determined fuel cell internal behaviour, the future fuel cell performance can be obtained by predicting the future model parameters. By comparing with prognostic results using adaptive neuro fuzzy inference system (ANFIS), the proposed prognostic analysis can provide better predictions for PEM fuel cell performance at dynamic condition, and with the understanding of variation in PEM fuel cell internal behaviour, mitigation strategies can be designed to extend the fuel cell performance.

Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma.

PubMed

Tadeo, Irene; Piqueras, Marta; Montaner, David; Villamón, Eva; Berbegall, Ana P; Cañete, Adela; Navarro, Samuel; Noguera, Rosa

2014-02-01

Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma.

Prognostic grouping of metastatic prostate cancer using conventional pretreatment prognostic factors.

PubMed

Mikkola, Arto; Aro, Jussi; Rannikko, Sakari; Ruutu, Mirja

2009-01-01

To develop three prognostic groups for disease specific mortality based on the binary classified pretreatment variables age, haemoglobin concentration (Hb), erythrocyte sedimentation rate (ESR), alkaline phosphatase (ALP), prostate-specific antigen (PSA), plasma testosterone and estradiol level in hormonally treated patients with metastatic prostate cancer (PCa). The present study comprised 200 Finnprostate 6 study patients, but data on all variables were not known for every patient. The patients were divided into three prognostic risk groups (Rgs) using the prognostically best set of pretreatment variables. The best set was found by backward stepwise selection and the effect of every excluded variable on the binary classification cut-off points of the remaining variables was checked and corrected when needed. The best group of variables was ALP, PSA, ESR and age. All data were known in 142 patients. Patients were given one risk point each for ALP > 180 U/l (normal value 60-275 U/l), PSA > 35 microg/l, ESR > 80 mm/h and age < 60 years. Three risk groups were formed: Rg-a (0-1 risk points), Rg-b (2 risk points) and Rg-c (3-4 risk points). The risk of death from PCa increased statistically significantly with advancing prognostic group. Patients with metastatic PCa can be divided into three statistically significantly different prognostic risk groups for PCa-specific mortality by using the binary classified pretreatment variables ALP, PSA, ESR and age.

Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method

NASA Astrophysics Data System (ADS)

He, Wei; Williard, Nicholas; Osterman, Michael; Pecht, Michael

A new method for state of health (SOH) and remaining useful life (RUL) estimations for lithium-ion batteries using Dempster-Shafer theory (DST) and the Bayesian Monte Carlo (BMC) method is proposed. In this work, an empirical model based on the physical degradation behavior of lithium-ion batteries is developed. Model parameters are initialized by combining sets of training data based on DST. BMC is then used to update the model parameters and predict the RUL based on available data through battery capacity monitoring. As more data become available, the accuracy of the model in predicting RUL improves. Two case studies demonstrating this approach are presented.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-07

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying explanation and elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article. © BMJ Publishing Group Ltd 2014.

Comparison of Cox's Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000-2012.

PubMed

Adelian, R; Jamali, J; Zare, N; Ayatollahi, S M T; Pooladfar, G R; Roustaei, N

2015-01-01

Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. To compare Cox's regression model with parametric models for determining the independent factors for predicting adults' and pediatrics' survival after liver transplantation. This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Parametric regression model is a good alternative for the Cox's regression model.