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.

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.

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.

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...

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 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.

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 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.

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.

Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs

NASA Technical Reports Server (NTRS)

Saha, Sankalita; Celaya, Jose Ramon; Vashchenko, Vladislav; Mahiuddin, Shompa; Goebel, Kai F.

2011-01-01

Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application. Index Terms Power MOSFET, accelerated aging, prognostics

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.

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.

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

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.

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.

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

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.

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.

[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.

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.

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

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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

Clinical Prediction Models for Patients With Nontraumatic Knee Pain in Primary Care: A Systematic Review and Internal Validation Study.

PubMed

Panken, Guus; Verhagen, Arianne P; Terwee, Caroline B; Heymans, Martijn W

2017-08-01

Study Design Systematic review and validation study. Background Many prognostic models of knee pain outcomes have been developed for use in primary care. Variability among published studies with regard to patient population, outcome measures, and relevant prognostic factors hampers the generalizability and implementation of these models. Objectives To summarize existing prognostic models in patients with knee pain in a primary care setting and to develop and internally validate new summary prognostic models. Methods After a sensitive search strategy, 2 reviewers independently selected prognostic models for patients with nontraumatic knee pain and assessed the methodological quality of the included studies. All predictors of the included studies were evaluated, summarized, and classified. The predictors assessed in multiple studies of sufficient quality are presented in this review. Using data from the Musculoskeletal System Study (BAS) cohort of patients with a new episode of knee pain, recruited consecutively by Dutch general medical practitioners (n = 372), we used predictors with a strong level of evidence to develop new prognostic models for each outcome measure and internally validated these models. Results Sixteen studies were eligible for inclusion. We considered 11 studies to be of sufficient quality. None of these studies validated their models. Five predictors with strong evidence were related to function and 6 to recovery, and were used to compose 2 prognostic models for patients with knee pain at 1 year. Running these new models in another data set showed explained variances (R 2 ) of 0.36 (function) and 0.33 (recovery). The area under the curve of the recovery model was 0.79. After internal validation, the adjusted R 2 values of the models were 0.30 (function) and 0.20 (recovery), and the area under the curve was 0.73. Conclusion We developed 2 valid prognostic models for function and recovery for patients with nontraumatic knee pain, based on predictors with strong evidence. A longer duration of complaints predicted poorer function but did not adequately predict chance of recovery. Level of Evidence Prognosis, levels 1a and 1b. J Orthop Sports Phys Ther 2017;47(8):518-529. Epub 16 Jun 2017. doi:10.2519/jospt.2017.7142.

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.

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

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 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.

Prognostic models for predicting posttraumatic seizures during acute hospitalization, and at 1 and 2 years following traumatic brain injury.

PubMed

Ritter, Anne C; Wagner, Amy K; Szaflarski, Jerzy P; Brooks, Maria M; Zafonte, Ross D; Pugh, Mary Jo V; Fabio, Anthony; Hammond, Flora M; Dreer, Laura E; Bushnik, Tamara; Walker, William C; Brown, Allen W; Johnson-Greene, Doug; Shea, Timothy; Krellman, Jason W; Rosenthal, Joseph A

2016-09-01

Posttraumatic seizures (PTS) are well-recognized acute and chronic complications of traumatic brain injury (TBI). Risk factors have been identified, but considerable variability in who develops PTS remains. Existing PTS prognostic models are not widely adopted for clinical use and do not reflect current trends in injury, diagnosis, or care. We aimed to develop and internally validate preliminary prognostic regression models to predict PTS during acute care hospitalization, and at year 1 and year 2 postinjury. Prognostic models predicting PTS during acute care hospitalization and year 1 and year 2 post-injury were developed using a recent (2011-2014) cohort from the TBI Model Systems National Database. Potential PTS predictors were selected based on previous literature and biologic plausibility. Bivariable logistic regression identified variables with a p-value < 0.20 that were used to fit initial prognostic models. Multivariable logistic regression modeling with backward-stepwise elimination was used to determine reduced prognostic models and to internally validate using 1,000 bootstrap samples. Fit statistics were calculated, correcting for overfitting (optimism). The prognostic models identified sex, craniotomy, contusion load, and pre-injury limitation in learning/remembering/concentrating as significant PTS predictors during acute hospitalization. Significant predictors of PTS at year 1 were subdural hematoma (SDH), contusion load, craniotomy, craniectomy, seizure during acute hospitalization, duration of posttraumatic amnesia, preinjury mental health treatment/psychiatric hospitalization, and preinjury incarceration. Year 2 significant predictors were similar to those of year 1: SDH, intraparenchymal fragment, craniotomy, craniectomy, seizure during acute hospitalization, and preinjury incarceration. Corrected concordance (C) statistics were 0.599, 0.747, and 0.716 for acute hospitalization, year 1, and year 2 models, respectively. The prognostic model for PTS during acute hospitalization did not discriminate well. Year 1 and year 2 models showed fair to good predictive validity for PTS. Cranial surgery, although medically necessary, requires ongoing research regarding potential benefits of increased monitoring for signs of epileptogenesis, PTS prophylaxis, and/or rehabilitation/social support. Future studies should externally validate models and determine clinical utility. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

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.

Variable selection under multiple imputation using the bootstrap in a prognostic study

PubMed Central

Heymans, Martijn W; van Buuren, Stef; Knol, Dirk L; van Mechelen, Willem; de Vet, Henrica CW

2007-01-01

Background Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection. Method In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels. Results We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found. Conclusion We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values. PMID:17629912

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

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.

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

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.

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

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

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.

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 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

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.

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.

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.

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.

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2011-01-01

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

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.

Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

PubMed

Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

2016-10-01

The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

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

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.

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.

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.

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.

Electromechanical actuators affected by multiple failures: Prognostic method based on spectral analysis techniques

NASA Astrophysics Data System (ADS)

Belmonte, D.; Vedova, M. D. L. Dalla; Ferro, C.; Maggiore, P.

2017-06-01

The proposal of prognostic algorithms able to identify precursors of incipient failures of primary flight command electromechanical actuators (EMA) is beneficial for the anticipation of the incoming failure: an early and correct interpretation of the failure degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. An innovative prognostic model-based approach, able to recognize the EMA progressive degradations before his anomalous behaviors become critical, is proposed: the Fault Detection and Identification (FDI) of the considered incipient failures is performed analyzing proper system operational parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. Subsequently, these operational parameters will be correlated with the actual EMA health condition by means of failure maps created by a reference monitoring model-based algorithm. In this work, the proposed method has been tested in case of EMA affected by combined progressive failures: in particular, partial stator single phase turn to turn short-circuit and rotor static eccentricity are considered. In order to evaluate the prognostic method, a numerical test-bench has been conceived. Results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual malfunctioning, minimizing the risk of fake alarms or unannounced failures.

Nutritional prognostic scores in patients with hilar cholangiocarcinoma treated by percutaneous transhepatic biliary stenting combined with 125I seed intracavitary irradiation: A retrospective observational study.

PubMed

Cui, Peiyuan; Pang, Qing; Wang, Yong; Qian, Zhen; Hu, Xiaosi; Wang, Wei; Li, Zongkuang; Zhou, Lei; Man, Zhongran; Yang, Song; Jin, Hao; Liu, Huichun

2018-06-01

We mainly aimed to preliminarily explore the prognostic values of nutrition-based prognostic scores in patients with advanced hilar cholangiocarcinoma (HCCA).We retrospectively analyzed 73 cases of HCCA, who underwent percutaneous transhepatic biliary stenting (PTBS) combined with I seed intracavitary irradiation from November 2012 to April 2017 in our department. The postoperative changes of total bilirubin (TBIL), direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and albumin (ALB) were observed. The preoperative clinical data were collected to calculate the nutrition-based scores, including controlling nutritional status (CONUT), C-reactive protein/albumin ratio (CAR), and prognostic nutritional index (PNI). Kaplan-Meier curve and Cox regression model were used for overall survival (OS) analyses.The serum levels of TBIL, DBIL, ALT, AST, and ALP significantly reduced, and ALB significantly increased at 1 month and 3 months postoperatively. The median survival time of the cohort was 12 months and the 1-year survival rate was 53.1%. Univariate analysis revealed that the statistically significant factors related to OS were CA19-9, TBIL, ALB, CONUT, and PNI. Multivariate analysis further identified CA19-9, CONUT, and PNI as independent prognostic factors.Nutrition-based prognostic scores, CONUT and PNI in particular, can be used as predictors of survival in unresectable HCCA.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Using Cox's proportional hazards model for prognostication in carcinoma of the upper aero-digestive tract.

PubMed

Wolfensberger, M

1992-01-01

One of the major short comings of the traditional TNM system is its limited potential for prognostication. With the development of multifactorial analysis techniques, such as Cox's proportional hazards model, it has become possible to simultaneously evaluate a large number of prognostic variables. Cox's model allows both the identification of prognostically relevant variables and the quantification of their prognostic influence. These characteristics make it a helpful tool for analysis as well as for prognostication. The goal of the present study was to develop a prognostic index for patients with carcinoma of the upper aero-digestive tract which makes use of all prognostically relevant variables. To accomplish this, the survival data of 800 patients with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx or larynx were analyzed. Sixty-one variables were screened for prognostic significance; of these only 19 variables (including age, tumor location, T, N and M stages, resection margins, capsular invasion of nodal metastases, and treatment modality) were found to significantly correlate with prognosis. With the help of Cox's equation, a prognostic index (PI) was computed for every combination of prognostic factors. To test the proposed model, the prognostic index was applied to 120 patients with carcinoma of the oral cavity or oropharynx. A comparison of predicted and observed survival showed good overall correlation, although actual survival tended to be better than predicted.

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

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.

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.

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.

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.

Gastric cancer, nutritional status, and outcome.

PubMed

Liu, Xuechao; Qiu, Haibo; Kong, Pengfei; Zhou, Zhiwei; Sun, Xiaowei

2017-01-01

We aim to investigate the prognostic value of several nutrition-based indices, including the prognostic nutritional index (PNI), performance status, body mass index, serum albumin, and preoperative body weight loss in patients with gastric cancer (GC). We retrospectively analyzed the records of 1,330 consecutive patients with GC undergoing curative surgery between October 2000 and September 2012. The relationship between nutrition-based indices and overall survival (OS) was examined using Kaplan-Meier analysis and Cox regression model. Following multivariate analysis, the PNI and preoperative body weight loss were the only nutritional-based indices independently associated with OS (hazard ratio [HR]: 1.356, 95% confidence interval [CI]: 1.051-1.748, P =0.019; HR: 1.152, 95% CI: 1.014-1.310, P =0.030, retrospectively). In stage-stratified analysis, multivariate analysis revealed that preoperative body weight loss was identified as an independent prognostic factor only in patients with stage III GC (HR: 1.223, 95% CI: 1.065-1.405, P =0.004), while the prognostic significance of PNI was not significant (all P >0.05). In patients with stage III GC, preoperative body weight loss stratified 5-year OS from 41.1% to 26.5%. When stratified by adjuvant chemotherapy, the prognostic significance of preoperative body weight loss was maintained in patients treated with surgery plus adjuvant chemotherapy and in patients treated with surgery alone ( P <0.001; P =0.003). Preoperative body weight loss is an independent prognostic factor for OS in patients with GC, especially in stage III disease. Preoperative body weight loss appears to be a superior predictor of outcome compared with other established nutrition-based indices.

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

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

NASA Technical Reports Server (NTRS)

Corbetta, M.; Sbarufatti, C.; Saxena, A.; Giglio, M.; Goebel, K.

2016-01-01

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

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

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.

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.

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.

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.

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

Risk stratification in myelodysplastic syndromes: is there a role for gene expression profiling?

PubMed

Zeidan, Amer M; Prebet, Thomas; Saad Aldin, Ehab; Gore, Steven David

2014-04-01

Evaluation of: Pellagatti A, Benner A, Mills KI et al. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J. Clin. Oncol. 31(28), 3557-3564 (2013). Patients with myelodysplastic syndromes (MDS) exhibit wide heterogeneity in clinical outcomes making accurate risk-stratification an integral part of the risk-adaptive management paradigm. Current prognostic schemes for MDS rely on clinicopathological parameters. Despite the increasing knowledge of the genetic landscape of MDS and the prognostic impact of many newly discovered molecular aberrations, none to date has been incorporated formally into the major risk models. Efforts are ongoing to use data generated from genome-wide high-throughput techniques to improve the 'individualized' outcome prediction for patients. We here discuss an important paper in which gene expression profiling (GEP) technology was applied to marrow CD34(+) cells from 125 MDS patients to generate and validate a standardized GEP-based prognostic signature.

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.

[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.

Urothelial cancer of the upper urinary tract: emerging biomarkers and integrative models for risk stratification.

PubMed

Mathieu, Romain; Vartolomei, Mihai D; Mbeutcha, Aurélie; Karakiewicz, Pierre I; Briganti, Alberto; Roupret, Morgan; Shariat, Shahrokh F

2016-08-01

The aim of this review was to provide an overview of current biomarkers and risk stratification models in urothelial cancer of the upper urinary tract (UTUC). A non-systematic Medline/PubMed literature search was performed using the terms "biomarkers", "preoperative models", "postoperative models", "risk stratification", together with "upper tract urothelial carcinoma". Original articles published between January 2003 and August 2015 were included based on their clinical relevance. Additional references were collected by cross referencing the bibliography of the selected articles. Various promising predictive and prognostic biomarkers have been identified in UTUC thanks to the increasing knowledge of the different biological pathways involved in UTUC tumorigenesis. These biomarkers may help identify tumors with aggressive biology and worse outcomes. Current tools aim at predicting muscle invasive or non-organ confined disease, renal failure after radical nephroureterectomy and survival outcomes. These models are still mainly based on imaging and clinicopathological feature and none has integrated biomarkers. Risk stratification in UTUC is still suboptimal, especially in the preoperative setting due to current limitations in staging and grading. Identification of novel biomarkers and external validation of current prognostic models may help improve risk stratification to allow evidence-based counselling for kidney-sparing approaches, perioperative chemotherapy and/or risk-based surveillance. Despite growing understanding of the biology underlying UTUC, management of this disease remains difficult due to the lack of validated biomarkers and the limitations of current predictive and prognostic tools. Further efforts and collaborations are necessaryry to allow their integration in daily practice.

Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data.

PubMed

Johnson, Brent A

2009-10-01

We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review.

PubMed

Chen, Jia-Mei; Li, Yan; Xu, Jun; Gong, Lei; Wang, Lin-Wei; Liu, Wen-Lou; Liu, Juan

2017-03-01

With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature-based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

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.

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.

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.

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.

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.

PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline- and taxane-based chemotherapy: correlative analysis of C9741 (Alliance)

PubMed Central

Liu, Minetta C; Pitcher, Brandelyn N; Mardis, Elaine R; Davies, Sherri R; Friedman, Paula N; Snider, Jacqueline E; Vickery, Tammi L; Reed, Jerry P; DeSchryver, Katherine; Singh, Baljit; Gradishar, William J; Perez, Edith A; Martino, Silvana; Citron, Marc L; Norton, Larry; Winer, Eric P; Hudis, Clifford A; Carey, Lisa A; Bernard, Philip S; Nielsen, Torsten O; Perou, Charles M; Ellis, Matthew J; Barry, William T

2016-01-01

PAM50 intrinsic breast cancer subtypes are prognostic independent of standard clinicopathologic factors. CALGB 9741 demonstrated improved recurrence-free (RFS) and overall survival (OS) with 2-weekly dose-dense (DD) versus 3-weekly therapy. A significant interaction between intrinsic subtypes and DD-therapy benefit was hypothesized. Suitable tumor samples were available from 1,471 (73%) of 2,005 subjects. Multiplexed gene-expression profiling generated the PAM50 subtype call, proliferation score, and risk of recurrence score (ROR-PT) for the evaluable subset of 1,311 treated patients. The interaction between DD-therapy benefit and intrinsic subtype was tested in a Cox proportional hazards model using two-sided alpha=0.05. Additional multivariable Cox models evaluated the proliferation and ROR-PT scores as continuous measures with selected clinical covariates. Improved outcomes for DD therapy in the evaluable subset mirrored results from the complete data set (RFS; hazard ratio=1.20; 95% confidence interval=0.99–1.44) with 12.3-year median follow-up. Intrinsic subtypes were prognostic of RFS (P<0.0001) irrespective of treatment assignment. No subtype-specific treatment effect on RFS was identified (interaction P=0.44). Proliferation and ROR-PT scores were prognostic for RFS (both P<0.0001), but no association with treatment benefit was seen (P=0.14 and 0.59, respectively). Results were similar for OS. The prognostic value of PAM50 intrinsic subtype was greater than estrogen receptor/HER2 immunohistochemistry classification. PAM50 gene signatures were highly prognostic but did not predict for improved outcomes with DD anthracycline- and taxane-based therapy. Clinical validation studies will assess the ability of PAM50 and other gene signatures to stratify patients and individualize treatment based on expected risks of distant recurrence. PMID:28691057

Prognostic Modeling of Valve Degradation within Power Stations

DTIC Science & Technology

2014-10-02

from the University of Strathclyde in 2013. His PhD focuses on condition monitoring and prognostics for tidal turbines , in collaboration with Andritz...Hydro Hammerfest, a leading tidal turbine manufacturer. Victoria M. Catterson is a Lecturer within the Institute for Energy and Environment at the...based method. Case study data is generated through simulation of valves within a 400MW Combined Cycle Gas Turbine power station. High fidelity

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

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.

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.

Systemic Inflammation-Based Biomarkers and Survival in HIV-Positive Subject With Solid Cancer in an Italian Multicenter Study.

PubMed

Raffetti, Elena; Donato, Francesco; Pezzoli, Chiara; Digiambenedetto, Simona; Bandera, Alessandra; Di Pietro, Massimo; Di Filippo, Elisa; Maggiolo, Franco; Sighinolfi, Laura; Fornabaio, Chiara; Castelnuovo, Filippo; Ladisa, Nicoletta; Castelli, Francesco; Quiros Roldan, Eugenia

2015-08-15

Recently, some systemic inflammation-based biomarkers have been demonstrated useful for predicting risk of death in patients with solid cancer independently of tumor characteristics. This study aimed to investigate the prognostic role of systemic inflammation-based biomarkers in HIV-infected patients with solid tumors and to propose a risk score for mortality in these subjects. Clinical and pathological data on solid AIDS-defining cancer (ADC) and non-AIDS-defining cancer (NADC), diagnosed between 1998 and 2012 in an Italian cohort, were analyzed. To evaluate the prognostic role of systemic inflammation- and nutrition-based markers, univariate and multivariable Cox regression models were applied. To compute the risk score equation, the patients were randomly assigned to a derivation and a validation sample. A total of 573 patients (76.3% males) with a mean age of 46.2 years (SD = 10.3) were enrolled. 178 patients died during a median of 3.2 years of follow-up. For solid NADCs, elevated Glasgow Prognostic Score, modified Glasgow Prognostic Score, neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and Prognostic Nutritional Index were independently associated with risk of death; for solid ADCs, none of these markers was associated with risk of death. For solid NADCs, we computed a mortality risk score on the basis of age at cancer diagnosis, intravenous drug use, and Prognostic Nutritional Index. The areas under the receiver operating characteristic curve were 0.67 (95% confidence interval: 0.58 to 0.75) in the derivation sample and 0.66 (95% confidence interval: 0.54 to 0.79) in the validation sample. Inflammatory biomarkers were associated with risk of death in HIV-infected patients with solid NADCs but not with ADCs.

Prognosis Research Strategy (PROGRESS) 3: prognostic model research.

PubMed

Steyerberg, Ewout W; Moons, Karel G M; van der Windt, Danielle A; Hayden, Jill A; Perel, Pablo; Schroter, Sara; Riley, Richard D; Hemingway, Harry; Altman, Douglas G

2013-01-01

Prognostic models are abundant in the medical literature yet their use in practice seems limited. In this article, the third in the PROGRESS series, the authors review how such models are developed and validated, and then address how prognostic models are assessed for their impact on practice and patient outcomes, illustrating these ideas with examples.

Using prognostic models in CLL to personalize approach to clinical care: Are we there yet?

PubMed

Mina, Alain; Sandoval Sus, Jose; Sleiman, Elsa; Pinilla-Ibarz, Javier; Awan, Farrukh T; Kharfan-Dabaja, Mohamed A

2018-03-01

Four decades ago, two staging systems were developed to help stratify CLL into different prognostic categories. These systems, the Rai and the Binet staging, depended entirely on abnormal exam findings and evidence of anemia and thrombocytopenia. Better understanding of biologic, genetic, and molecular characteristics of CLL have contributed to better appreciating its clinical heterogeneity. New prognostic models, the GCLLSG prognostic index and the CLL-IPI, emerged. They incorporate biologic and genetic information related to CLL and are capable of predicting survival outcomes and cases anticipated to need therapy earlier in the disease course. Accordingly, these newer models are helping develop better informed surveillance strategies and ultimately tailor treatment intensity according to presence (or lack thereof) of certain prognostic markers. This represents a step towards personalizing care of CLL patients. We anticipate that as more prognostic factors continue to be identified, the GCLLSG prognostic index and CLL-IPI models will undergo further revisions. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

[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.

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

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.

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

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.

Diffuse and Focal Brain Injury in a Large Animal Model of PTE: Mechanisms Underlying Epileptogenesis

DTIC Science & Technology

2017-10-01

subacute and chronic post -injury periods as a potential prognostic marker for PTE. The SNTF blood test is an electrochemiluminescence-based sandwich...contribution of each of these types of injury to epileptogenic brain activity and ultimately post traumatic epilepsy (PTE) is unclear, as are the mechanisms...nine months post injury, and blood biomarkers are being analyzed throughout in order to evaluate them as potential prognostic measures for the

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.

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.

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 tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer.

PubMed

Vollan, Hans Kristian Moen; Rueda, Oscar M; Chin, Suet-Feung; Curtis, Christina; Turashvili, Gulisa; Shah, Sohrab; Lingjærde, Ole Christian; Yuan, Yinyin; Ng, Charlotte K; Dunning, Mark J; Dicks, Ed; Provenzano, Elena; Sammut, Stephen; McKinney, Steven; Ellis, Ian O; Pinder, Sarah; Purushotham, Arnie; Murphy, Leigh C; Kristensen, Vessela N; Brenton, James D; Pharoah, Paul D P; Børresen-Dale, Anne-Lise; Aparicio, Samuel; Caldas, Carlos

2015-01-01

Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (n = 159), 17q (n = 176) and 11q (n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER-) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (r = 0.27 and r = 0.42, p < 0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PFS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER- disease. None of the expression-based predictors were prognostic in the ER- subset. We found that a model including CAAI and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAI. Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAAI as an independent predictor of survival in both ER+ and ER- breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

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.

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.

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.

Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods.

PubMed

Parkinson, Craig; Foley, Kieran; Whybra, Philip; Hills, Robert; Roberts, Ashley; Marshall, Chris; Staffurth, John; Spezi, Emiliano

2018-04-11

Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

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

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

Distributed Prognostic Health Management with Gaussian Process Regression

NASA Technical Reports Server (NTRS)

Saha, Sankalita; Saha, Bhaskar; Saxena, Abhinav; Goebel, Kai Frank

2010-01-01

Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

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

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

A utility-based design for randomized comparative trials with ordinal outcomes and prognostic subgroups.

PubMed

Murray, Thomas A; Yuan, Ying; Thall, Peter F; Elizondo, Joan H; Hofstetter, Wayne L

2018-01-22

A design is proposed for randomized comparative trials with ordinal outcomes and prognostic subgroups. The design accounts for patient heterogeneity by allowing possibly different comparative conclusions within subgroups. The comparative testing criterion is based on utilities for the levels of the ordinal outcome and a Bayesian probability model. Designs based on two alternative models that include treatment-subgroup interactions are considered, the proportional odds model and a non-proportional odds model with a hierarchical prior that shrinks toward the proportional odds model. A third design that assumes homogeneity and ignores possible treatment-subgroup interactions also is considered. The three approaches are applied to construct group sequential designs for a trial of nutritional prehabilitation versus standard of care for esophageal cancer patients undergoing chemoradiation and surgery, including both untreated patients and salvage patients whose disease has recurred following previous therapy. A simulation study is presented that compares the three designs, including evaluation of within-subgroup type I and II error probabilities under a variety of scenarios including different combinations of treatment-subgroup interactions. © 2018, The International Biometric Society.

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

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.

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.

An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.

PubMed

Remontet, L; Bossard, N; Belot, A; Estève, J

2007-05-10

Relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death. We propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors. The baseline hazard was modelled until 10 years follow-up using parametric continuous functions. Six models including cubic regression splines were considered and the Akaike Information Criterion was used to select the final model. This approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information. Splines were also used to model simultaneously non-linear effects of continuous covariates and time-dependent hazard ratios. This led to a graphical representation of the hazard ratio that can be useful for clinical interpretation. Estimates of these models were obtained by likelihood maximization. We showed that these estimates could be also obtained using standard algorithms for Poisson regression. Copyright 2006 John Wiley & Sons, Ltd.

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.

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers.

PubMed

Long, Nguyen Phuoc; Jung, Kyung Hee; Yoon, Sang Jun; Anh, Nguyen Hoang; Nghi, Tran Diem; Kang, Yun Pyo; Yan, Hong Hua; Min, Jung Eun; Hong, Soon-Sun; Kwon, Sung Won

2017-12-12

Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner. Deep learning-based diagnostic models were established based on the genetic panels of intrinsic genes of cervical carcinogenesis as well as on the unbiased variable selection approach. Survival analysis was also conducted to explore the potential biomarker candidates for prognostic assessment. Our results showed that cell cycle, RNA transport, mRNA surveillance, and one carbon pool by folate were the key regulatory mechanisms involved in the initiation, progression, and metastasis of CxCa. Various genetic panels combined with machine learning algorithms successfully differentiated CxCa from CIN and normalcy in cross-study normalized data sets. In particular, the 168-gene deep learning model for the differentiation of cancer from normalcy achieved an externally validated accuracy of 97.96% (99.01% sensitivity and 95.65% specificity). Survival analysis revealed that ZNF281 and EPHB6 were the two most promising prognostic genetic markers for CxCa among others. Our findings open new opportunities to enhance current understanding of the characteristics of CxCa pathobiology. In addition, the combination of transcriptomics-based signatures and deep learning classification may become an important approach to improve CxCa diagnosis and management in clinical practice.

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers

PubMed Central

Long, Nguyen Phuoc; Jung, Kyung Hee; Yoon, Sang Jun; Anh, Nguyen Hoang; Nghi, Tran Diem; Kang, Yun Pyo; Yan, Hong Hua; Min, Jung Eun; Hong, Soon-Sun; Kwon, Sung Won

2017-01-01

Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner. Deep learning-based diagnostic models were established based on the genetic panels of intrinsic genes of cervical carcinogenesis as well as on the unbiased variable selection approach. Survival analysis was also conducted to explore the potential biomarker candidates for prognostic assessment. Our results showed that cell cycle, RNA transport, mRNA surveillance, and one carbon pool by folate were the key regulatory mechanisms involved in the initiation, progression, and metastasis of CxCa. Various genetic panels combined with machine learning algorithms successfully differentiated CxCa from CIN and normalcy in cross-study normalized data sets. In particular, the 168-gene deep learning model for the differentiation of cancer from normalcy achieved an externally validated accuracy of 97.96% (99.01% sensitivity and 95.65% specificity). Survival analysis revealed that ZNF281 and EPHB6 were the two most promising prognostic genetic markers for CxCa among others. Our findings open new opportunities to enhance current understanding of the characteristics of CxCa pathobiology. In addition, the combination of transcriptomics-based signatures and deep learning classification may become an important approach to improve CxCa diagnosis and management in clinical practice. PMID:29312619

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

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.

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.

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 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.

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.

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.

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

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

Fried, David V.; Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas; Tucker, Susan L.

2014-11-15

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation wasmore » used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78.8% (±3.9% SD) classification reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions: Pretreatment tumor texture may provide prognostic information beyond that obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ∼80%. External validation would be required to establish texture as a prognostic factor.« less

[Prognostic value of three different staging schemes based on pN, MLR and LODDS in patients with T3 esophageal cancer].

PubMed

Wang, L; Cai, L; Chen, Q; Jiang, Y H

2017-10-23

Objective: To evaluate the prognostic value of three different staging schemes based on positive lymph nodes (pN), metastatic lymph nodes ratio (MLR) and log odds of positive lymph nodes (LODDS) in patients with T3 esophageal cancer. Methods: From 2007 to 2014, clinicopathological characteristics of 905 patients who were pathologically diagnosed as T3 esophageal cancer and underwent radical esophagectomy in Zhejiang Cancer Hospital were retrospectively analyzed. Kaplan-Meier curves and Multivariate Cox proportional hazards models were used to evaluate the independent prognostic factors. The values of three lymph node staging schemes for predicting 5-year survival were analyzed by using receiver operating characteristic (ROC) curves. Results: The 1-, 3- and 5-year overall survival rates of patients with T3 esophageal cancer were 80.9%, 50.0% and 38.4%, respectively. Multivariate analysis showed that MLR stage, LODDS stage and differentiation were independent prognostic survival factors ( P <0.05 for all). ROC curves showed that the area under the curve of pN stage, MLR stage, LODDS stage was 0.607, 0.613 and 0.618, respectively. However, the differences were not statistically significant ( P >0.05). Conclusions: LODDS is an independent prognostic factor for patients with T3 esophageal cancer. The value of LODDS staging system may be superior to pN staging system for evaluating the prognosis of these patients.

A Thermal-based Two-Source Energy Balance Model for Estimating Evapotranspiration over Complex Canopies

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation describes a robust but relatively simple LS...

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

Prognostic value of platelet-to-lymphocyte ratio in pancreatic cancer: a comprehensive meta-analysis of 17 cohort studies.

PubMed

Zhou, Yongping; Cheng, Sijin; Fathy, Abdel Hamid; Qian, Haixin; Zhao, Yongzhao

2018-01-01

Several studies were conducted to explore the prognostic value of platelet-to-lymphocyte ratio (PLR) in pancreatic cancer and have reported contradictory results. This study aims to summarize the prognostic role of PLR in pancreatic cancer. Embase, PubMed and Cochrane Library were completely searched. The cohort studies focusing on the prognostic role of PLR in pancreatic cancer were eligible. The overall survival (OS) and progression-free survival (PFS) were analyzed. Fifteen papers containing 17 cohort studies with pancreatic cancer were identified. The results showed patients that with low PLR might have longer OS when compared to the patients with high PLR (hazard ratio=1.28, 95% CI=1.17-1.40, P <0.00001; I 2 =42%). Similar results were observed in the subgroup analyses of OS, which was based on the analysis model, ethnicity, sample size and cut-off value. Further analyses based on the adjusted potential confounders were conducted, including CA199, neutrophil-to-lymphocyte ratio, modified Glasgow Prognostic Score, albumin, C-reactive protein, Eastern Cooperative Oncology Group, stage, tumor size, nodal involvement, tumor differentiation, margin status, age and gender, which confirmed that low PLR was a protective factor in pancreatic cancer. In addition, low PLR was significantly associated with longer PFS when compared to high PLR in pancreatic cancer (hazard ratio=1.27, 95% CI=1.03-1.57, P =0.03; I 2 =33%). In conclusion, it was found that high PLR is an unfavorable predictor of OS and PFS in patients with pancreatic cancer, and PLR is a promising prognostic biomarker for pancreatic cancer.

The prognostic value of preoperative inflammation-based prognostic scores and nutritional status for overall survival in resected patients with nonmetastatic Siewert type II/III adenocarcinoma of esophagogastric junction.

PubMed

Zhang, Lixiang; Su, Yezhou; Chen, Zhangming; Wei, Zhijian; Han, Wenxiu; Xu, Aman

2017-07-01

Immune and nutritional status of patients have been reported to predict postoperative complications, recurrence, and prognosis of patients with cancer. Therefore, this retrospective study aimed to explore the prognostic value of preoperative inflammation-based prognostic scores [neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR)] and nutritional status [prognostic nutritional index (PNI), body mass index (BMI), hemoglobin, albumin, and prealbumin] for overall survival (OS) in adenocarcinoma of esophagogastric junction (AEG) patients. A total of 355 patients diagnosed with Siewert type II/III AEG and underwent surgery between October 2010 and December 2011 were followed up until October 2016. Receiver operating characteristic (ROC) curve analysis was used to determine the cutoff values of NLR, PLR, and PNI. Kaplan-Meier curves and Cox regression analyses were used to calculate the OS characteristics. The ideal cutoff values for predicting OS were 3.5 for NLR, 171 for PLR, and 51.3 for PNI according to the ROC curve. The patients with hemoglobin <120 g/L (P = .001), prealbumin <180 mg/L (P = .000), PNI <51.3 (P = .010), NLR >3.5 (P = .000), PLR >171 (P = .006), and low BMI group (P = .000) had shorter OS. And multivariate survival analysis using the Cox proportional hazards model showed that the tumor-node-metastasis stage, BMI, NLR, and prealbumin levels were independent risk factors for the OS. Our study demonstrated that preoperative prealbumin, BMI, and NLR were independent prognostic factors of AEG patients.

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

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

Prognostic and health management of active assets in nuclear power plants

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

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

This study presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and twomore » wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets.« less

Prognostic and health management of active assets in nuclear power plants

DOE PAGES

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

2015-06-04

This study presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and twomore » wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets.« less

A comparison of the prognostic value of preoperative inflammation-based scores and TNM stage in patients with gastric cancer.

PubMed

Pan, Qun-Xiong; Su, Zi-Jian; Zhang, Jian-Hua; Wang, Chong-Ren; Ke, Shao-Ying

2015-01-01

People's Republic of China is one of the countries with the highest incidence of gastric cancer, accounting for 45% of all new gastric cancer cases in the world. Therefore, strong prognostic markers are critical for the diagnosis and survival of Chinese patients suffering from gastric cancer. Recent studies have begun to unravel the mechanisms linking the host inflammatory response to tumor growth, invasion and metastasis in gastric cancers. Based on this relationship between inflammation and cancer progression, several inflammation-based scores have been demonstrated to have prognostic value in many types of malignant solid tumors. To compare the prognostic value of inflammation-based prognostic scores and tumor node metastasis (TNM) stage in patients undergoing gastric cancer resection. The inflammation-based prognostic scores were calculated for 207 patients with gastric cancer who underwent surgery. Glasgow prognostic score (GPS), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), prognostic nutritional index (PNI), and prognostic index (PI) were analyzed. Linear trend chi-square test, likelihood ratio chi-square test, and receiver operating characteristic were performed to compare the prognostic value of the selected scores and TNM stage. In univariate analysis, preoperative serum C-reactive protein (P<0.001), serum albumin (P<0.001), GPS (P<0.001), PLR (P=0.002), NLR (P<0.001), PI (P<0.001), PNI (P<0.001), and TNM stage (P<0.001) were significantly associated with both overall survival and disease-free survival of patients with gastric cancer. In multivariate analysis, GPS (P=0.024), NLR (P=0.012), PI (P=0.001), TNM stage (P<0.001), and degree of differentiation (P=0.002) were independent predictors of gastric cancer survival. GPS and TNM stage had a comparable prognostic value and higher linear trend chi-square value, likelihood ratio chi-square value, and larger area under the receiver operating characteristic curve as compared to other inflammation-based prognostic scores. The present study indicates that preoperative GPS and TNM stage are robust predictors of gastric cancer survival as compared to NLR, PLR, PI, and PNI in patients undergoing tumor resection.

A comparison of the prognostic value of preoperative inflammation-based scores and TNM stage in patients with gastric cancer

PubMed Central

Pan, Qun-Xiong; Su, Zi-Jian; Zhang, Jian-Hua; Wang, Chong-Ren; Ke, Shao-Ying

2015-01-01

Background People’s Republic of China is one of the countries with the highest incidence of gastric cancer, accounting for 45% of all new gastric cancer cases in the world. Therefore, strong prognostic markers are critical for the diagnosis and survival of Chinese patients suffering from gastric cancer. Recent studies have begun to unravel the mechanisms linking the host inflammatory response to tumor growth, invasion and metastasis in gastric cancers. Based on this relationship between inflammation and cancer progression, several inflammation-based scores have been demonstrated to have prognostic value in many types of malignant solid tumors. Objective To compare the prognostic value of inflammation-based prognostic scores and tumor node metastasis (TNM) stage in patients undergoing gastric cancer resection. Methods The inflammation-based prognostic scores were calculated for 207 patients with gastric cancer who underwent surgery. Glasgow prognostic score (GPS), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), prognostic nutritional index (PNI), and prognostic index (PI) were analyzed. Linear trend chi-square test, likelihood ratio chi-square test, and receiver operating characteristic were performed to compare the prognostic value of the selected scores and TNM stage. Results In univariate analysis, preoperative serum C-reactive protein (P<0.001), serum albumin (P<0.001), GPS (P<0.001), PLR (P=0.002), NLR (P<0.001), PI (P<0.001), PNI (P<0.001), and TNM stage (P<0.001) were significantly associated with both overall survival and disease-free survival of patients with gastric cancer. In multivariate analysis, GPS (P=0.024), NLR (P=0.012), PI (P=0.001), TNM stage (P<0.001), and degree of differentiation (P=0.002) were independent predictors of gastric cancer survival. GPS and TNM stage had a comparable prognostic value and higher linear trend chi-square value, likelihood ratio chi-square value, and larger area under the receiver operating characteristic curve as compared to other inflammation-based prognostic scores. Conclusion The present study indicates that preoperative GPS and TNM stage are robust predictors of gastric cancer survival as compared to NLR, PLR, PI, and PNI in patients undergoing tumor resection. PMID:26124667

The 3′UTR signature defines a highly metastatic subgroup of triple-negative breast cancer

PubMed Central

Wang, Lei; Hu, Xin; Wang, Peng; Shao, Zhi-Ming

2016-01-01

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with an aggressive clinical course. Prognostic models are needed to chart potential patient outcomes. To address this, we used alternative 3′UTR patterns to improve postoperative risk stratification. We collected 327 publicly available microarrays and generated the 3′UTR landscape based on expression ratios of alternative 3′UTR. After initial feature filtering, we built a 17-3′UTR-based classifier using an elastic net model. Time-dependent ROC comparisons and Kaplan–Meier analyses confirmed an outstanding discriminating power of our prognostic model for TNBC patients. In the training cohort, 5-year event-free survival (EFS) was 78.6% (95% CI 71.2–86.0) for the low-risk group, and 16.3% (95% CI 2.3–30.4) for the high-risk group (log-rank p<0.0001; hazard ratio [HR] 8.29, 95% CI 4.78–14.4), In the validation set, 5-year EFS was 75.6% (95% CI 68.0–83.2) for the low-risk group, and 33.2% (95% CI 17.1–49.3) for the high-risk group (log-rank p<0.0001; HR 3.17, 95% CI 1.66–5.42). In conclusion, the 17-3′UTR-based classifier provides a superior prognostic performance for estimating disease recurrence and metastasis in TNBC patients and it may permit personalized management strategies. PMID:27494850

A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

The Predictive Accuracy of PREDICT: A Personalized Decision-Making Tool for Southeast Asian Women With Breast Cancer

PubMed Central

Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M.; Hartman, Mikael; Bhoo-Pathy, Nirmala

2015-01-01

Abstract Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients’ actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: −1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74–0.81) and 0.73 (95% CI: 0.68–0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings. PMID:25715267

The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

PubMed

Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M; Hartman, Mikael; Bhoo-Pathy, Nirmala

2015-02-01

Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.

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.

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.

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

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.

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.

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.

Prognostic scoring systems for myelodysplastic syndromes (MDS) in a population-based setting: a report from the Swedish MDS register.

PubMed

Moreno Berggren, Daniel; Folkvaljon, Yasin; Engvall, Marie; Sundberg, Johan; Lambe, Mats; Antunovic, Petar; Garelius, Hege; Lorenz, Fryderyk; Nilsson, Lars; Rasmussen, Bengt; Lehmann, Sören; Hellström-Lindberg, Eva; Jädersten, Martin; Ejerblad, Elisabeth

2018-06-01

The myelodysplastic syndromes (MDS) have highly variable outcomes and prognostic scoring systems are important tools for risk assessment and to guide therapeutic decisions. However, few population-based studies have compared the value of the different scoring systems. With data from the nationwide Swedish population-based MDS register we validated the International Prognostic Scoring System (IPSS), revised IPSS (IPSS-R) and the World Health Organization (WHO) Classification-based Prognostic Scoring System (WPSS). We also present population-based data on incidence, clinical characteristics including detailed cytogenetics and outcome from the register. The study encompassed 1329 patients reported to the register between 2009 and 2013, 14% of these had therapy-related MDS (t-MDS). Based on the MDS register, the yearly crude incidence of MDS in Sweden was 2·9 per 100 000 inhabitants. IPSS-R had a significantly better prognostic power than IPSS (P < 0·001). There was a trend for better prognostic power of IPSS-R compared to WPSS (P = 0·05) and for WPSS compared to IPSS (P = 0·07). IPSS-R was superior to both IPSS and WPSS for patients aged ≤70 years. Patients with t-MDS had a worse outcome compared to de novo MDS (d-MDS), however, the validity of the prognostic scoring systems was comparable for d-MDS and t-MDS. In conclusion, population-based studies are important to validate prognostic scores in a 'real-world' setting. In our nationwide cohort, the IPSS-R showed the best predictive power. © 2018 John Wiley & Sons Ltd.

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

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

[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.

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.

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.

An Overview of Prognosis Health Management Research at Glenn Research Center for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

NASA Technical Reports Server (NTRS)

Arnold, Steven M.; Goldberg, Robert K.; Lerch, Bradley A.; Saleeb, Atef F.

2009-01-01

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s). Key technologies which comprise the proposed integrated approach include (1) diagnostic/detection methodology, (2) prognosis/lifing methodology, (3) diagnostic/prognosis linkage, (4) experimental validation, and (5) material data information management system. A specific prognosis lifing methodology, experimental characterization and validation and data information management are the focal point of current activities being pursued within this integrated approach. The prognostic lifing methodology is based on an advanced multimechanism viscoelastoplastic model which accounts for both stiffness and/or strength reduction damage variables. Methods to characterize both the reversible and irreversible portions of the model are discussed. Once the multiscale model is validated the intent is to link it to appropriate diagnostic methods to provide a full-featured structural health monitoring system.

An Overview of Prognosis Health Management Research at GRC for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

NASA Technical Reports Server (NTRS)

Arnold, Steven M.; Goldberg, Robert K.; Lerch, Bradley A.; Saleeb, Atef F.

2009-01-01

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s). Key technologies which comprise the proposed integrated approach include 1) diagnostic/detection methodology, 2) prognosis/lifing methodology, 3) diagnostic/prognosis linkage, 4) experimental validation and 5) material data information management system. A specific prognosis lifing methodology, experimental characterization and validation and data information management are the focal point of current activities being pursued within this integrated approach. The prognostic lifing methodology is based on an advanced multi-mechanism viscoelastoplastic model which accounts for both stiffness and/or strength reduction damage variables. Methods to characterize both the reversible and irreversible portions of the model are discussed. Once the multiscale model is validated the intent is to link it to appropriate diagnostic methods to provide a full-featured structural health monitoring system.

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.

Current Pressure Transducer Application of Model-based Prognostics Using Steady State Conditions

NASA Technical Reports Server (NTRS)

Teubert, Christopher; Daigle, Matthew J.

2014-01-01

Prognostics is the process of predicting a system's future states, health degradation/wear, and remaining useful life (RUL). This information plays an important role in preventing failure, reducing downtime, scheduling maintenance, and improving system utility. Prognostics relies heavily on wear estimation. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, describes how this is used to predict future states, and presents a case study of a current/pressure (I/P) Transducer. I/P Transducer nominal and off-nominal behaviors are characterized using a physics-based model, and validated against expected and observed component behavior. This model is used to map observed steady-state responses to corresponding fault parameter values in the form of a lookup table. This method was chosen because of its fast, efficient nature, and its ability to be applied to both linear and non-linear systems. Using measurements of the steady state output, and the lookup table, wear is estimated. A regression is used to estimate the wear propagation parameter and characterize the damage progression function, which are used to predict future states and the remaining useful life of the system.

Spin-Up and Tuning of the Global Carbon Cycle Model Inside the GISS ModelE2 GCM

NASA Technical Reports Server (NTRS)

Aleinov, Igor; Kiang, Nancy Y.; Romanou, Anastasia

2015-01-01

Planetary carbon cycle involves multiple phenomena, acting at variety of temporal and spacial scales. The typical times range from minutes for leaf stomata physiology to centuries for passive soil carbon pools and deep ocean layers. So, finding a satisfactory equilibrium state becomes a challenging and computationally expensive task. Here we present the spin-up processes for different configurations of the GISS Carbon Cycle model from the model forced with MODIS observed Leaf Area Index (LAI) and prescribed ocean to the prognostic LAI and to the model fully coupled to the dynamic ocean and ocean biology. We investigate the time it takes the model to reach the equilibrium and discuss the ways to speed up this process. NASA Goddard Institute for Space Studies General Circulation Model (GISS ModelE2) is currently equipped with all major algorithms necessary for the simulation of the Global Carbon Cycle. The terrestrial part is presented by Ent Terrestrial Biosphere Model (Ent TBM), which includes leaf biophysics, prognostic phenology and soil biogeochemistry module (based on Carnegie-Ames-Stanford model). The ocean part is based on the NASA Ocean Biogeochemistry Model (NOBM). The transport of atmospheric CO2 is performed by the atmospheric part of ModelE2, which employs quadratic upstream algorithm for this purpose.

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

Autophagy-related prognostic signature for breast cancer.

PubMed

Gu, Yunyan; Li, Pengfei; Peng, Fuduan; Zhang, Mengmeng; Zhang, Yuanyuan; Liang, Haihai; Zhao, Wenyuan; Qi, Lishuang; Wang, Hongwei; Wang, Chenguang; Guo, Zheng

2016-03-01

Autophagy is a process that degrades intracellular constituents, such as long-lived or damaged proteins and organelles, to buffer metabolic stress under starvation conditions. Deregulation of autophagy is involved in the progression of cancer. However, the predictive value of autophagy for breast cancer prognosis remains unclear. First, based on gene expression profiling, we found that autophagy genes were implicated in breast cancer. Then, using the Cox proportional hazard regression model, we detected autophagy prognostic signature for breast cancer in a training dataset. We identified a set of eight autophagy genes (BCL2, BIRC5, EIF4EBP1, ERO1L, FOS, GAPDH, ITPR1 and VEGFA) that were significantly associated with overall survival in breast cancer. The eight autophagy genes were assigned as a autophagy-related prognostic signature for breast cancer. Based on the autophagy-related signature, the training dataset GSE21653 could be classified into high-risk and low-risk subgroups with significantly different survival times (HR = 2.72, 95% CI = (1.91, 3.87); P = 1.37 × 10(-5)). Inactivation of autophagy was associated with shortened survival of breast cancer patients. The prognostic value of the autophagy-related signature was confirmed in the testing dataset GSE3494 (HR = 2.12, 95% CI = (1.48, 3.03); P = 1.65 × 10(-3)) and GSE7390 (HR = 1.76, 95% CI = (1.22, 2.54); P = 9.95 × 10(-4)). Further analysis revealed that the prognostic value of the autophagy signature was independent of known clinical prognostic factors, including age, tumor size, grade, estrogen receptor status, progesterone receptor status, ERBB2 status, lymph node status and TP53 mutation status. Finally, we demonstrated that the autophagy signature could also predict distant metastasis-free survival for breast cancer. © 2015 Wiley Periodicals, Inc.

A Generic Software Architecture For Prognostics

NASA Technical Reports Server (NTRS)

Teubert, Christopher; Daigle, Matthew J.; Sankararaman, Shankar; Goebel, Kai; Watkins, Jason

2017-01-01

Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. As a result, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use.

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.

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

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.

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.

Independent Prognostic Value of Serum Markers in Diffuse Large B-Cell Lymphoma in the Era of the NCCN-IPI.

PubMed

Melchardt, Thomas; Troppan, Katharina; Weiss, Lukas; Hufnagl, Clemens; Neureiter, Daniel; Tränkenschuh, Wolfgang; Schlick, Konstantin; Huemer, Florian; Deutsch, Alexander; Neumeister, Peter; Greil, Richard; Pichler, Martin; Egle, Alexander

2015-12-01

Several serum parameters have been evaluated for adding prognostic value to clinical scoring systems in diffuse large B-cell lymphoma (DLBCL), but none of the reports used multivariate testing of more than one parameter at a time. The goal of this study was to validate widely available serum parameters for their independent prognostic impact in the era of the National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI) score to determine which were the most useful. This retrospective bicenter analysis includes 515 unselected patients with DLBCL who were treated with rituximab and anthracycline-based chemoimmunotherapy between 2004 and January 2014. Anemia, high C-reactive protein, and high bilirubin levels had an independent prognostic value for survival in multivariate analyses in addition to the NCCN-IPI, whereas neutrophil-to-lymphocyte ratio, high gamma-glutamyl transferase levels, and platelets-to-lymphocyte ratio did not. In our cohort, we describe the most promising markers to improve the NCCN-IPI. Anemia and high C-reactive protein levels retain their power in multivariate testing even in the era of the NCCN-IPI. The negative role of high bilirubin levels may be associated as a marker of liver function. Further studies are warranted to incorporate these markers into prognostic models and define their role opposite novel molecular markers. Copyright © 2015 by the National Comprehensive Cancer Network.

Mode of detection: an independent prognostic factor for women with breast cancer.

PubMed

Hofvind, Solveig; Holen, Åsne; Román, Marta; Sebuødegård, Sofie; Puig-Vives, Montse; Akslen, Lars

2016-06-01

To investigate breast cancer survival and risk of breast cancer death by detection mode (screen-detected, interval, and detected outside the screening programme), adjusting for prognostic and predictive tumour characteristics. Information about detection mode, prognostic (age, tumour size, histologic grade, lymph node status) and predictive factors (molecular subtypes based on immunohistochemical analyses of hormone receptor status (estrogen and progesterone) and Her2 status) were available for 8344 women in Norway aged 50-69 at diagnosis of breast cancer, 2005-2011. A total of 255 breast cancer deaths were registered by the end of 2011. Kaplan-Meier method was used to estimate six years breast cancer specific survival and Cox proportional hazard model to estimate hazard ratio (HR) for breast cancer death by detection mode, adjusting for prognostic and predictive factors. Women with screen-detected cancer had favourable prognostic and predictive tumour characteristics compared with interval cancers and those detected outside the screening programme. The favourable characteristics were present for screen-detected cancers, also within the subtypes. Adjusted HR of dying from breast cancer was two times higher for women with symptomatic breast cancer (interval or outside the screening), using screen-detected tumours as the reference. Detection mode is an independent prognostic factor for women diagnosed with breast cancer. Information on detection mode might be relevant for patient management to avoid overtreatment. © The Author(s) 2015.

Prognostic factors and scoring system for survival in colonic perforation.

PubMed

Komatsu, Shuhei; Shimomatsuya, Takumi; Nakajima, Masayuki; Amaya, Hirokazu; Kobuchi, Taketsune; Shiraishi, Susumu; Konishi, Sayuri; Ono, Susumu; Maruhashi, Kazuhiro

2005-01-01

No ideal and generally accepted prognostic factors and scoring systems exist to determine the prognosis of peritonitis associated with colonic perforation. This study was designed to investigate prognostic factors and evaluate the various scoring systems to allow identification of high-risk patients. Between 1996 and 2003, excluding iatrogenic and trauma cases, 26 consecutive patients underwent emergency operations for colorectal perforation and were selected for this retrospective study. Several clinical factors were analyzed as possible predictive factors, and APACHE II, SOFA, MPI, and MOF scores were calculated. The overall mortality was 26.9%. Compared with the survivors, non-survivors were found more frequently in Hinchey's stage III-IV, a low preoperative marker of pH, base excess (BE), and a low postoperative marker of white blood cell count, PaO2/FiO2 ratio, and renal output (24h). According to the logistic regression model, BE was a significant independent variable. Concerning the prognostic scoring systems, an APACHE II score of 19, a SOFA score of 8, an MPI score of 30, and an MOF score of 7 or more were significantly related to poor prognosis. Preoperative BE and postoperative white blood cell count were reliable prognostic factors and early classification using prognostic scoring systems at specific points in the disease process are useful to improve our understanding of the problems involved.

Prognostic categories and timing of negative prognostic communication from critical care physicians to family members at end-of-life in an intensive care unit.

PubMed

Gutierrez, Karen M

2013-09-01

Negative prognostic communication is often delayed in intensive care units, which limits time for families to prepare for end-of-life. This descriptive study, informed by ethnographic methods, was focused on exploring critical care physician communication of negative prognoses to families and identifying timing influences. Prognostic communication of critical care physicians to nurses and family members was observed and physicians and family members were interviewed. Physician perception of prognostic certainty, based on an accumulation of empirical data, and the perceived need for decision-making, drove the timing of prognostic communication, rather than family needs. Although prognoses were initially identified using intuitive knowledge for patients in one of the six identified prognostic categories, utilizing decision-making to drive prognostic communication resulted in delayed prognostic communication to families until end-of-life (EOL) decisions could be justified with empirical data. Providers will better meet the needs of families who desire earlier prognostic information by separating prognostic communication from decision-making and communicating the possibility of a poor prognosis based on intuitive knowledge, while acknowledging the uncertainty inherent in prognostication. This sets the stage for later prognostic discussions focused on EOL decisions, including limiting or withdrawing treatment, which can be timed when empirical data substantiate intuitive prognoses. This allows additional time for families to anticipate and prepare for end-of-life decision-making. © 2012 John Wiley & Sons Ltd.

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.

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.

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 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

A Testbed for Data Fusion for Helicopter Diagnostics and Prognostics

DTIC Science & Technology

2003-03-01

and algorithm design and tuning in order to develop advanced diagnostic and prognostic techniques for air craft health monitoring . Here a...and development of models for diagnostics, prognostics , and anomaly detection . Figure 5 VMEP Server Browser Interface 7 Download... detections , and prognostic prediction time horizons. The VMEP system and in particular the web component are ideal for performing data collection

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

Gene expression profiling of canine osteosarcoma reveals genes associated with short and long survival times

PubMed Central

Selvarajah, Gayathri T; Kirpensteijn, Jolle; van Wolferen, Monique E; Rao, Nagesha AS; Fieten, Hille; Mol, Jan A

2009-01-01

Background Gene expression profiling of spontaneous tumors in the dog offers a unique translational opportunity to identify prognostic biomarkers and signaling pathways that are common to both canine and human. Osteosarcoma (OS) accounts for approximately 80% of all malignant bone tumors in the dog. Canine OS are highly comparable with their human counterpart with respect to histology, high metastatic rate and poor long-term survival. This study investigates the prognostic gene profile among thirty-two primary canine OS using canine specific cDNA microarrays representing 20,313 genes to identify genes and cellular signaling pathways associated with survival. This, the first report of its kind in dogs with OS, also demonstrates the advantages of cross-species comparison with human OS. Results The 32 tumors were classified into two prognostic groups based on survival time (ST). They were defined as short survivors (dogs with poor prognosis: surviving fewer than 6 months) and long survivors (dogs with better prognosis: surviving 6 months or longer). Fifty-one transcripts were found to be differentially expressed, with common upregulation of these genes in the short survivors. The overexpressed genes in short survivors are associated with possible roles in proliferation, drug resistance or metastasis. Several deregulated pathways identified in the present study, including Wnt signaling, Integrin signaling and Chemokine/cytokine signaling are comparable to the pathway analysis conducted on human OS gene profiles, emphasizing the value of the dog as an excellent model for humans. Conclusion A molecular-based method for discrimination of outcome for short and long survivors is useful for future prognostic stratification at initial diagnosis, where genes and pathways associated with cell cycle/proliferation, drug resistance and metastasis could be potential targets for diagnosis and therapy. The similarities between human and canine OS makes the dog a suitable pre-clinical model for future 'novel' therapeutic approaches where the current research has provided new insights on prognostic genes, molecular pathways and mechanisms involved in OS pathogenesis and disease progression. PMID:19735553

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.

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.

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.

Red blood cell distribution width as a predictor of survival in nasal-type, extranodal natural killer/T-cell lymphoma

PubMed Central

He, Qiao; Cai, Shaolei; Li, Shi; Zeng, Jian; Zhang, Qing; Gao, Yu; Yu, Sisi

2017-01-01

We retrospectively enrolled 191 nasal-type, extranodal natural killer/T-cell lymphoma (ENKTL) patients newly diagnosed from 2008 to 2016 at the Sichuan Cancer Hospital, in order to evaluate the relationship between disease outcomes, demographic and clinical factors, and red blood cell distribution width (RDW). C-index, fisher's exact test, univariate analysis, and cox regression analysis were applied. The median age of patients was 44 years and 134 (70%) were men. The cutoff of RDW was 46.2 fL determined by Cutoff Finder. Patients with RDW≤46.2 fL had significantly better progression-free survival (PFS) (3-year PFS, 80.4% vs. 63.1%; P=0.01) and overall survival (OS) (3-year OS, 83.2% vs. 65.5%; P=0.004) than those with RDW>46.2 fL. Multivariate analysis demonstrated that elevated RDW is an independent adverse predictor of OS (P=0.021, HR=2.04). RDW is an independent predictor of survival outcomes in ENKTL, which we found to be superior to both the prognostic index of natural killer lymphoma (PINK) and the Korean Prognostic Index (KPI) in discriminating patients with different outcomes in low-risk and high-risk groups (all P < 0.05). The new models combining RDW with the International Prognostic Index (IPI), KPI, and PINK showed more powerful prognostic value than corresponding original models. RDW represents an easily available and inexpensive marker for risk stratification in patients with ENKTL treated with radiotherapy-based treatment. Further prospective studies are warranted to confirm the prognostic value of RDW in ENKTL. PMID:29190934

Red blood cell distribution width as a predictor of survival in nasal-type, extranodal natural killer/T-cell lymphoma.

PubMed

Luo, Huaichao; Quan, Xiaoying; Song, Xiao-Yu; Zhang, Li; Yin, Yilin; He, Qiao; Cai, Shaolei; Li, Shi; Zeng, Jian; Zhang, Qing; Gao, Yu; Yu, Sisi

2017-11-03

We retrospectively enrolled 191 nasal-type, extranodal natural killer/T-cell lymphoma (ENKTL) patients newly diagnosed from 2008 to 2016 at the Sichuan Cancer Hospital, in order to evaluate the relationship between disease outcomes, demographic and clinical factors, and red blood cell distribution width (RDW). C-index, fisher's exact test, univariate analysis, and cox regression analysis were applied. The median age of patients was 44 years and 134 (70%) were men. The cutoff of RDW was 46.2 fL determined by Cutoff Finder. Patients with RDW≤46.2 fL had significantly better progression-free survival (PFS) (3-year PFS, 80.4% vs. 63.1%; P =0.01) and overall survival (OS) (3-year OS, 83.2% vs. 65.5%; P =0.004) than those with RDW>46.2 fL. Multivariate analysis demonstrated that elevated RDW is an independent adverse predictor of OS ( P =0.021, HR=2.04). RDW is an independent predictor of survival outcomes in ENKTL, which we found to be superior to both the prognostic index of natural killer lymphoma (PINK) and the Korean Prognostic Index (KPI) in discriminating patients with different outcomes in low-risk and high-risk groups (all P < 0.05). The new models combining RDW with the International Prognostic Index (IPI), KPI, and PINK showed more powerful prognostic value than corresponding original models. RDW represents an easily available and inexpensive marker for risk stratification in patients with ENKTL treated with radiotherapy-based treatment. Further prospective studies are warranted to confirm the prognostic value of RDW in ENKTL.

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.

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

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.

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.

Cancer of the ovary, fallopian tube, and peritoneum: a population-based comparison of the prognostic factors and outcomes.

PubMed

Rottmann, Miriam; Burges, A; Mahner, S; Anthuber, C; Beck, T; Grab, D; Schnelzer, A; Kiechle, M; Mayr, D; Pölcher, M; Schubert-Fritschle, G; Engel, J

2017-09-01

The objective was to compare the prognostic factors and outcomes among primary ovarian cancer (OC), fallopian tube cancer (FC), and peritoneal cancer (PC) patients in a population-based setting. We analysed 5399 OC, 327 FC, and 416 PC patients diagnosed between 1998 and 2014 in the catchment area of the Munich Cancer Registry (meanwhile 4.8 million inhabitants). Tumour site differences were examined by comparing prognostic factors, treatments, the time to progression, and survival. The effect of the tumour site was additionally analysed by a Cox regression model. The median age at diagnosis, histology, and FIGO stage significantly differed among the tumour sites (p < 0.001); PC patients were older, more often diagnosed with a serous subtype, and in FIGO stage III or IV. The time to progression and survival significantly differed among the tumour sites. When stratified by FIGO stage, the differences in time to progression disappeared, and the differences in survival considerably weakened. The differences in the multivariate survival analysis showed an almost identical outcome in PC patients (HR 1.07 [0.91-1.25]) and an improved survival of FC patients (HR 0.63 [0.49-0.81]) compared to that of OC patients. The comparison of OC, FC, and PC patients in this large-scale population-based study showed differences in the prognostic factors. These differences primarily account for the inferior outcome of PC patients, and for the improved survival of FC compared to OC patients.

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.

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.

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.

Physics Based Modeling and Prognostics of Electrolytic Capacitors

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan; Ceyla, Jose R.; Biswas, Gautam; Goebel, Kai

2012-01-01

This paper proposes first principles based modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors have become critical components in electronics systems in aeronautics and other domains. Degradations and faults in DC-DC converter unit propagates to the GPS and navigation subsystems and affects the overall solution. Capacitors and MOSFETs are the two major components, which cause degradations and failures in DC-DC converters. This type of capacitors are known for its low reliability and frequent breakdown on critical systems like power supplies of avionics equipment and electrical drivers of electromechanical actuators of control surfaces. Some of the more prevalent fault effects, such as a ripple voltage surge at the power supply output can cause glitches in the GPS position and velocity output, and this, in turn, if not corrected will propagate and distort the navigation solution. 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.

A Hybrid Stochastic-Neuro-Fuzzy Model-Based System for In-Flight Gas Turbine Engine Diagnostics

DTIC Science & Technology

2001-04-05

Margin (ADM) and (ii) Fault Detection Margin (FDM). Key Words: ANFIS, Engine Health Monitoring , Gas Path Analysis, and Stochastic Analysis Adaptive Network...The paper illustrates the application of a hybrid Stochastic- Fuzzy -Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both...operational history monitored on-line by the engine health management (EHM) system. To capture the complex functional relationships between different

Realizing the Translational Potential of Telomere Length Variation as a Tissue-Based Prognostic Marker for Prostate Cancer

DTIC Science & Technology

2014-10-01

Telomere Length Variation as a Tissue- Based Prognostic Marker for Prostate Cancer PRINCIPAL INVESTIGATOR: Elizabeth A. Platz CONTRACTING...Translational Potential of Telomere Length Variation as a Tissue- Based Prognostic Marker for Prostate Cancer 5b. GRANT NUMBER W81XWH-12-1-0545 5c...combination of telomere length variability in prostate cancer cells and short telomere length in cancer-associated stromal cells is an independent

Potential role of nuclear PD-L1 expression in cell-surface vimentin positive circulating tumor cells as a prognostic marker in cancer patients.

PubMed

Satelli, Arun; Batth, Izhar Singh; Brownlee, Zachary; Rojas, Christina; Meng, Qing H; Kopetz, Scott; Li, Shulin

2016-07-01

Although circulating tumor cells (CTCs) have potential as diagnostic biomarkers for cancer, determining their prognostic role in cancer patients undergoing treatment is a challenge. We evaluated the prognostic value of programmed death-ligand 1 (PD-L1) expression in CTCs in colorectal and prostate cancer patients undergoing treatment. Peripheral blood samples were collected from 62 metastatic colorectal cancer patients and 30 metastatic prostate cancer patients. CTCs were isolated from the samples using magnetic separation with the cell-surface vimentin(CSV)-specific 84-1 monoclonal antibody that detects epithelial-mesenchymal transitioned (EMT) CTCs. CTCs were enumerated and analyzed for PD-L1 expression using confocal microscopy. PD-L1 expression was detectable in CTCs and was localized in the membrane and/or cytoplasm and nucleus. CTC detection alone was not associated with poor progression-free or overall survival in colorectal cancer or prostate cancer patients, but nuclear PD-L1 (nPD-L1) expression in these patients was significantly associated with short survival durations. These results demonstrated that nPD-L1 has potential as a clinically relevant prognostic biomarker for colorectal and prostate cancer. Our data thus suggested that use of CTC-based models of cancer for risk assessment can improve the standard cancer staging criteria and supported the incorporation of nPD-L1 expression detection in CTCs detection in such models.

Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

PubMed

Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

2007-12-15

Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

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.

LPL is the strongest prognostic factor in a comparative analysis of RNA-based markers in early chronic lymphocytic leukemia.

PubMed

Kaderi, Mohd Arifin; Kanduri, Meena; Buhl, Anne Mette; Sevov, Marie; Cahill, Nicola; Gunnarsson, Rebeqa; Jansson, Mattias; Smedby, Karin Ekström; Hjalgrim, Henrik; Jurlander, Jesper; Juliusson, Gunnar; Mansouri, Larry; Rosenquist, Richard

2011-08-01

The expression levels of LPL, ZAP70, TCL1A, CLLU1 and MCL1 have recently been proposed as prognostic factors in chronic lymphocytic leukemia. However, few studies have systematically compared these different RNA-based markers. Using real-time quantitative PCR, we measured the mRNA expression levels of these genes in unsorted samples from 252 newly diagnosed chronic lymphocytic leukemia patients and correlated our data with established prognostic markers (for example Binet stage, CD38, IGHV gene mutational status and genomic aberrations) and clinical outcome. High expression levels of all RNA-based markers, except MCL1, predicted shorter overall survival and time to treatment, with LPL being the most significant. In multivariate analysis including the RNA-based markers, LPL expression was the only independent prognostic marker for overall survival and time to treatment. When studying LPL expression and the established markers, LPL expression retained its independent prognostic strength for overall survival. All of the RNA-based markers, albeit with varying ability, added prognostic information to established markers, with LPL expression giving the most significant results. Notably, high LPL expression predicted a worse outcome in good-prognosis subgroups, such as patients with mutated IGHV genes, Binet stage A, CD38 negativity or favorable cytogenetics. In particular, the combination of LPL expression and CD38 could further stratify Binet stage A patients. LPL expression is the strongest RNA-based prognostic marker in chronic lymphocytic leukemia that could potentially be applied to predict outcome in the clinical setting, particularly in the large group of patients with favorable prognosis.

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.

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

Prognostic value of Ki-67 index in adult medulloblastoma after accounting for molecular subgroup: a retrospective clinical and molecular analysis.

PubMed

Zhao, Fu; Zhang, Jing; Li, Peng; Zhou, Qiangyi; Zhang, Shun; Zhao, Chi; Wang, Bo; Yang, Zhijun; Li, Chunde; Liu, Pinan

2018-04-23

Medulloblastoma (MB) is a rare primary brain tumor in adults. We previously evaluated that combining both clinical and molecular classification could improve current risk stratification for adult MB. In this study, we aimed to identify the prognostic value of Ki-67 index in adult MB. Ki-67 index of 51 primary adult MBs was reassessed using a computer-based image analysis (Image-Pro Plus). All patients were followed up ranging from 12 months up to 15 years. Gene expression profiling and immunochemistry were used to establish the molecular subgroups in adult MB. Combined risk stratification models were designed based on clinical characteristics, molecular classification and Ki-67 index, and identified by multivariable Cox proportional hazards analysis. In our cohort, the mean Ki-67 value was 30.0 ± 11.3% (range 6.56-63.55%). The average Ki-67 value was significantly higher in LC/AMB than in CMB and DNMB (P = .001). Among three molecular subgroups, Group 4-tumors had the highest average Ki-67 value compared with WNT- and SHH-tumors (P = .004). Patients with Ki-67 index large than 30% displayed poorer overall survival (OS) and progression free survival (PFS) than those with Ki-67 less than 30% (OS: P = .001; PFS: P = .006). Ki-67 index (i.e. > 30%, < 30%) was identified as an independent significant prognostic factor (OS: P = .017; PFS: P = .024) by using multivariate Cox proportional hazards model. In conclusion, Ki-67 index can be considered as a valuable independent prognostic biomarker for adult patients with MB.

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

BCL-2 system analysis identifies high-risk colorectal cancer patients.

PubMed

Lindner, Andreas U; Salvucci, Manuela; Morgan, Clare; Monsefi, Naser; Resler, Alexa J; Cremona, Mattia; Curry, Sarah; Toomey, Sinead; O'Byrne, Robert; Bacon, Orna; Stühler, Michael; Flanagan, Lorna; Wilson, Richard; Johnston, Patrick G; Salto-Tellez, Manuel; Camilleri-Broët, Sophie; McNamara, Deborah A; Kay, Elaine W; Hennessy, Bryan T; Laurent-Puig, Pierre; Van Schaeybroeck, Sandra; Prehn, Jochen H M

2017-12-01

The mitochondrial apoptosis pathway is controlled by an interaction of multiple BCL-2 family proteins, and plays a key role in tumour progression and therapy responses. We assessed the prognostic potential of an experimentally validated, mathematical model of BCL-2 protein interactions (DR_MOMP) in patients with stage III colorectal cancer (CRC). Absolute protein levels of BCL-2 family proteins were determined in primary CRC tumours collected from n=128 resected and chemotherapy-treated patients with stage III CRC. We applied DR_MOMP to categorise patients as high or low risk based on model outputs, and compared model outputs with known prognostic factors (T-stage, N-stage, lymphovascular invasion). DR_MOMP signatures were validated on protein of n=156 patients with CRC from the Cancer Genome Atlas (TCGA) project. High-risk stage III patients identified by DR_MOMP had an approximately fivefold increased risk of death compared with patients identified as low risk (HR 5.2, 95% CI 1.4 to 17.9, p=0.02). The DR_MOMP signature ranked highest among all molecular and pathological features analysed. The prognostic signature was validated in the TCGA colon adenocarcinoma (COAD) cohort (HR 4.2, 95% CI 1.1 to 15.6, p=0.04). DR_MOMP also further stratified patients identified by supervised gene expression risk scores into low-risk and high-risk categories. BCL-2-dependent signalling critically contributed to treatment responses in consensus molecular subtypes 1 and 3, linking for the first time specific molecular subtypes to apoptosis signalling. DR_MOMP delivers a system-based biomarker with significant potential as a prognostic tool for stage III CRC that significantly improves established histopathological risk factors. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

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

Integrated analysis of DNA methylation, immunohistochemistry and mRNA expression, data identifies a Methylation Expression Index (MEI) robustly associated with survival of ER-positive breast cancer patients

PubMed Central

Garcia-Closas, Montserrat; Davis, Sean; Meltzer, Paul; Lissowska, Jolanta; Horne, Hisani N.; Sherman, Mark E.; Lee, Maxwell

2015-01-01

Identification of prognostic gene expression signatures may enable improved decisions about management of breast cancer. To identify a prognostic signature for breast cancer, we performed DNA methylation profiling and identified methylation markers that were associated with expression of ER, PR, HER2, CK5/6 and EGFR proteins. Methylation markers that were correlated with corresponding mRNA expression levels were identified using 208 invasive tumors from a population-based case-control study conducted in Poland. Using this approach, we defined the Methylation Expression Index (MEI) signature that was based on a weighted sum of mRNA levels of 57 genes. Classification of cases as low or high MEI scores were related to survival using Cox regression models. In the Polish study, women with ER-positive low MEI cancers had reduced survival at a median of 5.20 years of follow-up, HR=2.85 95%CI=1.25-6.47. Low MEI was also related to decreased survival in four independent datasets totaling over 2500 ER-positive breast cancers. These results suggest that integrated analysis of tumor expression markers, DNA methylation, and mRNA data can be an important approach for identifying breast cancer prognostic signatures. Prospective assessment of MEI along with other prognostic signatures should be evaluated in future studies. PMID:25773928

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

Comparison of Cox's and relative survival models when estimating the effects of prognostic factors on disease-specific mortality: a simulation study under proportional excess hazards.

PubMed

Le Teuff, Gwenaël; Abrahamowicz, Michal; Bolard, Philippe; Quantin, Catherine

2005-12-30

In many prognostic studies focusing on mortality of persons affected by a particular disease, the cause of death of individual patients is not recorded. In such situations, the conventional survival analytical methods, such as the Cox's proportional hazards regression model, do not allow to discriminate the effects of prognostic factors on disease-specific mortality from their effects on all-causes mortality. In the last decade, the relative survival approach has been proposed to deal with the analyses involving population-based cancer registries, where the problem of missing information on the cause of death is very common. However, some questions regarding the ability of the relative survival methods to accurately discriminate between the two sources of mortality remain open. In order to systematically assess the performance of the relative survival model proposed by Esteve et al., and to quantify its potential advantages over the Cox's model analyses, we carried out a series of simulation experiments, based on the population-based colon cancer registry in the French region of Burgundy. Simulations showed a systematic bias induced by the 'crude' conventional Cox's model analyses when individual causes of death are unknown. In simulations where only about 10 per cent of patients died of causes other than colon cancer, the Cox's model over-estimated the effects of male gender and oldest age category by about 17 and 13 per cent, respectively, with the coverage rate of the 95 per cent CI for the latter estimate as low as 65 per cent. In contrast, the effect of higher cancer stages was under-estimated by 8-28 per cent. In contrast to crude survival, relative survival model largely reduced such problems and handled well even such challenging tasks as separating the opposite effects of the same variable on cancer-related versus other-causes mortality. Specifically, in all the cases discussed above, the relative bias in the estimates from the Esteve et al.'s model was always below 10 per cent, with the coverage rates above 81 per cent. Copyright 2005 John Wiley & Sons, Ltd.

Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data—a preliminary study

NASA Astrophysics Data System (ADS)

Xia, Wei; Chen, Ying; Zhang, Rui; Yan, Zhuangzhi; Zhou, Xiaobo; Zhang, Bo; Gao, Xin

2018-02-01

Our objective was to identify prognostic imaging biomarkers for hepatocellular carcinoma in contrast-enhanced computed tomography (CECT) with biological interpretations by associating imaging features and gene modules. We retrospectively analyzed 371 patients who had gene expression profiles. For the 38 patients with CECT imaging data, automatic intra-tumor partitioning was performed, resulting in three spatially distinct subregions. We extracted a total of 37 quantitative imaging features describing intensity, geometry, and texture from each subregion. Imaging features were selected after robustness and redundancy analysis. Gene modules acquired from clustering were chosen for their prognostic significance. By constructing an association map between imaging features and gene modules with Spearman rank correlations, the imaging features that significantly correlated with gene modules were obtained. These features were evaluated with Cox’s proportional hazard models and Kaplan-Meier estimates to determine their prognostic capabilities for overall survival (OS). Eight imaging features were significantly correlated with prognostic gene modules, and two of them were associated with OS. Among these, the geometry feature volume fraction of the subregion, which was significantly correlated with all prognostic gene modules representing cancer-related interpretation, was predictive of OS (Cox p  =  0.022, hazard ratio  =  0.24). The texture feature cluster prominence in the subregion, which was correlated with the prognostic gene module representing lipid metabolism and complement activation, also had the ability to predict OS (Cox p  =  0.021, hazard ratio  =  0.17). Imaging features depicting the volume fraction and textural heterogeneity in subregions have the potential to be predictors of OS with interpretable biological meaning.

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...

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

[A prognostic model for assessment of outcome of root canal treatment in teeth with pulpitis or apical periodontitis].

PubMed

Zhang, M M; Zheng, Y D; Liang, Y H

2018-02-18

To present a prognostic model for evaluating the outcome of root canal treatment in teeth with pulpitis or apical periodontitis 2 years after treatment. The implementation of this study was based on a retrospective study on the 2-year outcome of root canal treatment. A cohort of 360 teeth, which received treatment and review, were chosen to build up the total sample size. In the study, 143 teeth with vital pulp and 217 teeth with apical periodontitis were included. About 67% of the samples were selected randomly to derive a training date set for modeling, and the others were used as validating date set for testing. Logistic regression models were used to produce the prognostic models. The dependent variable was defined as absence of periapical lesion or reduction of periapical lesion. The predictability of the models was evaluated by the area under the receiver-operating characteristic (ROC) curve (AUC). Four predictors were included in model one (absence of apical lesion): pre-operative periapical radiolucency, canal curvature, density and apical extent of root fillings. The AUC was 0.802 (95%CI: 0.744-0.859). And the AUC of the testing date was 0.688. Only the density and apical extent of root fillings were included to present model two (reduction of apical lesion). The AUC of training dates and testing dates were 0.734 (95%CI: 0.612-0.856) and 0.681, respectively. As predicted by model one, the probability of absence of periapical lesion 2 years after endodontic treatment was 90% in pulpitis teeth with sever root-canal curvature and adequate root canal fillings, but 51% in teeth with apical periodontitis. When using prognostic model two for prediction, in teeth with apical periodontitis, the probability of detecting lesion reduction with adequate or inadequate root fillings was 95% and 39% 2 years after treatment. The pre-operative periapical status, canal curvature and quality of root canal treatment could be used to predict the 2-year outcome of root canal treatment.

A review on prognostics approaches for remaining useful life of lithium-ion battery

NASA Astrophysics Data System (ADS)

Su, C.; Chen, H. J.

2017-11-01

Lithium-ion (Li-ion) battery is a core component for various industrial systems, including satellite, spacecraft and electric vehicle, etc. The mechanism of performance degradation and remaining useful life (RUL) estimation correlate closely to the operating state and reliability of the aforementioned systems. Furthermore, RUL prediction of Li-ion battery is crucial for the operation scheduling, spare parts management and maintenance decision for such kinds of systems. In recent years, performance degradation prognostics and RUL estimation approaches have become a focus of the research concerning with Li-ion battery. This paper summarizes the approaches used in Li-ion battery RUL estimation. Three categories are classified accordingly, i.e. model-based approach, data-based approach and hybrid approach. The key issues and future trends for battery RUL estimation are also discussed.

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.

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.

Functional proteomics outlines the complexity of breast cancer molecular subtypes.

PubMed

Gámez-Pozo, Angelo; Trilla-Fuertes, Lucía; Berges-Soria, Julia; Selevsek, Nathalie; López-Vacas, Rocío; Díaz-Almirón, Mariana; Nanni, Paolo; Arevalillo, Jorge M; Navarro, Hilario; Grossmann, Jonas; Gayá Moreno, Francisco; Gómez Rioja, Rubén; Prado-Vázquez, Guillermo; Zapater-Moros, Andrea; Main, Paloma; Feliú, Jaime; Martínez Del Prado, Purificación; Zamora, Pilar; Ciruelos, Eva; Espinosa, Enrique; Fresno Vara, Juan Ángel

2017-08-30

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

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...

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.

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 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.

Comparison of Comorbidity Collection Methods

PubMed Central

Kallogjeri, Dorina; Gaynor, Sheila M; Piccirillo, Marilyn L; Jean, Raymond A; Spitznagel, Edward L; Piccirillo, Jay F

2014-01-01

Background Multiple valid comorbidity indices exist to quantify the presence and role of comorbidities in cancer patient survival. Our goal was to compare chart-based Adult Comorbidity Evaluation-27 index (ACE-27), and claims-based Charlson Comorbidity Index (CCI) methods of identifying comorbid ailments, and their prognostic ability. Study Design Prospective cohort study of 6138 newly-diagnosed cancer patients at 12 different institutions. Participating registrars were trained to collect comorbidities from the abstracted chart using the ACE-27 method. ACE-27 assessment was compared with comorbidities captured through hospital discharge face-sheets using ICD-coding. The prognostic accomplishments of each comorbidity method was examined using follow-up data assessed at 24 months after data abstraction. Results Distribution of the ACE-27 scores was: “None” for 1453 (24%) of the patients; “Mild” for 2388 (39%); “Moderate” for 1344 (22%) and “Severe” for 950 (15%) of the patients. Deyo’s adaption of the Charlson Comorbidity Index (CCI) identified 4265 (69%) patients with a CCI score of 0, and the remaining 31% had CCI scores of 1 (n=1341, 22%), 2 (n=365, 6%), or 3 or more (n=167, 3%). Of the 4265 patients with a CCI score of 0, 394 (9%) were coded with severe comorbidities based on ACE-27 method. A higher comorbidity score was significantly associated with higher risk of death for both comorbidity indices. The multivariable Cox model including both comorbidity indices had the best performance (Nagelkerke’s R-square=0.37) and the best discrimination (c-index=0.827). Conclusion The number, type, and overall severity of comorbid ailments identified by chart- and claims-based approaches in newly-diagnosed cancer patients were notably different. Both indices were prognostically significant and able to provide unique prognostic information. PMID:24933715

Centrosome-Based Mechanisms, Prognostics and Therapeutics in Prostate Cancer

DTIC Science & Technology

2006-12-01

progression of prostate carcinomas. The specific aims of the original proposal were designed to test several features of this model . 1. Are centrosome...features of this model . 1. Are centrosome defects present in early prostate cancer and can they predict aggressive disease? 2. Do pericentrin’s...cells, supports this model . The ability to block the cell cycle in prostate cells by depletion of any of 14 centrosome proteins identifies several

[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.

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

Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram

Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under differentmore » levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.« less

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.

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.

Electronic Health Management

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Saha, Sankalita; Goebel, Kai

2011-01-01

Accelerated aging methodologies for electrolytic components have been designed and accelerated aging experiments have been carried out. The methodology is based on imposing electrical and/or thermal overstresses via electrical power cycling in order to mimic the real world operation behavior. Data are collected in-situ and offline in order to periodically characterize the devices' electrical performance as it ages. The data generated through these experiments are meant to provide capability for the validation of prognostic algorithms (both model-based and data-driven). Furthermore, the data allow validation of physics-based and empirical based degradation models for this type of capacitor. A first set of models and algorithms has been designed and tested on the data.

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.

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.

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.

The prognostic value of reactive stroma on prostate needle biopsy: a population-based study.

PubMed

Saeter, Thorstein; Vlatkovic, Ljiljana; Waaler, Gudmund; Servoll, Einar; Nesland, Jahn M; Axcrona, Karol; Axcrona, Ulrika

2015-05-01

Reactive tumor stroma has been shown to play an active role in prostatic carcinogenesis. A grading system for reactive stroma in prostate cancer (PC) has recently been established and found to predict biochemical recurrence and prostate cancer-specific mortality (PCSM) in prostatectomized patients. To the best of our knowledge, there has been no study investigating the prognostic value of reactive stromal grading (RSG) with regard to PCSM when evaluated in diagnostic prostate needle biopsies. A population-based study on 318 patients, encompassing all cases of PC diagnosed by needle biopsies and without evidence of systemic metastasis at the time of diagnosis in Aust-Agder County in the period 1991-1999. Patients were identified by cross-referencing the Cancer Registry of Norway. Clinical data were obtained by review of medical charts. The endpoint was PCSM. RSG was evaluated on haematoxylin and eosin stained sections according to previously described criteria; grade 0, 0-5% reactive stroma; grade 1, 6-15%; grade 2, 16-50%; grade 3, 51-100%. RSG could be evaluated in 278 patients. The median follow- up time was 110 months (interquartile range: 51-171). The 10-year PC - specific survival rate for RSGs of 0, 1, 2, and 3 was 96%, 81%, 69%, and 63%, respectively (P < 0.005). RSG remained independently associated with PCSM in a multivariate Cox regression analysis adjusting for prostate-specific antigen level, clinical stage, Gleason score, and mode of treatment. The concordance index of the multivariate model was 0.814 CONCLUSIONS: Our study demonstrates that RSG in diagnostic prostate needle biopsies predicts PCSM independently of other evaluable prognostic factors. Hence, RSG could be used in addition to traditional prognostic factors for prognostication and treatment stratification of PC patients. © 2015 Wiley Periodicals, Inc.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs.

NASA Astrophysics Data System (ADS)

Belochitski, A.; Krueger, S. K.; Moorthi, S.; Bogenschutz, P.; Cheng, A.

2017-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity, and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation, and cloudiness. Unlike other similar methods, comparatively few new prognostic variables needs to be introduced, making the technique computationally efficient. In the base version of SHOC it is SGS turbulent kinetic energy (TKE), and in the developmental version — SGS TKE, and variances of total water and moist static energy (MSE). SHOC is now incorporated into a version of GFS that will become a part of the NOAA Next Generation Global Prediction System based around NOAA GFDL's FV3 dynamical core, NOAA Environmental Modeling System (NEMS) coupled modeling infrastructure software, and a set novel physical parameterizations. Turbulent diffusion coefficients computed by SHOC are now used in place of those produced by the boundary layer turbulence and shallow convection parameterizations. Large scale microphysics scheme is no longer used to calculate cloud fraction or the large-scale condensation/deposition. Instead, SHOC provides these quantities. Radiative transfer parameterization uses cloudiness computed by SHOC. An outstanding problem with implementation of SHOC in the NCEP global models is excessively large high level tropical cloudiness. Comparison of the moments of the SGS PDF diagnosed by SHOC to the moments calculated in a GigaLES simulation of tropical deep convection case (GATE), shows that SHOC diagnoses too narrow PDF distributions of total cloud water and MSE in the areas of deep convective detrainment. A subsequent sensitivity study of SHOC's diagnosed cloud fraction (CF) to higher order input moments of the SGS PDF demonstrated that CF is improved if SHOC is provided with correct variances of total water and MSE. Consequently, SHOC was modified to include two new prognostic equations for variances of total water and MSE, and coupled with the Chikira-Sugiyama parameterization of deep convection to include effects of detrainment on the prognostic variances.

[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.

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.

The proliferation marker Ki67, but not neuroendocrine expression, is an independent factor in the prediction of prognosis of primary prostate cancer patients

PubMed Central

Pascale, Mariarosa; Aversa, Cinzia; Barbazza, Renzo; Marongiu, Barbara; Siracusano, Salvatore; Stoffel, Flavio; Sulfaro, Sando; Roggero, Enrico; Stanta, Giorgio

2016-01-01

Abstract Background Neuroendocrine markers, which could indicate for aggressive variants of prostate cancer and Ki67 (a well-known marker in oncology for defining tumor proliferation), have already been associated with clinical outcome in prostate cancer. The aim of this study was to investigate the prognostic value of those markers in primary prostate cancer patients. Patients and methods NSE (neuron specific enolase), ChrA (chromogranin A), Syp (Synaptophysin) and Ki67 staining were performed by immunohistochemistry. Then, the prognostic impact of their expression on overall survival was investigated in 166 primary prostate cancer patients by univariate and multivariate analyses. Results NSE, ChrA, Syp and Ki67 were positive in 50, 45, 54 and 146 out of 166 patients, respectively. In Kaplan-Meier analysis only diffuse NSE staining (negative vs diffuse, p = 0.004) and Ki67 (≤ 10% vs > 10%, p < 0.0001) were significantly associated with overall survival. Ki67 expression, but not NSE, resulted as an independent prognostic factor for overall survival in multivariate analysis. Conclusions A prognostic model incorporating Ki67 expression with clinical-pathological covariates could provide additional prognostic information. Ki67 may thus improve prediction of prostate cancer outcome based on standard clinical-pathological parameters improving prognosis and management of prostate cancer patients. PMID:27679548

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

Multiplex polymerase chain reaction-based prognostic models in diffuse large B-cell lymphoma patients treated with R-CHOP.

PubMed

Green, Tina M; Jensen, Andreas K; Holst, René; Falgreen, Steffen; Bøgsted, Martin; de Stricker, Karin; Plesner, Torben; Mourits-Andersen, Torben; Frederiksen, Mikael; Johnsen, Hans E; Pedersen, Lars M; Møller, Michael B

2016-09-01

We present a multiplex analysis for genes known to have prognostic value in an attempt to design a clinically useful classification model in patients with diffuse large B-cell lymphoma (DLBCL). Real-time polymerase chain reaction was used to measure transcript levels of 28 relevant genes in 194 de novo DLBCL patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone). Including International Prognostic Index (IPI) as a variable in a penalized Cox regression, we investigated the association with disease progression for single genes or gene combinations in four models. The best model was validated in data from an online available R-CHOP treated cohort. With progression-free survival (PFS) as primary endpoint, the best performing IPI independent model incorporated the LMO2 and HLADQA1 as well as gene interactions for GCSAMxMIB1, GCSAMxCTGF and FOXP1xPDE4B. This model assigned 33% of patients (n = 60) to poor outcome with an estimated 3-year PFS of 40% vs. 87% for low risk (n = 61) and intermediate (n = 60) risk groups (P < 0·001). However, a simpler, IPI independent model incorporated LMO2 and BCL2 and assigned 33% of the patients with a 3-year PFS of 35% vs. 82% for low risk group (P < 0·001). We have documented the impact of a few single genes added to IPI for assignment in new drug trials. © 2016 John Wiley & Sons Ltd.

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.

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

DTIC Science & Technology

2012-09-01

interpreting the state vector as the health indicator and a threshold is used on this variable in order to compute EOL (end-of-life) and RUL. Here, we...End-of-life ( EOL ) would match the true spread and would not change from one experiment to another. This is, however, in practice impossible to achieve

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.

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.

A New Prognostic Staging System for Rectal Cancer

PubMed Central

Ueno, Hideki; Price, Ashley B.; Wilkinson, Kay H.; Jass, Jeremy R.; Mochizuki, Hidetaka; Talbot, Ian C.

2004-01-01

Objective: To clarify the appropriateness of tumor “budding,” a quantifiable histologic variable, as 1 parameter in the construction of a new prognostic grading system for rectal cancer. Summary Background Data: Patient division according to an accurate prognostic prediction could enhance the effectiveness of postoperative adjuvant therapy and follow-up. Patients and Methods: Tumor budding was defined as an isolated cancer cell or a cluster composed of fewer than 5 cells in the invasive frontal region, and was divided into 2 grades based on its number within a microscopic field of ×250. We analyzed 2 discrete cohorts comprising 638 and 476 patients undergoing potentially curative surgery. Results: In the first cohort, high-grade budding (10 or more foci in a field) was observed in 30% of patients and was significantly associated with a lower 5-year survival rate (41%) than low-grade budding (84%). Similarly, in the second cohort, the 5-year survival rate was 43% in high-grade budding patients and 83% in low-grade budding patients. In both cohorts, multivariate analyses verified budding to be an independent prognosticator, together with nodal involvement and extramural spread. These 3 variables were given weighted scores, and the score range was divided to provide 5 prognostic groups (97%; 86%; 61%; 39%; 17% 5-year survival). The model was tested on the second cohort, and similar prognostic results were obtained. Conclusions: We propose that because of its relevance to prognosis and its reproducibility, budding is an excellent parameter for use in a grading system to provide a confident prediction of clinical outcome. PMID:15492565

The modified glasgow prognostic score is an independent prognostic indicator in neoadjuvantly treated adenocarcinoma of the esophagogastric junction

PubMed Central

Jomrich, Gerd; Hollenstein, Marlene; John, Maximilian; Baierl, Andreas; Paireder, Matthias; Kristo, Ivan; Ilhan-Mutlu, Aysegül; Asari, Reza; Preusser, Matthias; Schoppmann, Sebastian F.

2018-01-01

The modified Glasgow Prognostic Score (mGPS) combines the indicators of decreased plasma albumin and elevated CRP. In a number of malignancies, elevated mGPS is associated with poor survival. Aim of this study was to investigate the prognostic role of mGPS in patients with neoadjuvantly treated adenocarcinomas of the esophagogastric junction 256 patients from a prospective database undergoing surgical resection after neoadjuvant treatment between 2003 and 2014 were evaluated. mGPS was scored as 0, 1, or 2 based on CRP (>1.0 mg/dl) and albumin (<35 g/L) from blood samples taken prior (preNT-mGPS) and after (postNT-mGPS) neoadjuvant therapy. Scores were correlated with clinicopathological patients’ characteristics. From 155 Patients, sufficient data was available. Median follow-up was 63.8 months (33.3–89.5 months). In univariate analysis, Cox proportional hazard model shows significant shorter patients OS (p = 0.04) and DFS (p = 0.02) for increased postNT-mGPS, preNT-hypoalbuminemia (OS: p = 0.003; DFS: p = 0.002) and post-NT-CRP (OS: p = 0.03; DFS: p = 0.04). Elevated postNT-mGPS and preNT-hypoalbuminemia remained significant prognostic factors in multivariate analysis for OS (p = 0.02; p = 0.005,) and DFS (p = 0.02, p = 0.004) with tumor differentiation and tumor staging as significant covariates. PostNT-mGPS and preNT-hypoalbuminemia are independent prognostic indicators in patients with neoadjuvantly treated adenocarcinomas of the esophagogastric junction and significantly associated with diminished OS and DFS. PMID:29467943

Prognostic significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with stage III and IV colorectal cancer

PubMed Central

Kim, Jae Hyun; Lee, Jun Yeop; Kim, Hae Koo; Lee, Jin Wook; Jung, Sung Gyu; Jung, Kyoungwon; Kim, Sung Eun; Moon, Won; Park, Moo In; Park, Seun Ja

2017-01-01

AIM To evaluate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in patients with colorectal cancer (CRC). METHODS Between April 1996 and December 2010, medical records from a total of 1868 patients with CRC were retrospectively reviewed. The values of simple inflammatory markers including NLR and PLR in predicting the long-term outcomes of these patients were evaluated using Kaplan-Meier curves and Cox regression models. RESULTS The median follow-up duration was 46 mo (interquartile range, 22-73). The estimation of NLR and PLR was based on the time of diagnosis. In multivariate Cox regression analysis, high NLR (≥ 3.0) and high PLR (≥ 160) were independent risk factors predicting poor long-term outcomes in patients with stage III and IV CRC. However, high NLR and high PLR were not prognostic factors in patients with stage I and II CRC. CONCLUSION In this study, we identified that high NLR (≥ 3.0) and high PLR (≥ 160) are useful prognostic factors to predict long-term outcomes in patients with stage III and IV CRC. PMID:28210087

Prognostic alternative mRNA splicing signature in non-small cell lung cancer.

PubMed

Li, Yuan; Sun, Nan; Lu, Zhiliang; Sun, Shouguo; Huang, Jianbing; Chen, Zhaoli; He, Jie

2017-05-01

Alternative splicing provides a major mechanism to generate protein diversity. Increasing evidence suggests a link of dysregulation of splicing associated with cancer. Genome-wide alternative splicing profiling in lung cancer remains largely unstudied. We generated alternative splicing profiles in 491 lung adenocarcinoma (LUAD) and 471 lung squamous cell carcinoma (LUSC) patients in TCGA using RNA-seq data, prognostic models and splicing networks were built by integrated bioinformatics analysis. A total of 3691 and 2403 alternative splicing events were significantly associated with patient survival in LUAD and LUSC, respectively, including EGFR, CD44, PIK3C3, RRAS2, MAPKAP1 and FGFR2. The area under the curve of the receiver-operator characteristic curve for prognostic predictor in NSCLC was 0.817 at 2000 days of overall survival which were also over 0.8 in LUAD and LUSC, separately. Interestingly, splicing correlation networks uncovered opposite roles of splicing factors in LUAD and LUSC. We created prognostic predictors based on alternative splicing events with high performances for risk stratification in NSCLC patients and uncovered interesting splicing networks in LUAD and LUSC which could be underlying mechanisms. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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 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.

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

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.

Tumor Volume and Patient Weight as Predictors of Outcome in Children with Intermediate Risk Rhabdomyosarcoma (RMS): A Report from the Children’s Oncology Group

PubMed Central

Rodeberg, David A.; Stoner, Julie A.; Garcia-Henriquez, Norbert; Randall, R. Lor; Spunt, Sheri L.; Arndt, Carola A.; Kao, Simon; Paidas, Charles N.; Million, Lynn; Hawkins, Douglas S.

2010-01-01

Background To compare tumor volume and patient weight vs. traditional factors of tumor diameter and patient age, to determine which parameters best discriminates outcome among intermediate risk RMS patients. Methods Complete patient information for non-metastatic RMS patients enrolled in the Children’s Oncology Group (COG) intermediate risk study D9803 (1999–2005) was available for 370 patients. The Kaplan-Meier method was used to estimate survival distributions. A recursive partitioning model was used to identify prognostic factors associated with event-free survival (EFS). Cox-proportional hazards regression models were used to estimate the association between patient characteristics and the risk of failure or death. Results For all intermediate risk patients with RMS, a recursive partitioning algorithm for EFS suggests that prognostic groups should optimally be defined by tumor volume (transition point 20 cm3), weight (transition point 50 kg), and embryonal histology. Tumor volume and patient weight added significant outcome information to the standard prognostic factors including tumor diameter and age (p=0.02). The ability to resect the tumor completely was not significantly associated with the size of the patient, and patient weight did not significantly modify the association between tumor volume and EFS after adjustment for standard risk factors (p=0.2). Conclusion The factors most strongly associated with EFS were tumor volume, patient weight, and histology. Based on regression modeling, volume and weight are superior predictors of outcome compared to tumor diameter and patient age in children with intermediate risk RMS. Prognostic performance of tumor volume and patient weight should be assessed in an independent prospective study. PMID:24048802

Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation modelORCHIDEE - Part 1: Simulating historical global burned area and fire regimes

Treesearch

C. Yue; P. Ciais; P. Cadule; K. Thonicke; S. Archibald; B. Poulter; W. M. Hao; S. Hantson; F. Mouillot; P. Friedlingstein; F. Maignan; N. Viovy

2014-01-01

Fire is an important global ecological process that influences the distribution of biomes, with consequences for carbon, water, and energy budgets. Therefore it is impossible to appropriately model the history and future of the terrestrial ecosystems and the climate system without including fire. This study incorporates the process-based prognostic fire module SPITFIRE...

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.

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

A novel prognostic six-CpG signature in glioblastomas.

PubMed

Yin, An-An; Lu, Nan; Etcheverry, Amandine; Aubry, Marc; Barnholtz-Sloan, Jill; Zhang, Lu-Hua; Mosser, Jean; Zhang, Wei; Zhang, Xiang; Liu, Yu-He; He, Ya-Long

2018-03-01

We aimed to identify a clinically useful biomarker using DNA methylation-based information to optimize individual treatment of patients with glioblastoma (GBM). A six-CpG panel was identified by incorporating genome-wide DNA methylation data and clinical information of three distinct discovery sets and was combined using a risk-score model. Different validation sets of GBMs and lower-grade gliomas and different statistical methods were implemented for prognostic evaluation. An integrative analysis of multidimensional TCGA data was performed to molecularly characterize different risk tumors. The six-CpG risk-score signature robustly predicted overall survival (OS) in all discovery and validation cohorts and in a treatment-independent manner. It also predicted progression-free survival (PFS) in available patients. The multimarker epigenetic signature was demonstrated as an independent prognosticator and had better performance than known molecular indicators such as glioma-CpG island methylator phenotype (G-CIMP) and proneural subtype. The defined risk subgroups were molecularly distinct; high-risk tumors were biologically more aggressive with concordant activation of proangiogenic signaling at multimolecular levels. Accordingly, we observed better OS benefits of bevacizumab-contained therapy to high-risk patients in independent sets, supporting its implication in guiding usage of antiangiogenic therapy. Finally, the six-CpG signature refined the risk classification based on G-CIMP and MGMT methylation status. The novel six-CpG signature is a robust and independent prognostic indicator for GBMs and is of promising value to improve personalized management. © 2018 John Wiley & Sons Ltd.

Prognostics and health management system for hydropower plant based on fog computing and docker container

NASA Astrophysics Data System (ADS)

Xiao, Jian; Zhang, Mingqiang; Tian, Haiping; Huang, Bo; Fu, Wenlong

2018-02-01

In this paper, a novel prognostics and health management system architecture for hydropower plant equipment was proposed based on fog computing and Docker container. We employed the fog node to improve the real-time processing ability of improving the cloud architecture-based prognostics and health management system and overcome the problems of long delay time, network congestion and so on. Then Storm-based stream processing of fog node was present and could calculate the health index in the edge of network. Moreover, the distributed micros-service and Docker container architecture of hydropower plants equipment prognostics and health management was also proposed. Using the micro service architecture proposed in this paper, the hydropower unit can achieve the goal of the business intercommunication and seamless integration of different equipment and different manufacturers. Finally a real application case is given in this paper.

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.

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

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.

Rotor Smoothing and Vibration Monitoring Results for the US Army VMEP

DTIC Science & Technology

2009-06-01

individual component CI detection thresholds, and development of models for diagnostics, prognostics , and anomaly detection . Figure 16 VMEP Server...and prognostics are of current interest. Development of those systems requires large amounts of data (collection, monitoring , manipulation) to capture...development of automated systems and for continuous updating of algorithms to improve detection , classification, and prognostic performance. A test

Identifying the most appropriate age threshold for TNM stage grouping of well-differentiated thyroid cancer.

PubMed

Hendrickson-Rebizant, J; Sigvaldason, H; Nason, R W; Pathak, K A

2015-08-01

Age is integrated in most risk stratification systems for well-differentiated thyroid cancer (WDTC). The most appropriate age threshold for stage grouping of WDTC is debatable. The objective of this study was to evaluate the best age threshold for stage grouping by comparing multivariable models designed to evaluate the independent impact of various prognostic factors, including age based stage grouping, on the disease specific survival (DSS) of our population-based cohort. Data from population-based thyroid cancer cohort of 2125 consecutive WDTC, diagnosed during 1970-2010, with a median follow-up of 11.5 years, was used to calculate DSS using the Kaplan Meier method. Multivariable analysis with Cox proportional hazard model was used to assess independent impact of different prognostic factors on DSS. The Akaike information criterion (AIC), a measure of statistical model fit, was used to identify the most appropriate age threshold model. Delta AIC, Akaike weight, and evidence ratios were calculated to compare the relative strength of different models. The mean age of the patients was 47.3 years. DSS of the cohort was 95.6% and 92.8% at 10 and 20 years respectively. A threshold of 55 years, with the lowest AIC, was identified as the best model. Akaike weight indicated an 85% chance that this age threshold is the best among the compared models, and is 16.8 times more likely to be the best model as compared to a threshold of 45 years. The age threshold of 55 years was found to be the best for TNM stage grouping. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Prognostic Significance of Progesterone Receptor–Positive Tumor Cells Within Immunohistochemically Defined Luminal A Breast Cancer

PubMed Central

Prat, Aleix; Cheang, Maggie Chon U.; Martín, Miguel; Parker, Joel S.; Carrasco, Eva; Caballero, Rosalía; Tyldesley, Scott; Gelmon, Karen; Bernard, Philip S.; Nielsen, Torsten O.; Perou, Charles M.

2013-01-01

Purpose Current immunohistochemical (IHC)-based definitions of luminal A and B breast cancers are imperfect when compared with multigene expression-based assays. In this study, we sought to improve the IHC subtyping by examining the pathologic and gene expression characteristics of genomically defined luminal A and B subtypes. Patients and Methods Gene expression and pathologic features were collected from primary tumors across five independent cohorts: British Columbia Cancer Agency (BCCA) tamoxifen-treated only, Grupo Español de Investigación en Cáncer de Mama 9906 trial, BCCA no systemic treatment cohort, PAM50 microarray training data set, and a combined publicly available microarray data set. Optimal cutoffs of percentage of progesterone receptor (PR) –positive tumor cells to predict survival were derived and independently tested. Multivariable Cox models were used to test the prognostic significance. Results Clinicopathologic comparisons among luminal A and B subtypes consistently identified higher rates of PR positivity, human epidermal growth factor receptor 2 (HER2) negativity, and histologic grade 1 in luminal A tumors. Quantitative PR gene and protein expression were also found to be significantly higher in luminal A tumors. An empiric cutoff of more than 20% of PR-positive tumor cells was statistically chosen and proved significant for predicting survival differences within IHC-defined luminal A tumors independently of endocrine therapy administration. Finally, no additional prognostic value within hormonal receptor (HR) –positive/HER2-negative disease was observed with the use of the IHC4 score when intrinsic IHC-based subtypes were used that included the more than 20% PR-positive tumor cells and vice versa. Conclusion Semiquantitative IHC expression of PR adds prognostic value within the current IHC-based luminal A definition by improving the identification of good outcome breast cancers. The new proposed IHC-based definition of luminal A tumors is HR positive/HER2 negative/Ki-67 less than 14%, and PR more than 20%. PMID:23233704

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

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.

Prognostic factors in breast phyllodes tumors: a nomogram based on a retrospective cohort study of 404 patients.

PubMed

Zhou, Zhi-Rui; Wang, Chen-Chen; Sun, Xiang-Jie; Yang, Zhao-Zhi; Chen, Xing-Xing; Shao, Zhi-Ming; Yu, Xiao-Li; Guo, Xiao-Mao

2018-04-01

The aim of this study was to explore the independent prognostic factors related to postoperative recurrence-free survival (RFS) in patients with breast phyllodes tumors (PTBs). A retrospective analysis was conducted in Fudan University Shanghai Cancer Center. According to histological type, patients with benign PTBs were classified as a low-risk group, while borderline and malignant PTBs were classified as a high-risk group. The Cox regression model was adopted to identify factors affecting postoperative RFS in the two groups, and a nomogram was generated to predict recurrence-free survival at 1, 3, and 5 years. Among the 404 patients, 168 (41.6%) patients had benign PTB, 184 (45.5%) had borderline PTB, and 52 (12.9%) had malignant PTB. Fifty-five patients experienced postoperative local recurrence, including six benign cases, 26 borderline cases, and 22 malignant cases; the three histological types of PTB had local recurrence rates of 3.6%, 14.1%, and 42.3%, respectively. Stromal cell atypia was an independent prognostic factor for RFS in the low-risk group, while the surgical approach and tumor border were independent prognostic factors for RFS in the high-risk group, and patients receiving simple excision with an infiltrative tumor border had a higher recurrence rate. A nomogram developed based on clinicopathologic features and surgical approaches could predict recurrence-free survival at 1, 3, and 5 years. For high-risk patients, this predictive nomogram based on tumor border, tumor residue, mitotic activity, degree of stromal cell hyperplasia, and atypia can be applied for patient counseling and clinical management. The efficacy of adjuvant radiotherapy remains uncertain. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

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.

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.

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.

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.

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

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer.

PubMed

Cheng, Nai-Ming; Fang, Yu-Hua Dean; Lee, Li-yu; Chang, Joseph Tung-Chieh; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Liao, Chun-Ta; Yang, Lan-Yan; Hsu, Ching-Han; Yen, Tzu-Chen

2015-03-01

The question as to whether the regional textural features extracted from PET images predict prognosis in oropharyngeal squamous cell carcinoma (OPSCC) remains open. In this study, we investigated the prognostic impact of regional heterogeneity in patients with T3/T4 OPSCC. We retrospectively reviewed the records of 88 patients with T3 or T4 OPSCC who had completed primary therapy. Progression-free survival (PFS) and disease-specific survival (DSS) were the main outcome measures. In an exploratory analysis, a standardized uptake value of 2.5 (SUV 2.5) was taken as the cut-off value for the detection of tumour boundaries. A fixed threshold at 42 % of the maximum SUV (SUVmax 42 %) and an adaptive threshold method were then used for validation. Regional textural features were extracted from pretreatment (18)F-FDG PET/CT images using the grey-level run length encoding method and grey-level size zone matrix. The prognostic significance of PET textural features was examined using receiver operating characteristic (ROC) curves and Cox regression analysis. Zone-size nonuniformity (ZSNU) was identified as an independent predictor of PFS and DSS. Its prognostic impact was confirmed using both the SUVmax 42 % and the adaptive threshold segmentation methods. Based on (1) total lesion glycolysis, (2) uniformity (a local scale texture parameter), and (3) ZSNU, we devised a prognostic stratification system that allowed the identification of four distinct risk groups. The model combining the three prognostic parameters showed a higher predictive value than each variable alone. ZSNU is an independent predictor of outcome in patients with advanced T-stage OPSCC, and may improve their prognostic stratification.

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.

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.

Assessment of the prognostic and predictive utility of the Breast Cancer Index (BCI): an NCIC CTG MA.14 study.

PubMed

Sgroi, Dennis C; Chapman, Judy-Anne W; Badovinac-Crnjevic, T; Zarella, Elizabeth; Binns, Shemeica; Zhang, Yi; Schnabel, Catherine A; Erlander, Mark G; Pritchard, Kathleen I; Han, Lei; Shepherd, Lois E; Goss, Paul E; Pollak, Michael

2016-01-04

Biomarkers that can be used to accurately assess the residual risk of disease recurrence in women with hormone receptor-positive breast cancer are clinically valuable. We evaluated the prognostic value of the Breast Cancer Index (BCI), a continuous risk index based on a combination of HOXB13:IL17BR and molecular grade index, in women with early breast cancer treated with either tamoxifen alone or tamoxifen plus octreotide in the NCIC MA.14 phase III clinical trial (ClinicalTrials.gov Identifier NCT00002864; registered 1 November 1999). Gene expression analysis of BCI by real-time polymerase chain reaction was performed blinded to outcome on RNA extracted from archived formalin-fixed, paraffin-embedded tumor samples of 299 patients with both lymph node-negative (LN-) and lymph node-positive (LN+) disease enrolled in the MA.14 trial. Our primary objective was to determine the prognostic performance of BCI based on relapse-free survival (RFS). MA.14 patients experienced similar RFS on both treatment arms. Association of gene expression data with RFS was evaluated in univariate analysis with a stratified log-rank test statistic, depicted with a Kaplan-Meier plot and an adjusted Cox survivor plot. In the multivariate assessment, we used stratified Cox regression. The prognostic performance of an emerging, optimized linear BCI model was also assessed in a post hoc analysis. Of 299 samples, 292 were assessed successfully for BCI for 146 patients accrued in each MA.14 treatment arm. BCI risk groups had a significant univariate association with RFS (stratified log-rank p = 0.005, unstratified log-rank p = 0.007). Adjusted 10-year RFS in BCI low-, intermediate-, and high-risk groups was 87.5 %, 83.9 %, and 74.7 %, respectively. BCI had a significant prognostic effect [hazard ratio (HR) 2.34, 95 % confidence interval (CI) 1.33-4.11; p = 0.004], although not a predictive effect, on RFS in stratified multivariate analysis, adjusted for pathological tumor stage (HR 2.22, 95 % CI 1.22-4.07; p = 0.01). In the post hoc multivariate analysis, higher linear BCI was associated with shorter RFS (p = 0.002). BCI had a strong prognostic effect on RFS in patients with early-stage breast cancer treated with tamoxifen alone or with tamoxifen and octreotide. BCI was prognostic in both LN- and LN+ patients. This retrospective study is an independent validation of the prognostic performance of BCI in a prospective trial.

A comparison of prognostic significance of strong ion gap (SIG) with other acid-base markers in the critically ill: a cohort study.

PubMed

Ho, Kwok M; Lan, Norris S H; Williams, Teresa A; Harahsheh, Yusra; Chapman, Andrew R; Dobb, Geoffrey J; Magder, Sheldon

2016-01-01

This cohort study compared the prognostic significance of strong ion gap (SIG) with other acid-base markers in the critically ill. The relationships between SIG, lactate, anion gap (AG), anion gap albumin-corrected (AG-corrected), base excess or strong ion difference-effective (SIDe), all obtained within the first hour of intensive care unit (ICU) admission, and the hospital mortality of 6878 patients were analysed. The prognostic significance of each acid-base marker, both alone and in combination with the Admission Mortality Prediction Model (MPM0 III) predicted mortality, were assessed by the area under the receiver operating characteristic curve (AUROC). Of the 6878 patients included in the study, 924 patients (13.4 %) died after ICU admission. Except for plasma chloride concentrations, all acid-base markers were significantly different between the survivors and non-survivors. SIG (with lactate: AUROC 0.631, confidence interval [CI] 0.611-0.652; without lactate: AUROC 0.521, 95 % CI 0.500-0.542) only had a modest ability to predict hospital mortality, and this was no better than using lactate concentration alone (AUROC 0.701, 95 % 0.682-0.721). Adding AG-corrected or SIG to a combination of lactate and MPM0 III predicted risks also did not substantially improve the latter's ability to differentiate between survivors and non-survivors. Arterial lactate concentrations explained about 11 % of the variability in the observed mortality, and it was more important than SIG (0.6 %) and SIDe (0.9 %) in predicting hospital mortality after adjusting for MPM0 III predicted risks. Lactate remained as the strongest predictor for mortality in a sensitivity multivariate analysis, allowing for non-linearity of all acid-base markers. The prognostic significance of SIG was modest and inferior to arterial lactate concentration for the critically ill. Lactate concentration should always be considered regardless whether physiological, base excess or physical-chemical approach is used to interpret acid-base disturbances in critically ill patients.

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.

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.

Multiple Score Comparison: a network meta-analysis approach to comparison and external validation of prognostic scores.

PubMed

Haile, Sarah R; Guerra, Beniamino; Soriano, Joan B; Puhan, Milo A

2017-12-21

Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them. Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined. We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small. We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.

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.

Evidence base and future research directions in the management of low back pain.

PubMed

Abbott, Allan

2016-03-18

Low back pain (LBP) is a prevalent and costly condition. Awareness of valid and reliable patient history taking, physical examination and clinical testing is important for diagnostic accuracy. Stratified care which targets treatment to patient subgroups based on key characteristics is reliant upon accurate diagnostics. Models of stratified care that can potentially improve treatment effects include prognostic risk profiling for persistent LBP, likely response to specific treatment based on clinical prediction models or suspected underlying causal mechanisms. The focus of this editorial is to highlight current research status and future directions for LBP diagnostics and stratified care.

A Comparison of Systemic Inflammation-Based Prognostic Scores in Patients on Regular Hemodialysis

PubMed Central

Kato, Akihiko; Tsuji, Takayuki; Sakao, Yukitoshi; Ohashi, Naro; Yasuda, Hideo; Fujimoto, Taiki; Takita, Takako; Furuhashi, Mitsuyoshi; Kumagai, Hiromichi

2013-01-01

Background/Aims Systemic inflammation-based prognostic scores have prognostic power in patients with cancer, independently of tumor stage and site. Although inflammatory status is associated with mortality in hemodialysis (HD) patients, it remains to be determined as to whether these composite scores are useful in predicting clinical outcomes. Methods We calculated the 6 prognostic scores [Glasgow prognostic score (GPS), modified GPS (mGPS), neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), prognostic index (PI) and prognostic nutritional index (PNI), which have been established as a useful scoring system in cancer patients. We enrolled 339 patients on regular HD (age: 64 ± 13 years; time on HD: 129 ± 114 months; males/females = 253/85) and followed them for 42 months. The area under the receiver-operating characteristics curve was used to determine which scoring system was more predictive of mortality. Results Elevated GPS, mGPS, NLR, PLR, PI and PNI were all associated with total mortality, independent of covariates. If GPS was raised, mGPS, NLR, PLR and PI were also predictive of all-cause mortality and/or hospitalization. GPS and PNI were associated with poor nutritional status. Using overall mortality as an endpoint, the area under the curve (AUC) was significant for a GPS of 0.701 (95% CI: 0.637-0.765; p < 0.01) and for a PNI of 0.616 (95% CI: 0.553-0.768; p = 0.01). However, AUC for hypoalbuminemia (<3.5 g/dl) was comparable to that of GPS (0.695, 95% CI: 0.632-0.759; p < 0.01). Conclusion GPS, based on serum albumin and highly sensitive C-reactive protein, has the most prognostic power for mortality prediction among the prognostic scores in HD patients. However, as the determination of serum albumin reflects mortality similarly to GPS, other composite combinations are needed to provide additional clinical utility beyond that of albumin alone in HD patients. PMID:24403910

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.

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.

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.

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.

Theoretical Background and Prognostic Modeling for Benchmarking SHM Sensors for Composite Structures

DTIC Science & Technology

2010-10-01

minimum flaw size can be detected by the existing SHM based monitoring methods. Sandwich panels with foam , WebCore and honeycomb structures were...Whether it be hat stiffened, corrugated sandwich, honeycomb sandwich, or foam filled sandwich, all composite structures have one basic handicap in...based monitoring methods. Sandwich panels with foam , WebCore and honeycomb structures were considered for use in this study. Eigenmode frequency

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.

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

A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model.

PubMed

Smith, Wade P; Doctor, Jason; Meyer, Jürgen; Kalet, Ira J; Phillips, Mark H

2009-06-01

The prognosis of cancer patients treated with intensity-modulated radiation-therapy (IMRT) is inherently uncertain, depends on many decision variables, and requires that a physician balance competing objectives: maximum tumor control with minimal treatment complications. In order to better deal with the complex and multiple objective nature of the problem we have combined a prognostic probabilistic model with multi-attribute decision theory which incorporates patient preferences for outcomes. The response to IMRT for prostate cancer was modeled. A Bayesian network was used for prognosis for each treatment plan. Prognoses included predicting local tumor control, regional spread, distant metastases, and normal tissue complications resulting from treatment. A Markov model was constructed and used to calculate a quality-adjusted life-expectancy which aids in the multi-attribute decision process. Our method makes explicit the tradeoffs patients face between quality and quantity of life. This approach has advantages over current approaches because with our approach risks of health outcomes and patient preferences determine treatment decisions.

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.

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

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.

Clostridium Difficile Infection Due to Pneumonia Treatment: Mortality Risk Models.

PubMed

Chmielewska, M; Zycinska, K; Lenartowicz, B; Hadzik-Błaszczyk, M; Cieplak, M; Kur, Z; Wardyn, K A

2017-01-01

One of the most common gastrointestinal infection after the antibiotic treatment of community or nosocomial pneumonia is caused by the anaerobic spore Clostridium difficile (C. difficile). The aim of this study was to retrospectively assess mortality due to C. difficile infection (CDI) in patients treated for pneumonia. We identified 94 cases of post-pneumonia CDI out of the 217 patients with CDI. The mortality issue was addressed by creating a mortality risk models using logistic regression and multivariate fractional polynomial analysis. The patients' demographics, clinical features, and laboratory results were taken into consideration. To estimate the influence of the preceding respiratory infection, a pneumonia severity scale was included in the analysis. The analysis showed two statistically significant and clinically relevant mortality models. The model with the highest prognostic strength entailed age, leukocyte count, serum creatinine and urea concentration, hematocrit, coexisting neoplasia or chronic obstructive pulmonary disease. In conclusion, we report on two prognostic models, based on clinically relevant factors, which can be of help in predicting mortality risk in C. difficile infection, secondary to the antibiotic treatment of pneumonia. These models could be useful in preventive tailoring of individual therapy.

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.

Mathematical Frameworks for Diagnostics, Prognostics and Condition Based Maintenance Problems

DTIC Science & Technology

2008-08-15

REPORT Mathematical Frameworks for Diagnostics, Prognostics and Condition Based Maintenance Problems (W911NF-05-1-0426) 14. ABSTRACT 16. SECURITY ...other documentation. 12. DISTRIBUTION AVAILIBILITY STATEMENT Approved for Public Release; Distribution Unlimited 9. SPONSORING/MONITORING AGENCY NAME...parallel and distributed computing environment were researched. In support of the Condition Based Maintenance (CBM) philosophy, a theoretical framework

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.

Ways of providing the patient with a prognosis: a terminology of employed strategies based on qualitative data.

PubMed

Graugaard, Peter Kjær; Rogg, Lotte; Eide, Hilde; Uhlig, Till; Loge, Jon Håvard

2011-04-01

To identify, denote, and structure strategies applied by physicians and patients when communicating information about prognosis. A descriptive qualitative study based on audiotaped physician-patient encounters between 23 haematologists and rheumatologists, and 89 patients in Oslo. Classification of identified prognostic sequences was based on consensus. Physicians seldom initiated communication with patients explicitly to find out their overall preferences for prognostic information (metacommunication). Instead, they used sounding and implicit strategies such as invitations, implicatures, and non-specific information that might result in further disclosure of information if requested by the patients. In order to balance the obligation to promote hope and provide (true) information, they used strategies such as bad news/good news spirals, authentications, safeguardings, and softenings. Identified strategies applied by the patients to adjust the physician-initiated prognostic information to their needs were requests for specification, requests for optimism, and emotional warnings. The study presents an empirically derived terminology so that clinicians and educators involved in medical communication can increase their awareness of prognostic communication. Based on qualitative data obtained from communication excerpts, we suggest that individual clinicians and researchers evaluate the possible benefits of more frequent use of metacommunication and explicit prognostic information. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

The generic MESSy submodel TENDENCY (v1.0) for process-based analyses in Earth system models

NASA Astrophysics Data System (ADS)

Eichinger, R.; Jöckel, P.

2014-07-01

The tendencies of prognostic variables in Earth system models are usually only accessible, e.g. for output, as a sum over all physical, dynamical and chemical processes at the end of one time integration step. Information about the contribution of individual processes to the total tendency is lost, if no special precautions are implemented. The knowledge on individual contributions, however, can be of importance to track down specific mechanisms in the model system. We present the new MESSy (Modular Earth Submodel System) infrastructure submodel TENDENCY and use it exemplarily within the EMAC (ECHAM/MESSy Atmospheric Chemistry) model to trace process-based tendencies of prognostic variables. The main idea is the outsourcing of the tendency accounting for the state variables from the process operators (submodels) to the TENDENCY submodel itself. In this way, a record of the tendencies of all process-prognostic variable pairs can be stored. The selection of these pairs can be specified by the user, tailor-made for the desired application, in order to minimise memory requirements. Moreover, a standard interface allows the access to the individual process tendencies by other submodels, e.g. for on-line diagnostics or for additional parameterisations, which depend on individual process tendencies. An optional closure test assures the correct treatment of tendency accounting in all submodels and thus serves to reduce the model's susceptibility. TENDENCY is independent of the time integration scheme and therefore the concept is applicable to other model systems as well. Test simulations with TENDENCY show an increase of computing time for the EMAC model (in a setup without atmospheric chemistry) of 1.8 ± 1% due to the additional subroutine calls when using TENDENCY. Exemplary results reveal the dissolving mechanisms of the stratospheric tape recorder signal in height over time. The separation of the tendency of the specific humidity into the respective processes (large-scale clouds, convective clouds, large-scale advection, vertical diffusion and methane oxidation) show that the upward propagating water vapour signal dissolves mainly because of the chemical and the advective contribution. The TENDENCY submodel is part of version 2.42 or later of MESSy.

The generic MESSy submodel TENDENCY (v1.0) for process-based analyses in Earth System Models

NASA Astrophysics Data System (ADS)

Eichinger, R.; Jöckel, P.

2014-04-01

The tendencies of prognostic variables in Earth System Models are usually only accessible, e.g., for output, as sum over all physical, dynamical and chemical processes at the end of one time integration step. Information about the contribution of individual processes to the total tendency is lost, if no special precautions are implemented. The knowledge on individual contributions, however, can be of importance to track down specific mechanisms in the model system. We present the new MESSy (Modular Earth Submodel System) infrastructure submodel TENDENCY and use it exemplarily within the EMAC (ECHAM/MESSy Atmospheric Chemistry) model to trace process-based tendencies of prognostic variables. The main idea is the outsourcing of the tendency accounting for the state variables from the process operators (submodels) to the TENDENCY submodel itself. In this way, a record of the tendencies of all process-prognostic variable pairs can be stored. The selection of these pairs can be specified by the user, tailor-made for the desired application, in order to minimise memory requirements. Moreover a standard interface allows the access to the individual process tendencies by other submodels, e.g., for on-line diagnostics or for additional parameterisations, which depend on individual process tendencies. An optional closure test assures the correct treatment of tendency accounting in all submodels and thus serves to reduce the models susceptibility. TENDENCY is independent of the time integration scheme and therefore applicable to other model systems as well. Test simulations with TENDENCY show an increase of computing time for the EMAC model (in a setup without atmospheric chemistry) of 1.8 ± 1% due to the additional subroutine calls when using TENDENCY. Exemplary results reveal the dissolving mechanisms of the stratospheric tape recorder signal in height over time. The separation of the tendency of the specific humidity into the respective processes (large-scale clouds, convective clouds, large-scale advection, vertical diffusion and methane-oxidation) show that the upward propagating water vapour signal dissolves mainly because of the chemical and the advective contribution. The TENDENCY submodel is part of version 2.42 or later of MESSy.

An original approach was used to better evaluate the capacity of a prognostic marker using published survival curves.

PubMed

Dantan, Etienne; Combescure, Christophe; Lorent, Marine; Ashton-Chess, Joanna; Daguin, Pascal; Classe, Jean-Marc; Giral, Magali; Foucher, Yohann

2014-04-01

Predicting chronic disease evolution from a prognostic marker is a key field of research in clinical epidemiology. However, the prognostic capacity of a marker is not systematically evaluated using the appropriate methodology. We proposed the use of simple equations to calculate time-dependent sensitivity and specificity based on published survival curves and other time-dependent indicators as predictive values, likelihood ratios, and posttest probability ratios to reappraise prognostic marker accuracy. The methodology is illustrated by back calculating time-dependent indicators from published articles presenting a marker as highly correlated with the time to event, concluding on the high prognostic capacity of the marker, and presenting the Kaplan-Meier survival curves. The tools necessary to run these direct and simple computations are available online at http://www.divat.fr/en/online-calculators/evalbiom. Our examples illustrate that published conclusions about prognostic marker accuracy may be overoptimistic, thus giving potential for major mistakes in therapeutic decisions. Our approach should help readers better evaluate clinical articles reporting on prognostic markers. Time-dependent sensitivity and specificity inform on the inherent prognostic capacity of a marker for a defined prognostic time. Time-dependent predictive values, likelihood ratios, and posttest probability ratios may additionally contribute to interpret the marker's prognostic capacity. Copyright © 2014 Elsevier Inc. All rights reserved.

Prognostic molecular markers with no impact on decision-making: the paradox of gliomas based on a prospective study.

PubMed

Wager, M; Menei, P; Guilhot, J; Levillain, P; Michalak, S; Bataille, B; Blanc, J-L; Lapierre, F; Rigoard, P; Milin, S; Duthe, F; Bonneau, D; Larsen, C-J; Karayan-Tapon, L

2008-06-03

This study assessed the prognostic value of several markers involved in gliomagenesis, and compared it with that of other clinical and imaging markers already used. Four-hundred and sixteen adult patients with newly diagnosed glioma were included over a 3-year period and tumour suppressor genes, oncogenes, MGMT and hTERT expressions, losses of heterozygosity, as well as relevant clinical and imaging information were recorded. This prospective study was based on all adult gliomas. Analyses were performed on patient groups selected according to World Health Organization histoprognostic criteria and on the entire cohort. The endpoint was overall survival, estimated by the Kaplan-Meier method. Univariate analysis was followed by multivariate analysis according to a Cox model. p14(ARF), p16(INK4A) and PTEN expressions, and 10p 10q23, 10q26 and 13q LOH for the entire cohort, hTERT expression for high-grade tumours, EGFR for glioblastomas, 10q26 LOH for grade III tumours and anaplastic oligodendrogliomas were found to be correlated with overall survival on univariate analysis and age and grade on multivariate analysis only. This study confirms the prognostic value of several markers. However, the scattering of the values explained by tumour heterogeneity prevents their use in individual decision-making.

Anomalies in Network Bridges Involved in Bile Acid Metabolism Predict Outcomes of Colorectal Cancer Patients

PubMed Central

Yoon, Seyeol; Lee, Jae W.; Lee, Doheon

2014-01-01

Biomarkers prognostic for colorectal cancer (CRC) would be highly desirable in clinical practice. Proteins that regulate bile acid (BA) homeostasis, by linking metabolic sensors and metabolic enzymes, also called bridge proteins, may be reliable prognostic biomarkers for CRC. Based on a devised metric, “bridgeness,” we identified bridge proteins involved in the regulation of BA homeostasis and identified their prognostic potentials. The expression patterns of these bridge proteins could distinguish between normal and diseased tissues, suggesting that these proteins are associated with CRC pathogenesis. Using a supervised classification system, we found that these bridge proteins were reproducibly prognostic, with high prognostic ability compared to other known markers. PMID:25259881

Biomarker-Based Risk Model to Predict Cardiovascular Mortality in Patients With Stable Coronary Disease.

PubMed

Lindholm, Daniel; Lindbäck, Johan; Armstrong, Paul W; Budaj, Andrzej; Cannon, Christopher P; Granger, Christopher B; Hagström, Emil; Held, Claes; Koenig, Wolfgang; Östlund, Ollie; Stewart, Ralph A H; Soffer, Joseph; White, Harvey D; de Winter, Robbert J; Steg, Philippe Gabriel; Siegbahn, Agneta; Kleber, Marcus E; Dressel, Alexander; Grammer, Tanja B; März, Winfried; Wallentin, Lars

2017-08-15

Currently, there is no generally accepted model to predict outcomes in stable coronary heart disease (CHD). This study evaluated and compared the prognostic value of biomarkers and clinical variables to develop a biomarker-based prediction model in patients with stable CHD. In a prospective, randomized trial cohort of 13,164 patients with stable CHD, we analyzed several candidate biomarkers and clinical variables and used multivariable Cox regression to develop a clinical prediction model based on the most important markers. The primary outcome was cardiovascular (CV) death, but model performance was also explored for other key outcomes. It was internally bootstrap validated, and externally validated in 1,547 patients in another study. During a median follow-up of 3.7 years, there were 591 cases of CV death. The 3 most important biomarkers were N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), and low-density lipoprotein cholesterol, where NT-proBNP and hs-cTnT had greater prognostic value than any other biomarker or clinical variable. The final prediction model included age (A), biomarkers (B) (NT-proBNP, hs-cTnT, and low-density lipoprotein cholesterol), and clinical variables (C) (smoking, diabetes mellitus, and peripheral arterial disease). This "ABC-CHD" model had high discriminatory ability for CV death (c-index 0.81 in derivation cohort, 0.78 in validation cohort), with adequate calibration in both cohorts. This model provided a robust tool for the prediction of CV death in patients with stable CHD. As it is based on a small number of readily available biomarkers and clinical factors, it can be widely employed to complement clinical assessment and guide management based on CV risk. (The Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy Trial [STABILITY]; NCT00799903). Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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.

Inflammation-based prognostic score and number of lymph node metastases are independent prognostic factors in esophageal squamous cell carcinoma.

PubMed

Kobayashi, Takashi; Teruya, Masanori; Kishiki, Tomokazu; Kaneko, Susumu; Endo, Daisuke; Takenaka, Yoshiharu; Miki, Kenji; Kobayashi, Kaoru; Morita, Koji

2010-08-01

Few studies have investigated whether the Glasgow Prognostic Score (GPS), an inflammation-based prognostic score, is useful for postoperative prognosis of esophageal squamous cell carcinoma. GPS was calculated on the basis of admission data as follows: patients with elevated C-reactive protein level (>10 mg/l) and hypoalbuminemia (<35 g/l) were assigned to GPS2. Patients with one or no abnormal value were assigned to GPS1 or GPS0. A new scoring system was constructed using independent prognostic variables and was evaluated on whether it could be used to dictate the choice of clinical options. 65 patients with esophageal squamous cell carcinoma were enrolled. GPS and the number of lymph node metastases were found to be independent prognostic variables. The scoring system comprising GPS and the number of lymph node metastases was found to be effective in the prediction of a long-term outcome (p < 0.0001). Preoperative GPS may be useful for postoperative prognosis of patients with esophageal squamous cell carcinoma. GPS and the number of lymph node metastases could be used to identify a subgroup of patients with esophageal squamous cell carcinoma who are eligible for radical resection but show poor prognosis.

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.

An Integrated Approach for Gear Health Prognostics

NASA Technical Reports Server (NTRS)

He, David; Bechhoefer, Eric; Dempsey, Paula; Ma, Jinghua

2012-01-01

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.

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 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.

Neurological prognostication of outcome in patients in coma after cardiac arrest.

PubMed

Rossetti, Andrea O; Rabinstein, Alejandro A; Oddo, Mauro

2016-05-01

Management of coma after cardiac arrest has improved during the past decade, allowing an increasing proportion of patients to survive, thus prognostication has become an integral part of post-resuscitation care. Neurologists are increasingly confronted with raised expectations of next of kin and the necessity to provide early predictions of long-term prognosis. During the past decade, as technology and clinical evidence have evolved, post-cardiac arrest prognostication has moved towards a multimodal paradigm combining clinical examination with additional methods, consisting of electrophysiology, blood biomarkers, and brain imaging, to optimise prognostic accuracy. Prognostication should never be based on a single indicator; although some variables have very low false positive rates for poor outcome, multimodal assessment provides resassurance about the reliability of a prognostic estimate by offering concordant evidence. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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.

Prognostic factors in operable breast cancer treated with neoadjuvant chemotherapy: towards a quantification of residual disease.

PubMed

Mombelli, Sarah; Kwiatkowski, Fabrice; Abrial, Catherine; Wang-Lopez, Qian; de Boissieu, Paul; Garbar, Christian; Bensussan, Armand; Curé, Hervé

2015-01-01

Neoadjuvant chemotherapy (NACT) allows for a more frequent use of breast-conservative surgery; it is also an in vivo model of individual tumor sensitivity which permits to determine new prognostic factors to personalize the therapeutic approach. Between 2000 and 2012, 318 patients with primary invasive breast cancer were treated with a median of 6 cycles of NACT; they received either an anthracycline-based FEC 100 protocol (31.1%), or anthracyclines + taxanes (53.5%), with trastuzumab if indicated (15.4%). After a median follow-up of 44.2 months, the pathological complete response rate according to the classification of Chevallier et al. [Am J Clin Oncol 1993;16:223-228] was 19.3%, and overall (OS) and disease-free survival (DFS) at 10 years were 60.2 and 69.6%, respectively. Univariate analyses demonstrated that the Residual Disease in Breast and Nodes (RDBN) index was the most significant prognostic factor for OS (p = 0.0082) and DFS (p = 0.0022), and multivariate analyses mainly revealed that the residual tumor size, residual involved node number and post-chemotherapy Scarff-Bloom-Richardson (SBR) grading were the most significant prognostic factors. In a cohort of patients who were all homogeneously treated with some of the most common drugs for breast cancer, we demonstrate that NACT may provide additional prognostic factors and confirm the RDBN index. As this index allows for the prediction of survival with different breast cancer subtypes, we suggest that it should be calculated routinely to help clinicians to select patients who need adjuvant treatments. 2015 S. Karger AG, Basel

The ratio of hemoglobin to red cell distribution width as a novel prognostic parameter in esophageal squamous cell carcinoma: a retrospective study from southern China

PubMed Central

Bi, Xiwen; Yang, Hang; An, Xin; Wang, Fenghua; Jiang, Wenqi

2016-01-01

Background We propose a novel prognostic parameter for esophageal squamous cell carcinoma (ESCC)—hemoglobin/red cell distribution width (HB/RDW) ratio. Its clinical prognostic value and relationship with other clinicopathological characteristics were investigated in ESCC patients. Results The optimal cut-off value was 0.989 for the HB/RDW ratio. The HB/RDW ratio (P= 0.035), tumor depth (P = 0.020) and lymph node status (P<0.001) were identified to be an independent prognostic factors of OS by multivariate analysis, which was validated by bootstrap resampling. Patients with a low HB/RDW ratio had a 1.416 times greater risk of dying during follow-up compared with those with a high HB/RDW (95% CI = 1.024–1.958, P = 0.035). Materials and Methods We retrospectively analyzed 362 patients who underwent curative treatment at a single institution between January 2007 and December 2008. The chi-square test was used to evaluate relationships between the HB/RDW ratio and other clinicopathological variables; the Kaplan–Meier method was used to analyze the 5-year overall survival (OS); and the Cox proportional hazards models were used for univariate and multivariate analyses of variables related to OS. Conclusion A significant association was found between the HB/RDW ratio and clinical characteristics and survival outcomes in ESCC patients. Based on these findings, we believe that the HB/RDW ratio is a novel and promising prognostic parameter for ESCC patients. PMID:27223088

A hybrid approach to survival model building using integration of clinical and molecular information in censored data.

PubMed

Choi, Ickwon; Kattan, Michael W; Wells, Brian J; Yu, Changhong

2012-01-01

In medical society, the prognostic models, which use clinicopathologic features and predict prognosis after a certain treatment, have been externally validated and used in practice. In recent years, most research has focused on high dimensional genomic data and small sample sizes. Since clinically similar but molecularly heterogeneous tumors may produce different clinical outcomes, the combination of clinical and genomic information, which may be complementary, is crucial to improve the quality of prognostic predictions. However, there is a lack of an integrating scheme for clinic-genomic models due to the P ≥ N problem, in particular, for a parsimonious model. We propose a methodology to build a reduced yet accurate integrative model using a hybrid approach based on the Cox regression model, which uses several dimension reduction techniques, L₂ penalized maximum likelihood estimation (PMLE), and resampling methods to tackle the problem. The predictive accuracy of the modeling approach is assessed by several metrics via an independent and thorough scheme to compare competing methods. In breast cancer data studies on a metastasis and death event, we show that the proposed methodology can improve prediction accuracy and build a final model with a hybrid signature that is parsimonious when integrating both types of variables.

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.

Peripheral blood lymphocyte/monocyte ratio as a useful prognostic factor in dogs with diffuse large B-cell lymphoma receiving chemoimmunotherapy.

PubMed

Marconato, Laura; Martini, Valeria; Stefanello, Damiano; Moretti, Pierangelo; Ferrari, Roberta; Comazzi, Stefano; Laganga, Paola; Riondato, Fulvio; Aresu, Luca

2015-11-01

Diffuse large B-cell lymphoma (DLBCL) is the most frequent canine lymphoid neoplasm. Despite treatment, the majority of dogs with DLBCL experience tumour relapse and consequently die, so practical models to characterise dogs with a poor prognosis are needed. This study examined whether the lymphocyte/monocyte ratio (LMR) can predict outcome in dogs with newly diagnosed DLBCL with regard to time-to-progression (TTP) and lymphoma specific survival (LSS). A retrospective study analysed the prognostic significance of LMR obtained at diagnosis by flow cytometry (based on morphological properties and CD45 expression) in 51 dogs that underwent complete staging and received the same treatment, comprising multi-agent chemotherapy and administration of an autologous vaccine. Dogs with an LMR ≤ 1.2 (30% of all cases) were found to have significantly shorter TTP and LSS, and it was concluded that LMR was a useful independent prognostic indicator with biological relevance in dogs with DLBCL treated with chemoimmunotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Vehicle Integrated Prognostic Reasoner (VIPR) 2010 Annual Final Report

NASA Technical Reports Server (NTRS)

Hadden, George D.; Mylaraswamy, Dinkar; Schimmel, Craig; Biswas, Gautam; Koutsoukos, Xenofon; Mack, Daniel

2011-01-01

Honeywell's Central Maintenance Computer Function (CMCF) and Aircraft Condition Monitoring Function (ACMF) represent the state-of-the art in integrated vehicle health management (IVHM). Underlying these technologies is a fault propagation modeling system that provides nose-to-tail coverage and root cause diagnostics. The Vehicle Integrated Prognostic Reasoner (VIPR) extends this technology to interpret evidence generated by advanced diagnostic and prognostic monitors provided by component suppliers to detect, isolate, and predict adverse events that affect flight safety. This report describes year one work that included defining the architecture and communication protocols and establishing the user requirements for such a system. Based on these and a set of ConOps scenarios, we designed and implemented a demonstration of communication pathways and associated three-tiered health management architecture. A series of scripted scenarios showed how VIPR would detect adverse events before they escalate as safety incidents through a combination of advanced reasoning and additional aircraft data collected from an aircraft condition monitoring system. Demonstrating VIPR capability for cases recorded in the ASIAS database and cross linking them with historical aircraft data is planned for year two.

Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE).

PubMed

Palacios-Rubio, Julián; Marina-Breysse, Manuel; Quintanilla, Jorge G; Gil-Perdomo, José Miguel; Juárez-Fernández, Miriam; Garcia-Gonzalez, Inés; Rial-Bastón, Verónica; Corcobado, María Carmen; Espinosa, María Carmen; Ruiz, Francisco; Gómez-Mascaraque Pérez, Francisco; Bringas-Bollada, María; Lillo-Castellano, José María; Pérez-Castellano, Nicasio; Martínez-Sellés, Manuel; López de Sá, Esteban; Martín-Benítez, Juan Carlos; Perez-Villacastín, Julián; Filgueiras-Rama, David

2018-06-06

Ventricular fibrillation (VF)-related sudden cardiac death (SCD) is a leading cause of mortality and morbidity. Current biological and imaging parameters show significant limitations on predicting cerebral performance at hospital admission. The AWAKE study (NCT03248557) is a multicentre observational study to validate a model based on spectral ECG analysis to early predict cerebral performance and survival in resuscitated comatose survivors. Data from VF ECG tracings of patients resuscitated from SCD will be collected using an electronic Case Report Form. Patients can be either comatose (Glasgow Coma Scale - GCS - ≤8) survivors undergoing temperature control after return of spontaneous circulation (RoSC), or those who regain consciousness (GCS=15) after RoSC; all admitted to Intensive Cardiac Care Units in 4 major university hospitals. VF tracings prior to the first direct current shock will be digitized and analyzed to derive spectral data and feed a predictive model to estimate favorable neurological performance (FNP). The results of the model will be compared to the actual prognosis. The primary clinical outcome is FNP during hospitalization. Patients will be categorized into 4 subsets of neurological prognosis according to the risk score obtained from the predictive model. The secondary clinical outcomes are survival to hospital discharge, and FNP and survival after 6 months of follow-up. The model-derived categorisation will be also compared with clinical variables to assess model sensitivity, specificity, and accuracy. A model based on spectral analysis of VF tracings is a promising tool to obtain early prognostic data after SCD. Copyright © 2018 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Learning predictive models that use pattern discovery--a bootstrap evaluative approach applied in organ functioning sequences.

PubMed

Toma, Tudor; Bosman, Robert-Jan; Siebes, Arno; Peek, Niels; Abu-Hanna, Ameen

2010-08-01

An important problem in the Intensive Care is how to predict on a given day of stay the eventual hospital mortality for a specific patient. A recent approach to solve this problem suggested the use of frequent temporal sequences (FTSs) as predictors. Methods following this approach were evaluated in the past by inducing a model from a training set and validating the prognostic performance on an independent test set. Although this evaluative approach addresses the validity of the specific models induced in an experiment, it falls short of evaluating the inductive method itself. To achieve this, one must account for the inherent sources of variation in the experimental design. The main aim of this work is to demonstrate a procedure based on bootstrapping, specifically the .632 bootstrap procedure, for evaluating inductive methods that discover patterns, such as FTSs. A second aim is to apply this approach to find out whether a recently suggested inductive method that discovers FTSs of organ functioning status is superior over a traditional method that does not use temporal sequences when compared on each successive day of stay at the Intensive Care Unit. The use of bootstrapping with logistic regression using pre-specified covariates is known in the statistical literature. Using inductive methods of prognostic models based on temporal sequence discovery within the bootstrap procedure is however novel at least in predictive models in the Intensive Care. Our results of applying the bootstrap-based evaluative procedure demonstrate the superiority of the FTS-based inductive method over the traditional method in terms of discrimination as well as accuracy. In addition we illustrate the insights gained by the analyst into the discovered FTSs from the bootstrap samples. Copyright 2010 Elsevier Inc. All rights reserved.

Evidence base and future research directions in the management of low back pain

PubMed Central

Abbott, Allan

2016-01-01

Low back pain (LBP) is a prevalent and costly condition. Awareness of valid and reliable patient history taking, physical examination and clinical testing is important for diagnostic accuracy. Stratified care which targets treatment to patient subgroups based on key characteristics is reliant upon accurate diagnostics. Models of stratified care that can potentially improve treatment effects include prognostic risk profiling for persistent LBP, likely response to specific treatment based on clinical prediction models or suspected underlying causal mechanisms. The focus of this editorial is to highlight current research status and future directions for LBP diagnostics and stratified care. PMID:27004162

Glasgow prognostic score is superior to other inflammation-based scores in predicting survival of diffuse large B-cell lymphoma

PubMed Central

Wei, Xiaolei; Zhou, Lizhi; Wei, Qi; Zhang, Yuankun; Huang, Weimin; Feng, Ru

2017-01-01

Inflammation-based prognostic scores, such as the glasgow prognostic score (GPS), prognostic index (PI), prognostic nutritional index (PNI), neutrophil lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR) were related to survival in many solid tumors. Recent study showed that GPS can be used to predict outcome in diffuse large B-cell lymphoma (DLBCL). However, other inflammation related scores had not been reported and it also remained unknown which of them was the most useful to evaluate the survival in DLBCLs. In this retrospective study, a number of 252 newly diagnosed and histologically proven DLBCLs from January 2003 to December 2014 were included. The high GPS, high PI, high NLR, high PLR and low PNI were all associated with poor overall survival (p < 0.05) and event-free survival (p < 0.05) in univariate analysis. Multivariate analysis indicated that GPS (HR = 1.781, 95% CI = 1.065–2.979, p = 0.028) remained an independent prognostic predictor in DLBCL. The c-index of GPS (0.735, 95% CI = 0.645–0.824) was greater than that of PI (0.710, 95% CI = 0.621–0.799, p = 0.602), PNI (0.600, 95% CI = 0.517–0.683, p = 0.001), PLR (0.599, 95% CI = 0.510–0.689, p = 0.029) and NLR (0.572, 95% CI = 0.503–0.642, p = 0.005) by Harrell's concordance index. Especially in DLBCLs treated with R-CHOP, GPS still remained the most powerful prognostic score when comparing with others (p = 0.001 and p < 0.001, respectively for OS and EFS). In conclusion, it is indicated that inflammation-based prognostic scores such as GPS, PI, NLR, PNI and PLR all could be used to predict the outcome of DLBCLs. Among them, GPS is the most powerful indicator in predicting survival in DLBCLs, even in the rituximab era. PMID:29100345

External prognostic validations and comparisons of age- and gender-adjusted exercise capacity predictions.

PubMed

Kim, Esther S H; Ishwaran, Hemant; Blackstone, Eugene; Lauer, Michael S

2007-11-06

The purpose of this study was to externally validate the prognostic value of age- and gender-based nomograms and categorical definitions of impaired exercise capacity (EC). Exercise capacity predicts death, but its use in routine clinical practice is hampered by its close correlation with age and gender. For a median of 5 years, we followed 22,275 patients without known heart disease who underwent symptom-limited stress testing. Models for predicted or impaired EC were identified by literature search. Gender-specific multivariable proportional hazards models were constructed. Four methods were used to assess validity: Akaike Information Criterion (AIC), right-censored c-index in 100 out-of-bootstrap samples, the Nagelkerke Index R2, and calculation of calibration error in 100 bootstrap samples. There were 646 and 430 deaths in 13,098 men and 9,177 women, respectively. Of the 7 models tested in men, a model based on a Veterans Affairs cohort (predicted metabolic equivalents [METs] = 18 - [0.15 x age]) had the highest AIC and R2. In women, a model based on the St. James Take Heart Project (predicted METs = 14.7 - [0.13 x age]) performed best. Categorical definitions of fitness performed less well. Even after accounting for age and gender, there was still an important interaction with age, whereby predicted EC was a weaker predictor in older subjects (p for interaction <0.001 in men and 0.003 in women). Several methods describe EC accounting for age and gender-related differences, but their ability to predict mortality differ. Simple cutoff values fail to fully describe EC's strong predictive value.

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.,

Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

PubMed

Gao, Haiyan; Yang, Mei; Zhang, Xiaolan

2018-04-01

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics

NASA Technical Reports Server (NTRS)

Ho, K. K.; Moody, G. B.; Peng, C. K.; Mietus, J. E.; Larson, M. G.; Levy, D.; Goldberger, A. L.

1997-01-01

BACKGROUND: Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted. METHODS AND RESULTS: We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7+/-8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD (P<.01), LF (P<.01), VLF (P<.05), and TP (P<.01) and the nonlinear measure DFA (P<.05) were predictors of survival over a mean follow-up period of 1.9 years; other measures, including ApEn (P>.3), were not. In multivariable models, DFA was of borderline predictive significance (P=.06) after adjustment for the diagnosis of CHF and SD. CONCLUSIONS: These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.

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.

Next-generation sequencing in systemic mastocytosis: Derivation of a mutation-augmented clinical prognostic model for survival.

PubMed

Pardanani, Animesh; Lasho, Terra; Elala, Yoseph; Wassie, Emnet; Finke, Christy; Reichard, Kaaren K; Chen, Dong; Hanson, Curtis A; Ketterling, Rhett P; Tefferi, Ayalew

2016-09-01

In routine practice, the World Health Organization classification of systemic mastocytosis (SM) is also the de facto prognostic system; a core value is distinguishing indolent (ISM) from advanced SM (includes aggressive SM [ASM], SM with associated hematological neoplasm [SM-AHN] and mast cell leukemia [MCL]). We sequenced 27 genes in 150 SM patients to identify mutations that could be integrated into a clinical-molecular prognostic model for survival. Forty four patients (29%) had ISM, 25 (17%) ASM, 80 (53%) SM-AHN and 1 (0.7%) MCL; overall KITD816V prevalence was 75%. In 87 patients, 148 non-KIT mutations were detected; the most frequently mutated genes were TET2 (29%), ASXL1 (17%), and CBL (11%), with significantly higher mutation frequency in SM-AHN > ASM > ISM (P < 0.0001). In advanced SM, ASXL1 and RUNX1 mutations were associated with inferior survival. In multivariate analysis, age > 60 years (HR = 2.4), hemoglobin < 10 g/dL or transfusion-dependence (HR = 1.7), platelet count < 150 × 10(9) /L (HR = 3.2), serum albumin < 3.5 g/dL (HR = 2.6), and ASXL1 mutation (HR = 2.3) were associated with inferior survival. A mutation-augmented prognostic scoring system (MAPSS) based on these parameters stratified advanced SM patients into high-, intermediate-, and low-risk groups with median survival of 5, 21 and 86 months, respectively (P < 0.0001). These data should optimize risk-stratification and treatment selection for advanced SM patients. Am. J. Hematol. 91:888-893, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

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.

Identifying prognostic signature in ovarian cancer using DirGenerank

PubMed Central

Wang, Jian-Yong; Chen, Ling-Ling; Zhou, Xiong-Hui

2017-01-01

Identifying the prognostic genes in cancer is essential not only for the treatment of cancer patients, but also for drug discovery. However, it's still a big challenge to select the prognostic genes that can distinguish the risk of cancer patients across various data sets because of tumor heterogeneity. In this situation, the selected genes whose expression levels are statistically related to prognostic risks may be passengers. In this paper, based on gene expression data and prognostic data of ovarian cancer patients, we used conditional mutual information to construct gene dependency network in which the nodes (genes) with more out-degrees have more chances to be the modulators of cancer prognosis. After that, we proposed DirGenerank (Generank in direct netowrk) algorithm, which concerns both the gene dependency network and genes’ correlations to prognostic risks, to identify the gene signature that can predict the prognostic risks of ovarian cancer patients. Using ovarian cancer data set from TCGA (The Cancer Genome Atlas) as training data set, 40 genes with the highest importance were selected as prognostic signature. Survival analysis of these patients divided by the prognostic signature in testing data set and four independent data sets showed the signature can distinguish the prognostic risks of cancer patients significantly. Enrichment analysis of the signature with curated cancer genes and the drugs selected by CMAP showed the genes in the signature may be drug targets for therapy. In summary, we have proposed a useful pipeline to identify prognostic genes of cancer patients. PMID:28615526

Evaluation of prognostic factors in liver-limited metastatic colorectal cancer: a preplanned analysis of the FIRE-1 trial

PubMed Central

Giessen, C; Fischer von Weikersthal, L; Laubender, R P; Stintzing, S; Modest, D P; Schalhorn, A; Schulz, C; Heinemann, V

2013-01-01

Background: Liver-limited disease (LLD) denotes a specific subgroup of metastatic colorectal cancer (mCRC) patients. Patients and Methods: A total of 479 patients with unresectable mCRC from an irinotecan-based randomised phase III trial were evaluated. Patients with LLD and non-LLD and hepatic resection were differentiated. Based on baseline patient characteristic, prognostic factors for hepatic resection were evaluated. Furthermore, prognostic factors for median overall survival (OS) were estimated via Cox regression in LLD patients. Results: Secondary liver resection was performed in 38 out of 479 patients (resection rate: 7.9%). Prognostic factors for hepatic resection were LLD, lactate dehydrogenase (LDH), node-negative primary, alkaline phosphatase (AP) and Karnofsky performance status (PS). Median OS was significantly increased after hepatic resection (48 months), whereas OS in LLD (17 months) and non-LLD (19 months) was comparable in non-resected patients. With the inapplicability of Koehne's risk classification in LLD patients, a new score based on only the independent prognostic factors LDH and white blood cell (WBC) provided markedly improved information on the outcome. Conclusion: Patients undergoing hepatic resection showed favourable long-term survival, whereas non-resected LLD patients and non-LLD patients did not differ with regard to progression-free survival and OS. The LDH levels and WBC count were confirmed as prognostic factors and provide a useful and simple score for OS-related risk stratification also in LLD. PMID:23963138

Experiments in monthly mean simulation of the atmosphere with a coarse-mesh general circulation model

NASA Technical Reports Server (NTRS)

Lutz, R. J.; Spar, J.

1978-01-01

The Hansen atmospheric model was used to compute five monthly forecasts (October 1976 through February 1977). The comparison is based on an energetics analysis, meridional and vertical profiles, error statistics, and prognostic and observed mean maps. The monthly mean model simulations suffer from several defects. There is, in general, no skill in the simulation of the monthly mean sea-level pressure field, and only marginal skill is indicated for the 850 mb temperatures and 500 mb heights. The coarse-mesh model appears to generate a less satisfactory monthly mean simulation than the finer mesh GISS model.

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.

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.

Circulating tumor cells and miRNAs as prognostic markers in neuroendocrine neoplasms.

PubMed

Zatelli, Maria Chiara; Grossrubatscher, Erika Maria; Guadagno, Elia; Sciammarella, Concetta; Faggiano, Antongiulio; Colao, Annamaria

2017-06-01

The prognosis of neuroendocrine neoplasms (NENs) is widely variable and has been shown to associate with several tissue- and blood-based biomarkers in different settings. The identification of prognostic factors predicting NEN outcome is of paramount importance to select the best clinical management for these patients. Prognostic markers have been intensively investigated, also taking advantage of the most modern techniques, in the perspective of personalized medicine and appropriate resource utilization. This review summarizes the available data on the possible role of circulating tumor cells and microRNAs as prognostic markers in NENs. © 2017 Society for Endocrinology.

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.

Pure fetal histology subtype was associated with better prognosis of children with hepatoblastoma: A Chinese population-based study.

PubMed

Qiao, Guo-liang; Chen, Zhen; Wang, Chen; Ge, Juntao; Zhang, Zhen; Li, Long; Ren, Jun

2016-03-01

The aim of this study is to identify the association between histologic types and the prognosis of hepatoblastoma (HB) in a large Asian cohort of a single institution and to explore the interaction of histologic types with other independently risk factors in the process of affecting prognosis of HB. We retrospectively reviewed 176 children with HB (82 female, 94 male) managed in our institution between May 1, 2001 and July 30, 2014. Prognostic factors were evaluated using Kaplan-Meier curves and Cox proportional hazards models. For the entire cohort of 176 patients, the overall median survival was 80.4 months(95% CI: 71.6-89.2 months), and the 5-year event-free survival and overall survival rates were 54.6 and 66.7%. Descriptive survival statistics and Kaplan-Meier curves suggested that alpha fetoprotein levels, tumor metastases, multifocality, histologic types, and Pre-Treatment Extent of Disease staging System stage had prognostic significance in this relatively selected cohort. Moreover, after eliminating the impact of the interaction of different classification methods of histologic types, pure fetal histologic (PFH) was an independent prognostic factor of HB (hazard ratio [HR]: 2.752, P = 0.021). Further stratification analysis showed that the impaction of other identified risk factors on the influence of PFH on the prognosis of HB patients was different. We have confirmed that the HB prognostic factors of HB and PFH was associated with better prognosis of children with HB based on an Asian population. PFH showed different significance in the process of affecting prognosis of HB with the interaction of other independent risk factors. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

PubMed

Drier, Yotam; Domany, Eytan

2011-03-14

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

[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.

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

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

DTIC Science & Technology

2012-09-01

94035, USA abhinav.saxena@nasa.gov ABSTRACT Prognostics deals with the prediction of the end of life ( EOL ) of a system. EOL is a random variable, due...future evolution of the system, accumulating additional uncertainty into the predicted EOL . Prediction algorithms that do not account for these sources of...uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in

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.

Modeling Developmental Language Difficulties from School Entry into Adulthood: Literacy, Mental Health, and Employment Outcomes

ERIC Educational Resources Information Center

Law, James; Rush, Robert; Schoon, Ingrid; Parsons, Samantha

2009-01-01

Purpose: Understanding the long-term outcomes of developmental language difficulties is key to knowing what significance to attach to them. To date, most prognostic studies have tended to be clinical rather than population-based, which necessarily affects the interpretation. This study sought to address this issue using data from a U.K. birth…

MCT4 surpasses the prognostic relevance of the ancillary protein CD147 in clear cell renal cell carcinoma.

PubMed

Fisel, Pascale; Stühler, Viktoria; Bedke, Jens; Winter, Stefan; Rausch, Steffen; Hennenlotter, Jörg; Nies, Anne T; Stenzl, Arnulf; Scharpf, Marcus; Fend, Falko; Kruck, Stephan; Schwab, Matthias; Schaeffeler, Elke

2015-10-13

Cluster of differentiation 147 (CD147/BSG) is a transmembrane glycoprotein mediating oncogenic processes partly through its role as binding partner for monocarboxylate transporter MCT4/SLC16A3. As demonstrated for MCT4, CD147 is proposed to be associated with progression in clear cell renal cell carcinoma (ccRCC). In this study, we evaluated the prognostic relevance of CD147 in comparison to MCT4/SLC16A3 expression and DNA methylation. CD147 protein expression was assessed in two independent ccRCC-cohorts (n = 186, n = 59) by immunohistochemical staining of tissue microarrays and subsequent manual as well as automated software-supported scoring (Tissue Studio, Definien sAG). Epigenetic regulation of CD147 was investigated using RNAseq and DNA methylation data of The Cancer Genome Atlas. These results were validated in our cohort. Relevance of prognostic models for cancer-specific survival, comprising CD147 and MCT4 expression or SLC16A3 DNA methylation, was compared using chi-square statistics. CD147 protein expression generated with Tissue Studio correlated significantly with those from manual scoring (P < 0.0001, rS = 0.85), indicating feasibility of software-based evaluation exemplarily for the membrane protein CD147 in ccRCC. Association of CD147 expression with patient outcome differed between cohorts. DNA methylation in the CD147/BSG promoter was not associated with expression. Comparison of prognostic relevance of CD147/BSG and MCT4/SLC16A3, showed higher significance for MCT4 expression and superior prognostic power for DNA methylation at specific CpG-sites in the SLC16A3 promoter (e.g. CD147 protein: P = 0.7780,Harrell's c-index = 53.7% vs. DNA methylation: P = 0.0076, Harrell's c-index = 80.0%). Prognostic significance of CD147 protein expression could not surpass that of MCT4, especially of SLC16A3 DNA methylation, corroborating the role of MCT4 as prognostic biomarker for ccRCC.

MCT4 surpasses the prognostic relevance of the ancillary protein CD147 in clear cell renal cell carcinoma

PubMed Central

Winter, Stefan; Rausch, Steffen; Hennenlotter, Jörg; Nies, Anne T.; Stenzl, Arnulf; Scharpf, Marcus; Fend, Falko; Kruck, Stephan; Schwab, Matthias; Schaeffeler, Elke

2015-01-01

Cluster of differentiation 147 (CD147/BSG) is a transmembrane glycoprotein mediating oncogenic processes partly through its role as binding partner for monocarboxylate transporter MCT4/SLC16A3. As demonstrated for MCT4, CD147 is proposed to be associated with progression in clear cell renal cell carcinoma (ccRCC). In this study, we evaluated the prognostic relevance of CD147 in comparison to MCT4/SLC16A3 expression and DNA methylation. Methods CD147 protein expression was assessed in two independent ccRCC-cohorts (n = 186, n = 59) by immunohistochemical staining of tissue microarrays and subsequent manual as well as automated software-supported scoring (Tissue Studio, Definien sAG). Epigenetic regulation of CD147 was investigated using RNAseq and DNA methylation data of The Cancer Genome Atlas. These results were validated in our cohort. Relevance of prognostic models for cancer-specific survival, comprising CD147 and MCT4 expression or SLC16A3 DNA methylation, was compared using chi-square statistics. Results CD147 protein expression generated with Tissue Studio correlated significantly with those from manual scoring (P < 0.0001, rS = 0.85), indicating feasibility of software-based evaluation exemplarily for the membrane protein CD147 in ccRCC. Association of CD147 expression with patient outcome differed between cohorts. DNA methylation in the CD147/BSG promoter was not associated with expression. Comparison of prognostic relevance of CD147/BSG and MCT4/SLC16A3, showed higher significance for MCT4 expression and superior prognostic power for DNA methylation at specific CpG-sites in the SLC16A3 promoter (e.g. CD147 protein: P = 0.7780, Harrell's c-index = 53.7% vs. DNA methylation: P = 0.0076, Harrell's c-index = 80.0%). Conclusions Prognostic significance of CD147 protein expression could not surpass that of MCT4, especially of SLC16A3 DNA methylation, corroborating the role of MCT4 as prognostic biomarker for ccRCC. PMID:26384346

Prognostics and Health Monitoring: Application to Electric Vehicles

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. 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.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

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

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

Prognostic factors of primary gastrointestinal stromal tumors: a cohort study based on high-volume centers.

PubMed

Liu, Xuechao; Qiu, Haibo; Zhang, Peng; Feng, Xingyu; Chen, Tao; Li, Yong; Tao, Kaixiong; Li, Guoxin; Sun, Xiaowei; Zhou, Zhiwei

2018-02-01

We aimed to evaluate the clinicopathologic characteristics, immunohistochemical expression and prognostic factors of patients with primary gastrointestinal stromal tumors (GISTs). Data from 2,570 consecutive GIST patients from four medical centers in China (January 2001-December 2015) were reviewed. Survival curves were constructed by the Kaplan-Meier method, and Cox regression models were used to identify independent prognostic factors. Of the included patients, 1,375 (53.5%) were male, and the patient age range was 18 to 95 (median, 58) years. The tumors were mostly found in the stomach (64.5%), small intestine (25.1%) and colorectal region (5.1%). At the time of diagnosis, the median tumor size was 4.0 (range: 0.1-55.0) cm, and the median mitotic index per 50 high power fields (HPFs) was 3 (range: 0-254). Of the 2,168 resected patients, 2,009 (92.7%) received curative resection. According to the modified National Institutes of Health (NIH) classification, 21.9%, 28.9%, 14.1% and 35.1% were very low-, low-, intermediate- and high-risk tumors, respectively. The rate of positivity was 96.4% for c-Kit, 87.1% for CD34, 96.9% for delay of germination 1 (DOG-1), 8.0% for S-100, 31.0% for smooth muscle actin (SMA) and 5.1% for desmin. However, the prognostic value of each was limited. Multivariate analysis showed that age, tumor size, mitotic index, tumor site, occurrence of curative resection and postoperative imatinib were independent prognostic factors. Furthermore, we found that high-risk patients benefited significantly from postoperative imatinib (P<0.001), whereas intermediate-risk patients did not (P=0.954). Age, tumor size, mitotic index, tumor site, occurrence of curative resection and postoperative imatinib were independent prognostic factors in patients with GISTs. Moreover, determining whether intermediate-risk patients can benefit from adjuvant imatinib would be of considerable interest in future studies.

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.

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.

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.

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.

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.

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.

Number of positive nodes is superior to the lymph node ratio and American Joint Committee on Cancer N staging for the prognosis of surgically treated head and neck squamous cell carcinomas.

PubMed

Roberts, Thomas J; Colevas, A Dimitrios; Hara, Wendy; Holsinger, F Christopher; Oakley-Girvan, Ingrid; Divi, Vasu

2016-05-01

Recent changes in head and neck cancer epidemiology have created a need for improved lymph node prognostics. This article compares the prognostic value of the number of positive nodes (pN) with the value of the lymph node ratio (LNR) and American Joint Committee on Cancer (AJCC) N staging in surgical patients. The Surveillance, Epidemiology, and End Results database was used to identify cases of head and neck squamous cell carcinomas from 2004 to 2012. The sample was grouped by the AJCC N stage, LNR, and pN and was analyzed with Kaplan-Meier and multivariate Cox proportional hazards models. The sample was also analyzed by the site of the primary tumor. This study identified 12,437 patients. Kaplan-Meier survival curves showed superior prognostic ability for LNR and pN staging in comparison with AJCC staging. Patients with a pN value > 5 had the worst overall survival (5-year survival rate, 16%). Patients with oropharyngeal tumors had better outcomes for all groupings, and a pN value > 5 for oropharyngeal cancers was associated with decreased survival. Multivariate regressions demonstrated larger hazard ratios (HRs) and a lower Akaike information criterion for the pN model versus the AJCC stage and LNR models. The HRs were 1.78 (95% confidence interval, 1.62-1.95) for a pN value of 1, 2.53 (95% confidence interval, 2.32-2.75) for a pN value of 2 to 5, and 4.64 (95% confidence interval, 4.18-5.14) for a pN value > 5. The pN models demonstrated superior prognostic value in comparison with the LNR and AJCC N staging. Future modifications of the nodal staging system should be based on the pN with a separate system for oropharyngeal cancers. Future trials should consider examining adjuvant treatment escalation in patients with >5 lymph nodes. Cancer 2016;122:1388-1397. © 2016 American Cancer Society. © 2015 American Cancer Society.

Representing Northern Peatland Hydrology and Biogeochemistry with ALM Land Surface Model

NASA Astrophysics Data System (ADS)

Shi, X.; Ricciuto, D. M.; Thornton, P. E.; Hanson, P. J.; Xu, X.; Mao, J.; Warren, J.; Yuan, F.; Norby, R. J.; Sebestyen, S.; Griffiths, N.; Weston, D. J.; Walker, A.

2017-12-01

Northern peatlands are likely to be important in future carbon cycle-climate feedbacks due to their large carbon pool and vulnerability to hydrological change. Predictive understanding of northern peatland hydrology is a necessary precursor to understanding the fate of massive carbon stores in these systems under the influence of present and future climate change. Current models have begun to address microtopographic controls on peatland hydrology, but none have included a prognostic calculation of peatland water table depth for a vegetated wetland, independent of prescribed regional water tables. Firstly, we introduce a new configuration of the land model (ALM) of Accelerated Climate model for Energy (ACME), which includes a fully prognostic water table calculation for a vegetated peatland. Secondly, we couple our new hydrology treatment with vertically structured soil organic matter pool, and the addition of components from methane biogeochemistry. Thirdly, we introduce a new PFT for mosses and implement the water content dynamics and physiology of mosses. We inform and test our model based on SPRUCE experiment to get the reasonable results for the seasonal dynamics water table depths, water content dynamics and physiology of mosses, and correct soil carbon profiles. Then, we use our new model structure to test the how the water table depth and CH4 emission will respond to elevated CO2 and different warming scenarios.

Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM

PubMed Central

Zhang, Chaolong; He, Yigang; Yuan, Lifeng; Xiang, Sheng; Wang, Jinping

2015-01-01

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately. PMID:26413090

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

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

Prognostic significance of number of nodes removed in patients with node-negative early cervical cancer.

PubMed

Mao, Siyue; Dong, Jun; Li, Sheng; Wang, Yiqi; Wu, Peihong

2016-10-01

The aim of this study was to investigate whether the number of removed lymph nodes was associated with survival of patients with node-negative early cervical cancer and to analyze the prognostic significance of clinical and pathologic features in these patients. Patients with FIGO stage IA-IIB cervical cancer who underwent radical hysterectomy with lymphadenectomy without receiving preoperative therapy were reviewed retrospectively. Patients were all proved to have lymph-node-negative disease and classified into five groups based on the number of nodes removed. The Kaplan-Meier method and Cox's proportional hazards regression model were used in prognostic analysis. The final dataset included 359 patients: 45 (12.5%) patients had ≤10 nodes removed, 93 (25.9%) had 11-15, 98 (27.3%) had 16-20, 64 (17.8%) had 21-25, and 59 (16.4%) had >25 nodes removed. There was no association between the number of nodes removed and survival of patients with node-negative early cervical cancer (χ 2  = 6.19, P = 0.185). Similarly, subgroup analyses for FIGO stage IB1-IIB also showed that the number of lymph nodes was not significantly related to survival in each stage. Multivariate analyses showed that histology and depth of invasion were independent prognostic factors for survival in these patients. If a standardized lymphadenectomy is performed, the number of lymph nodes removed is not an independent prognostic factor for patients with node-negative early cervical cancer. Our study suggests that there is inconclusive evidence to support survival benefit of complete lymphadenectomy among these patients. © 2016 Japan Society of Obstetrics and Gynecology.

Caspase-3 activity, response to chemotherapy and clinical outcome in patients with colon cancer.

PubMed

de Oca, Javier; Azuara, Daniel; Sanchez-Santos, Raquel; Navarro, Matilde; Capella, Gabriel; Moreno, Victor; Sola, Anna; Hotter, Georgina; Biondo, Sebastiano; Osorio, Alfonso; Martí-Ragué, Joan; Rafecas, Antoni

2008-01-01

The prognostic value of the degree of apoptosis in colorectal cancer is controversial. This study evaluates the putative clinical usefulness of measuring caspase-3 activity as a prognostic factor in colonic cancer patients receiving 5-fluoracil adjuvant chemotherapy. We evaluated caspase-3-like protease activity in tumours and in normal colon tissue. Specimens were studied from 54 patients. These patients had either stage III cancer (Dukes stage C) or high-risk stage II cancer (Dukes stage B2 with invasion of adjacent organs, lymphatic or vascular infiltration or carcinoembryonic antigen [CEA] >5). Median follow-up was 73 months. Univariate analysis was performed previously to explore the relation of different variables (age, sex, preoperative CEA, tumour size, Dukes stage, vascular invasion, lymphatic invasion, caspase-3 activity in tumour and caspase-3 activity in normal mucosa) as prognostic factors of tumour recurrence after chemotherapy treatment. Subsequently, a multivariate Cox regression model was performed. Median values of caspase-3 activity in tumours were more than twice those in normal mucosa (88.1 vs 40.6 U, p=0.001), showing a statistically significant correlation (r=0.34). Significant prognostic factors of recurrence in multivariate analysis were: male sex (odds ratio, OR=3.53 [1.13-10.90], p=0.02), age (OR=1.09 [1.01-1.18], p=0.03), Dukes stage (OR=1.93 [1.01-3.70]), caspase-3 activity in normal mucosa (OR=1.02 [1.01-1.04], p=0.017) and caspase-3 activity in tumour (OR=1.02 [1.01-1.03], p=0.013). Low caspase-3 activity in the normal mucosa and tumour are independent prognostic factors of tumour recurrence in patients receiving adjuvant 5-fluoracil-based treatment in colon cancer, correlating with poor disease-free survival and higher recurrence rate.

Complex karyotype in mantle cell lymphoma is a strong prognostic factor for the time to treatment and overall survival, independent of the MCL international prognostic index.

PubMed

Sarkozy, Clémentine; Terré, Christine; Jardin, Fabrice; Radford, Isabelle; Roche-Lestienne, Catherine; Penther, Dominique; Bastard, Christian; Rigaudeau, Sophie; Pilorge, Sylvain; Morschhauser, Franck; Bouscary, Didier; Delarue, Richard; Farhat, Hassan; Rousselot, Philippe; Hermine, Olivier; Tilly, Hervé; Chevret, Sylvie; Castaigne, Sylvie

2014-01-01

Mantle cell lymphoma (MCL) is usually an aggressive disease. However, a few patients do have an "indolent" evolution (iMCL) defined by a long survival time without intensive therapy. Many studies highlight the prognostic role of additional genetic abnormalities, but these abnormalities are not routinely tested for and do not yet influence the treatment decision. We aimed to evaluate the prognostic impact of these additional abnormalities detected by conventional cytogenetic testing, as well as their relationships with the clinical characteristics and their value in identifying iMCL. All consecutive MCL cases diagnosed between 1995 and 2011 at four institutions were retrospectively selected on the basis of an informative karyotype with a t(11;14) translocation at the time of diagnosis. A total of 125 patients were included and followed for an actual median time of 35 months. The median overall survival (OS) and survival without treatment (TFS) were 73.7 and 1.3 months, respectively. In multivariable Cox models, a high mantle cell lymphoma international prognostic index score, a complex karyotype, and blastoid morphology were independently associated with a shortened OS. Spleen enlargement, nodal presentation, extra-hematological involvement, and complex karyotypes were associated with shorter TFS. A score based on these factors allowed for the identification of "indolent" patients (median TFS 107 months) from other patients (median TFS: 1 month). In conclusion, in this multicentric cohort of MCL patients, a complex karyotype was associated with a shorter survival time and allowed for the identification of iMCL at the time of diagnosis. Copyright © 2013 Wiley Periodicals, Inc.

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.

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

PubMed

Janssen, Ronald J; Mourão-Miranda, Janaina; Schnack, Hugo G

2018-04-22

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine-learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine-learning-based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning.

PubMed

Schmidt, Rainer; Gierl, Lothar

2005-03-01

Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning. It is useful for application domains where neither well-known standards, nor known periodicity, nor a complete domain theory exist. We have used our method in two prognostic applications. The first one deals with prognosis of the kidney function for intensive care patients. The idea is to elicit impairments on time, especially to warn against threatening kidney failures. Our second application deals with a completely different domain, namely geographical medicine. Its intention is to compute early warnings against approaching infectious diseases, which are characterised by irregular cyclic occurrences. So far, we have applied our program on influenza and bronchitis. In this paper, we focus on influenza forecast and show first experimental results.

Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?

NASA Astrophysics Data System (ADS)

Grützun, V.; Quaas, J.; Morcrette, C. J.; Ament, F.

2013-09-01

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the "perfect model approach." This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme.

Clinical significance of circulating miR-25-3p as a novel diagnostic and prognostic biomarker in osteosarcoma.

PubMed

Fujiwara, Tomohiro; Uotani, Koji; Yoshida, Aki; Morita, Takuya; Nezu, Yutaka; Kobayashi, Eisuke; Yoshida, Akihiko; Uehara, Takenori; Omori, Toshinori; Sugiu, Kazuhisa; Komatsubara, Tadashi; Takeda, Ken; Kunisada, Toshiyuki; Kawamura, Machiko; Kawai, Akira; Ochiya, Takahiro; Ozaki, Toshifumi

2017-05-16

Emerging evidence has suggested that circulating microRNAs (miRNAs) in body fluids have novel diagnostic and prognostic significance for patients with malignant diseases. The lack of useful biomarkers is a crucial problem of bone and soft tissue sarcomas; therefore, we investigated the circulating miRNA signature and its clinical relevance in osteosarcoma. Global miRNA profiling was performed using patient serum collected from a discovery cohort of osteosarcoma patients and controls and cell culture media. The secretion of the detected miRNAs from osteosarcoma cells and clinical relevance of serum miRNA levels were evaluated using in vitro and in vivo models and a validation patient cohort. Discovery screening identified 236 serum miRNAs that were highly expressed in osteosarcoma patients compared with controls, and eight among these were also identified in the cell culture media. Upregulated expression levels of miR-17-5p and miR-25-3p were identified in osteosarcoma cells, and these were abundantly secreted into the culture media in tumor-derived exosomes. Serum miR-25-3p levels were significantly higher in osteosarcoma patients than in control individuals in the validation cohort, with favorable sensitivity and specificity compared with serum alkaline phosphatase. Furthermore, serum miR-25-3p levels at diagnosis were correlated with patient prognosis and reflected tumor burden in both in vivo models and patients; these associations were more sensitive than those of serum alkaline phosphatase. Serum-based circulating miR-25-3p may serve as a non-invasive blood-based biomarker for tumor monitoring and prognostic prediction in osteosarcoma patients.

Clinical significance of circulating miR-25-3p as a novel diagnostic and prognostic biomarker in osteosarcoma

PubMed Central

Fujiwara, Tomohiro; Uotani, Koji; Yoshida, Aki; Morita, Takuya; Nezu, Yutaka; Kobayashi, Eisuke; Yoshida, Akihiko; Uehara, Takenori; Omori, Toshinori; Sugiu, Kazuhisa; Komatsubara, Tadashi; Takeda, Ken; Kunisada, Toshiyuki; Kawamura, Machiko; Kawai, Akira; Ochiya, Takahiro; Ozaki, Toshifumi

2017-01-01

Background Emerging evidence has suggested that circulating microRNAs (miRNAs) in body fluids have novel diagnostic and prognostic significance for patients with malignant diseases. The lack of useful biomarkers is a crucial problem of bone and soft tissue sarcomas; therefore, we investigated the circulating miRNA signature and its clinical relevance in osteosarcoma. Methods Global miRNA profiling was performed using patient serum collected from a discovery cohort of osteosarcoma patients and controls and cell culture media. The secretion of the detected miRNAs from osteosarcoma cells and clinical relevance of serum miRNA levels were evaluated using in vitro and in vivo models and a validation patient cohort. Results Discovery screening identified 236 serum miRNAs that were highly expressed in osteosarcoma patients compared with controls, and eight among these were also identified in the cell culture media. Upregulated expression levels of miR-17-5p and miR-25-3p were identified in osteosarcoma cells, and these were abundantly secreted into the culture media in tumor-derived exosomes. Serum miR-25-3p levels were significantly higher in osteosarcoma patients than in control individuals in the validation cohort, with favorable sensitivity and specificity compared with serum alkaline phosphatase. Furthermore, serum miR-25-3p levels at diagnosis were correlated with patient prognosis and reflected tumor burden in both in vivo models and patients; these associations were more sensitive than those of serum alkaline phosphatase. Conclusions Serum-based circulating miR-25-3p may serve as a non-invasive blood-based biomarker for tumor monitoring and prognostic prediction in osteosarcoma patients. PMID:28380419

Aortic-Brachial Arterial Stiffness Gradient and Cardiovascular Risk in the Community: The Framingham Heart Study.

PubMed

Niiranen, Teemu J; Kalesan, Bindu; Larson, Martin G; Hamburg, Naomi M; Benjamin, Emelia J; Mitchell, Gary F; Vasan, Ramachandran S

2017-06-01

A recent study reported that the aortic-brachial arterial stiffness gradient, defined as carotid-radial/carotid-femoral pulse wave velocity (PWV ratio), predicts all-cause mortality better than carotid-femoral pulse wave velocity (CFPWV) alone in dialysis patients. However, the prognostic significance of PWV ratio for cardiovascular disease (CVD) in the community remains unclear. Accordingly, we assessed the correlates and prognostic value of the PWV ratio in 2114 Framingham Heart Study participants (60±10 years; 56% women) free of overt CVD. Mean PWV ratio decreased from 1.36±0.19 in participants aged <40 years to 0.73±0.21 in those aged ≥80 years. In multivariable linear regression, older age, male sex, higher body mass index, diabetes mellitus, lower high-density lipoprotein cholesterol, higher mean arterial pressure, and higher heart rate were associated with lower PWV ratio ( P <0.001 for all). During a median follow-up of 12.6 years, 248 first CVD events occurred. In Cox regression models adjusted for standard CVD risk factors, 1-SD changes in CFPWV (hazard ratio, 1.33; 95% confidence interval, 1.10-1.61) and PWV ratio (hazard ratio, 1.32; 95% confidence interval, 1.09-1.59) were associated with similar CVD risks. Models that included conventional CVD risk factors plus CFPWV or PWV ratio gave the same C statistics (C=0.783). Although PWV ratio has been reported to provide incremental predictive value over CFPWV in dialysis patients, we could not replicate these findings in our community-based sample. Our findings suggest that the prognostic significance of PWV ratio may vary based on baseline CVD risk, and CFPWV should remain the criterion standard for assessing vascular stiffness in the community. © 2017 American Heart Association, Inc.

Software framework for prognostic health monitoring of ocean-based power generation

NASA Astrophysics Data System (ADS)

Bowren, Mark

On August 5, 2010 the U.S. Department of Energy (DOE) has designated the Center for Ocean Energy Technology (COET) at Florida Atlantic University (FAU) as a national center for ocean energy research and development of prototypes for open-ocean power generation. Maintenance on ocean-based machinery can be very costly. To avoid unnecessary maintenance it is necessary to monitor the condition of each machine in order to predict problems. This kind of prognostic health monitoring (PHM) requires a condition-based maintenance (CBM) system that supports diagnostic and prognostic analysis of large amounts of data. Research in this field led to the creation of ISO13374 and the development of a standard open-architecture for machine condition monitoring. This thesis explores an implementation of such a system for ocean-based machinery using this framework and current open-standard technologies.

Contribution of artificial intelligence to the knowledge of prognostic factors in laryngeal carcinoma.

PubMed

Zapater, E; Moreno, S; Fortea, M A; Campos, A; Armengot, M; Basterra, J

2000-11-01

Many studies have investigated prognostic factors in laryngeal carcinoma, with sometimes conflicting results. Apart from the importance of environmental factors, the different statistical methods employed may have influenced such discrepancies. A program based on artificial intelligence techniques is designed to determine the prognostic factors in a series of 122 laryngeal carcinomas. The results obtained are compared with those derived from two classical statistical methods (Cox regression and mortality tables). Tumor location was found to be the most important prognostic factor by all methods. The proposed intelligent system is found to be a sound method capable of detecting exceptional cases.

A dynamic multi-scale Markov model based methodology for remaining life prediction

NASA Astrophysics Data System (ADS)

Yan, Jihong; Guo, Chaozhong; Wang, Xing

2011-05-01

The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov model, experimental results illustrate the effectiveness of the methodology.

Early Prognostication Markers in Cardiac Arrest Patients Treated with Hypothermia

PubMed Central

Karapetkova, Maria; Koenig, Matthew A.; Jia, Xiaofeng

2015-01-01

Background and purpose Established prognostication markers, such as clinical findings, electroencephalography (EEG), and biochemical markers, used by clinicians to predict neurologic outcome after cardiac arrest (CA) are altered under therapeutic hypothermia (TH) conditions and their validity remains uncertain. Methods MEDLINE and EMBASE were searched for evidence on the current standards for neurologic outcome prediction for out-of-hospital CA patients treated with TH and the validity of a wide range of prognostication markers. Relevant studies that suggested one or several established biomarkers, and multimodal approaches for prognostication were included and reviewed. Results While the prognostic accuracy of various tests has been questioned after TH, pupillary light reflexes and somatosensory evoked potentials (SSEP) are still strongly associated with negative outcome for early prognostication. Increasingly, EEG background activity has also been identified as a valid predictor for outcome after 72 hours after CA and a preferred prognostic method in clinical settings. Neuroimaging techniques, such as MRI and CT, can identify functional and structural brain injury, but are not readily available at the patient’s bedside because of limited availability and high costs. Conclusions A multimodal algorithm composed of neurological examination, EEG-based quantitative testing, and SSEP, in conjunction with newer MRI sequences, if available, holds promise for accurate prognostication in CA patients treated with TH. In order to avoid premature withdrawal of care, prognostication should be performed later than 72 hours after CA. PMID:26228521

Early prognostication markers in cardiac arrest patients treated with hypothermia.

PubMed

Karapetkova, M; Koenig, M A; Jia, X

2016-03-01

Established prognostication markers, such as clinical findings, electroencephalography (EEG) and biochemical markers, used by clinicians to predict neurological outcome after cardiac arrest (CA) are altered under therapeutic hypothermia (TH) conditions and their validity remains uncertain. MEDLINE and Embase were searched for evidence on the current standards for neurological outcome prediction for out-of-hospital CA patients treated with TH and the validity of a wide range of prognostication markers. Relevant studies that suggested one or several established biomarkers and multimodal approaches for prognostication are included and reviewed. Whilst the prognostic accuracy of various tests after TH has been questioned, pupillary light reflexes and somatosensory evoked potentials are still strongly associated with negative outcome for early prognostication. Increasingly, EEG background activity has also been identified as a valid predictor for outcome after 72 h after CA and a preferred prognostic method in clinical settings. Neuroimaging techniques, such as magnetic resonance imaging and computed tomography, can identify functional and structural brain injury but are not readily available at the patient's bedside because of limited availability and high costs. A multimodal algorithm composed of neurological examination, EEG-based quantitative testing and somatosensory evoked potentials, in conjunction with newer magnetic resonance imaging sequences, if available, holds promise for accurate prognostication in CA patients treated with TH. In order to avoid premature withdrawal of care, prognostication should be performed more than 72 h after CA. © 2015 EAN.

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.

Prognostic Value of Pretherapeutic Tumor-to-Blood Standardized Uptake Ratio in Patients with Esophageal Carcinoma.

PubMed

Bütof, Rebecca; Hofheinz, Frank; Zöphel, Klaus; Stadelmann, Tobias; Schmollack, Julia; Jentsch, Christina; Löck, Steffen; Kotzerke, Jörg; Baumann, Michael; van den Hoff, Jörg

2015-08-01

Despite ongoing efforts to develop new treatment options, the prognosis for patients with inoperable esophageal carcinoma is still poor and the reliability of individual therapy outcome prediction based on clinical parameters is not convincing. The aim of this work was to investigate whether PET can provide independent prognostic information in such a patient group and whether the tumor-to-blood standardized uptake ratio (SUR) can improve the prognostic value of tracer uptake values. (18)F-FDG PET/CT was performed in 130 consecutive patients (mean age ± SD, 63 ± 11 y; 113 men, 17 women) with newly diagnosed esophageal cancer before definitive radiochemotherapy. In the PET images, the metabolically active tumor volume (MTV) of the primary tumor was delineated with an adaptive threshold method. The blood standardized uptake value (SUV) was determined by manually delineating the aorta in the low-dose CT. SUR values were computed as the ratio of tumor SUV and blood SUV. Uptake values were scan-time-corrected to 60 min after injection. Univariate Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), distant metastases-free survival (DM), and locoregional tumor control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. In multivariate Cox regression with respect to OS, including T stage, N stage, and smoking state, MTV- and SUR-based parameters were significant prognostic factors for OS with similar effect size. Multivariate analysis with respect to DM revealed smoking state, MTV, and all SUR-based parameters as significant prognostic factors. The highest hazard ratios (HRs) were found for scan-time-corrected maximum SUR (HR = 3.9) and mean SUR (HR = 4.4). None of the PET parameters was associated with LRC. Univariate Cox regression with respect to LRC revealed a significant effect only for N stage greater than 0 (P = 0.048). PET provides independent prognostic information for OS and DM but not for LRC in patients with locally advanced esophageal carcinoma treated with definitive radiochemotherapy in addition to clinical parameters. Among the investigated uptake-based parameters, only SUR was an independent prognostic factor for OS and DM. These results suggest that the prognostic value of tracer uptake can be improved when characterized by SUR instead of SUV. Further investigations are required to confirm these preliminary results. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Prognostic value of inflammation-based markers in patients with pancreatic cancer administered gemcitabine and erlotinib.

PubMed

Lee, Jae Min; Lee, Hong Sik; Hyun, Jong Jin; Choi, Hyuk Soon; Kim, Eun Sun; Keum, Bora; Seo, Yeon Seok; Jeen, Yoon Tae; Chun, Hoon Jai; Um, Soon Ho; Kim, Chang Duck

2016-07-15

To evaluate the value of systemic inflammation-based markers as prognostic factors for advanced pancreatic cancer (PC). Data from 82 patients who underwent combination chemotherapy with gemcitabine and erlotinib for PC from 2011 to 2014 were collected retrospectively. Data that included the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio, and the C-reactive protein (CRP)-to-albumin (CRP/Alb) ratio were analyzed. Kaplan-Meier curves, and univariate and multivariate Cox proportional hazards regression analyses were used to identify the prognostic factors associated with progression-free survival (PFS) and overall survival (OS). The univariate analysis demonstrated the prognostic value of the NLR (P = 0.049) and the CRP/Alb ratio (P = 0.047) in relation to PFS, and a positive relationship between an increase in inflammation-based markers and a poor prognosis in relation to OS. The multivariate analysis determined that an increased NLR (hazard ratio = 2.76, 95%CI: 1.33-5.75, P = 0.007) is an independent prognostic factor for poor OS. There was no association between the PLR and the patients' prognoses in those who had received chemotherapy that comprised gemcitabine and erlotinib in combination. The Kaplan-Meier method and the log-rank test determined significantly worse outcomes in relation to PFS and OS in patients with an NLR > 5 or a CRP/Alb ratio > 5. Systemic inflammation-based markers, including increases in the NLR and the CRP/Alb ratio, may be useful for predicting PC prognoses.

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.

PubMed

Pourahmad, Saeedeh; Hafizi-Rastani, Iman; Khalili, Hosseinali; Paydar, Shahram

2016-10-17

Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome. This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process. The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA). The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes' order by DT method was more consistent with the clinical literature. The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.

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.

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.

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.

The use of multiple models in case-based diagnosis

NASA Technical Reports Server (NTRS)

Karamouzis, Stamos T.; Feyock, Stefan

1993-01-01

The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation.

Prognostic value of Child-Turcotte criteria in medically treated cirrhosis.

PubMed

Christensen, E; Schlichting, P; Fauerholdt, L; Gluud, C; Andersen, P K; Juhl, E; Poulsen, H; Tygstrup, N

1984-01-01

The Child- Turcotte criteria (CTC) (based on serum bilirubin and albumin, ascites, neurological disorder and nutrition) are established prognostic factors in patients with cirrhosis having portacaval shunt surgery. The objective of this study was to evaluate the prognostic value of CTC in conservatively treated cirrhosis. Patients (n = 245) with histologically verified cirrhosis from a control group of a controlled clinical trial were studied. Data at entry into the trial were used to classify patients according to CTC. Survival curves for up to 16 years were made, and survival rates were compared using the log-rank test. Survival decreased significantly with increasing degree of abnormality (A----B----C) of albumin (p less than 0.001), ascites (p less than 0.001), bilirubin (p = 0.02) and nutritional status (p = 0.03). Survival was insignificantly influenced by neurological status (p = 0.11) probably because none of the patients had hepatic coma at entry into the trial. The five variables in CTC were combined to a score. With increasing score, the median survival time decreased from 6.4 years (score 5) to 2 months (scores 12 or more). Furthermore, the mortality from hepatic failure, gastrointestinal bleeding or hepatocellular carcinoma increased significantly with increasing score. CTC provide valuable and easily obtainable prognostic information in cirrhosis. However, CTC are inferior to a prognostic index based on multivariate analysis of prognostic factors.

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.

An accurate, simple prognostic model consisting of age, JAK2, CALR, and MPL mutation status for patients with primary myelofibrosis.

PubMed

Rozovski, Uri; Verstovsek, Srdan; Manshouri, Taghi; Dembitz, Vilma; Bozinovic, Ksenija; Newberry, Kate; Zhang, Ying; Bove, Joseph E; Pierce, Sherry; Kantarjian, Hagop; Estrov, Zeev

2017-01-01

In most patients with primary myelofibrosis, one of three mutually exclusive somatic mutations is detected. In approximately 60% of patients, the Janus kinase 2 gene is mutated, in 20%, the calreticulin gene is mutated, and in 5%, the myeloproliferative leukemia virus gene is mutated. Although patients with mutated calreticulin or myeloproliferative leukemia genes have a favorable outcome, and those with none of these mutations have an unfavorable outcome, prognostication based on mutation status is challenging due to the heterogeneous survival of patients with mutated Janus kinase 2. To develop a prognostic model based on mutation status, we screened primary myelofibrosis patients seen at the MD Anderson Cancer Center, Houston, USA, between 2000 and 2013 for the presence of Janus kinase 2, calreticulin, and myeloproliferative leukemia mutations. Of 344 primary myelofibrosis patients, Janus kinase 2 V617F was detected in 226 (66%), calreticulin mutation in 43 (12%), and myeloproliferative leukemia mutation in 16 (5%); 59 patients (17%) were triple-negatives. A 50% cut-off dichotomized Janus kinase 2-mutated patients into those with high Janus kinase 2 V617F allele burden and favorable survival and those with low Janus kinase 2 V617F allele burden and unfavorable survival. Patients with a favorable mutation status (high Janus kinase 2 V617F allele burden/myeloproliferative leukemia/calreticulin mutation) and aged 65 years or under had a median survival of 126 months. Patients with one risk factor (low Janus kinase 2 V617F allele burden/triple-negative or age >65 years) had an intermediate survival duration, and patients aged over 65 years with an adverse mutation status (low Janus kinase 2 V617F allele burden or triple-negative) had a median survival of only 35 months. Our simple and easily applied age- and mutation status-based scoring system accurately predicted the survival of patients with primary myelofibrosis. Copyright© Ferrata Storti Foundation.

SU-F-R-52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients

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

Huynh, E; Coroller, T; Narayan, V

Purpose: There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non-invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lungmore » cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence. Methods: FB and AIP images of 113 Stage I-II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p-values were corrected for multiple testing using the false discovery rate. Results: All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66). Conclusion: Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments. R.M. had consulting interest with Amgen (ended in 2015).« less

Methods Developed by the Tools for Engine Diagnostics Task to Monitor and Predict Rotor Damage in Real Time

NASA Technical Reports Server (NTRS)

Baaklini, George Y.; Smith, Kevin; Raulerson, David; Gyekenyesi, Andrew L.; Sawicki, Jerzy T.; Brasche, Lisa

2003-01-01

Tools for Engine Diagnostics is a major task in the Propulsion System Health Management area of the Single Aircraft Accident Prevention project under NASA s Aviation Safety Program. The major goal of the Aviation Safety Program is to reduce fatal aircraft accidents by 80 percent within 10 years and by 90 percent within 25 years. The goal of the Propulsion System Health Management area is to eliminate propulsion system malfunctions as a primary or contributing factor to the cause of aircraft accidents. The purpose of Tools for Engine Diagnostics, a 2-yr-old task, is to establish and improve tools for engine diagnostics and prognostics that measure the deformation and damage of rotating engine components at the ground level and that perform intermittent or continuous monitoring on the engine wing. In this work, nondestructive-evaluation- (NDE-) based technology is combined with model-dependent disk spin experimental simulation systems, like finite element modeling (FEM) and modal norms, to monitor and predict rotor damage in real time. Fracture mechanics time-dependent fatigue crack growth and damage-mechanics-based life estimation are being developed, and their potential use investigated. In addition, wireless eddy current and advanced acoustics are being developed for on-wing and just-in-time NDE engine inspection to provide deeper access and higher sensitivity to extend on-wing capabilities and improve inspection readiness. In the long run, these methods could establish a base for prognostic sensing while an engine is running, without any overt actions, like inspections. This damage-detection strategy includes experimentally acquired vibration-, eddy-current- and capacitance-based displacement measurements and analytically computed FEM-, modal norms-, and conventional rotordynamics-based models of well-defined damages and critical mass imbalances in rotating disks and rotors.

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

NASA Astrophysics Data System (ADS)

Lei, Yaguo; Li, Naipeng; Guo, Liang; Li, Ningbo; Yan, Tao; Lin, Jing

2018-05-01

Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes, i.e., data acquisition, health indicator (HI) construction, health stage (HS) division, and RUL prediction. Over recent years, a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process, i.e., RUL prediction. However, there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap, this paper provides a review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction. First, in data acquisition, several prognostic datasets widely used in academic literature are introduced systematically. Then, commonly used HI construction approaches and metrics are discussed. After that, the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards, the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally, the paper provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

DOE PAGES

Wang, Pin; Wang, Yunshan; Hang, Bo; ...

2016-07-11

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

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.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

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

Wang, Pin; Wang, Yunshan; Hang, Bo

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

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.

Defining a New Prognostic index for Stage I Non-seminomatous Germ Cell Tumors using CXCL12 Expression and Proportion of Embryonal Carcinoma

PubMed Central

Gilbert, Duncan C; Al-Saadi, Reem; Thway, Khin; Chandler, Ian; Berney, Daniel; Gabe, Rhian; Stenning, Sally P; Sweet, Joan; Huddart, Robert; Shipley, Janet M

2015-01-01

Purpose Up to 50% of patients diagnosed with stage I non-seminomatous germ cell tumors (NSGCT) harbor occult metastases. Patients are managed by surveillance with chemotherapy at relapse or adjuvant treatment up-front. Late toxicities from chemotherapy are increasingly recognised. Based on a potential biological role in germ cells/tumors and pilot data, our aim was to evaluate tumor expression of the chemokine CXCL12 alongside previously proposed markers as clinically useful biomarkers of relapse. Experimental design Immunohistochemistry for tumor expression of CXCL12 was assessed as a biomarker of relapse alongside vascular invasion, histology (percentage embryonal carcinoma) and MIB1 staining for proliferationin formalin fixed paraffin-embedded orchidectomy samples from patients enrolled in the Medical Research Council’s TE08/22 prospective trials of surveillance in stage I NSGCT. Results TE08/TE22 trial patients had a 76.4% 2-year relapse free rate (RFR) and both CXCL12 expression and percentage embryonal carcinoma provided prognostic value independently of vascular invasion (stratified log rank test p=0.006 for both). There was no additional prognostic value for MIB1 staining. A model using CXCL12, percentage embryonal carcinoma and VI defines 3 prognostic groups that were independantly validated. Conclusions CXCL12 and percentage embryonal carcinoma both stratify patients’ relapse risk over and above vascular invasion alone. This is anticipated to improve the stratification of patients and identify high-risk cases to be considered for adjuvant therapy. PMID:26453693

Outcome and prognostic factors in metastatic urothelial carcinoma patients receiving second-line chemotherapy: an analysis of real-world clinical practice data in Japan.

PubMed

Matsumoto, Ryuji; Abe, Takashige; Ishizaki, Junji; Kikuchi, Hiroshi; Harabayashi, Toru; Minami, Keita; Sazawa, Ataru; Mochizuki, Tango; Akino, Tomoshige; Murakumo, Masashi; Osawa, Takahiro; Maruyama, Satoru; Murai, Sachiyo; Shinohara, Nobuo

2018-06-25

The objective of the present study was to investigate the survival outcome and prognostic factors of metastatic urothelial carcinoma patients treated with second-line systemic chemotherapy in real-world clinical practice. Overall, 114 patients with metastatic urothelial carcinoma undergoing second-line systemic chemotherapy were included in this retrospective analysis. The dominant second-line chemotherapy was a paclitaxel-based combination regimen (60%, 68/114). We assessed the progression-free survival and overall survival times using the Kaplan-Meier method. The Cox proportional hazards model was applied to identify the factors affecting overall survival. The median progression-free survival and overall survival times were 4 and 9 months, respectively. In the multivariate analysis, an Eastern Cooperative Oncology Group performance status score greater than 0 at presentation, C-reactive protein level ≧1 mg/dl and poor response to prior chemotherapy were adverse prognostic indicators. Patients with 0, 1, 2 and 3 of those risk factors had a median overall survival of 17, 12, 7 and 3 months, respectively. The Eastern Cooperative Oncology Group performance status at presentation, C-reactive protein level and response to prior chemotherapy were prognostic factors for metastatic urothelial carcinoma patients undergoing second-line chemotherapy. In the future, this information might help guide the choice of salvage treatment, such as second-line chemotherapy or immune checkpoint inhibitors, after the failure of first-line chemotherapy.

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.

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

Epigenetic Reprogramming Strategies to Reverse Global Loss of 5-Hydroxymethylcytosine, a Prognostic Factor for Poor Survival in High-grade Serous Ovarian Cancer

PubMed Central

Tucker, Douglass W.; Getchell, Christopher R.; McCarthy, Eric T.; Ohman, Anders W.; Sasamoto, Naoko; Xu, Shuyun; Ko, Joo Yeon; Gupta, Mamta; Shafrir, Amy; Medina, Jamie E.; Lee, Jonathan J.; MacDonald, Lauren A.; Malik, Ammara; Hasselblatt, Kathleen T; Li, Wenjing; Zhang, Hong; Kaplan, Samuel J.; Murphy, George F.; Hirsch, Michelle S.; Liu, Joyce F.; Matulonis, Ursula A.; Terry, Kathryn L.; Lian, Christine G.; Dinulescu, Daniela M.

2018-01-01

Purpose A major challenge in platinum-based cancer therapy is the clinical management of chemoresistant tumors, which have a largely unknown pathogenesis at the level of epigenetic regulation. Experimental Design We evaluated the potential of using global loss of 5-hydroxymethylcytosine (5-hmC) levels as a novel diagnostic and prognostic epigenetic marker to better assess platinum-based chemotherapy response and clinical outcome in high-grade serous tumors (HGSOC), the most common and deadliest subtype of ovarian cancer. Furthermore, we identified a targetable pathway to reverse these epigenetic changes, both genetically and pharmacologically. Results This study shows that decreased 5-hmC levels are an epigenetic hallmark for malignancy and tumor progression in HGSOC. In addition, global 5-hmC loss is associated with a decreased response to platinum-based chemotherapy, shorter time to relapse, and poor overall survival in patients newly diagnosed with HGSOC. Interestingly, the rescue of 5-hmC loss restores sensitivity to platinum chemotherapy in vitro and in vivo, decreases the percentage of tumor cells with cancer stem cell markers, and increases overall survival in an aggressive animal model of platinum-resistant disease. Conclusions Consequently, a global analysis of patient 5-hmC levels should be included in future clinical trials, which use pretreatment with epigenetic adjuvants to elevate 5-hmC levels and improve the efficacy of current chemotherapies. Identifying prognostic epigenetic markers and altering chemotherapeutic regimens to incorporate DNMTi pretreatment in tumors with low 5-hmC levels could have important clinical implications for newly diagnosed HGSOC disease. PMID:29263182

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.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

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.

The Prognostic Value of the 8th Edition of the American Joint Committee on Cancer (AJCC) Staging System in HER2-Enriched Subtype Breast Cancer, a Retrospective Analysis.

PubMed

Zhou, Bin; Xu, Ling; Ye, Jingming; Xin, Ling; Duan, Xuening; Liu, Yinhua

2017-08-01

The American Joint Committee on Cancer (AJCC) released its 8th edition of tumor staging which is to be implemented in early 2018. The present study aimed to analyze the prognostic value of AJCC 8th edition Cancer Staging System in HER2-enriched breast cancer, on a retrospective cohort. This study was a retrospective single-center study of HER2-enriched breast cancer cases diagnosed from January 2008 to December 2014. Clinicopathological features and follow up data including disease-free survival (DFS) and overall survival (OS) were analyzed to explore prognostic factors for disease outcome. We restaged patients based on the 8th edition of the AJCC cancer staging system and analyzed prognostic value of the Anatomic Stage Group and the Prognostic Stage Group. The study enrolled 170 HER2-enriched subtype breast cancer patients with 5-year disease free survival (DFS) of 85.1% and 5-year overall survival (OS) of 86.8%. Prognostic stages of 117 cases (68.8%) changed compared with anatomic stages, with 116 upstaged cases and 1 downstaged case. The Anatomic Stage Groups had a significant prognostic impact on DFS (χ 2 =16.752, p<0.001) and OS (χ 2 =25.038, p<0.001). The Prognostic Staging Groups had a significant prognostic impact on DFS (χ 2 =6.577, p=0.037) and OS (χ 2 =21.762, p<0.001). In the multivariate analysis, both stage groups were independent predictors of OS. Both Anatomic and Prognostic Stage Groups in the 8th edition of the AJCC breast cancer staging system had prognostic value in HER2-enriched subtype breast cancer. The Prognostic Stage system was a breakthrough on the basis of anatomic staging system. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Evaluation of an inflammation-based prognostic score in patients with metastatic renal cancer.

PubMed

Ramsey, Sara; Lamb, Gavin W A; Aitchison, Michael; Graham, John; McMillan, Donald C

2007-01-15

Recently, it was shown that an inflammation-based prognostic score, the Glasgow Prognostic Score (GPS), provides additional prognostic information in patients with advanced cancer. The objective of the current study was to examine the value of the GPS compared with established scoring systems in predicting cancer-specific survival in patients with metastatic renal cancer. One hundred nineteen patients who underwent immunotherapy for metastatic renal cancer were recruited. The Memorial Sloan-Kettering Cancer Center (MSKCC) score and the Metastatic Renal Carcinoma Comprehensive Prognostic System (MRCCPS) score were calculated as described previously. Patients who had both an elevated C-reactive protein level (>10 mg/L) and hypoalbuminemia (<35 g/L) were allocated a GPS of 2. Patients who had only 1 of those 2 biochemical abnormalities were allocated a GPS of 1. Patients who had neither abnormality were allocated a GPS of 0. On multivariate analysis of significant individual factors, only calcium (hazard ratio [HR], 3.21; 95% confidence interval [95% CI], 1.51-6.83; P = .002), white cell count (HR, 1.66; 95% CI, 1.17-2.35; P = .004), albumin (HR, 2.63; 95% CI, 1.38-5.03; P = .003), and C-reactive protein (HR, 2.85; 95% CI; 1.49-5.45; P = .002) were associated independently with cancer-specific survival. On multivariate analysis of the different scoring systems, the MSKCC (HR, 1.88; 95% CI, 1.22-2.88; P = .004), the MRCCPS (HR, 1.42; 95% CI, 0.97-2.09; P = .071), and the GPS (HR, 2.35; 95% CI, 1.51-3.67; P < .001) were associated independently with cancer-specific survival. An inflammation-based prognostic score (GPS) predicted survival independent of established scoring systems in patients with metastatic renal cancer.

High pretreatment plasma D-dimer predicts poor survival of colorectal cancer: insight from a meta-analysis of observational studies

PubMed Central

Lu, Shao-Long; Ye, Zhi-Hua; Ling, Tong; Liang, Si-Yuan; Li, Hui; Tang, Xiao-Zhun; Xu, Yan-Song; Tang, Wei-Zhong

2017-01-01

D-dimer, one of the canonical markers of hypercoagulability, was reported to be a potential prognostic marker of colorectal cancer. However, an inconsistent conclusion existed in several published studies. Thus, we performed this meta-analysis to provide a comprehensive insight into the prognostic role for pretreatment D-dimer in colorectal cancer. Six databases (English: Pubmed, Embase and Web of Science; Chinese: CNKI, Wangfang and VIP) were utilized for the literature retrieval. Hazard ratio (HR) was pooled by Stata 12.0. A total of fifteen studies (2283 cases) corresponded to this meta-analysis and provided available data to evaluate the prognostic role of D-dimer for colorectal cancer. The pooled HR reached 2.167 (95%. CI (confidence interval): 1.672–2.809, P < 0.001) utilizing random effect model due to obvious heterogeneity among the included studies (I2: 73.3%; P < 0.001). To explore the heterogeneity among the studies, we conducted a sensitivity analysis and found a heterogeneous study. After removing it, the heterogeneity reduced substantially (I2: 0%; P = 0.549) and we obtained a more convincing result by fixed effect model (HR = 2.143, 95% CI = 1.922–2.390, P < 0.001, 14 studies with 2179 cases). In summary, high pretreatment plasma D-dimer predicts poor survival of colorectal cancer based on the current evidence. Further prospective researches are necessary to confirm the role of D-dimer in colorectal cancer. PMID:29113378

Mortality and recurrence rates among systemically untreated high risk breast cancer patients included in the DBCG 77 trials.

PubMed

Jensen, Maj-Britt; Nielsen, Torsten O; Knoop, Ann S; Laenkholm, Anne-Vibeke; Balslev, Eva; Ejlertsen, Bent

2018-01-01

Following loco-regional treatment for early breast cancer accurate prognostication is essential for communicating benefits of systemic treatment. The aim of this study was to determine time to recurrence and long-term mortality rates in high risk patients according to patient characteristics and subtypes as assigned by immunohistochemistry panels. In November 1977 through January 1983, 2862 patients with tumors larger than 5 cm or positive axillary nodes were included in the DBCG 77 trials. Archival tumor tissue from patients randomly assigned to no systemic treatment was analyzed for ER, PR, Ki67, EGFR and HER2. Intrinsic subtypes were defined as follows: Luminal A, ER or PR >0%, HER2-negative, PR >10% and Ki67 < 14%; Luminal B, ER or PR >0%, (PR ≤10% or HER2-positive or Ki67 ≥ 14%); HER2E, ER 0%, PR 0%, HER2 positive; Core basal, ER 0%, PR 0%, HER2 negative and EGFR positive. Multivariate categorical and fractional polynomials (MFP) models were used to construct prognostic subsets by clinicopathologic characteristics. In a multivariate model, mortality rate was significantly associated with age, tumor size, nodal status, invasion, histological type and grade, as well as subtype classification. With 35 years of follow-up, in this population of high-risk patients with no systemic therapy, no subgroup based on a composite prognostic score and/or molecular subtypes could be identified without excess mortality as compared to the background population.

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.

Implications of prognostic factors and risk groups in the management of differentiated thyroid cancer.

PubMed

Shaha, Ashok R

2004-03-01

The outcome in differentiated thyroid cancer generally depends on the stage of the disease at the time of presentation; prognostic factors such as age, grade, size, extension, or distant metastasis; and risk groups (eg, low or high risk). The author has reviewed a large number of patients with differentiated thyroid cancer to analyze their hypothesis and to confirm that various risk groups have a major implication in relation to extent of the treatment and outcome. Differentiated thyroid cancers make up 90% of all thyroid tumors. The prognostic factors are well defined, such as age, size of the tumor, extrathyroidal extension, presence of distant metastasis, histological appearance, and grade of the tumor. The author has previously divided the risk groups into low-, intermediate-, and high-risk categories based on prognostic factors. The study describes the author's treatment approach related to the extent of thyroidectomy and adjuvant therapy based on various risk groups and the long-term survival. Retrospective. In a retrospective review of 1038 patients with differentiated thyroid carcinoma, various prognostic factors were studied by univariate and multivariate analysis. The significant prognostic factors were studied in detail and, based on these prognostic factors, the patients were divided into low-, intermediate- and high-risk groups. The survival curves were plotted by Kaplan-Meier method. The long-term survivals in low-, intermediate- and high-risk groups were 99%, 87%, and 57% respectively. Based on these risk groups, a decision tree was made regarding extent of thyroidectomy and adjuvant treatment. In the high-risk group and selected patients in the intermediate-risk group, aggressive surgery including removal of all gross disease and extrathyroidal extension with postoperative radioactive iodine ablation is recommended. In the low-risk group and selected patients in the intermediate-risk group, lobectomy appears to be satisfactory with excellent long-term outcome. The surgical treatment offers the best long-term results in low-risk patients, and the role of adjuvant treatment in this group is questionable. The decisions in the management of well-differentiated thyroid cancer should be based on various prognostic factors and risk groups. The long-term survival in the low-risk group is excellent, and consideration should be given to conservative surgical resection depending on the extent of the disease. In the high-risk group and selected patients in the intermediate-risk group, total thyroidectomy with radioactive ablation is warranted. A consideration may be given to external-beam radiation therapy in selected high-risk patients. It is apparent, based on the author's clinical experience and critical retrospective analysis, that the author's hypothesis that risk groups are extremely important in the long-term outcome of patients with differentiated thyroid cancer is correct. Based on various risk groups, the author currently is able to guide the treatment policies for thyroid cancer.

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.

Prognostic Model for Resected Squamous Cell Lung Cancer: External Multicenter Validation and Propensity Score Analysis exploring the Impact of Adjuvant and Neoadjuvant Treatment.

PubMed

Pilotto, Sara; Sperduti, Isabella; Leuzzi, Giovanni; Chiappetta, Marco; Mucilli, Felice; Ratto, Giovanni Battista; Lococo, Filippo; Filosso, Pier Lugigi; Spaggiari, Lorenzo; Novello, Silvia; Milella, Michele; Santo, Antonio; Scarpa, Aldo; Infante, Maurizio; Tortora, Giampaolo; Facciolo, Francesco; Bria, Emilio

2018-04-01

We developed one of the first clinicopathological prognostic nomograms for resected squamous cell lung cancer (SQLC). Herein, we validate the model in a larger multicenter cohort and we explore the impact of adjuvant and neoadjuvant treatment (ANT). Patients with resected SQLC from January 2002 to December 2012 in six institutions were eligible. Each patient was assigned a prognostic score based on the clinicopathological factors included in the model (age, T descriptor according to seventh edition of the TNM classification, lymph node status, and grading). Kaplan-Meier analysis for disease-free survival, cancer-specific survival (CSS), and overall survival was performed according to a three-class risk model. Harrell's C-statistics were adopted for model validation. The effect of ANT was adjusted with propensity score. Data on 1375 patients were gathered (median age, 68 years; male sex, 86.8%; T descriptor 1 or 2 versus 3 or 4, 71.7% versus 24.9%; nodes negative versus positive, 53.4% versus 46.6%; and grading of 1 or 2 versus 3, 35.0% versus 41.1%). Data for survival analysis were available for 1097 patients. With a median follow-up of 55 months, patients at low risk had a significantly longer disease-free survival than did patients at intermediate risk (hazard ratio [HR] = 1.67, 95% confidence interval [CI]: 1.40-2.01) and patients at high risk (HR = 2.46, 95% CI: 1.90-3.19); they also had a significantly longer CSS (HR = 2.46, 95% CI: 1.80-3.36 versus HR = 4.30, 95% CI: 2.92-6.33) and overall survival (HR = 1.79, 95% CI: 1.48-2.17 versus HR = 2.33, 95% CI: 1.76-3.07). A trend in favor of ANT was observed for intermediate-risk/high-risk patients, particularly for CSS (p = 0.06 [5-year CSS 72.7% versus 60.8%]). A model based on a combination of easily available clinicopathological factors effectively stratifies patients with resected SQLC into three risk classes. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

ExSurv: A Web Resource for Prognostic Analyses of Exons Across Human Cancers Using Clinical Transcriptomes

PubMed Central

Hashemikhabir, Seyedsasan; Budak, Gungor; Janga, Sarath Chandra

2016-01-01

Survival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients’ clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing – a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. To address this gap and to provide a platform for rational identification of prognostic exons from cancer transcriptomes, we developed ExSurv (https://exsurv.soic.iupui.edu), a web-based platform for predicting the survival contribution of all annotated exons in the human genome using RNA sequencing-based expression profiles for cancer samples from four cancer types available from The Cancer Genome Atlas. ExSurv enables users to search for a gene of interest and shows survival probabilities for all the exons associated with a gene and found to be significant at the chosen threshold. ExSurv also includes raw expression values across the cancer cohort as well as the survival plots for prognostic exons. Our analysis of the resulting prognostic exons across four cancer types revealed that most of the survival-associated exons are unique to a cancer type with few processes such as cell adhesion, carboxylic, fatty acid metabolism, and regulation of T-cell signaling common across cancer types, possibly suggesting significant differences in the posttranscriptional regulatory pathways contributing to prognosis. PMID:27528797

Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Li, Dan; Yang, Haizhen; Liang, XiaoFeng

2010-11-01

Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

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.

Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry.

PubMed

van Rosendael, Alexander R; Maliakal, Gabriel; Kolli, Kranthi K; Beecy, Ashley; Al'Aref, Subhi J; Dwivedi, Aeshita; Singh, Gurpreet; Panday, Mohit; Kumar, Amit; Ma, Xiaoyue; Achenbach, Stephan; Al-Mallah, Mouaz H; Andreini, Daniele; Bax, Jeroen J; Berman, Daniel S; Budoff, Matthew J; Cademartiri, Filippo; Callister, Tracy Q; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J W; Cury, Ricardo C; DeLago, Augustin; Feuchtner, Gudrun; Hadamitzky, Martin; Hausleiter, Joerg; Kaufmann, Philipp A; Kim, Yong-Jin; Leipsic, Jonathon A; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert L; Rubinshtein, Ronen; Shaw, Leslee J; Villines, Todd C; Gransar, Heidi; Lu, Yao; Jones, Erica C; Peña, Jessica M; Lin, Fay Y; Min, James K

Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores. From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data). In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events). A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification. Published by Elsevier Inc.

Prognostic Value of Procalcitonin in Adult Patients with Sepsis: A Systematic Review and Meta-Analysis.

PubMed

Liu, Dan; Su, Longxiang; Han, Gencheng; Yan, Peng; Xie, Lixin

2015-01-01

Procalcitonin (PCT) has been widely investigated for its prognostic value in septic patients. However, studies have produced conflicting results. The purpose of the present meta-analysis is to explore the diagnostic accuracy of a single PCT concentration and PCT non-clearance in predicting all-cause sepsis mortality. We searched PubMed, Embase, Web of Knowledge and the Cochrane Library. Articles written in English were included. A 2 × 2 contingency table was constructed based on all-cause mortality and PCT level or PCT non-clearance in septic patients. Two authors independently evaluated study eligibility and extracted data. The diagnostic value of PCT in predicting prognosis was determined using a bivariate meta-analysis model. We used the Q-test and I2 index to test heterogeneity. Twenty-three studies with 3,994 patients were included. An elevated PCT level was associated with a higher risk of death. The pooled relative risk (RR) was 2.60 (95% confidence interval (CI), 2.05-3.30) using a random-effects model (I(2) = 63.5%). The overall area under the summary receiver operator characteristic (SROC) curve was 0.77 (95% CI, 0.73-0.80), with a sensitivity and specificity of 0.76 (95% CI, 0.67-0.82) and 0.64 (95% CI, 0.52-0.74), respectively. There was significant evidence of heterogeneity for the PCT testing time (P = 0.020). Initial PCT values were of limited prognostic value in patients with sepsis. PCT non-clearance was a prognostic factor of death in patients with sepsis. The pooled RR was 3.05 (95% CI, 2.35-3.95) using a fixed-effects model (I(2) = 37.9%). The overall area under the SROC curve was 0.79 (95% CI, 0.75-0.83), with a sensitivity and specificity of 0.72 (95% CI, 0.58-0.82) and 0.77 (95% CI, 0.55-0.90), respectively. Elevated PCT concentrations and PCT non-clearance are strongly associated with all-cause mortality in septic patients. Further studies are needed to define the optimal cut-off point and the optimal definition of PCT non-clearance for accurate risk assessment.

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.

TU-CD-BRB-08: Radiomic Analysis of FDG-PET Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated with SBRT

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

Cui, Y; Shirato, H; Song, J

2015-06-15

Purpose: This study aims to identify novel prognostic imaging biomarkers in locally advanced pancreatic cancer (LAPC) using quantitative, high-throughput image analysis. Methods: 86 patients with LAPC receiving chemotherapy followed by SBRT were retrospectively studied. All patients had a baseline FDG-PET scan prior to SBRT. For each patient, we extracted 435 PET imaging features of five types: statistical, morphological, textural, histogram, and wavelet. These features went through redundancy checks, robustness analysis, as well as a prescreening process based on their concordance indices with respect to the relevant outcomes. We then performed principle component analysis on the remaining features (number ranged frommore » 10 to 16), and fitted a Cox proportional hazard regression model using the first 3 principle components. Kaplan-Meier analysis was used to assess the ability to distinguish high versus low-risk patients separated by median predicted survival. To avoid overfitting, all evaluations were based on leave-one-out cross validation (LOOCV), in which each holdout patient was assigned to a risk group according to the model obtained from a separate training set. Results: For predicting overall survival (OS), the most dominant imaging features were wavelet coefficients. There was a statistically significant difference in OS between patients with predicted high and low-risk based on LOOCV (hazard ratio: 2.26, p<0.001). Similar imaging features were also strongly associated with local progression-free survival (LPFS) (hazard ratio: 1.53, p=0.026) on LOOCV. In comparison, neither SUVmax nor TLG was associated with LPFS (p=0.103, p=0.433) (Table 1). Results for progression-free survival and distant progression-free survival showed similar trends. Conclusion: Radiomic analysis identified novel imaging features that showed improved prognostic value over conventional methods. These features characterize the degree of intra-tumor heterogeneity reflected on FDG-PET images, and their biological underpinnings warrant further investigation. If validated in large, prospective cohorts, this method could be used to stratify patients based on individualized risk.« less

Re-resection rates after breast-conserving surgery as a performance indicator: introduction of a case-mix model to allow comparison between Dutch hospitals.

PubMed

Talsma, A K; Reedijk, A M J; Damhuis, R A M; Westenend, P J; Vles, W J

2011-04-01

Re-resection rate after breast-conserving surgery (BCS) has been introduced as an indicator of quality of surgical treatment in international literature. The present study aims to develop a case-mix model for re-resection rates and to evaluate its performance in comparing results between hospitals. Electronic records of eligible patients diagnosed with in-situ and invasive breast cancer in 2006 and 2007 were derived from 16 hospitals in the Rotterdam Cancer Registry (RCR) (n = 961). A model was built in which prognostic factors for re-resections after BCS were identified and expected re-resection rate could be assessed for hospitals based on their case mix. To illustrate the opportunities of monitoring re-resections over time, after risk adjustment for patient profile, a VLAD chart was drawn for patients in one hospital. In general three out of every ten women had re-surgery; in about 50% this meant an additive mastectomy. Independent prognostic factors of re-resection after multivariate analysis were histological type, sublocalisation, tumour size, lymph node involvement and multifocal disease. After correction for case mix, one hospital was performing significantly less re-resections compared to the reference hospital. On the other hand, two were performing significantly more re-resections than was expected based on their patient mix. Our population-based study confirms earlier reports that re-resection is frequently required after an initial breast-conserving operation. Case-mix models such as the one we constructed can be used to correct for variation between hospitals performances. VLAD charts are valuable tools to monitor quality of care within individual hospitals. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

PubMed Central

Brown, Fred; Adelson, David; White, Deborah; Hughes, Timothy; Chaudhri, Naeem

2017-01-01

Background Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Principle Findings The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models. PMID:28045960

Interpretative variability and its impact on the prognostic value of myocardial fatty acid imaging in asymptomatic hemodialysis patients in a multicenter trial in Japan.

PubMed

Kiriyama, Tomonari; Kumita, Shin-Ichiro; Moroi, Masao; Nishimura, Tsunehiko; Tamaki, Nagara; Hasebe, Naoyuki; Kikuchi, Kenjiro

2015-01-01

The severity of impaired fatty acid utilization in the myocardium can predict cardiac death in asymptomatic patients on hemodialysis. However, interpretive variability and its impact on the prognostic value of myocardial fatty acid imaging are unknown. A total of 677 patients who received hemodialysis for ≥ 20 years and had one or more cardiovascular risk factors underwent (123)I-labeled β-methyl iodophenyl-pentadecanoic acid (BMIPP) single-photon emission computed tomography (SPECT) at 48 hospitals across Japan. SPECT images were interpreted by experts at the nuclear core laboratory and by readers with varying skill levels at clinical centers, based on the standard 17-segment model and 5-point scoring systems, independently. The κ values only reached fair agreement both for overall impression (κ=0.298, normal vs. abnormal) and for categorical impression (κ=0.244, normal vs. mildly abnormal vs. severely abnormal). The normalcy rate was lower in readers at the clinical centers (60.9%) than in experts (69.9%). In contrast to the results assessed by experts, a Kaplan-Meier analysis based on the interpretation by readers at the clinical centers failed to distinguish the risk of events in patients with normal scans from that of patients with mildly abnormal scans. Considerable variability and its impact on prognostic value were observed in the visual interpretation of BMIPP SPECT images between experts and readers at the clinical centers.

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.

Estimation of group means when adjusting for covariates in generalized linear models.

PubMed

Qu, Yongming; Luo, Junxiang

2015-01-01

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

Prognostic value of fasting versus nonfasting low-density lipoprotein cholesterol levels on long-term mortality: insight from the National Health and Nutrition Examination Survey III (NHANES-III).

PubMed

Doran, Bethany; Guo, Yu; Xu, Jinfeng; Weintraub, Howard; Mora, Samia; Maron, David J; Bangalore, Sripal

2014-08-12

National and international guidelines recommend fasting lipid panel measurement for risk stratification of patients for prevention of cardiovascular events. However, the prognostic value of fasting versus nonfasting low-density lipoprotein cholesterol (LDL-C) is uncertain. Patients enrolled in the National Health and Nutrition Examination Survey III (NHANES-III), a nationally representative cross-sectional survey performed from 1988 to 1994, were stratified on the basis of fasting status (≥8 or <8 hours) and followed for a mean of 14.0 (±0.22) years. Propensity score matching was used to assemble fasting and nonfasting cohorts with similar baseline characteristics. The risk of outcomes as a function of LDL-C and fasting status was assessed with the use of receiver operating characteristic curves and bootstrapping methods. The interaction between fasting status and LDL-C was assessed with Cox proportional hazards modeling. Primary outcome was all-cause mortality. Secondary outcome was cardiovascular mortality. One-to-one matching based on propensity score yielded 4299 pairs of fasting and nonfasting individuals. For the primary outcome, fasting LDL-C yielded prognostic value similar to that for nonfasting LDL-C (C statistic=0.59 [95% confidence interval, 0.57-0.61] versus 0.58 [95% confidence interval, 0.56-0.60]; P=0.73), and LDL-C by fasting status interaction term in the Cox proportional hazards model was not significant (Pinteraction=0.11). Similar results were seen for the secondary outcome (fasting versus nonfasting C statistic=0.62 [95% confidence interval, 0.60-0.66] versus 0.62 [95% confidence interval, 0.60-0.66]; P=0.96; Pinteraction=0.34). Nonfasting LDL-C has prognostic value similar to that of fasting LDL-C. National and international agencies should consider reevaluating the recommendation that patients fast before obtaining a lipid panel. © 2014 American Heart Association, Inc.

Value of Excess Pressure Integral for Predicting 15-Year All-Cause and Cardiovascular Mortalities in End-Stage Renal Disease Patients.

PubMed

Huang, Jui-Tzu; Cheng, Hao-Min; Yu, Wen-Chung; Lin, Yao-Ping; Sung, Shih-Hsien; Wang, Jiun-Jr; Wu, Chung-Li; Chen, Chen-Huan

2017-11-29

The excess pressure integral (XSPI), derived from analysis of the arterial pressure curve, may be a significant predictor of cardiovascular events in high-risk patients. We comprehensively investigated the prognostic value of XSPI for predicting long-term mortality in end-stage renal disease patients undergoing regular hemodialysis. A total of 267 uremic patients (50.2% female; mean age 54.2±14.9 years) receiving regular hemodialysis for more than 6 months were enrolled. Cardiovascular parameters were obtained by echocardiography and applanation tonometry. Calibrated carotid arterial pressure waveforms were analyzed according to the wave-transmission and reservoir-wave theories. Multivariable Cox proportional hazard models were constructed to account for age, sex, diabetes mellitus, albumin, body mass index, and hemodialysis treatment adequacy. Incremental utility of the parameters to risk stratification was assessed by net reclassification improvement. During a median follow-up of 15.3 years, 124 deaths (46.4%) incurred. Baseline XSPI was significantly predictive of all-cause (hazard ratio per 1 SD 1.4, 95% confidence interval 1.15-1.70, P =0.0006) and cardiovascular mortalities (1.47, 1.18-1.84, P =0.0006) after accounting for the covariates. The addition of XSPI to the base prognostic model significantly improved prediction of both all-cause mortality (net reclassification improvement=0.1549, P =0.0012) and cardiovascular mortality (net reclassification improvement=0.1535, P =0.0033). XSPI was superior to carotid-pulse wave velocity, forward and backward wave amplitudes, and left ventricular ejection fraction in consideration of overall independent and incremental prognostics values. In end-stage renal disease patients undergoing regular hemodialysis, XSPI was significantly predictive of long-term mortality and demonstrated an incremental value to conventional prognostic factors. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

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.

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