Practical prognostic index for patients with metastatic recurrent breast cancer: retrospective analysis of 2,322 patients from the GEICAM Spanish El Alamo Register.

PubMed

Puente, Javier; López-Tarruella, Sara; Ruiz, Amparo; Lluch, Ana; Pastor, Miguel; Alba, Emilio; de la Haba, Juan; Ramos, Manuel; Cirera, Luis; Antón, Antonio; Llombart, Antoni; Plazaola, Arrate; Fernández-Aramburo, Antonio; Sastre, Javier; Díaz-Rubio, Eduardo; Martin, Miguel

2010-07-01

Women with recurrent metastatic breast cancer from a Spanish hospital registry (El Alamo, GEICAM) were analyzed in order to identify the most helpful prognostic factors to predict survival and to ultimately construct a practical prognostic index. The inclusion criteria covered women patients diagnosed with operable invasive breast cancer who had metastatic recurrence between 1990 and 1997 in GEICAM hospitals. Patients with stage IV breast cancer at initial diagnosis or with isolated loco-regional recurrence were excluded from this analysis. Data from 2,322 patients with recurrent breast cancer after primary treatment (surgery, radiation and systemic adjuvant treatment) were used to construct the prognostic index. The prognostic index score for each individual patient was calculated by totalling up the scores of each independent variable. The maximum score obtainable was 26.1. Nine-hundred and sixty-two patients who had complete data for all the variables were used in the computation of the prognostic index score. We were able to stratify them into three prognostic groups based on the prognostic index score: 322 patients in the good risk group (score < or =13.5), 308 patients in the intermediate risk group (score 13.51-15.60) and 332 patients in the poor risk group (score > or =15.61). The median survivals for these groups were 3.69, 2.27 and 1.02 years, respectively (P < 0.0001). In conclusion, risk scores are extraordinarily valuable tools, highly recommendable in the clinical practice.

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.

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.

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

Incorporating prognostic imaging biomarkers into clinical practice

PubMed Central

Miles, Kenneth A.

2013-01-01

Abstract A prognostic imaging biomarker can be defined as an imaging characteristic that is objectively measurable and provides information on the likely outcome of the cancer disease in an untreated individual and should be distinguished from predictive imaging biomarkers and imaging markers of response. A range of tumour characteristics of potential prognostic value can be measured using a variety imaging modalities. However, none has currently been adopted into routine clinical practice. This article considers key examples of emerging prognostic imaging biomarkers and proposes an evaluation framework that aims to demonstrate clinical efficacy and so support their introduction into the clinical arena. With appropriate validation within an established evaluation framework, prognostic imaging biomarkers have the potential to contribute to individualized cancer care, in some cases reducing the financial burden of expensive cancer treatments by facilitating their more rational use. PMID:24060808

Chronic lymphocytic leukemia: A prognostic model comprising only two biomarkers (IGHV mutational status and FISH cytogenetics) separates patients with different outcome and simplifies the CLL-IPI.

PubMed

Delgado, Julio; Doubek, Michael; Baumann, Tycho; Kotaskova, Jana; Molica, Stefano; Mozas, Pablo; Rivas-Delgado, Alfredo; Morabito, Fortunato; Pospisilova, Sarka; Montserrat, Emili

2017-04-01

Rai and Binet staging systems are important to predict the outcome of patients with chronic lymphocytic leukemia (CLL) but do not reflect the biologic diversity of the disease nor predict response to therapy, which ultimately shape patients' outcome. We devised a biomarkers-only CLL prognostic system based on the two most important prognostic parameters in CLL (i.e., IGHV mutational status and fluorescence in situ hybridization [FISH] cytogenetics), separating three different risk groups: (1) low-risk (mutated IGHV + no adverse FISH cytogenetics [del(17p), del(11q)]); (2) intermediate-risk (either unmutated IGHV or adverse FISH cytogenetics) and (3) high-risk (unmutated IGHV + adverse FISH cytogenetics). In 524 unselected subjects with CLL, the 10-year overall survival was 82% (95% CI 76%-88%), 52% (45%-62%), and 27% (17%-42%) for the low-, intermediate-, and high-risk groups, respectively. Patients with low-risk comprised around 50% of the series and had a life expectancy comparable to the general population. The prognostic model was fully validated in two independent cohorts, including 417 patients representative of general CLL population and 337 patients with Binet stage A CLL. The model had a similar discriminatory value as the CLL-IPI. Moreover, it applied to all patients with CLL independently of age, and separated patients with different risk within Rai or Binet clinical stages. The biomarkers-only CLL prognostic system presented here simplifies the CLL-IPI and could be useful in daily practice and to stratify patients in clinical trials. © 2017 Wiley Periodicals, Inc.

Prognostic models based on patient snapshots and time windows: Predicting disease progression to assisted ventilation in Amyotrophic Lateral Sclerosis.

PubMed

Carreiro, André V; Amaral, Pedro M T; Pinto, Susana; Tomás, Pedro; de Carvalho, Mamede; Madeira, Sara C

2015-12-01

Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient's condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient's current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians' experience. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning.

PubMed

Fusar-Poli, P; Schultze-Lutter, F

2016-02-01

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes' theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment. 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/

Systematic review of renal carcinoma prognostic factors.

PubMed

Lorente, D; Trilla, E; Meseguer, A; Planas, J; Placer, J; Celma, A; Salvador, C; Regis, L; Morote, J

2017-05-01

The natural history of renal cell carcinoma is heterogeneous. Some scenarios can be found in terms of clinical presentation, clinical evolution or type of recurrence (local/metastatic). The aim of this publication is to analyze the most important prognostic factors published in the literature. A literature review ob published papers was performed using the Pubmed, from first Motzer's classification published in 1999 to 2015, according to PRISMA declaration. Search was done using the following keywords: kidney neoplasm, kidney cancer, renal cell carcinoma, prognostic factors, mortality, survival and disease progression. Papers were classified according to level of evidence, the number of patients included and the type of study performed. The evolution in the knowledge of molecular pathways related to renal oncogenesis and the new targeted therapies has left to remain obsolete the old prognostic models. It's necessary to perform a continuous review to actualize nomograms and to adapt them to the new scenarios. Is necessary to perform a proper external validation of existing prognostic factors using prospective and multicentric studies to add them into the daily urologist clinical practice. Copyright © 2016 AEU. Publicado por Elsevier España, S.L.U. All rights reserved.

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.

Prognostic Disclosures to Children: A Historical Perspective.

PubMed

Sisk, Bryan A; Bluebond-Langner, Myra; Wiener, Lori; Mack, Jennifer; Wolfe, Joanne

2016-09-01

Prognostic disclosure to children has perpetually challenged clinicians and parents. In this article, we review the historical literature on prognostic disclosure to children in the United States using cancer as an illness model. Before 1948, there was virtually no literature focused on prognostic disclosure to children. As articles began to be published in the 1950s and 1960s, many clinicians and researchers initially recommended a "protective" approach to disclosure, where children were shielded from the harms of bad news. We identified 4 main arguments in the literature at this time supporting this "protective" approach. By the late 1960s, however, a growing number of clinicians and researchers were recommending a more "open" approach, where children were included in discussions of diagnosis, which at the time was often synonymous with a terminal prognosis. Four different arguments in the literature were used at this time supporting this "open" approach. Then, by the late 1980s, the recommended approach to prognostic disclosure in pediatrics shifted largely from "never tell" to "always tell." In recent years, however, there has been a growing appreciation for the complexity of prognostic disclosure in pediatrics. Current understanding of pediatric disclosure does not lead to simple "black-and-white" recommendations for disclosure practices. As with most difficult questions, we are left to balance competing factors on a case-by-case basis. We highlight 4 categories of current considerations related to prognostic disclosure in pediatrics, and we offer several approaches to prognostic disclosure for clinicians who care for these young patients and their families. Copyright © 2016 by the American Academy of Pediatrics.

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.

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.

Numerical Modeling of the Global Atmosphere

NASA Technical Reports Server (NTRS)

Arakawa, Akio; Mechoso, Carlos R.

1996-01-01

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

[Neuroendocrine neoplasm of digestive system with different grades: a clinicopathologic and prognostic study].

PubMed

Zhang, Ming-hui; Liu, Yan-hui; Luo, Xin-lan; Lin, Xing-tao; Zhuang, Heng-guo

2012-07-01

To study the clinicopathologic and prognostic features of neuroendocrine neoplasm of digestive system with different grades. The clinicopathologic features of 139 cases of neuroendocrine neoplasm occurring in digestive system were retrospectively reviewed and graded according to the 2010 World Health Organization classification of tumours of the digestive system. Immunohistochemical study for synaptophysin, chromogranin A and Ki-67 was carried out. The follow-up and survival data were analysed using Kaplan-Meier method. Prognostic factors were tested by Log-rank testing and independent risk factors were analysed using Cox regression model. Amongst the 139 cases studied, there were 88 cases (63.3%) of grade 1 tumors, 9 cases (6.5%) of grade 2 tumors and 42 cases (30.2%) of grade 3 tumors. There was diffusely positive staining for synaptophysin and chromogranin A in most of the grade 1 and grade 2 tumors. The staining in grade 3 tumors however was focal (P < 0.05). The differences in tumor size, depth of invasion, presence of tumor emboli, perineural permeation, nodal involvement, distant metastasis and survival rate amongst the three groups was statistically significant (P < 0.05). There is significant difference in the clinicopathologic and prognostic features of neuroendocrine neoplasm of digestive system with different grades. It is considered as an independent prognostic factor and represents a useful tool for prognostic evaluation of such tumors, both in clinical practice and research.

[Molecular characterization of breast cancer in clinical practice].

PubMed

Zemmouri, Y; De Croze, D; Vincent Salomon, A; Rouzier, R; Bonneau, C

2016-05-01

Breast cancer involves various types of tumors. The objective of this review was to provide a summary of the main methods currently available in clinical practice to characterize breast cancers at a molecular level and to discuss their prognostic and predictive values. Hormonal receptors expression and the HER2 status are prognostic markers and can also predict the response to targeted therapies. Their analysis through immunohistochemistry is systematical. Ki67 is an effective prognostic marker, but its reliability is debated because of its low reproducibility between laboratories and between pathologists. Commercial genomic signatures are all considered valid prognostic tools and may guide physicians to make therapeutic choices. These signatures are costly and should therefore be restricted to situations in which the use of chemotherapy remains equivocal. Copyright © 2016. Published by Elsevier SAS.

Model-Based Prognostics of Hybrid Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal

2015-01-01

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

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 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.

Predictive & Prognostic Controller for Wide Band Gap (Silicon Carbide) Power Conversion (Preprint)

DTIC Science & Technology

2006-11-01

that is required for them to achieve their full potential, or other elements that are different from the traditional practice of Power Conditioning...symptoms, medical clinicians rely strongly on family history, individual history, environmental conditions or exposures and lifestyle to ascertain the...the model and possibly related models of the particular converter design. The lifestyle , the stress and the exposure that is considered in human

Validation of the International Metastatic Renal-Cell Carcinoma Database Consortium (IMDC) prognostic model for first-line pazopanib in metastatic renal carcinoma: the Spanish Oncologic Genitourinary Group (SOGUG) SPAZO study.

PubMed

Pérez-Valderrama, B; Arranz Arija, J A; Rodríguez Sánchez, A; Pinto Marín, A; Borrega García, P; Castellano Gaunas, D E; Rubio Romero, G; Maximiano Alonso, C; Villa Guzmán, J C; Puertas Álvarez, J L; Chirivella González, I; Méndez Vidal, M J; Juan Fita, M J; León-Mateos, L; Lázaro Quintela, M; García Domínguez, R; Jurado García, J M; Vélez de Mendizábal, E; Lambea Sorrosal, J J; García Carbonero, I; González del Alba, A; Suárez Rodríguez, C; Jiménez Gallego, P; Meana García, J A; García Marrero, R D; Gajate Borau, P; Santander Lobera, C; Molins Palau, C; López Brea, M; Fernández Parra, E M; Reig Torras, O; Basterretxea Badiola, L; Vázquez Estévez, S; González Larriba, J L

2016-04-01

Patients with metastatic renal carcinoma (mRCC) treated with first-line pazopanib were not included in the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) prognostic model. SPAZO (NCT02282579) was a nation-wide retrospective observational study designed to assess the effectiveness and validate the IMDC prognostic model in patients treated with first-line pazopanib in clinical practice. Data of 278 patients, treated with first-line pazopanib for mRCC in 34 centres in Spain, were locally recorded and externally validated. Mean age was 66 years, there were 68.3% male, 93.5% clear-cell type, 74.8% nephrectomized, and 81.3% had ECOG 0-1. Metastatic sites were: lung 70.9%, lymph node 43.9%, bone 26.3%, soft tissue/skin 20.1%, liver 15.1%, CNS 7.2%, adrenal gland 6.5%, pleura/peritoneum 5.8%, pancreas 5%, and kidney 2.2%. After median follow-up of 23 months, 76.4% had discontinued pazopanib (57.2% due to progression), 47.9% had received second-line targeted therapy, and 48.9% had died. According to IMDC prognostic model, 19.4% had favourable risk (FR), 57.2% intermediate risk (IR), and 23.4% poor risk (PR). No unexpected toxicities were recorded. Response rate was 30.3% (FR: 44%, IR: 30% PR: 17.3%). Median progression-free survival (whole population) was 11 months (32 in FR, 11 in IR, 4 in PR). Median and 2-year overall survival (whole population) were 22 months and 48.1%, respectively (FR: not reached and 81.6%, IR: 22 and 48.7%, PR: 7 and 18.8%). These estimations and their 95% confidence intervals are fully consistent with the outcomes predicted by the IMDC prognostic model. Our results validate the IMDC model for first-line pazopanib in mRCC and confirm the effectiveness and safety of this treatment. © The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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.

Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

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

2014-01-01

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

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.

Biomarkers in Breast Cancer – An Update

PubMed Central

Schmidt, M.; Fasching, P. A.; Beckmann, M. W.; Kölbl, H.

2012-01-01

The therapy of choice for breast cancer patients requiring adjuvant chemo- or radiotherapy is increasingly guided by the principle of weighing the individual effectiveness of the therapy against the associated side effects. This has only been made possible by the discovery and validation of modern biomarkers. In the last decades and in the last few years some biomarkers have been integrated in clinical practice and a number have been included in modern study concepts. The importance of biomarkers lies not merely in their prognostic value indicating the future course of disease but also in their use to predict patient response to therapy. Due to the many subgroups, mathematical models and computer-assisted analysis are increasingly being used to assess the prognostic information obtained from established clinical and histopathological factors. In addition to describing some recent computer programmes this overview will focus on established molecular markers which have already been extensively validated in clinical practice and on new molecular markers identified by genome-wide studies. PMID:26640290

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

Introduction to the Special Issue on Neuropsychology Practices in Integrated Care Teams.

PubMed

Festa, Joanne R

2018-05-01

This special issue on neuropsychology practices in integrated healthcare teams demonstrates how neuropsychologists have transformed their practices in an evolving healthcare landscape. These contributions are an overview of the many ways in which neuropsychologists function in integrated care teams. The experiences of integrated neuropsychologists serve as a model for those seeking new practice opportunities by providing highly practical, clinically relevant information. Included in this volume are articles on education and reimbursement issues, information about clinical practices that address diagnostic issues, prognostics and clinical management, as well as surgical treatment planning and outcome prediction. Authors highlight the value of their services, their contribution to improving team and patient communication, as well as the biopsychosocial understanding of the patient. Several unexpected challenges are detailed among the pearls and pitfalls of these practices.

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.

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

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

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

Diagnostic and Prognostic Models for Generator Step-Up Transformers

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

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

In 2014, the online monitoring (OLM) of active components project under the Light Water Reactor Sustainability program at Idaho National Laboratory (INL) focused on diagnostic and prognostic capabilities for generator step-up transformers. INL worked with subject matter experts from the Electric Power Research Institute (EPRI) to augment and revise the GSU fault signatures previously implemented in the Electric Power Research Institute’s (EPRI’s) Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. Two prognostic models were identified and implemented for GSUs in the FW-PHM Suite software. INL and EPRI demonstrated the use of prognostic capabilities for GSUs. The complete set of faultmore » signatures developed for GSUs in the Asset Fault Signature Database of the FW-PHM Suite for GSUs is presented in this report. Two prognostic models are described for paper insulation: the Chendong model for degree of polymerization, and an IEEE model that uses a loading profile to calculates life consumption based on hot spot winding temperatures. Both models are life consumption models, which are examples of type II prognostic models. Use of the models in the FW-PHM Suite was successfully demonstrated at the 2014 August Utility Working Group Meeting, Idaho Falls, Idaho, to representatives from different utilities, EPRI, and the Halden Research Project.« less

A Model-Based Prognostics Approach Applied to Pneumatic Valves

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Goebel, Kai

2011-01-01

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

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

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

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.

Discussing prognosis with patients with osteoarthritis: a cross-sectional survey in general practice.

PubMed

Clarson, L E; Nicholl, B I; Bishop, A; Daniel, R; Mallen, C D

2016-04-01

Osteoarthritis is a leading cause of chronic pain and disability and one of the most common conditions diagnosed and managed in primary care. Despite the evidence that patients would value discussions about the course of osteoarthritis to help them make informed treatment decisions and plan for the future, little is known of GPs' practice of, or views regarding, discussing prognosis with these patients. A cross-sectional postal survey asked 2500 randomly selected UK GPs their views on discussing prognosis with patients with osteoarthritis and potential barriers or facilitators to such discussions. They were also asked if prognostic discussions were part of their current practice and what indicators they considered important in assessing the prognosis associated with osteoarthritis. Of 768 respondents (response rate 30.7 %), the majority felt it necessary to discuss prognosis with osteoarthritis patients (n = 738, 96.1 %), but only two thirds reported that it was part of their routine practice (n = 498, 64.8 %). Most respondents found predicting the course of osteoarthritis (n = 703, 91.8 %) and determining the prognosis of patients difficult (n = 589, 76.7 %). Obesity, level of physical disability and pain severity were considered the most important prognostic indicators in osteoarthritis. Although GPs consider prognostic discussions necessary for patients with osteoarthritis, few prioritise these discussions. Lack of time and perceived difficulties in predicting the disease course and determining prognosis for patients with osteoarthritis may be barriers to engaging in prognostic discussions. Further research is required to identify ways to assist GPs making prognostic predictions for patients with osteoarthritis and facilitate engagement in these discussions.

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.

Incorporating Neutrophil-to-lymphocyte Ratio and Platelet-to-lymphocyte Ratio in Place of Neutrophil Count and Platelet Count Improves Prognostic Accuracy of the International Metastatic Renal Cell Carcinoma Database Consortium Model

PubMed Central

Chrom, Pawel; Stec, Rafal; Bodnar, Lubomir; Szczylik, Cezary

2018-01-01

Purpose The study investigated whether a replacement of neutrophil count and platelet count by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) within the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model would improve its prognostic accuracy. Materials and Methods This retrospective analysis included consecutive patients with metastatic renal cell carcinoma treated with first-line tyrosine kinase inhibitors. The IMDC and modified-IMDC models were compared using: concordance index (CI), bias-corrected concordance index (BCCI), calibration plots, the Grønnesby and Borgan test, Bayesian Information Criterion (BIC), generalized R2, Integrated Discrimination Improvement (IDI), and continuous Net Reclassification Index (cNRI) for individual risk factors and the three risk groups. Results Three hundred and twenty-one patients were eligible for analyses. The modified-IMDC model with NLR value of 3.6 and PLR value of 157 was selected for comparison with the IMDC model. Both models were well calibrated. All other measures favoured the modified-IMDC model over the IMDC model (CI, 0.706 vs. 0.677; BCCI, 0.699 vs. 0.671; BIC, 2,176.2 vs. 2,190.7; generalized R2, 0.238 vs. 0.202; IDI, 0.044; cNRI, 0.279 for individual risk factors; and CI, 0.669 vs. 0.641; BCCI, 0.669 vs. 0.641; BIC, 2,183.2 vs. 2,198.1; generalized R2, 0.163 vs. 0.123; IDI, 0.045; cNRI, 0.165 for the three risk groups). Conclusion Incorporation of NLR and PLR in place of neutrophil count and platelet count improved prognostic accuracy of the IMDC model. These findings require external validation before introducing into clinical practice. PMID:28253564

Incorporating Neutrophil-to-lymphocyte Ratio and Platelet-to-lymphocyte Ratio in Place of Neutrophil Count and Platelet Count Improves Prognostic Accuracy of the International Metastatic Renal Cell Carcinoma Database Consortium Model.

PubMed

Chrom, Pawel; Stec, Rafal; Bodnar, Lubomir; Szczylik, Cezary

2018-01-01

The study investigated whether a replacement of neutrophil count and platelet count by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) within the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model would improve its prognostic accuracy. This retrospective analysis included consecutive patients with metastatic renal cell carcinoma treated with first-line tyrosine kinase inhibitors. The IMDC and modified-IMDC models were compared using: concordance index (CI), bias-corrected concordance index (BCCI), calibration plots, the Grønnesby and Borgan test, Bayesian Information Criterion (BIC), generalized R 2 , Integrated Discrimination Improvement (IDI), and continuous Net Reclassification Index (cNRI) for individual risk factors and the three risk groups. Three hundred and twenty-one patients were eligible for analyses. The modified-IMDC model with NLR value of 3.6 and PLR value of 157 was selected for comparison with the IMDC model. Both models were well calibrated. All other measures favoured the modified-IMDC model over the IMDC model (CI, 0.706 vs. 0.677; BCCI, 0.699 vs. 0.671; BIC, 2,176.2 vs. 2,190.7; generalized R 2 , 0.238 vs. 0.202; IDI, 0.044; cNRI, 0.279 for individual risk factors; and CI, 0.669 vs. 0.641; BCCI, 0.669 vs. 0.641; BIC, 2,183.2 vs. 2,198.1; generalized R 2 , 0.163 vs. 0.123; IDI, 0.045; cNRI, 0.165 for the three risk groups). Incorporation of NLR and PLR in place of neutrophil count and platelet count improved prognostic accuracy of the IMDC model. These findings require external validation before introducing into clinical practice.

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.

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms

PubMed Central

Montalban-Bravo, Guillermo; Takahashi, Koichi; Patel, Keyur; Wang, Feng; Xingzhi, Song; Nogueras, Graciela M.; Huang, Xuelin; Pierola, Ana Alfonso; Jabbour, Elias; Colla, Simona; Gañan-Gomez, Irene; Borthakur, Gautham; Daver, Naval; Estrov, Zeev; Kadia, Tapan; Pemmaraju, Naveen; Ravandi, Farhad; Bueso-Ramos, Carlos; Chamseddine, Ali; Konopleva, Marina; Zhang, Jianhua; Kantarjian, Hagop; Futreal, Andrew; Garcia-Manero, Guillermo

2018-01-01

The prognostic and predictive value of sequencing analysis in myelodysplastic syndromes (MDS) has not been fully integrated into clinical practice. We performed whole exome sequencing (WES) of bone marrow samples from 83 patients with MDS and 31 with MDS/MPN identifying 218 driver mutations in 31 genes in 98 (86%) patients. A total of 65 (57%) patients received therapy with hypomethylating agents. By univariate analysis, mutations in BCOR, STAG2, TP53 and SF3B1 significantly influenced survival. Increased number of mutations (≥ 3), but not clonal heterogeneity, predicted for shorter survival and LFS. Presence of 3 or more mutations also predicted for lower likelihood of response (26 vs 50%, p = 0.055), and shorter response duration (3.6 vs 26.5 months, p = 0.022). By multivariate analysis, TP53 mutations (HR 3.1, CI 1.3–7.5, p = 0.011) and number of mutations (≥ 3) (HR 2.5, CI 1.3–4.8, p = 0.005) predicted for shorter survival. A novel prognostic model integrating this mutation data with IPSS-R separated patients into three categories with median survival of not reached, 29 months and 12 months respectively (p < 0.001) and increased stratification potential, compared to IPSS-R, in patients with high/very-high IPSS-R. This model was validated in a separate cohort of 413 patients with untreated MDS. Although the use of WES did not provide significant more information than that obtained with targeted sequencing, our findings indicate that increased number of mutations is an independent prognostic factor in MDS and that mutation data can add value to clinical prognostic models. PMID:29515765

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms.

PubMed

Montalban-Bravo, Guillermo; Takahashi, Koichi; Patel, Keyur; Wang, Feng; Xingzhi, Song; Nogueras, Graciela M; Huang, Xuelin; Pierola, Ana Alfonso; Jabbour, Elias; Colla, Simona; Gañan-Gomez, Irene; Borthakur, Gautham; Daver, Naval; Estrov, Zeev; Kadia, Tapan; Pemmaraju, Naveen; Ravandi, Farhad; Bueso-Ramos, Carlos; Chamseddine, Ali; Konopleva, Marina; Zhang, Jianhua; Kantarjian, Hagop; Futreal, Andrew; Garcia-Manero, Guillermo

2018-02-09

The prognostic and predictive value of sequencing analysis in myelodysplastic syndromes (MDS) has not been fully integrated into clinical practice. We performed whole exome sequencing (WES) of bone marrow samples from 83 patients with MDS and 31 with MDS/MPN identifying 218 driver mutations in 31 genes in 98 (86%) patients. A total of 65 (57%) patients received therapy with hypomethylating agents. By univariate analysis, mutations in BCOR, STAG2, TP53 and SF3B1 significantly influenced survival. Increased number of mutations (≥ 3), but not clonal heterogeneity, predicted for shorter survival and LFS. Presence of 3 or more mutations also predicted for lower likelihood of response (26 vs 50%, p = 0.055), and shorter response duration (3.6 vs 26.5 months, p = 0.022). By multivariate analysis, TP53 mutations (HR 3.1, CI 1.3-7.5, p = 0.011) and number of mutations (≥ 3) (HR 2.5, CI 1.3-4.8, p = 0.005) predicted for shorter survival. A novel prognostic model integrating this mutation data with IPSS-R separated patients into three categories with median survival of not reached, 29 months and 12 months respectively ( p < 0.001) and increased stratification potential, compared to IPSS-R, in patients with high/very-high IPSS-R. This model was validated in a separate cohort of 413 patients with untreated MDS. Although the use of WES did not provide significant more information than that obtained with targeted sequencing, our findings indicate that increased number of mutations is an independent prognostic factor in MDS and that mutation data can add value to clinical prognostic models.

The present status and future growth of maintenance in US manufacturing: results from a pilot survey.

PubMed

Jin, Xiaoning; Siegel, David; Weiss, Brian A; Gamel, Ellen; Wang, Wei; Lee, Jay; Ni, Jun

A research study was conducted (1) to examine the practices employed by US manufacturers to achieve productivity goals and (2) to understand what level of intelligent maintenance technologies and strategies are being incorporated into these practices. This study found that the effectiveness and choice of maintenance strategy were strongly correlated to the size of the manufacturing enterprise; there were large differences in adoption of advanced maintenance practices and diagnostics and prognostics technologies between small and medium-sized enterprises (SMEs). Despite their greater adoption of maintenance practices and technologies, large manufacturing organizations have had only modest success with respect to diagnostics and prognostics and preventive maintenance projects. The varying degrees of success with respect to preventative maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and use of advanced prognostics and health management (PHM) technology. The future outlook for manufacturing PHM technology among the manufacturing organizations considered in this study was overwhelmingly positive; many manufacturing organizations have current and planned projects in this area. Given the current modest state of implementation and positive outlook for this technology, gaps, future trends, and roadmaps for manufacturing PHM and maintenance strategy are presented.

The present status and future growth of maintenance in US manufacturing: results from a pilot survey

PubMed Central

Jin, Xiaoning; Siegel, David; Weiss, Brian A.; Gamel, Ellen; Wang, Wei; Lee, Jay; Ni, Jun

2016-01-01

A research study was conducted (1) to examine the practices employed by US manufacturers to achieve productivity goals and (2) to understand what level of intelligent maintenance technologies and strategies are being incorporated into these practices. This study found that the effectiveness and choice of maintenance strategy were strongly correlated to the size of the manufacturing enterprise; there were large differences in adoption of advanced maintenance practices and diagnostics and prognostics technologies between small and medium-sized enterprises (SMEs). Despite their greater adoption of maintenance practices and technologies, large manufacturing organizations have had only modest success with respect to diagnostics and prognostics and preventive maintenance projects. The varying degrees of success with respect to preventative maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and use of advanced prognostics and health management (PHM) technology. The future outlook for manufacturing PHM technology among the manufacturing organizations considered in this study was overwhelmingly positive; many manufacturing organizations have current and planned projects in this area. Given the current modest state of implementation and positive outlook for this technology, gaps, future trends, and roadmaps for manufacturing PHM and maintenance strategy are presented. PMID:27525253

Practices and evaluations of prognostic disclosure for Japanese cancer patients and their families from the family's point of view.

PubMed

Yoshida, Saran; Shiozaki, Mariko; Sanjo, Makiko; Morita, Tatsuya; Hirai, Kei; Tsuneto, Satoru; Shima, Yasuo

2013-10-01

The primary end points of this analysis were to explore 1) the practices of prognostic disclosure for patients with cancer and their family members in Japan, 2) the person who decided on the degree of prognosis communication, and 3) family evaluations of the type of prognostic disclosure. Semistructured face-to-face interviews were conducted with 60 bereaved family members of patients with cancer who were admitted to palliative care units in Japan. Twenty-five percent of patients and 75% of family members were informed of the predicted survival time of the patient. Thirty-eight percent of family members answered that they themselves decided on to what degree to communicate the prognosis to patients and 83% of them chose not to disclose to patients their prognosis or incurability. In the overall evaluation of prognosis communication, 30% of the participants said that they regretted or felt doubtful about the degree of prognostic disclosure to patients, whereas 37% said that they were satisfied with the degree of prognostic disclosure and 5% said that they had made a compromise. Both in the “prognostic disclosure” group and the “no disclosure” group, there were family members who said that they regretted or felt doubtful (27% and 31%, respectively) and family members who said that they were satisfied with the degree of disclosure (27% and 44%, respectively). In conclusion, family members assume the predominant role as the decision-making source regarding prognosis disclosure to patients, and they often even prevent prognostic disclosure to patients. From the perspective of family members, any one type of disclosure is not necessarily the most acceptable choice. Future surveys should explore the reasons why family members agree or disagree with prognostic disclosures to patients and factors correlated with family evaluations.

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.

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.

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

Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries

NASA Astrophysics Data System (ADS)

Wang, Dong; Zhao, Yang; Yang, Fangfang; Tsui, Kwok-Leung

2017-09-01

Brownian motion with adaptive drift has attracted much attention in prognostics because its first hitting time is highly relevant to remaining useful life prediction and it follows the inverse Gaussian distribution. Besides linear degradation modeling, nonlinear-drifted Brownian motion has been developed to model nonlinear degradation. Moreover, the first hitting time distribution of the nonlinear-drifted Brownian motion has been approximated by time-space transformation. In the previous studies, the drift coefficient is the only hidden state used in state space modeling of the nonlinear-drifted Brownian motion. Besides the drift coefficient, parameters of a nonlinear function used in the nonlinear-drifted Brownian motion should be treated as additional hidden states of state space modeling to make the nonlinear-drifted Brownian motion more flexible. In this paper, a prognostic method based on nonlinear-drifted Brownian motion with multiple hidden states is proposed and then it is applied to predict remaining useful life of rechargeable batteries. 26 sets of rechargeable battery degradation samples are analyzed to validate the effectiveness of the proposed prognostic method. Moreover, some comparisons with a standard particle filter based prognostic method, a spherical cubature particle filter based prognostic method and two classic Bayesian prognostic methods are conducted to highlight the superiority of the proposed prognostic method. Results show that the proposed prognostic method has lower average prediction errors than the particle filter based prognostic methods and the classic Bayesian prognostic methods for battery remaining useful life prediction.

Molecular Pathology: Predictive, Prognostic, and Diagnostic Markers in Uterine Tumors.

PubMed

Ritterhouse, Lauren L; Howitt, Brooke E

2016-09-01

This article focuses on the diagnostic, prognostic, and predictive molecular biomarkers in uterine malignancies, in the context of morphologic diagnoses. The histologic classification of endometrial carcinomas is reviewed first, followed by the description and molecular classification of endometrial epithelial malignancies in the context of histologic classification. Taken together, the molecular and histologic classifications help clinicians to approach troublesome areas encountered in clinical practice and evaluate the utility of molecular alterations in the diagnosis and subclassification of endometrial carcinomas. Putative prognostic markers are reviewed. The use of molecular alterations and surrogate immunohistochemistry as prognostic and predictive markers is also discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

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

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.

CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer

PubMed Central

Nguyen, Dinh Truong; Kim, Jin-Hwan; Jo, Yong Hwa; Shahid, Muhammad; Akter, Salima; Aryal, Saurav Nath; Yoo, Ji Youn; Ahn, Yong-Joo; Cho, Kyoung Min; Lee, Ju-Seog; Choe, Wonchae; Kang, Insug; Ha, Joohun; Kim, Sung Soo

2015-01-01

Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies. PMID:26397224

CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer.

PubMed

Nguyen, Minh Nam; Choi, Tae Gyu; Nguyen, Dinh Truong; Kim, Jin-Hwan; Jo, Yong Hwa; Shahid, Muhammad; Akter, Salima; Aryal, Saurav Nath; Yoo, Ji Youn; Ahn, Yong-Joo; Cho, Kyoung Min; Lee, Ju-Seog; Choe, Wonchae; Kang, Insug; Ha, Joohun; Kim, Sung Soo

2015-10-13

Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies.

A Clinical Decision Support System for Breast Cancer Patients

NASA Astrophysics Data System (ADS)

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

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

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.

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 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.

A Distributed Approach to System-Level Prognostics

NASA Technical Reports Server (NTRS)

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

2012-01-01

Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component lifetimes that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative Jack of scalability and efficiency of typical prognostics approaches. In order to address these is ues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resul ting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.

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.

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.

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

PubMed Central

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

2009-01-01

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

Prognosis: the "missing link" within the CanMEDS competency framework.

PubMed

Maida, Vincent; Cheon, Paul M

2014-05-13

The concept of prognosis dates back to antiquity. Quantum advances in diagnostics and therapeutics have relegated this once highly valued core competency to an almost negligible role in modern medical practice. Medical curricula are devoid of teaching opportunities focused on prognosis. This void is driven by a corresponding relative dearth within physician competency frameworks. This study aims to assess the level of content related to prognosis within CanMEDS (Canadian Medical Education Directives for Specialists), a leading and prototypical physician competency framework. A quantitative content analysis of CanMEDS competency framework was carried out to measure the extent of this deficiency. Foxit Reader 5.1 (Foxit Corporation), a keyword scanning software, was used to assess the CanMEDS 2005 framework documents of 29 physician specialties and 37 subspecialties across the seven physician roles (medical expert, communicator, collaborator, manager, health advocate, scholar, and professional). The keywords used in the search included prognosis, prognostic, prognosticate, and prognostication. Of the 29 specialties six (20.7%) contained at least one citation of the keyword "prognosis", and one (3.4%) contained one citation of the keyword "prognostic". Of the 37 subspecialties, sixteen (43.2%) contained at least one citation of the keyword "prognosis", and three (8.1%) contained at least one citation of the keyword "prognostic". The terms "prognosticate" and "prognostication" were completely absent from all CanMEDS 2005 documents. Overall, the combined citations for "prognosis" and "prognostic" were linked with the following competency roles: Medical Expert (80.3%), Scholar (11.5%), and Communicator (8.2%). Given the fundamental and foundational importance of prognosis within medical practice, it is recommended that physicians develop appropriate attitudes, skills and knowledge related to the formulation and communication of prognosis. The deficiencies within CanMEDS, demonstrated by this study, should be addressed in advance of the launch of its updated version in 2015.

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.

Survey on current practices for neurological prognostication after cardiac arrest.

PubMed

Friberg, Hans; Cronberg, Tobias; Dünser, Martin W; Duranteau, Jacques; Horn, Janneke; Oddo, Mauro

2015-05-01

To investigate current practices and timing of neurological prognostication in comatose cardiac arrest patients. An anonymous questionnaire was distributed to the 8000 members of the European Society of Intensive Care Medicine during September and October 2012. The survey had 27 questions divided into three categories: background data, clinical data, decision-making and consequences. A total of 1025 respondents (13%) answered the survey with complete forms in more than 90%. Twenty per cent of respondents practiced outside of Europe. Overall, 22% answered that they had national recommendations, with the highest percentage in the Netherlands (>80%). Eighty-nine per cent used induced hypothermia (32-34 °C) for comatose cardiac arrest patients, while 11% did not. Twenty per cent had separate prognostication protocols for hypothermia patients. Seventy-nine per cent recognized that neurological examination alone is not enough to predict outcome and a similar number (76%) used additional methods. Intermittent electroencephalography (EEG), brain computed tomography (CT) scan and evoked potentials (EP) were considered most useful. Poor prognosis was defined as cerebral performance category (CPC) 3-5 (58%) or CPC 4-5 (39%) or other (3%). When prognosis was considered poor, 73% would actively withdraw intensive care while 20% would not and 7% were uncertain. National recommendations for neurological prognostication after cardiac arrest are uncommon and only one physician out of five uses a separate protocol for hypothermia treated patients. A neurological examination alone was considered insufficient to predict outcome in comatose patients and most respondents advocated a multimodal approach: EEG, brain CT and EP were considered most useful. Uncertainty regarding neurological prognostication and decisions on level of care was substantial. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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

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.

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

PubMed

D'Amico, Gennaro; De Franchis, Roberto

2003-09-01

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

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

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.

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

PubMed

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

2016-08-01

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

Multiple Damage Progression Paths in Model-Based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Goebel, Kai Frank

2011-01-01

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active

A Prognostic Modeling Approach for Predicting Recurring Maintenance for Shipboard Propulsion Systems

DTIC Science & Technology

2001-06-01

CIT CDT 1 CIP CDP η γ 1γ T T adb (1) Compressor fouling has also been shown to increase vibration , (Ozgur et al (2000) and Tsalavoutas et al) but... vibration increases and secondly is the poor reliability with which performance degradation severity may be assessed. In lieu of these practical...Industrial Gas Turbines” International Gas Turbine and Aeroengine Congress and Exposition, Belgium, June 1990 4. Kurtz, Rainer, Brun, Klaus, and

New Breast Cancer Recursive Partitioning Analysis Prognostic Index in Patients With Newly Diagnosed Brain Metastases

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

Niwinska, Anna, E-mail: alphaonetau@poczta.onet.pl; Murawska, Magdalena

2012-04-01

Purpose: The aim of the study was to present a new breast cancer recursive partitioning analysis (RPA) prognostic index for patients with newly diagnosed brain metastases as a guide in clinical decision making. Methods and Materials: A prospectively collected group of 441 consecutive patients with breast cancer and brain metastases treated between the years 2003 and 2009 was assessed. Prognostic factors significant for univariate analysis were included into RPA. Results: Three prognostic classes of a new breast cancer RPA prognostic index were selected. The median survival of patients within prognostic Classes I, II, and III was 29, 9, and 2.4more » months, respectively (p < 0.0001). Class I included patients with one or two brain metastases, without extracranial disease or with controlled extracranial disease, and with Karnofsky performance status (KPS) of 100. Class III included patients with multiple brain metastases with KPS of {<=}60. Class II included all other cases. Conclusions: The breast cancer RPA prognostic index is an easy and valuable tool for use in clinical practice. It can select patients who require aggressive treatment and those in whom whole-brain radiotherapy or symptomatic therapy is the most reasonable option. An individual approach is required for patients from prognostic Class II.« less

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

PubMed

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

2014-05-01

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

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.

Prognostic Value of Lymphocyte G Protein-Coupled Receptor Kinase-2 Protein Levels in Patients With Heart Failure

PubMed Central

Rengo, Giuseppe; Pagano, Gennaro; Filardi, Pasquale Perrone; Femminella, Grazia Daniela; Parisi, Valentina; Cannavo, Alessandro; Liccardo, Daniela; Komici, Klara; Gambino, Giuseppina; D’Amico, Maria Loreta; de Lucia, Claudio; Paolillo, Stefania; Trimarco, Bruno; Vitale, Dino Franco; Ferrara, Nicola; Koch, Walter J; Leosco, Dario

2016-01-01

Rationale Sympathetic nervous system (SNS) hyperactivity is associated with poor prognosis in patients with HF, yet routine assessment of SNS activation is not recommended for clinical practice. Myocardial G protein-coupled receptor kinase 2 (GRK2) is up-regulated in heart failure (HF) patients, causing dysfunctional β-adrenergic receptor signaling. Importantly, myocardial GRK2 levels correlate with levels found in peripheral lymphocytes of HF patients. Objective The independent prognostic value of blood GRK2 measurements in HF patients has never been investigated, thus, the purpose of the present study was to evaluate whether lymphocyte GRK2 levels predict clinical outcome in HF patients. Methods and Results We prospectively studied 257 HF patients with mean left ventricular ejection fraction (LVEF) of 31.4±8.5%. At the time of enrollment, plasma norepinephrine, serum NT-proBNP and lymphocyte GRK2 levels, as well as clinical and instrumental variables were measured. The prognostic value of GRK2 to predict cardiovascular (CV) death and all-cause mortality was assessed using the Cox proportional hazard model including demographic, clinical, instrumental and laboratory data. Over a mean follow-up period of 37.5±20.2 months (range: 3–60 months) there were 102 CV deaths. Age, LVEF, NYHA class, Chronic Obstructive Pulmonary Disease, Chronic Kidney Disease, N-terminal-pro Brain Natriuretic Peptide, and lymphocyte GRK2 protein levels were independent predictors of CV mortality in HF patients. GRK2 levels showed an additional prognostic and clinical value over demographic and clinical variables. The independent prognostic value of lymphocyte GRK2 levels was also confirmed for all-cause mortality. Conclusion Lymphocyte GRK2 protein levels can independently predict prognosis in patients with HF. PMID:26884616

Depression, Anxiety, and Regret Before and After Testing to Estimate Uveal Melanoma Prognosis.

PubMed

Schuermeyer, Isabel; Maican, Anca; Sharp, Richard; Bena, James; Triozzi, Pierre L; Singh, Arun D

2016-01-01

To our knowledge, longitudinal assessment of depression, anxiety, and decision regret (a sense of disappointment or dissatisfaction in the decision) in patients undergoing prognostication for uveal melanoma does not exist. To report on depression, anxiety, and decision regret before and after testing to estimate uveal melanoma prognosis. Prospective interventional case series conducted at an institutional referral practice of 96 patients with clinical diagnosis of uveal melanoma who underwent prognostication at the time of primary therapy. Depression, anxiety, and decision regret prior to prognostication (baseline) and at 3 and 12 months afterwards. The Hospital Anxiety and Depression Scale (HADS) and Decision Regret Scale were self-administered by the patients prior to prognostication (baseline) and at 3 and 12 months afterwards. Data were summarized using means and standard deviations for continuous measures, frequencies, and percentages for categorical factors. A mixed model was used to assess the trajectory of HADS anxiety and the associations between HADS anxiety and baseline HADS depression, baseline decision regret, prognostication test result, and adjuvant therapy, respectively, while adjusting for age and sex. Ninety-six patients (median age 60.7 years) completed baseline questionnaires. The mean (SD) HADS anxiety score at baseline (7.4 [4.0]) was higher than at 3 months (5.4 [3.7]; P < .001) or 12 months (4.7 [3.4]; P < .001), and decreased with older age (coefficient estimate [SD], -0.06 [0.02]; P < .001). The decision regret score was associated with baseline HADS depression score (coefficient estimate [SE], -1.17 [0.43]; P < .007), and HADS depression score increased with baseline HADS anxiety score (coefficient estimate [SE], 0.39 [0.06]; P < .001). Our study raises questions about decision regret in patients who agree to have a prognostic test that may not help guide treatment. Although decision regret appears to lessen or dissipate with time, study on larger numbers of patients is necessary to elucidate factors that may be addressed to mitigate decision regret.

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.

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

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.

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 Structural Health Monitoring Software Tool for Optimization, Diagnostics and Prognostics

DTIC Science & Technology

2011-01-01

A Structural Health Monitoring Software Tool for Optimization, Diagnostics and Prognostics Seth S . Kessler1, Eric B. Flynn2, Christopher T...technology more accessible, and commercially practical. 1. INTRODUCTION Currently successful laboratory non- destructive testing and monitoring...PROGRAM ELEMENT NUMBER 6. AUTHOR( S ) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES

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.

Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing.

PubMed

Jin, Xiaoning; Weiss, Brian A; Siegel, David; Lee, Jay

2016-01-01

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.

Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing

PubMed Central

Jin, Xiaoning; Weiss, Brian A.; Siegel, David; Lee, Jay

2016-01-01

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy. PMID:28058173

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

The Evolution of Prognostic Factors in Multiple Myeloma

PubMed Central

Hassanein, Mona; Rasheed, Walid; Aljurf, Mahmoud; Alsharif, Fahad

2017-01-01

Multiple myeloma (MM) is a heterogeneous hematologic malignancy involving the proliferation of plasma cells derived by different genetic events contributing to the development, progression, and prognosis of this disease. Despite improvement in treatment strategies of MM over the last decade, the disease remains incurable. All efforts are currently focused on understanding the prognostic markers of the disease hoping to incorporate the new therapeutic modalities to convert the disease into curable one. We present this comprehensive review to summarize the current standard prognostic markers used in MM along with novel techniques that are still in development and highlight their implications in current clinical practice. PMID:28321258

Towards evidence-based emergency medicine: best BETs from the Manchester Royal Infirmary. BET 4: Prognostic value of B-type natriuretic peptides (BNP and NT-proBNP) in community-acquired pneumonia.

PubMed

Hodgson, David; Nee, Patrick; Sultan, Laith

2012-10-01

A short cut review was carried out to establish the prognostic value of B-type natriuretic peptides (BNP and NT-proBNP) in community acquired pneumonia (CAP). Three cohort studies were directly relevant to the question. The author, date and country of publication, patient group studied, study type, relevant outcomes, results and study weaknesses of these papers are tabulated. The clinical bottom line was that B-type natriuretic peptides have prognostic value in CAP but further prospective studies were needed to assess their application in clinical practice.

Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry.

PubMed

Canepa, Marco; Fonseca, Candida; Chioncel, Ovidiu; Laroche, Cécile; Crespo-Leiro, Maria G; Coats, Andrew J S; Mebazaa, Alexandre; Piepoli, Massimo F; Tavazzi, Luigi; Maggioni, Aldo P

2018-06-01

This study compared the performance of major heart failure (HF) risk models in predicting mortality and examined their utilization using data from a contemporary multinational registry. Several prognostic risk scores have been developed for ambulatory HF patients, but their precision is still inadequate and their use limited. This registry enrolled patients with HF seen in participating European centers between May 2011 and April 2013. The following scores designed to estimate 1- to 2-year all-cause mortality were calculated in each participant: CHARM (Candesartan in Heart Failure-Assessment of Reduction in Mortality), GISSI-HF (Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure), MAGGIC (Meta-analysis Global Group in Chronic Heart Failure), and SHFM (Seattle Heart Failure Model). Patients with hospitalized HF (n = 6,920) and ambulatory HF patients missing any variable needed to estimate each score (n = 3,267) were excluded, leaving a final sample of 6,161 patients. At 1-year follow-up, 5,653 of 6,161 patients (91.8%) were alive. The observed-to-predicted survival ratios (CHARM: 1.10, GISSI-HF: 1.08, MAGGIC: 1.03, and SHFM: 0.98) suggested some overestimation of mortality by all scores except the SHFM. Overprediction occurred steadily across levels of risk using both the CHARM and the GISSI-HF, whereas the SHFM underpredicted mortality in all risk groups except the highest. The MAGGIC showed the best overall accuracy (area under the curve [AUC] = 0.743), similar to the GISSI-HF (AUC = 0.739; p = 0.419) but better than the CHARM (AUC = 0.729; p = 0.068) and particularly better than the SHFM (AUC = 0.714; p = 0.018). Less than 1% of patients received a prognostic estimate from their enrolling physician. Performance of prognostic risk scores is still limited and physicians are reluctant to use them in daily practice. The need for contemporary, more precise prognostic tools should be considered. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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.

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.

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.

Prostate Specific or Enriched Genes as Composite Biomarkers for Prostate Cancer

DTIC Science & Technology

2008-02-01

isotope dilution by comparing to the 13C- or 15N-labeled reference peptides. The MRM method is best practiced utilizing a triple quadrupole mass...specific, androgen- regulated gene. Here, we evaluate its utility as a prostate cancer tissue marker for diagnosis and prognostic evaluation. Experimental...prevalence of adverse prognostic factors such as capsular penetration, seminal vesicle invasion, and positive surgical margins is rather high compared with

A Survey of Attitudes towards the Clinical Application of Systemic Inflammation Based Prognostic Scores in Cancer.

PubMed

Watt, David G; Roxburgh, Campbell S; White, Mark; Chan, Juen Zhik; Horgan, Paul G; McMillan, Donald C

2015-01-01

The systemic inflammatory response (SIR) plays a key role in determining nutritional status and survival of patients with cancer. A number of objective scoring systems have been shown to have prognostic value; however, their application in routine clinical practice is not clear. The aim of the present survey was to examine the range of opinions internationally on the routine use of these scoring systems. An online survey was distributed to a target group consisting of individuals worldwide who have reported an interest in systemic inflammation in patients with cancer. Of those invited by the survey (n = 238), 65% routinely measured the SIR, mainly for research and prognostication purposes and clinically for allocation of adjuvant therapy or palliative chemotherapy. 40% reported that they currently used the Glasgow Prognostic Score/modified Glasgow Prognostic Score (GPS/mGPS) and 81% reported that a measure of systemic inflammation should be incorporated into clinical guidelines, such as the definition of cachexia. The majority of respondents routinely measured the SIR in patients with cancer, mainly using the GPS/mGPS for research and prognostication purposes. The majority reported that a measure of the SIR should be adopted into clinical guidelines.

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...

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

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.

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

PubMed Central

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

2016-01-01

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

A 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.

[Chronic lymphocytic leukaemia: current management].

PubMed

Aurran-Schleinitz, T; Arnoulet, C; Ivanov, V; Coso, D; Rey, J; Schiano, J-M; Stoppa, A-M; Bouabdallah, R; Gastaut, J-A

2008-05-01

Chronic lymphocytic leukemia (CLL) is the most common leukaemia in the Western world. Recent advancement in the aetiology, pathophysiology and the development of new therapeutics tools have significantly modified the current management of CLL. The cellular origin of CLL is still unknown. The current main hypothesis will be first briefly described. This review will then focus on the newly defined prognostic factors and the development and use of new drugs for the treatment of CLL. To describe the modern and practical management of CLL, we will compare classical and new prognostic markers. Then, we will discuss the various therapeutic options including chemotherapy and immunotherapy (monoclonal antibodies, allogenic transplantation), and define their current respective indications. These new diagnostic and prognostic markers will allow the characterization of new prognostic subgroups of patients. This will lead to a targeted and individualized therapeutic approach. We will present the first results of clinical trials and the on-going studies conducted in this disease.

Breast Cancer Prognosis for Young Patients

PubMed Central

OWRANG, MEHDI; COPELAND, L. ROBERT JR; RICKS-SANTI, J. LUISEL; GASKINS, MELVIN; BEYENE, DESTA; DEWITTY, L. ROBERT JR; KANAAN, M. YASMINE

2017-01-01

Background/Aims: Breast cancer (BCa) prognostication is a vital element for providing effective treatment for patients with BCa. Studies suggest that ethnicity plays a greater role in the incidence and poor prognosis of BCa in younger women than in their older counterparts. Therefore, the goal of this study was to assess the association between age and ethnicity on the overall final prognosis. Materials and Methods: Nottingham Prognostic Index (NPI) was used to analyze BCa prognosis using Howard University Cancer Center Tumor Registry and the National Cancer Institute’s Surveillance, Epidemiology, and End Results BCa datasets. Patients were grouped according to their predicted prognosis based on NPI scheme. Results: There was no correlation between the younger patients compared to their older counterparts for any of the prognostic clusters. The significance of ethnicity in poorer prognosis for younger age is not conclusive either. Conclusion: An extended prognostic tool/system needs to be evaluated for its usefulness in a clinical practice environment. PMID:28652435

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

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

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

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

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.

Management of hepatocellular carcinoma: Predictive value of immunohistochemical markers for postoperative survival

PubMed Central

Niu, Zhao-Shan; Niu, Xiao-Jun; Wang, Mei

2015-01-01

Hepatocellular carcinoma (HCC) accounts for over 90% of all primary liver cancers. With an ever increasing incidence trend year by year, it has become the third most common cause of death from cancer worldwide. Hepatic resection is generally considered to be one of the most effective therapies for HCC patients, however, there is a high risk of recurrence in postoperative HCC. In clinical practice, there exists an urgent need for valid prognostic markers to identify patients with prognosis, hence the importance of studies on prognostic markers in improving the prediction of HCC prognosis. This review focuses on the most promising immunohistochemical prognostic markers in predicting the postoperative survival of HCC patients. PMID:25624992

Diagnostic and prognostic epigenetic biomarkers in cancer.

PubMed

Costa-Pinheiro, Pedro; Montezuma, Diana; Henrique, Rui; Jerónimo, Carmen

2015-01-01

Growing cancer incidence and mortality worldwide demands development of accurate biomarkers to perfect detection, diagnosis, prognostication and monitoring. Urologic (prostate, bladder, kidney), lung, breast and colorectal cancers are the most common and despite major advances in their characterization, this has seldom translated into biomarkers amenable for clinical practice. Epigenetic alterations are innovative cancer biomarkers owing to stability, frequency, reversibility and accessibility in body fluids, entailing great potential of assay development to assist in patient management. Several studies identified putative epigenetic cancer biomarkers, some of which have been commercialized. However, large multicenter validation studies are required to foster translation to the clinics. Herein we review the most promising epigenetic detection, diagnostic, prognostic and predictive biomarkers for the most common cancers.

Australasian haematologist referral patterns to palliative care: lack of consensus on when and why.

PubMed

Auret, K; Bulsara, C; Joske, D

2003-12-01

Patients with haematological malignancies are not referred to palliative care services as frequently as those with solid cancers (non-haematological malignancies). The present study surveyed haematologists in Australia and New Zealand. We aimed to record theoretical referral times, identify problems with referral to palliative care and clarify elements used to decide whether a patient was "terminally ill". A questionnaire based on the case-histories of three patients (with acute leukaemia, lymphoma or multiple myeloma) was distributed at the Haematology Society of Australia and New Zealand Congress 2000, Perth, Australia. Each case was divided into stages by transitional points in the illness to include issues or prognostic variables that may stimulate referral to palliative care. Questions were asked about: (i) referral-triggers, (ii) problems previously experienced, (iii) definition of when the patient was "terminally ill", (iv) prognostication difficulties and (v) communication about prognosis. The response rate was 11%, which may represent up to 32% of Australian specialists. Eighty per cent had access to all types of palliative care services and refer for symptom control, regardless of illness stage. Twenty-nine per cent had experienced difficulties in referring. There was a variation as to exactly when referral would occur and when each case was considered "terminally ill". Reasons for early or later referral were explored. Prognostication difficulties were common. In theory there is a willingness to refer to palliative care, however this has yet to be translated to day-to-day practice. This may be due to prognostication difficulties, logistical factors and medical concerns. Models of referral are suggested for further study.

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

GPU Accelerated Prognostics

NASA Technical Reports Server (NTRS)

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

2017-01-01

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

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

USDA-ARS?s Scientific Manuscript database

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

Application of Model-based Prognostics to a Pneumatic Valves Testbed

NASA Technical Reports Server (NTRS)

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

2014-01-01

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

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.

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.

The prognostic value of heart rate variability in the elderly, changing the perspective: from sympathovagal balance to chaos theory.

PubMed

Nicolini, Paola; Ciulla, Michele M; De Asmundis, Carlo; Magrini, Fabio; Brugada, Pedro

2012-05-01

Heart rate variability (HRV) is the temporal beat-to-beat variation in successive RR intervals on an electrocardiographic (ECG) recording and it reflects the regulation of the heart rate (HR) by the autonomic nervous system (ANS). HRV analysis is a noninvasive tool for the assessment of autonomic function that gained momentum in the late 1980s when its clinical relevance as a predictor of mortality was established by a milestone study by Kleiger et al. in patients with postacute myocardial infarction. In the last few decades, the increasing availability of commercial ECG devices offering HRV analysis has made HRV a favorite marker for risk stratification in the setting of cardiovascular disease. The rapid aging of the world population and the growing popularity of HRV have also fueled interest for the prognostic value of HRV in the elderly, outside a specific cardiological context. However, the discussion of HRV measures in the elderly is still very much centered on the rather reductionistic model of sympathovagal balance, with the orthosympathetic and parasympathetic limbs of the ANS exercising opposing effects on the heart via autonomic tone. The expanding application of nonlinear dynamics to medicine has brought to the forefront the notion of system complexity, embedded in the mathematical concepts of chaos theory and fractals, and provides an opportunity to suggest a broader interpretation for the prognostic significance of HRV, especially in the elderly. Although the use of novel indices of HRV may be hampered by practical issues, a more holistic approach to HRV may still be safeguarded if traditional time- and frequency-domain measures are viewed in terms of autonomic modulation. This review focuses on HRV in geriatric populations. It considers studies on the prognostic value of HRV in elderly subjects, discussing the potential confounding effect of erratic rhythm, and concentrates on the conceptual distinction between autonomic tone and autonomic modulation. It also briefly addresses the question of the practicality of ECG recordings and identifies a promising area for future research in the effects of common noncardioactive drugs on HRV. ©2012, The Authors. Journal compilation ©2012 Wiley Periodicals, Inc.

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...

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

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk.

PubMed

Jieyi Li; Arandjelovic, Ognjen

2017-07-01

Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories, or display cumulative long term prognostics, in an intuitive and easily interpretable manner.

The role of the Internet in cancer patients' engagement with complementary and alternative treatments.

PubMed

Broom, Alex; Tovey, Philip

2008-04-01

This article draws on a study of 80 National Health Service cancer patients and their experiences of using the Internet within disease and treatment processes. It focuses on the role the Internet plays in the context of potential or actual engagement with complementary and alternative medicine (CAM). The results depart from previous conceptualizations of the Internet as a major source of CAM knowledge, and second, as a major pathway to patient CAM usage. Moreover, the results highlight significant anxiety as patients attempt to process vast amounts of complex biomedical diagnostic and prognostic information online. For patients attempting to embrace alternative therapeutic models of cancer care, exposure to prognostic data may pose considerable risks to individual well-being and engagement with healing practices. On the basis of these results we problematize social theorizations of the Internet as contributing to such things as: the democratization of knowledge; the deprofessionalization of medicine; and patient empowerment. We emphasize, instead, the potential role of the Internet in reinforcing biomedicine's paradigmatic dominance in cancer care.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

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

PubMed

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

2018-04-01

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

Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance.

PubMed

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

2018-05-01

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

[PROGNOSTIC MODELS IN MODERN MANAGEMENT OF VULVAR CANCER].

PubMed

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

2016-01-01

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

Investigating the Effect of Damage Progression Model Choice on Prognostics Performance

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Methodological issues and recommendations for systematic reviews of prognostic studies: an example from cardiovascular disease.

PubMed

Dretzke, Janine; Ensor, Joie; Bayliss, Sue; Hodgkinson, James; Lordkipanidzé, Marie; Riley, Richard D; Fitzmaurice, David; Moore, David

2014-12-03

Prognostic factors are associated with the risk of future health outcomes in individuals with a particular health condition. The prognostic ability of such factors is increasingly being assessed in both primary research and systematic reviews. Systematic review methodology in this area is continuing to evolve, reflected in variable approaches to key methodological aspects. The aim of this article was to (i) explore and compare the methodology of systematic reviews of prognostic factors undertaken for the same clinical question, (ii) to discuss implications for review findings, and (iii) to present recommendations on what might be considered to be 'good practice' approaches. The sample was comprised of eight systematic reviews addressing the same clinical question, namely whether 'aspirin resistance' (a potential prognostic factor) has prognostic utility relative to future vascular events in patients on aspirin therapy for secondary prevention. A detailed comparison of methods around study identification, study selection, quality assessment, approaches to analysis, and reporting of findings was undertaken and the implications discussed. These were summarised into key considerations that may be transferable to future systematic reviews of prognostic factors. Across systematic reviews addressing the same clinical question, there were considerable differences in the numbers of studies identified and overlap between included studies, which could only partially be explained by different study eligibility criteria. Incomplete reporting and differences in terminology within primary studies hampered study identification and selection process across reviews. Quality assessment was highly variable and only one systematic review considered a checklist for studies of prognostic questions. There was inconsistency between reviews in approaches towards analysis, synthesis, addressing heterogeneity and reporting of results. Different methodological approaches may ultimately affect the findings and interpretation of systematic reviews of prognostic research, with implications for clinical decision-making.

How Radiation Oncologists Evaluate and Incorporate Life Expectancy Estimates Into the Treatment of Palliative Cancer Patients: A Survey-Based Study

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

Tseng, Yolanda D., E-mail: ydtseng@partners.org; Krishnan, Monica S.; Sullivan, Adam J.

2013-11-01

Purpose: We surveyed how radiation oncologists think about and incorporate a palliative cancer patient’s life expectancy (LE) into their treatment recommendations. Methods and Materials: A 41-item survey was e-mailed to 113 radiation oncology attending physicians and residents at radiation oncology centers within the Boston area. Physicians estimated how frequently they assessed the LE of their palliative cancer patients and rated the importance of 18 factors in formulating LE estimates. For 3 common palliative case scenarios, physicians estimated LE and reported whether they had an LE threshold below which they would modify their treatment recommendation. LE estimates were considered accurate whenmore » within the 95% confidence interval of median survival estimates from an established prognostic model. Results: Among 92 respondents (81%), the majority were male (62%), from an academic practice (75%), and an attending physician (70%). Physicians reported assessing LE in 91% of their evaluations and most frequently rated performance status (92%), overall metastatic burden (90%), presence of central nervous system metastases (75%), and primary cancer site (73%) as “very important” in assessing LE. Across the 3 cases, most (88%-97%) had LE thresholds that would alter treatment recommendations. Overall, physicians’ LE estimates were 22% accurate with 67% over the range predicted by the prognostic model. Conclusions: Physicians often incorporate LE estimates into palliative cancer care and identify important prognostic factors. Most have LE thresholds that guide their treatment recommendations. However, physicians overestimated patient survival times in most cases. Future studies focused on improving LE assessment are needed.« less

Neural cell adhesion molecule-180 expression as a prognostic criterion in colorectal carcinoma: Feasible or not?

PubMed Central

Tascilar, Oge; Cakmak, Güldeniz Karadeniz; Tekin, Ishak Ozel; Emre, Ali Ugur; Ucan, Bulent Hamdi; Irkorucu, Oktay; Karakaya, Kemal; Gül, Mesut; Engin, Hüseyin Bülent; Comert, Mustafa

2007-01-01

AIM: To evaluate the frequency of neural cell adhesion molecule (NCAM)-180 expression in fresh tumor tissue samples and to discuss the prognostic value of NCAM-180 in routine clinical practice. METHODS: Twenty-six patients (16 men, 10 women) with colorectal cancer were included in the study. Fresh tumor tissue samples and macroscopically healthy proximal margins of each specimen were subjected to flow-cytometric analysis for NCAM-180 expression. RESULTS: Flow-cytometric analysis determined NCAM-180 expression in whole tissue samples of macroscopically healthy colorectal tissues. However, NCAM-180 expression was positive in only one case (3.84%) with well-differentiated Stage II disease who experienced no active disease at 30 mon follow-up. CONCLUSION: As a consequence of the limited number of cases in our series, it might not be possible to make a generalisation, nevertheless the routine use of NCAM-180 expression as a prognostic marker for colorectal carcinoma seems to be unfeasible and not cost-effective in clinical practice due to its very low incidence. PMID:17907291

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.

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.

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

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.

Should culture affect practice? A comparison of prognostic discussions in consultations with immigrant versus native-born cancer patients.

PubMed

Butow, Phyllis N; Sze, Ming; Eisenbruch, Maurice; Bell, Melaine L; Aldridge, Lynley J; Abdo, Sarah; Tanious, Michelle; Dong, Skye; Iedema, Rick; Vardy, Janette; Hui, Rina; Boyle, Francis; Liauw, Winston; Goldstein, David

2013-08-01

Poor prognosis is difficult to impart, particularly across a cultural divide. This study compared prognostic communication with immigrants (with and without interpreters) versus native-born patients in audio-taped oncology consultations. Ten oncologists, 78 patients (31 Australian-born, 47 immigrants) and 115 family members participated. The first two consultations after diagnosis of incurable disease were audiotaped, transcribed and coded. 142 consultations were included in the analysis. Fifty percent of doctor and 59% of patient prognostic speech units were not interpreted or interpreted non-equivalently when an interpreter was present. Immigrant status predicted few prognostic facts, and oncologist characteristics no prognostic facts, disclosed. Oncologists were significantly less likely to convey hope to immigrants (p=0.0004), and more likely to use medical jargon (p=0.009) than with Australian-born patients. Incurable disease status and a limited life span were commonly acknowledged, generally with no timeframe provided. Physical issues were discussed more commonly than emotional aspects. While culture did not appear to influence doctor speech, interpreters filtered or blocked much prognostic communication. Initiatives to empower all patients to attain needed information, optimise communication when an interpreter is present and train cancer health professionals in culturally appropriate care, are urgently required. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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.

German dentists' websites on periodontitis have low quality of information.

PubMed

Schwendicke, Falk; Stange, Jörg; Stange, Claudia; Graetz, Christian

2017-08-02

The internet is an increasingly relevant source of health information. We aimed to assess the quality of German dentists' websites on periodontitis, hypothesizing that it was significantly associated with a number of practice-specific parameters. We searched four electronic search engines and included pages which were freely accessible, posted by a dental practice in Germany, and mentioned periodontal disease/therapy. Websites were assessed for (1) technical and functional aspects, (2) generic quality and risk of bias, (3) disease-specific information. For 1 and 2, validated tools (LIDA/DISCERN) were used for assessment. For 3, we developed a criterion catalogue encompassing items on etiologic and prognostic factors for periodontitis, the diagnostic and treatment process, and the generic chance of tooth retention in periodontitis patients. Inter- and intra-rater reliabilities were largely moderate. Generalized linear modeling was used to assess the association between the information quality (measured as % of maximally available scores) and practice-specific characteristics. Seventy-one websites were included. Technical and functional aspects were reported in significantly higher quality (median: 71%, 25/75th percentiles: 67/79%) than all other aspects (p < 0.05). Generic risk of bias and most disease-specific aspects showed significantly lower reporting quality (median range was 0-40%), with poorest reporting for prognostic factors (9;0/27%), diagnostic process (0;0/33%) and chances of tooth retention (0;0/2%). We found none of the practice-specific parameters to have significant impact on the overall quality of the websites. Most German dentists' websites on periodontitis are not fully trustworthy and relevant information are not or insufficiently considered. There is great need to improve the information quality from such websites at least with regards to periodontitis.

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

PubMed

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

2018-01-02

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

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

PubMed

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

2018-05-16

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

Establishing a Traumatic Brain Injury Program of Care: Benchmarking Outcomes after Institutional Adoption of Evidence-Based Guidelines.

PubMed

Tarapore, Phiroz E; Vassar, Mary J; Cooper, Shelly; Lay, Twyila; Galletly, Julia; Manley, Geoffrey T; Huang, Michael C

2016-11-15

Traumatic brain injury (TBI) is a widespread global disease, often with widely varying outcomes. Standardization of care and adherence to established guidelines are central to the effort to improve outcomes. At our level I urban trauma center, we developed and implemented a Joint Commission-certified TBI Program of Care in 2011 and compared our post-implementation patient data set with historical controls, using the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic model. Historical controls were drawn from the San Francisco General Hospital Traumatic Coma Data Bank (SFGH/TCDB) from 1987 to 1996. Recent era patients were drawn from the NeuroTracker database, a customized electronic medical record used in our clinical practice. Descriptive statistics were calculated. Adherence to four quality-of-care metrics on the clinical service was tracked for 2011-2013. The IMPACT prognostic model was used to calculate expected versus observed mortality for current and historical patient groups. In the historical control group, 832 patients were identified and 6-month mortality was available for 592. Observed 6-month mortality was 49%. In the recent era patient group, 211 patients were identified and 6-month mortality was 38%. The IMPACT prognostic model was applied to each patient group. Areas under the curve for each analysis were >0.85 and goodness of fit was satisfactory, indicating good performance of the IMPACT model. Comparison of observed versus expected deaths in the recent versus the control patient sets revealed a drop of 59% in early mortality. The greatest reductions in mortality were observed in the group of patients with IMPACT-predicted mortality ≤50%. Significant progress has been made in reducing the percentage of unexpected deaths in TBI patients. It is likely that major factors include more aggressive management and tracking of compliance with the implementation of guidelines for the management of TBI patients.

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

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.

Profiles of neurological outcome prediction among intensivists.

PubMed

Racine, Eric; Dion, Marie-Josée; Wijman, Christine A C; Illes, Judy; Lansberg, Maarten G

2009-12-01

Advances in intensive care medicine have increased survival rates of patients with critical neurological conditions. The focus of prognostication for such patients is therefore shifting from predicting chances of survival to meaningful neurological recovery. This study assessed the variability in long-term outcome predictions among physicians and aimed to identify factors that may account for this variability. Based on a clinical vignette describing a comatose patient suffering from post-anoxic brain injury intensivists were asked in a semi-structured interview about the patient's specific neurological prognosis and about prognostication in general. Qualitative research methods were used to identify areas of variability in prognostication and to classify physicians according to specific prognostication profiles. Quantitative statistics were used to assess for associations between prognostication profiles and physicians' demographic and practice characteristics. Eighteen intensivists participated. Functional outcome predictions varied along an evaluative dimension (fair/good-poor) and a confidence dimension (certain-uncertain). More experienced physicians tended to be more pessimistic about the patient's functional outcome and more certain of their prognosis. Attitudes toward quality of life varied along an evaluative dimension (good-poor) and a "style" dimension (objective-subjective). Older and more experienced physicians were more likely to express objective judgments of quality of life and to predict a worse quality of life for the patient than their younger and less experienced counterparts. Various prognostication profiles exist among intensivists. These may be dictated by factors such as physicians' age and clinical experience. Awareness of these associations may be a first step to more uniform prognostication.

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

PubMed

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

2015-07-31

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

The time has come for new models in febrile neutropenia: a practical demonstration of the inadequacy of the MASCC score.

PubMed

Carmona-Bayonas, A; Jiménez-Fonseca, P; Virizuela Echaburu, J; Sánchez Cánovas, M; Ayala de la Peña, F

2017-09-01

Since its publication more than 15 years ago, the MASCC score has been internationally validated any number of times and recommended by most clinical practice guidelines for the management of febrile neutropenia (FN) around the world. We have used an empirical data-supported simulated scenario to demonstrate that, despite everything, the MASCC score is impractical as a basis for decision-making. A detailed analysis of reasons supporting the clinical irrelevance of this model is performed. First, seven of its eight variables are "innocent bystanders" that contribute little to selecting low-risk candidates for ambulatory management. Secondly, the training series was hardly representative of outpatients with solid tumors and low-risk FN. Finally, the simultaneous inclusion of key variables both in the model and in the outcome explains its successful validation in various series of patients. Alternative methods of prognostic classification, such as the Clinical Index of Stable Febrile Neutropenia, have been specifically validated for patients with solid tumors and should replace the MASCC model in situations of clinical uncertainty.

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.

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

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

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

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

Breast Cancer Prognosis for Young Patients.

PubMed

Owrang, Mehdi; Copeland, Robert L; Ricks-Santi, Luisel J; Gaskins, Melvin; Beyene, Desta; Dewitty, Robert L; Kanaan, Yasmine M

2017-01-01

Breast cancer (BCa) prognostication is a vital element for providing effective treatment for patients with BCa. Studies suggest that ethnicity plays a greater role in the incidence and poor prognosis of BCa in younger women than in their older counterparts. Therefore, the goal of this study was to assess the association between age and ethnicity on the overall final prognosis. Nottingham Prognostic Index (NPI) was used to analyze BCa prognosis using Howard University Cancer Center Tumor Registry and the National Cancer Institute's Surveillance, Epidemiology, and End Results BCa datasets. Patients were grouped according to their predicted prognosis based on NPI scheme. There was no correlation between the younger patients compared to their older counterparts for any of the prognostic clusters. The significance of ethnicity in poorer prognosis for younger age is not conclusive either. An extended prognostic tool/system needs to be evaluated for its usefulness in a clinical practice environment. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

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.

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

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

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

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.

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.

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

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

Sheng, Shuangwen

The global wind industry has seen tremendous growth during the past two decades. However, the industry is challenged by premature component failures, which lead to increased turbine downtime and subsequently, cost of energy for wind power. To mitigate the impacts from these failures, the wind industry has been exploring various areas for improvements ranging from product design, new materials or lubricants, to operation and maintenance (O&M) practices. Condition-based maintenance or prognostics and health management (PHM) has been explored as one enabling technology for improving O&M practices. This chapter provides a brief overview of wind turbine PHM with a focus onmore » operational data mining and condition monitoring of drivetrains. Some future research and development opportunities in wind turbine PHM are also briefly discussed.« less

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.

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.

Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer.

PubMed

Smith, Wade P; Kim, Minsun; Holdsworth, Clay; Liao, Jay; Phillips, Mark H

2016-03-11

To build a new treatment planning approach that extends beyond radiation transport and IMRT optimization by modeling the radiation therapy process and prognostic indicators for more outcome-focused decision making. An in-house treatment planning system was modified to include multiobjective inverse planning, a probabilistic outcome model, and a multi-attribute decision aid. A genetic algorithm generated a set of plans embodying trade-offs between the separate objectives. An influence diagram network modeled the radiation therapy process of prostate cancer using expert opinion, results of clinical trials, and published research. A Markov model calculated a quality adjusted life expectancy (QALE), which was the endpoint for ranking plans. The Multiobjective Evolutionary Algorithm (MOEA) was designed to produce an approximation of the Pareto Front representing optimal tradeoffs for IMRT plans. Prognostic information from the dosimetrics of the plans, and from patient-specific clinical variables were combined by the influence diagram. QALEs were calculated for each plan for each set of patient characteristics. Sensitivity analyses were conducted to explore changes in outcomes for variations in patient characteristics and dosimetric variables. The model calculated life expectancies that were in agreement with an independent clinical study. The radiation therapy model proposed has integrated a number of different physical, biological and clinical models into a more comprehensive model. It illustrates a number of the critical aspects of treatment planning that can be improved and represents a more detailed description of the therapy process. A Markov model was implemented to provide a stronger connection between dosimetric variables and clinical outcomes and could provide a practical, quantitative method for making difficult clinical decisions.

Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees.

PubMed

Shi, K-Q; Zhou, Y-Y; Yan, H-D; Li, H; Wu, F-L; Xie, Y-Y; Braddock, M; Lin, X-Y; Zheng, M-H

2017-02-01

At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification. © 2016 John Wiley & Sons Ltd.

Communicating about prognosis: ethical responsibilities of pediatricians and parents.

PubMed

Mack, Jennifer W; Joffe, Steven

2014-02-01

Clinicians are sometimes reluctant to discuss prognosis with parents of children with life-threatening illness, usually because they worry about the emotional impact of this information. However, parents often want this prognostic information because it underpins informed decision-making, especially near the end of life. In addition, despite understandable clinician concerns about its emotional impact, prognostic disclosure can actually support hope and peace of mind among parents struggling to live with a child's illness. Children, too, may need to understand what is ahead to manage uncertainty and make plans for the ways their remaining life will be lived. In this article, we describe the ethical issues involved in disclosure of prognostic information to parents and children with life-threatening illness and offer practical guidance for these conversations.

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.

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

NASA Technical Reports Server (NTRS)

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

2007-01-01

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

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

PubMed

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

2016-07-01

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

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

PubMed

Hattori, Satoshi; Zhou, Xiao-Hua

2016-11-20

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

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

PubMed

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

2017-01-01

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

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

PubMed

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

2011-07-01

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

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.

Prognostic indicators associated with early mortality of wild raptors admitted to a wildlife rehabilitation centre in Spain.

PubMed

Molina-López, Rafael A; Casal, Jordi; Darwich, Laila

2015-03-01

Assessment of the prognostic indicators of wildlife casualties is critical in wildlife rehabilitation practice, to optimize the use of economical resources, and to protect animal welfare. Few studies have been conducted in this field. To identify the prognostic indicators associated with raptor mortality during the first week of hospitalization. Complete medical records of 1722 wild raptor cases admitted to a wildlife rehabilitation centre from 1995 to 2007 were used. Regression models were created to determine mortality-related factors for different variables (order, sex, body condition (BC), clinical signs, and available haematological and biochemical parameters). In the bivariate analysis, the presence of nervous (OR = 11.9, 95%CI:5.1-27.6) or musculoskeletal (OR = 12.1, 95%CI:5.8-25.3) signs, a poor BC (OR = 32.9, 95%CI:19-81.2), and low values of packed cell volume (PCV), haemoglobin or total solids (TS), were all associated with early mortality. After adjusting variables in the multivariate model, BC was excluded due to co-linearity with other variables, and alteration of the nervous system was the only significant risk factor (OR = 4.0; 95%CI:1.9-8.8). In species specific analysis, poor prognosis was related to neurological signs in Athene noctua, poor BC in Strix aluco, trauma in Acciptiter nisus and Tyto alba, low PCV in Buteo buteo and Falco tinnunculus and low TS in Falco tinnunculus. Raptors with a poor BC, low values of PCV and those presenting with neurological signs, had the highest risk of dying in the first days of admittance. Thus, either medical care or humane euthanasia for poor prognosis should be performed to address animal welfare.

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.

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.

Contemporary approach to neurologic prognostication of coma after cardiac arrest.

PubMed

Ben-Hamouda, Nawfel; Taccone, Fabio S; Rossetti, Andrea O; Oddo, Mauro

2014-11-01

Coma after cardiac arrest (CA) is an important cause of admission to the ICU. Prognosis of post-CA coma has significantly improved over the past decade, particularly because of aggressive postresuscitation care and the use of therapeutic targeted temperature management (TTM). TTM and sedatives used to maintain controlled cooling might delay neurologic reflexes and reduce the accuracy of clinical examination. In the early ICU phase, patients' good recovery may often be indistinguishable (based on neurologic examination alone) from patients who eventually will have a poor prognosis. Prognostication of post-CA coma, therefore, has evolved toward a multimodal approach that combines neurologic examination with EEG and evoked potentials. Blood biomarkers (eg, neuron-specific enolase [NSE] and soluble 100-β protein) are useful complements for coma prognostication; however, results vary among commercial laboratory assays, and applying one single cutoff level (eg, > 33 μg/L for NSE) for poor prognostication is not recommended. Neuroimaging, mainly diffusion MRI, is emerging as a promising tool for prognostication, but its precise role needs further study before it can be widely used. This multimodal approach might reduce false-positive rates of poor prognosis, thereby providing optimal prognostication of comatose CA survivors. The aim of this review is to summarize studies and the principal tools presently available for outcome prediction and to describe a practical approach to the multimodal prognostication of coma after CA, with a particular focus on neuromonitoring tools. We also propose an algorithm for the optimal use of such multimodal tools during the early ICU phase of post-CA coma.

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

PubMed

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

2012-10-01

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

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.

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

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.

Gene Expression Analysis Of Circulating Hormone Refractory Prostate Cancer Micrometastases

DTIC Science & Technology

2011-02-01

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

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

DTIC Science & Technology

2012-06-01

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

A Distributed Approach to System-Level Prognostics

DTIC Science & Technology

2012-09-01

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

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

PubMed

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

2013-07-09

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

Mutations with epigenetic effects in myeloproliferative neoplasms and recent progress in treatment: Proceedings from the 5th International Post-ASH Symposium

PubMed Central

Tefferi, A; Abdel-Wahab, O; Cervantes, F; Crispino, J D; Finazzi, G; Girodon, F; Gisslinger, H; Gotlib, J; Kiladjian, J-J; Levine, R L; Licht, J D; Mullally, A; Odenike, O; Pardanani, A; Silver, R T; Solary, E; Mughal, T

2011-01-01

Immediately following the 2010 annual American Society of Hematology (ASH) meeting, the 5th International Post-ASH Symposium on Chronic Myelogenous Leukemia and BCR-ABL1-Negative Myeloproliferative Neoplasms (MPNs) took place on 7–8 December 2010 in Orlando, Florida, USA. During this meeting, the most recent advances in laboratory research and clinical practice, including those that were presented at the 2010 ASH meeting, were discussed among recognized authorities in the field. The current paper summarizes the proceedings of this meeting in BCR-ABL1-negative MPN. We provide a detailed overview of new mutations with putative epigenetic effects (TET oncogene family member 2 (TET2), additional sex comb-like 1 (ASXL1), isocitrate dehydrogenase (IDH) and enhancer of zeste homolog 2 (EZH2)) and an update on treatment with Janus kinase (JAK) inhibitors, pomalidomide, everolimus, interferon-α, midostaurin and cladribine. In addition, the new ‘Dynamic International Prognostic Scoring System (DIPSS)-plus' prognostic model for primary myelofibrosis (PMF) and the clinical relevance of distinguishing essential thrombocythemia from prefibrotic PMF are discussed. PMID:23471017

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

Double positivity for HPV-DNA/p16ink4a is the biomarker with strongest diagnostic accuracy and prognostic value for human papillomavirus related oropharyngeal cancer patients.

PubMed

Mena, Marisa; Taberna, Miren; Tous, Sara; Marquez, Sandra; Clavero, Omar; Quiros, Beatriz; Lloveras, Belen; Alejo, Maria; Leon, Xavier; Quer, Miquel; Bagué, Silvia; Mesia, Ricard; Nogués, Julio; Gomà, Montserrat; Aguila, Anton; Bonfill, Teresa; Blazquez, Carmen; Guix, Marta; Hijano, Rafael; Torres, Montserrat; Holzinger, Dana; Pawlita, Michael; Pavon, Miguel Angel; Bravo, Ignacio G; de Sanjosé, Silvia; Bosch, Francesc Xavier; Alemany, Laia

2018-03-01

The etiologic role of human papillomaviruses (HPV) in oropharyngeal cancer (OPC) is well established. Nevertheless, information on survival differences by anatomic sub-site or treatment remains scarce, and it is still unclear the HPV-relatedness definition with best diagnostic accuracy and prognostic value. We conducted a retrospective cohort study of all patients diagnosed with a primary OPC in four Catalonian hospitals from 1990 to 2013. Formalin-fixed, paraffin-embedded cancer tissues were subjected to histopathological evaluation, DNA quality control, HPV-DNA detection, and p16 INK4a /pRb/p53/Cyclin-D1 immunohistochemistry. HPV-DNA positive and a random sample of HPV-DNA negative cases were subjected to HPV-E6*I mRNA detection. Demographic, tobacco/alcohol use, clinical and follow-up data were collected. Multivariate models were used to evaluate factors associated with HPV positivity as defined by four different HPV-relatedness definitions. Proportional-hazards models were used to compare the risk of death and recurrence among HPV-related and non-related OPC. 788 patients yielded a valid HPV-DNA result. The percentage of positive cases was 10.9%, 10.2%, 8.5% and 7.4% for p16 INK4a , HPV-DNA, HPV-DNA/HPV-E6*I mRNA, and HPV-DNA/p16 INK4a , respectively. Being non-smoker or non-drinker was consistently associated across HPV-relatedness definitions with HPV positivity. A suggestion of survival differences between anatomic sub-sites and treatments was observed. Double positivity for HPV-DNA/p16 INK4a showed strongest diagnostic accuracy and prognostic value. Double positivity for HPV-DNA/p16 INK4a , a test that can be easily implemented in the clinical practice, has optimal diagnostic accuracy and prognostic value. Our results have strong clinical implications for patients' classification and handling and also suggest that not all the HPV-related OPC behave similarly. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Forecasting Brassica rapa: Merging climate models with genotype specific process models for evaluation whole species response to climate change.

NASA Astrophysics Data System (ADS)

Pleban, J. R.; Mackay, D. S.; Ewers, B. E.; Weinig, C.; Guadagno, C. L.

2016-12-01

Human society has modified agriculture management practices and utilized a variety of breeding approaches to adapt to changing environments. Presently a dual pronged challenge has emerged as environmental change is occurring more rapidly while the demand of population growth on food supply is rising. Knowledge of how current agricultural practices will respond to these challenges can be informed through crafted prognostic modeling approaches. Amongst the uncertainties associated with forecasting agricultural production in a changing environment is evaluation of the responses across the existing genotypic diversity of crop species. Mechanistic models of plant productivity provide a means of genotype level parameterization allowing for a prognostic evaluation of varietal performance under changing climate. Brassica rapa represents an excellent species for this type of investigation because of its wide cultivation as well as large morphological and physiological diversity. We incorporated genotypic parameterization of B. rapa genotypes based on unique CO2 assimilation strategies, vulnerabilities to cavitation, and root to leaf area relationships into the TREES model. Three climate drivers, following the "business-as-usual" greenhouse gas emissions scenario (RCP 8.5) from Coupled Model Intercomparison Project, Phase 5 (CMIP5) were considered: temperature (T) along with associated changes in vapor pressure deficit (VPD), increasing CO2, as well as alternatives in irrigation regime across a temporal scale of present day to 2100. Genotypic responses to these drivers were evaluated using net primary productivity (NPP) and percent loss hydraulic conductance (PLC) as a measure of tolerance for a particular watering regime. Genotypic responses to T were witnessed as water demand driven by increases in VPD at 2050 and 2100 drove some genotypes to greater PLC and in a subset of these saw periodic decreases in NPP during a growing season. Genotypes able to withstand the greater water demand showed lower NPP yields relative to hydraulically aggressive genotypes but saw limited PLC. Expansion of this analysis to large recombinant inbred populations may inform breeders in identification of trait combinations needed to meet the coupled challenge of rapid environmental change and increase food demand.

The practice of therapeutic hypothermia after cardiac arrest in France: a national survey.

PubMed

Orban, Jean-Christophe; Cattet, Florian; Lefrant, Jean-Yves; Leone, Marc; Jaber, Samir; Constantin, Jean-Michel; Allaouchiche, Bernard; Ichai, Carole

2012-01-01

Cardiac arrest is a major health concern worldwide accounting for 375,000 cases per year in Europe with a survival rate of <10%. Therapeutic hypothermia has been shown to improve patients' neurological outcome and is recommended by scientific societies. Despite these guidelines, different surveys report a heterogeneous application of this treatment. The aim of the present study was to evaluate the clinical practice of therapeutic hypothermia in cardiac arrest patients. This self-declarative web based survey was proposed to all registered French adult intensive care units (ICUs) (n=357). Paediatrics and neurosurgery ICUs were excluded. The different questions addressed the structure, the practical modalities of therapeutic hypothermia and the use of prognostic factors in patients admitted after cardiac arrest. One hundred and thirty-two out of 357 ICUs (37%) answered the questionnaire. Adherence to recommendations regarding the targeted temperature and hypothermia duration were 98% and 94% respectively. Both guidelines were followed in 92% ICUs. During therapeutic hypothermia, sedative drugs were given in 99% ICUs, mostly midazolam (77%) and sufentanil (59%). Neuromuscular blocking agents (NMBA) were used in 97% ICUs, mainly cisatracurium (77%). Numerous prognostic factors were used after cardiac arrest such as clinical factors (95%), biomarkers (53%), electroencephalography (78%) and evoked potentials (35%). In France, adherence to recommendations for therapeutic hypothermia after cardiac arrest is higher than those previously reported in other countries. Numerous prognostic factors are widely used even if their reliability remains controversial.

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

PubMed

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

1993-12-01

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

Towards Prognostics of Electrolytic Capacitors

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, kai

2007-01-01

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

Hot Spot and Whole-Tumor Enumeration of CD8+ Tumor-Infiltrating Lymphocytes Utilizing Digital Image Analysis Is Prognostic in Triple-Negative Breast Cancer.

PubMed

McIntire, Patrick J; Irshaid, Lina; Liu, Yifang; Chen, Zhengming; Menken, Faith; Nowak, Eugene; Shin, Sandra J; Ginter, Paula S

2018-05-07

CD8 + tumor-infiltrating lymphocytes (TILs) have emerged as a prognostic indicator in triple-negative breast cancer (TNBC). There is debate surrounding the prognostic value of hot spots for CD8 + TIL enumeration. We compared hot spot versus whole-tumor CD8 + TIL enumeration in prognosticating TNBC using immunohistochemistry on whole tissue sections and quantification by digital image analysis (Halo imaging analysis software; Indica Labs, Corrales, NM). A wide range of clinically relevant hot spot sizes was evaluated. CD8 + TIL enumeration was independently statistically significant for all hot spot sizes and whole-tumor annotations for disease-free survival by multivariate analysis. A 10× objective (2.2 mm diameter) hot spot was found to correlate significantly with overall survival (P = .04), while the remaining hot spots and whole-tumor CD8 + TIL enumeration did not (P > .05). Statistical significance was not demonstrated when comparing between hot spots and whole-tumor annotations, as the groups had overlapping confidence intervals. CD8 + TIL hot spot enumeration is equivalent to whole-tumor enumeration for prognostication in TNBC and may serve as a good alternative methodology in future studies and clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

Osteosarcoma: Diagnostic dilemmas in histopathology and prognostic factors

PubMed Central

Wadhwa, Neelam

2014-01-01

Osteosarcoma (OS), the commonest malignancy of osteoarticular origin, is a very aggressive neoplasm. Divergent histologic differentiation is common in OS; hence triple diagnostic approach is essential in all cases. 20% cases are atypical owing to lack of concurrence among clinicoradiologic and pathologic features necessitating resampling. Recognition of specific anatomic and histologic variants is essential in view of better outcome. Traditional prognostic factors of OS do stratify patients for short term outcome, but often fail to predict their long term outcome. Considering the negligible improvement in the patient outcome during the last 20 years, search for novel prognostic factors is in progress like ezrin vascular endothelial growth factor, chemokine receptors, dysregulation of various micro ribonucleic acid are potentially promising. Their utility needs to be validated by long term followup studies before they are incorporated in routine clinical practice. PMID:24932029

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

PubMed Central

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

2016-01-01

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

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

DTIC Science & Technology

2012-09-01

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

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.

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

A model-based economic evaluation of improved primary care management of patients with type 2 diabetes in Australia.

PubMed

Haji Ali Afzali, Hossein; Gray, Jodi; Beilby, Justin; Holton, Christine; Karnon, Jonathan

2013-12-01

There are few studies investigating the economic value of the Australian practice nurse workforce on the management of chronic conditions. This is particularly important in Australia, where the government needs evidence to inform decisions on whether to maintain or redirect current financial incentives that encourage practices to recruit practice nurses. The objective of this study was to estimate the lifetime costs and quality-adjusted life-years (QALYs) associated with two models of practice nurse involvement in clinical-based activities (high and low level) in the management of type 2 diabetes within the primary care setting. A previously validated state transition model (the United Kingdom Prospective Diabetes Study Outcomes Model) was adapted, which uses baseline prognostic factors (e.g. gender, haemoglobin A1c [HbA1c]) to predict the risk of occurrence of diabetes-related complications (e.g. stroke). The model was populated by data from Australian and UK observational studies. Costs and utility values associated with complications were summed over patients' lifetimes to estimate costs and QALY gains from the perspective of the health care system. All costs were expressed in 2011 Australian dollars (AU$). The base-case analysis assumed a 40-year time horizon with an annual discount rate of 5 %. Relative to low-level involvement of practice nurses in the provision of clinical-based activities, the high-level model was associated with lower mean lifetime costs of management of complications (-AU$8,738; 95 % confidence interval [CI] -AU$12,522 to -AU$4,954), and a greater average gain in QALYs (0.3; 95 % CI 0.2-0.4). A range of sensitivity analyses were performed, in which the high-level model was dominant in all cases. Our results suggest that the high-level model is a dominant management strategy over the low-level model in all modelled scenarios. These findings indicate the need for effective primary care-based incentives to encourage general practices not only to employ practice nurses, but to better integrate them into the provision of clinical services.

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.

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

PubMed

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

2018-03-14

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

Cytogenetic prognostication within medulloblastoma subgroups.

PubMed

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

2014-03-20

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

Cytogenetic Prognostication Within Medulloblastoma Subgroups

PubMed Central

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

2014-01-01

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

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

PubMed Central

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

2001-01-01

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

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

PubMed

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

2018-06-01

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

Prognostic factors in canine appendicular osteosarcoma – a meta-analysis

PubMed Central

2012-01-01

Background Appendicular osteosarcoma is the most common malignant primary canine bone tumor. When treated by amputation or tumor removal alone, median survival times (MST) do not exceed 5 months, with the majority of dogs suffering from metastatic disease. This period can be extended with adequate local intervention and adjuvant chemotherapy, which has become common practice. Several prognostic factors have been reported in many different studies, e.g. age, breed, weight, sex, neuter status, location of tumor, serum alkaline phosphatase (SALP), bone alkaline phosphatase (BALP), infection, percentage of bone length affected, histological grade or histological subtype of tumor. Most of these factors are, however, only reported as confounding factors in larger studies. Insight in truly significant prognostic factors at time of diagnosis may contribute to tailoring adjuvant therapy for individual dogs suffering from osteosarcoma. The objective of this study was to systematically review the prognostic factors that are described for canine appendicular osteosarcoma and validate their scientific importance. Results A literature review was performed on selected studies and eligible data were extracted. Meta-analyses were done for two of the three selected possible prognostic factors (SALP and location), looking at both survival time (ST) and disease free interval (DFI). The third factor (age) was studied in a qualitative manner. Both elevated SALP level and the (proximal) humerus as location of the primary tumor are significant negative prognostic factors for both ST and DFI in dogs with appendicular osteosarcoma. Increasing age was associated with shorter ST and DFI, however, was not statistically significant because information of this factor was available in only a limited number of papers. Conclusions Elevated SALP and proximal humeral location are significant negative prognosticators for canine osteosarcoma. PMID:22587466

Prognostic factors in canine appendicular osteosarcoma - a meta-analysis.

PubMed

Boerman, Ilse; Selvarajah, Gayathri T; Nielen, Mirjam; Kirpensteijn, Jolle

2012-05-15

Appendicular osteosarcoma is the most common malignant primary canine bone tumor. When treated by amputation or tumor removal alone, median survival times (MST) do not exceed 5 months, with the majority of dogs suffering from metastatic disease. This period can be extended with adequate local intervention and adjuvant chemotherapy, which has become common practice. Several prognostic factors have been reported in many different studies, e.g. age, breed, weight, sex, neuter status, location of tumor, serum alkaline phosphatase (SALP), bone alkaline phosphatase (BALP), infection, percentage of bone length affected, histological grade or histological subtype of tumor. Most of these factors are, however, only reported as confounding factors in larger studies. Insight in truly significant prognostic factors at time of diagnosis may contribute to tailoring adjuvant therapy for individual dogs suffering from osteosarcoma. The objective of this study was to systematically review the prognostic factors that are described for canine appendicular osteosarcoma and validate their scientific importance. A literature review was performed on selected studies and eligible data were extracted. Meta-analyses were done for two of the three selected possible prognostic factors (SALP and location), looking at both survival time (ST) and disease free interval (DFI). The third factor (age) was studied in a qualitative manner. Both elevated SALP level and the (proximal) humerus as location of the primary tumor are significant negative prognostic factors for both ST and DFI in dogs with appendicular osteosarcoma. Increasing age was associated with shorter ST and DFI, however, was not statistically significant because information of this factor was available in only a limited number of papers. Elevated SALP and proximal humeral location are significant negative prognosticators for canine osteosarcoma.

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

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.

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.

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.

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.

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.

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).

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.

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

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.

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.

[Prognostic scores for pulmonary embolism].

PubMed

Junod, Alain

2016-03-23

Nine prognostic scores for pulmonary embolism (PE), based on retrospective and prospective studies, published between 2000 and 2014, have been analyzed and compared. Most of them aim at identifying PE cases with a low risk to validate their ambulatory care. Important differences in the considered outcomes: global mortality, PE-specific mortality, other complications, sizes of low risk groups, exist between these scores. The most popular score appears to be the PESI and its simplified version. Few good quality studies have tested the applicability of these scores to PE outpatient care, although this approach tends to already generalize in the medical practice.

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.

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.

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.

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.

ATRX, IDH1-R132H and Ki-67 immunohistochemistry as a classification scheme for astrocytic tumors.

PubMed

Cai, Jinquan; Zhang, Chuanbao; Zhang, Wei; Wang, Guangzhi; Yao, Kun; Wang, Zhiliang; Li, Guanzhang; Qian, Zenghui; Li, Yongli; Jiang, Tao; Jiang, Chuanlu

2016-01-01

Recurrence and progression to higher grade lesions are key biological events and characteristic behaviors in the evolution process of glioma. Malignant astrocytic tumors such as glioblastoma (GBM) are the most lethal intracranial tumors. However, the clinical practicability and significance of molecular parameters for the diagnostic and prognostic prediction of astrocytic tumors is still limited. In this study, we detected ATRX, IDH1-R132H and Ki-67 by immunohistochemistry and observed the association of IDH1-R132H with ATRX and Ki-67 expression. There was a strong association between ATRX loss and IDH1-R132H (p<0.0001). However, Ki-67 high expression restricted in the tumors with IDH1-R132H negative (p=0.0129). Patients with IDH1-R132H positive or ATRX loss astrocytic tumors had a longer progressive- free survival (p<0.0001, p=0.0044, respectively). High Ki-67 expression was associated with shorter PFS in patients with astrocytic tumors (p=0.002). Then we characterized three prognostic subgroups of astrocytic tumors (referred to as A1, A2 and A3). The new model demonstrated a remarkable separation of the progression interval in the three molecular subgroups and the distribution of patients' age in the A1-A2-A3 model was also significant different. This model will aid predicting the overall survival and progressive time of astrocytic tumors' patients.

ATRX, IDH1-R132H and Ki-67 immunohistochemistry as a classification scheme for astrocytic tumors

PubMed Central

Zhang, Wei; Wang, Guangzhi; Yao, Kun; Wang, Zhiliang; Li, Guanzhang; Qian, Zenghui; Li, Yongli; Jiang, Tao; Jiang, Chuanlu

2016-01-01

Recurrence and progression to higher grade lesions are key biological events and characteristic behaviors in the evolution process of glioma. Malignant astrocytic tumors such as glioblastoma (GBM) are the most lethal intracranial tumors. However, the clinical practicability and significance of molecular parameters for the diagnostic and prognostic prediction of astrocytic tumors is still limited. In this study, we detected ATRX, IDH1-R132H and Ki-67 by immunohistochemistry and observed the association of IDH1-R132H with ATRX and Ki-67 expression. There was a strong association between ATRX loss and IDH1-R132H (p<0.0001). However, Ki-67 high expression restricted in the tumors with IDH1-R132H negative (p=0.0129). Patients with IDH1-R132H positive or ATRX loss astrocytic tumors had a longer progressive- free survival (p<0.0001, p=0.0044, respectively). High Ki-67 expression was associated with shorter PFS in patients with astrocytic tumors (p=0.002). Then we characterized three prognostic subgroups of astrocytic tumors (referred to as A1, A2 and A3). The new model demonstrated a remarkable separation of the progression interval in the three molecular subgroups and the distribution of patients’ age in the A1-A2-A3 model was also significant different. This model will aid predicting the overall survival and progressive time of astrocytic tumors’ patients. PMID:27713914

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.

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.

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.

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.

[A prognostic model of a cholera epidemic].

PubMed

Boev, B V; Bondarenko, V M; Prokop'eva, N V; San Román, R T; Raygoza-Anaya, M; García de Alba, R

1994-01-01

A new model for the prognostication of cholera epidemic on the territory of a large city is proposed. This model reflects the characteristic feature of contacting infection by sensitive individuals due to the preservation of Vibrio cholerae in their water habitat. The mathematical model of the epidemic quantitatively reflects the processes of the spread of infection by kinetic equations describing the interaction of the streams of infected persons, the causative agents and susceptible persons. The functions and parameters of the model are linked with the distribution of individuals according to the duration of the incubation period and infectious process, as well as the period of asymptomatic carrier state. The computer realization of the model by means of IBM PC/AT made it possible to study the cholera epidemic which took place in Mexico in 1833. The verified model of the cholera epidemic was used for the prognostication of the possible spread of this infection in Guadalajara, taking into account changes in the epidemiological situation and the size of the population, as well as improvements in sanitary and hygienic conditions, in the city.

Evaluation of Simulated Marine Aerosol Production Using the WaveWatchIII Prognostic Wave Model Coupled to the Community Atmosphere Model within the Community Earth System Model

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

Long, M. S.; Keene, William C.; Zhang, J.

2016-11-08

Primary marine aerosol (PMA) is emitted into the atmosphere via breaking wind waves on the ocean surface. Most parameterizations of PMA emissions use 10-meter wind speed as a proxy for wave action. This investigation coupled the 3 rd generation prognostic WAVEWATCH-III wind-wave model within a coupled Earth system model (ESM) to drive PMA production using wave energy dissipation rate – analogous to whitecapping – in place of 10-meter wind speed. The wind speed parameterization did not capture basin-scale variability in relations between wind and wave fields. Overall, the wave parameterization did not improve comparison between simulated versus measured AOD ormore » Na +, thus highlighting large remaining uncertainties in model physics. Results confirm the efficacy of prognostic wind-wave models for air-sea exchange studies coupled with laboratory- and field-based characterizations of the primary physical drivers of PMA production. No discernible correlations were evident between simulated PMA fields and observed chlorophyll or sea surface temperature.« less

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.

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

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.

Time-dependent changes in mortality and transformation risk in MDS

PubMed Central

Tuechler, Heinz; Sanz, Guillermo; Schanz, Julie; Garcia-Manero, Guillermo; Solé, Francesc; Bennett, John M.; Bowen, David; Fenaux, Pierre; Dreyfus, Francois; Kantarjian, Hagop; Kuendgen, Andrea; Malcovati, Luca; Cazzola, Mario; Cermak, Jaroslav; Fonatsch, Christa; Le Beau, Michelle M.; Slovak, Marilyn L.; Levis, Alessandro; Luebbert, Michael; Maciejewski, Jaroslaw; Machherndl-Spandl, Sigrid; Magalhaes, Silvia M. M.; Miyazaki, Yasushi; Sekeres, Mikkael A.; Sperr, Wolfgang R.; Stauder, Reinhard; Tauro, Sudhir; Valent, Peter; Vallespi, Teresa; van de Loosdrecht, Arjan A.; Germing, Ulrich; Haase, Detlef; Greenberg, Peter L.

2016-01-01

In myelodysplastic syndromes (MDSs), the evolution of risk for disease progression or death has not been systematically investigated despite being crucial for correct interpretation of prognostic risk scores. In a multicenter retrospective study, we described changes in risk over time, the consequences for basal prognostic scores, and their potential clinical implications. Major MDS prognostic risk scoring systems and their constituent individual predictors were analyzed in 7212 primary untreated MDS patients from the International Working Group for Prognosis in MDS database. Changes in risk of mortality and of leukemic transformation over time from diagnosis were described. Hazards regarding mortality and acute myeloid leukemia transformation diminished over time from diagnosis in higher-risk MDS patients, whereas they remained stable in lower-risk patients. After approximately 3.5 years, hazards in the separate risk groups became similar and were essentially equivalent after 5 years. This fact led to loss of prognostic power of different scoring systems considered, which was more pronounced for survival. Inclusion of age resulted in increased initial prognostic power for survival and less attenuation in hazards. If needed for practicability in clinical management, the differing development of risks suggested a reasonable division into lower- and higher-risk MDS based on the IPSS-R at a cutoff of 3.5 points. Our data regarding time-dependent performance of prognostic scores reflect the disparate change of risks in MDS subpopulations. Lower-risk patients at diagnosis remain lower risk whereas initially high-risk patients demonstrate decreasing risk over time. This change of risk should be considered in clinical decision making. PMID:27335276

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

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.

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.

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.

Prospective validation of a lymphocyte infiltration prognostic test in stage III colon cancer patients treated with adjuvant FOLFOX.

PubMed

Emile, Jean-François; Julié, Catherine; Le Malicot, Karine; Lepage, Come; Tabernero, Josep; Mini, Enrico; Folprecht, Gunnar; Van Laethem, Jean-Luc; Dimet, Stéphanie; Boulagnon-Rombi, Camille; Allard, Marc-Antoine; Penault-Llorca, Frédérique; Bennouna, Jaafar; Laurent-Puig, Pierre; Taieb, Julien

2017-09-01

The prognostic value of lymphocyte infiltration (LI) of colorectal carcinoma (CC) has been demonstrated by several groups. However, no validated test is currently available for clinical practice. We previously described an automated and reproducible method for testing LI and aimed to validate it for clinical use. According to National Institutes of Health criteria, we designed a prospective validation of this biomarker in patients included in the PETACC8 phase III study. Primary objective was to compare percentage of patients alive and without recurrence at 2 years in patients with high versus low LI (#NCT02364024). Associations of LI with patient recurrence and survival were analysed, and multivariable models were adjusted for treatment and relevant factors. Automated testing of LI was performed on virtual slides without access to clinical data. Among the 1220 CC patients enrolled, LI was high, low and not evaluable in 241 (19.8%), 790 (64.8%) and 189 (15.5%), respectively. Primary objective was met with a 2-year recurrence rate of 14.4% versus 21.1% in patients with high and low LI, respectively (p = 0.02). Patients with high LI also had better disease free survival (DFS) and overall survival (OS). Tumour stage, grade, RAS status and BRAF status were with LI the only prognostic markers in multivariable analysis for OS. Subgroup analyses revealed that high LI had better DFS and OS in mismatch repair (MMR) proficient patients, and in patients without RAS mutation, but not in MMR deficient and RAS mutated patients. Although this is the first validation with high level of evidence (IIB) of the prognostic value of a LI test in colon cancers, it still needs to be confirmed in independent series of colon cancer patients. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prognostic index for chronic- and smoldering-type adult T-cell leukemia-lymphoma.

PubMed

Katsuya, Hiroo; Shimokawa, Mototsugu; Ishitsuka, Kenji; Kawai, Kazuhiro; Amano, Masahiro; Utsunomiya, Atae; Hino, Ryosuke; Hanada, Shuichi; Jo, Tatsuro; Tsukasaki, Kunihiro; Moriuchi, Yukiyoshi; Sueoka, Eisaburo; Yoshida, Shinichiro; Suzushima, Hitoshi; Miyahara, Masaharu; Yamashita, Kiyoshi; Eto, Tetsuya; Suzumiya, Junji; Tamura, Kazuo

2017-07-06

Adult T-cell leukemia-lymphoma (ATL) has been divided into 4 clinical subtypes: acute, lymphoma, chronic, and smoldering. The aim of this study is to develop a novel prognostic index (PI) for chronic and smoldering ATL. We conducted a nationwide retrospective survey on ATL patients, and 248 fully eligible individuals were used in this analysis. In the univariate analysis, sex, performance status, log 10 (soluble interleukin-2 receptor [sIL-2R]), neutrophils count, and lymphadenopathy showed values of P < .05 in training samples. A multivariate analysis was performed on these factors, and only log 10 (sIL-2R) was identified as an independent prognostic factor in training samples. Using a regression coefficient of this variable, a prognostic model was formulated to identify different levels of risk: indolent ATL-PI (iATL-PI) = 1.51 × log 10 (sIL-2R [U/mL]). The values calculated by iATL-PI were divided into 3 groups using a quartile point. In the validation sample, median survival times (MSTs) were 1.6 years, 5.5 years, and not reached for patients in the high-, intermediate-, and low-risk groups, respectively ( P < .0001). To make the scoring system clinically practicable, we simplified iATL-PI according to trichotomizing sIL-2R at 1000 and 6000 U/mL, using a quartile point. Patients with more than 6000 U/mL sIL-2R were categorized into the high-risk group, less than and equal to 1000 U/mL into the low-risk group, and the others into the intermediate-risk group, and MSTs were 1.6 years, not reached, and 5.5 years, respectively ( P < .0001). iATL-PI has potential as a novel tool for a risk-adapted therapeutic approach. © 2017 by The American Society of Hematology.

The Hijdra scale has significant prognostic value for the functional outcome of Fisher grade 3 patients with subarachnoid hemorrhage.

PubMed

Bretz, Julia S; Von Dincklage, Falk; Woitzik, Johannes; Winkler, Maren K L; Major, Sebastian; Dreier, Jens P; Bohner, Georg; Scheel, Michael

2017-09-01

Despite its high prevalence among patients with aneurysmal subarachnoid hemorrhage (aSAH) and high risk of delayed cerebral ischemia (DCI), the Fisher grade 3 category remains a poorly studied subgroup. The aim of this cohort study has been to investigate the prognostic value of the Hijdra sum scoring system for the functional outcome in patients with Fisher grade 3 aSAH, in order to improve the risk stratification within this Fisher category. Initial CT scans of 72 prospectively enrolled patients with Fisher grade 3 aSAH were analyzed, and cisternal, ventricular, and total amount of blood were graded according to the Hijdra scale. Additionally, space-occupying subarachnoid blood clots were assessed. Outcome was evaluated after 6 months. Within the subgroup of Fisher grade 3, aSAH patients with an unfavorable outcome showed a significantly larger cisternal Hijdra sum score (HSS: 21.1 ± 5.2) than patients with a favorable outcome (HSS: 17.6 ± 5.9; p = 0.009). However, both the amount of ventricular blood (p = 0.165) and space-occupying blood clots (p = 0.206) appeared to have no prognostic relevance. After adjusting for the patient's age, gender, tobacco use, clinical status at admission, and presence of intracerebral hemorrhage, the cisternal and total HSS remained the only independent parameters included in multivariate logistic regression models to predict functional outcome (p < 0.01). The cisternal Hijdra score is fairly easy to perform and the present study indicates that it has an additional predictive value for the functional outcome within the Fisher 3 category. We suggest that the Hijdra scale is a practically useful prognostic instrument for the risk evaluation after aSAH and should be applied more often in the clinical setting.

Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.

PubMed

Qin, Li-Xuan; Levine, Douglas A

2016-06-10

Accurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task, partly due to the combination of the typically weak genomic signal for a clinical outcome and the frequently strong noise due to microarray handling effects. Effective strategies to resolve this challenge are in dire need. We set out to assess the use of careful study design and data normalization for the discovery of prognostic molecular biomarkers. Taking progression free survival in advanced serous ovarian cancer as an example, we conducted empirical analysis on two sets of microRNA arrays for the same set of tumor samples: arrays in one set were collected using careful study design (that is, uniform handling and randomized array-to-sample assignment) and arrays in the other set were not. We found that (1) handling effects can confound the clinical outcome under study as a result of chance even with randomization, (2) the level of confounding handling effects can be reduced by data normalization, and (3) good study design cannot be replaced by post-hoc normalization. In addition, we provided a practical approach to define positive and negative control markers for detecting handling effects and assessing the performance of a normalization method. Our work showcased the difficulty of finding prognostic biomarkers for a clinical outcome of weak genomic signals, illustrated the benefits of careful study design and data normalization, and provided a practical approach to identify handling effects and select a beneficial normalization method. Our work calls for careful study design and data analysis for the discovery of robust and translatable molecular biomarkers.

Moderate efficiency of clinicians' predictions decreased for blurred clinical conditions and benefits from the use of BRASS index. A longitudinal study on geriatric patients' outcomes.

PubMed

Signorini, Giulia; Dagani, Jessica; Bulgari, Viola; Ferrari, Clarissa; de Girolamo, Giovanni

2016-01-01

Accurate prognosis is an essential aspect of good clinical practice and efficient health services, particularly for chronic and disabling diseases, as in geriatric populations. This study aims to examine the accuracy of clinical prognostic predictions and to devise prediction models combining clinical variables and clinicians' prognosis for a geriatric patient sample. In a sample of 329 consecutive older patients admitted to 10 geriatric units, we evaluated the accuracy of clinicians' prognosis regarding three outcomes at discharge: global functioning, length of stay (LoS) in hospital, and destination at discharge (DD). A comprehensive set of sociodemographic, clinical, and treatment-related information were also collected. Moderate predictive performance was found for all three outcomes: area under receiver operating characteristic curve of 0.79 and 0.78 for functioning and LoS, respectively, and moderate concordance, Cohen's K = 0.45, between predicted and observed DD. Predictive models found the Blaylock Risk Assessment Screening Score together with clinicians' judgment relevant to improve predictions for all outcomes (absolute improvement in adjusted and pseudo-R(2) up to 19%). Although the clinicians' estimates were important factors in predicting global functioning, LoS, and DD, more research is needed regarding both methodological aspects and clinical measurements, to improve prognostic clinical indices. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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

Prognostic value of serum heavy/light chain ratios in patients with POEMS syndrome.

PubMed

Wang, Chen; Su, Wei; Cai, Qian-Qian; Cai, Hao; Ji, Wei; Di, Qian; Duan, Ming-Hui; Cao, Xin-Xin; Zhou, Dao-Bin; Li, Jian

2016-07-01

POEMS syndrome is a rare plasma cell dyscrasia. Serum concentrations of the monoclonal protein in this disorder are typically low, and inapplicable to monitor disease activity in most cases, resulting in limited practical and prognostic values. Novel immunoassays measuring isotype-specific heavy/light chain (HLC) pairs showed its utility in disease monitoring and outcome prediction in several plasma cell dyscrasias. We report results of HLC measurements in 90 patients with POEMS syndrome. Sixty-six patients (73%; 95% confidence interval, 63-82%) had an abnormal HLC ratio at baseline. It could stratify the risk of disease relapse and was strongly associated with worse progression-free survival in a multivariate analysis (P = 0.021; hazard ratio [HR] 6.89, 95% CI 1.34-35.43). After therapy, HLC ratios improved, with 43 patients (48%) remaining abnormal. The post-therapeutic HLC ratio, if abnormal, also remained as an independent prognostic factor associated with worse progression-free survival (P = 0.019; HR 4.30, 95% CI 1.27-14.56). These results suggest the prognostic utility of HLC ratios in clinical management of POEMS patients. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The evolving field of prognostication and risk stratification in MDS: Recent developments and future directions.

PubMed

Lee, Eun-Ju; Podoltsev, Nikolai; Gore, Steven D; Zeidan, Amer M

2016-01-01

The clinical course of patients with myelodysplastic syndromes (MDS) is characterized by wide variability reflecting the underlying genetic and biological heterogeneity of the disease. Accurate prediction of outcomes for individual patients is an integral part of the evidence-based risk/benefit calculations that are necessary for tailoring the aggressiveness of therapeutic interventions. While several prognostication tools have been developed and validated for risk stratification, each of these systems has limitations. The recent progress in genomic sequencing techniques has led to discoveries of recurrent molecular mutations in MDS patients with independent impact on relevant clinical outcomes. Reliable assays of these mutations have already entered the clinic and efforts are currently ongoing to formally incorporate mutational analysis into the existing clinicopathologic risk stratification tools. Additionally, mutational analysis holds promise for going beyond prognostication to therapeutic selection and individualized treatment-specific prediction of outcomes; abilities that would revolutionize MDS patient care. Despite these exciting developments, the best way of incorporating molecular testing for use in prognostication and prediction of outcomes in clinical practice remains undefined and further research is warranted. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

PubMed Central

Shedden, Kerby; Taylor, Jeremy M.G.; Enkemann, Steve A.; Tsao, Ming S.; Yeatman, Timothy J.; Gerald, William L.; Eschrich, Steve; Jurisica, Igor; Venkatraman, Seshan E.; Meyerson, Matthew; Kuick, Rork; Dobbin, Kevin K.; Lively, Tracy; Jacobson, James W.; Beer, David G.; Giordano, Thomas J.; Misek, David E.; Chang, Andrew C.; Zhu, Chang Qi; Strumpf, Dan; Hanash, Samir; Shepherd, Francis A.; Ding, Kuyue; Seymour, Lesley; Naoki, Katsuhiko; Pennell, Nathan; Weir, Barbara; Verhaak, Roel; Ladd-Acosta, Christine; Golub, Todd; Gruidl, Mike; Szoke, Janos; Zakowski, Maureen; Rusch, Valerie; Kris, Mark; Viale, Agnes; Motoi, Noriko; Travis, William; Sharma, Anupama

2009-01-01

Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. PMID:18641660

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

PubMed

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

A comparative analysis of prognostic factor models for follicular lymphoma based on a phase III trial of CHOP-rituximab versus CHOP + 131iodine--tositumomab.

PubMed

Press, Oliver W; Unger, Joseph M; Rimsza, Lisa M; Friedberg, Jonathan W; LeBlanc, Michael; Czuczman, Myron S; Kaminski, Mark; Braziel, Rita M; Spier, Catherine; Gopal, Ajay K; Maloney, David G; Cheson, Bruce D; Dakhil, Shaker R; Miller, Thomas P; Fisher, Richard I

2013-12-01

There is currently no consensus on optimal frontline therapy for patients with follicular lymphoma. We analyzed a phase III randomized intergroup trial comparing six cycles of CHOP-R (cyclophosphamide-Adriamycin-vincristine-prednisone (Oncovin)-rituximab) with six cycles of CHOP followed by iodine-131 tositumomab radioimmunotherapy (RIT) to assess whether any subsets benefited more from one treatment or the other, and to compare three prognostic models. We conducted univariate and multivariate Cox regression analyses of 532 patients enrolled on this trial and compared the prognostic value of the FLIPI (follicular lymphoma international prognostic index), FLIPI2, and LDH + β2M (lactate dehydrogenase + β2-microglobulin) models. Outcomes were excellent, but not statistically different between the two study arms [5-year progression-free survival (PFS) of 60% with CHOP-R and 66% with CHOP-RIT (P = 0.11); 5-year overall survival (OS) of 92% with CHOP-R and 86% with CHOP-RIT (P = 0.08); overall response rate of 84% for both arms]. The only factor found to potentially predict the impact of treatment was serum β2M; among patients with normal β2M, CHOP-RIT patients had better PFS compared with CHOP-R patients, whereas among patients with high serum β2M, PFS by arm was similar (interaction P value = 0.02). All three prognostic models (FLIPI, FLIPI2, and LDH + β2M) predicted both PFS and OS well, though the LDH + β2M model is easiest to apply and identified an especially poor risk subset. In an exploratory analysis using the latter model, there was a statistically significant trend suggesting that low-risk patients had superior observed PFS if treated with CHOP-RIT, whereas high-risk patients had a better PFS with CHOP-R. ©2013 AACR.

Setting the vision: applied patient-reported outcomes and smart, connected digital healthcare systems to improve patient-centered outcomes prediction in critical illness.

PubMed

Wysham, Nicholas G; Abernethy, Amy P; Cox, Christopher E

2014-10-01

Prediction models in critical illness are generally limited to short-term mortality and uncommonly include patient-centered outcomes. Current outcome prediction tools are also insensitive to individual context or evolution in healthcare practice, potentially limiting their value over time. Improved prognostication of patient-centered outcomes in critical illness could enhance decision-making quality in the ICU. Patient-reported outcomes have emerged as precise methodological measures of patient-centered variables and have been successfully employed using diverse platforms and technologies, enhancing the value of research in critical illness survivorship and in direct patient care. The learning health system is an emerging ideal characterized by integration of multiple data sources into a smart and interconnected health information technology infrastructure with the goal of rapidly optimizing patient care. We propose a vision of a smart, interconnected learning health system with integrated electronic patient-reported outcomes to optimize patient-centered care, including critical care outcome prediction. A learning health system infrastructure integrating electronic patient-reported outcomes may aid in the management of critical illness-associated conditions and yield tools to improve prognostication of patient-centered outcomes in critical illness.

Pretreatment Dysphagia Inventory and videofluorographic swallowing study as prognostic indicators of early survival outcomes in head and neck cancer.

PubMed

Yang, Chan Joo; Roh, Jong-Lyel; Choi, Kyoung Hyo; Kim, Min-Ju; Choi, Seung-Ho; Nam, Soon Yuhl; Kim, Sang Yoon

2015-05-15

The prognostic role of swallowing-related, pretreatment subjective and objective findings has not been investigated in detail. The authors evaluated the association between pretreatment MD Anderson Dysphagia Inventory (MDADI) or videofluorographic swallowing study (VFSS) results and standard outcomes, including early recurrence and survival, in patients with treatment-naïve head and neck squamous cell carcinoma (HNSCC). Patients with HNSCC (n = 191) who received treatment at the authors' institution and were examined by self-administered MDADI questionnaires and VFSS were prospectively enrolled. MDADI and VFSS findings were analyzed in correlation with clinicopathologic variables, and factors that predicted 2-year disease-free survival (DFS) and overall survival (OS) were identified using a Cox proportional-hazards regression model. The 2-year OS and DFS rates were 80.1% and 77.5%, respectively. Clinical tumor (T) and lymph node (N) classifications, overall TNM stage, sex, tumor site, and educational level were significantly associated with specific MDADI subdomains, whereas Karnofsky performance score was significantly associated with all MDADI subdomains. After controlling for clinical factors, total scores, global assessment scores, and emotional and physical MDADI subscores were significantly predictive of 2-year OS and DFS (P < .05 for each). VFSS findings were not significantly associated with survival (P > .05). The current results provide evidence of the prognostic role of the MDADI in predicting early survival outcomes in patients with HNSCC. The MDADI may be a practical and noninvasive method for the identification of patients at risk who would benefit from close follow-up. © 2015 American Cancer Society.

Physics Based Electrolytic Capacitor Degradation Models for Prognostic Studies under Thermal Overstress

NASA Technical Reports Server (NTRS)

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

2012-01-01

Electrolytic capacitors are used in several applications ranging from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining useful life of components or systems based on their current state of health and their anticipated future use and operational conditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress conditions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

Retrospective cohort study of prognostic factors in patients with oral cavity and oropharyngeal squamous cell carcinoma.

PubMed

Carrillo, José F; Carrillo, Liliana C; Cano, Ana; Ramirez-Ortega, Margarita C; Chanona, Jorge G; Avilés, Alejandro; Herrera-Goepfert, Roberto; Corona-Rivera, Jaime; Ochoa-Carrillo, Francisco J; Oñate-Ocaña, Luis F

2016-04-01

Prognostic factors in oral cavity and oropharyngeal squamous cell carcinoma (SCC) are debated. The purpose of this study was to investigate the association of prognostic factors with oncologic outcomes. Patients with oral cavity and oropharyngeal SCC treated from 1997 to 2012 were included in this retrospective cohort study. Associations of prognostic factors with locoregional recurrence (LRR) or overall survival (OS) were analyzed using the logistic regression and the Cox models. Six hundred thirty-four patients were included in this study; tumor size, surgical margins, and N classification were associated with LRR (p < .0001); considering histopathology: perineural invasion, lymphocytic infiltration, infiltrative borders, and N classification were significant determinants of LRR. Tumor size, N classification, alcoholism, and surgical margins were associated with OS (p < .0001); considering pathologic prognostic factors, perivascular invasion, islands borders, and surgical margins were independently associated with OS (p < .0001). Surgical margins, perineural and perivascular invasion, lymphocytic infiltration, and infiltrative patterns of tumor invasion are significant prognostic factors in oral cavity and oropharyngeal SCC. © 2015 Wiley Periodicals, Inc.

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.

Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma.

PubMed

Giridhar, Karthik V; Sosa, Carlos P; Hillman, David W; Sanhueza, Cristobal; Dalpiaz, Candace L; Costello, Brian A; Quevedo, Fernando J; Pitot, Henry C; Dronca, Roxana S; Ertz, Donna; Cheville, John C; Donkena, Krishna Vanaja; Kohli, Manish

2017-11-03

The Memorial Sloan Kettering Cancer Center (MSKCC) prognostic score is based on clinical parameters. We analyzed whole blood mRNA expression in metastatic clear cell renal cell carcinoma (mCCRCC) patients and compared it to the MSKCC score for predicting overall survival. In a discovery set of 19 patients with mRCC, we performed whole transcriptome RNA sequencing and selected eighteen candidate genes for further evaluation based on associations with overall survival and statistical significance. In an independent validation of set of 47 patients with mCCRCC, transcript expression of the 18 candidate genes were quantified using a customized NanoString probeset. Cox regression multivariate analysis confirmed that two of the candidate genes were significantly associated with overall survival. Higher expression of BAG1 [hazard ratio (HR) of 0.14, p < 0.0001, 95% confidence interval (CI) 0.04-0.36] and NOP56 (HR 0.13, p < 0.0001, 95% CI 0.05-0.34) were associated with better prognosis. A prognostic model incorporating expression of BAG1 and NOP56 into the MSKCC score improved prognostication significantly over a model using the MSKCC prognostic score only ( p < 0.0001). Prognostic value of using whole blood mRNA gene profiling in mCCRCC is feasible and should be prospectively confirmed in larger studies.

Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma

PubMed Central

Sosa, Carlos P.; Hillman, David W.; Sanhueza, Cristobal; Dalpiaz, Candace L.; Costello, Brian A.; Quevedo, Fernando J.; Pitot, Henry C.; Dronca, Roxana S.; Ertz, Donna; Cheville, John C.; Donkena, Krishna Vanaja; Kohli, Manish

2017-01-01

The Memorial Sloan Kettering Cancer Center (MSKCC) prognostic score is based on clinical parameters. We analyzed whole blood mRNA expression in metastatic clear cell renal cell carcinoma (mCCRCC) patients and compared it to the MSKCC score for predicting overall survival. In a discovery set of 19 patients with mRCC, we performed whole transcriptome RNA sequencing and selected eighteen candidate genes for further evaluation based on associations with overall survival and statistical significance. In an independent validation of set of 47 patients with mCCRCC, transcript expression of the 18 candidate genes were quantified using a customized NanoString probeset. Cox regression multivariate analysis confirmed that two of the candidate genes were significantly associated with overall survival. Higher expression of BAG1 [hazard ratio (HR) of 0.14, p < 0.0001, 95% confidence interval (CI) 0.04–0.36] and NOP56 (HR 0.13, p < 0.0001, 95% CI 0.05–0.34) were associated with better prognosis. A prognostic model incorporating expression of BAG1 and NOP56 into the MSKCC score improved prognostication significantly over a model using the MSKCC prognostic score only (p < 0.0001). Prognostic value of using whole blood mRNA gene profiling in mCCRCC is feasible and should be prospectively confirmed in larger studies. PMID:29099775

Big genomics and clinical data analytics strategies for precision cancer prognosis.

PubMed

Ow, Ghim Siong; Kuznetsov, Vladimir A

2016-11-07

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

Hand therapist management of the lateral epicondylosis: a survey of expert opinion and practice patterns.

PubMed

MacDermid, Joy C; Wojkowski, Sarah; Kargus, Cristin; Marley, Meghan; Stevenson, Emily

2010-01-01

Lateral epicondylosis (LE) is a common condition. Knowledge on practice patterns underlies identification of knowledge to practice gaps. The purpose was to determine the practice patterns and beliefs of hand therapists in managing LE. The study design used was a descriptive survey. A survey of Certified Hand Therapists and members of the American Society of Hand Therapists was conducted (n=693). Questions were framed around frequency and perceived effectiveness of interventions, examination techniques, outcome measures, and prognostic factors. More than 80% of therapists use education/activity modification, home exercise, LE orthoses, and stretching for both the acute and chronic LE. Therapists perceive education, orthoses and home exercise are the most effective for acute cases, whereas in chronic cases, orthoses dropped to ninth in ranked perceived effectiveness. Grip strength (80%) and numeric pain rating (71%) were the most commonly used outcome measures. Most (>70%) therapists perceived occupation and duration of symptoms are prognostic in terms of resolution of symptoms, whereas compliance with exercise (78%) and work factors are important for return to work. Therapists rely on impairment measures to evaluate hand therapy outcomes in patients with LE. Hand therapists are aligned with a number of recommendations from the available systematic reviews, although the use of outcome measures and optimal definition of education and exercise exhibit evidence to practice gaps. Level 5. Copyright (c) 2010 Hanley & Belfus. Published by Elsevier Inc. All rights reserved.

Validation of the Complexity INdex in SARComas prognostic signature on formalin-fixed, paraffin-embedded, soft tissue sarcomas.

PubMed

Le Guellec, S; Lesluyes, T; Sarot, E; Valle, C; Filleron, T; Rochaix, P; Valentin, T; Pérot, G; Coindre, J-M; Chibon, F

2018-05-31

Prediction of metastatic outcome in sarcomas is challenging for clinical management since they are aggressive and carry a high metastatic risk. A 67-gene expression signature, the Complexity INdex in SARComas (CINSARC), has been identified as a better prognostic factor than the reference pathological grade. Since it cannot be applied easily in standard laboratory practice, we assessed its prognostic value using nanoString on formalin-fixed, paraffin-embedded (FFPE) blocks to evaluate its potential in clinical routine practice and guided therapeutic management. A code set consisting of 67 probes derived from the 67 genes of the CINSARC signature was built and named NanoCind®. To compare the performance of RNA-seq and nanoString (NanoCind®), we used expressions of various sarcomas (n=124, frozen samples) using both techniques and compared predictive values based on CINSARC risk groups and clinical annotations. We also used nanoString on FFPE blocks (n=67) and matching frozen and FFPE samples (n=45) to compare their level of agreement. Metastasis-free survival and agreement values in classification groups were evaluated. CINSARC strongly predicted metastatic outcome using nanoString on frozen samples (HR = 2.9, 95% CI 1.23-6.82) with similar risk-group classifications (86%). While more than 50% of FFPE blocks were not analyzable by RNA-seq owing to poor RNA quality, all samples were analyzable with nanoString. When similar (risk-group) classifications were measured with frozen tumors (RNA-seq) compared to FFPE blocks (84% agreement), the CINSARC signature was still a predictive factor of metastatic outcome with nanoString on FFPE samples (HR = 4.43, 95% CI 1.25-15.72). CINSARC is a material-independent prognostic signature for metastatic outcome in sarcomas and outperforms histological grade. Unlike RNA-seq, nanoString is not influenced by the poor quality of RNA extracted from FFPE blocks. The CINSARC signature can potentially be used in combination with nanoString (NanoCind®) in routine clinical practice on FFPE blocks to predict metastatic outcome.

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.

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.

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.

Real-World Data on Prognostic Factors for Overall Survival in EGFR Mutation-Positive Advanced Non-Small Cell Lung Cancer Patients Treated with First-Line Gefitinib.

PubMed

Yao, Zong-Han; Liao, Wei-Yu; Ho, Chao-Chi; Chen, Kuan-Yu; Shih, Jin-Yuan; Chen, Jin-Shing; Lin, Zhong-Zhe; Lin, Chia-Chi; Chih-Hsin Yang, James; Yu, Chong-Jen

2017-09-01

This study aimed to identify independent prognostic factors for overall survival (OS) of patients with advanced non-small cell lung cancer (NSCLC) harboring an activating epidermal growth factor receptor (EGFR) mutation and receiving gefitinib as first-line treatment in real-world practice. We enrolled 226 patients from June 2011 to May 2013. During this period, gefitinib was the only EGFR-tyrosine kinase inhibitor reimbursed by the Bureau of National Health Insurance of Taiwan. The median progression-free survival and median OS were 11.9 months (95% confidence interval [CI]: 9.7-14.2) and 26.9 months (21.2-32.5), respectively. The Cox proportional hazards regression model revealed that postoperative recurrence, performance status (Eastern Cooperative Oncology Grade [ECOG] ≥2), smoking index (≥20 pack-years), liver metastasis at initial diagnosis, and chronic hepatitis C virus (HCV) infection were independent prognostic factors for OS (hazard ratio [95% CI] 0.3 [0.11-0.83], p  = .02; 2.69 [1.60-4.51], p  < .001; 1.92 [1.24-2.97], p  = .003; 2.26 [1.34-3.82], p  = .002; 3.38 [1.85-7.78], p  < .001, respectively). However, brain metastasis (BM) at initial diagnosis or intracranial progression during gefitinib treatment had no impact on OS (1.266 [0.83-1.93], p  = .275 and 0.75 [0.48-1.19], p  = .211, respectively). HCV infection, performance status (ECOG ≥2), newly diagnosed advanced NSCLC without prior operation, and liver metastasis predicted poor OS in EGFR mutation-positive advanced NSCLC patients treated with first-line gefitinib; however, neither BM at initial diagnosis nor intracranial progression during gefitinib treatment had an impact on OS. The finding that chronic hepatitis C virus (HCV) infection might predict poor overall survival (OS) in epidermal growth factor receptor mutation-positive advanced non-small cell lung cancer (NSCLC) patients treated with first-line gefitinib may raise awareness of benefit from anti-HCV treatment in this patient population. Brain metastasis in the initial diagnosis or intracranial progression during gefitinib treatment is not a prognostic factor for OS. This study, which enrolled a real-world population of NSCLC patients, including sicker patients who were not eligible for a clinical trial, may have impact on guiding usual clinical practice. © AlphaMed Press 2017.

The Interval to Biochemical Failure Is Prognostic for Metastasis, Prostate Cancer-Specific Mortality, and Overall Mortality After Salvage Radiation Therapy for Prostate Cancer

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

Johnson, Skyler, E-mail: Skylerjohn3101@gmail.com; Jackson, William; Li, Darren

2013-07-01

Purpose: To investigate the utility of the interval to biochemical failure (IBF) after salvage radiation therapy (SRT) after radical prostatectomy (RP) for prostate cancer as a surrogate endpoint for distant metastasis (DM), prostate cancer-specific mortality (PCSM), and overall mortality (OM). Methods and Materials: A retrospective analysis of 575 patients treated with SRT after RP from a single institution. Of those, 250 patients experienced biochemical failure (BF), with the IBF defined as the time from commencement of SRT to BF. The IBF was evaluated by Kaplan-Meier and Cox proportional hazards models for its association with DM, PCSM, and OM. Results: Themore » median follow-up time was 85 (interquartile range [IQR] 49.8-121.1) months, with a median IBF of 16.8 (IQR, 8.5-37.1) months. With a cutoff time of 18 months, as previously used, 129 (52%) of patients had IBF ≤18 months. There were no differences among any clinical or pathologic features between those with IBF ≤ and those with IBF >18 months. On log–rank analysis, IBF ≤18 months was prognostic for increased DM (P<.0001, HR 4.9, 95% CI 3.2-7.4), PCSM (P<.0001, HR 4.1, 95% CI 2.4-7.1), and OM (P<.0001, HR 2.7, 95% CI 1.7-4.1). Cox proportional hazards models with adjustment for other clinical variables demonstrated that IBF was independently prognostic for DM (P<.001, HR 4.9), PCSM (P<.0001, HR 4.0), and OM (P<.0001, HR 2.7). IBF showed minimal change in performance regardless of androgen deprivation therapy (ADT) use. Conclusion: After SRT, a short IBF can be used for early identification of patients who are most likely to experience progression to DM, PCSM, and OM. IBF ≤18 months may be useful in clinical practice or as an endpoint for clinical trials.« less

Stratification by interferon-γ release assay level predicts risk of incident TB.

PubMed

Winje, Brita Askeland; White, Richard; Syre, Heidi; Skutlaberg, Dag Harald; Oftung, Fredrik; Mengshoel, Anne Torunn; Blix, Hege Salvesen; Brantsæter, Arne Broch; Holter, Ellen Kristine; Handal, Nina; Simonsen, Gunnar Skov; Afset, Jan Egil; Bakken Kran, Anne Marte

2018-04-05

Targeted testing and treatment of latent TB infection (LTBI) are priorities on the global health agenda, but LTBI management remains challenging. We aimed to evaluate the prognostic value of the QuantiFERON TB-Gold (QFT) test for incident TB, focusing on the interferon (IFN)-γ level, when applied in routine practice in a low TB incidence setting. In this large population-based prospective cohort, we linked QFT results in Norway (1 January 2009-30 June 2014) with national registry data (Norwegian Surveillance System for Infectious Diseases, Norwegian Prescription Database, Norwegian Patient Registry and Statistics Norway) to assess the prognostic value of QFT for incident TB. Participants were followed until 30 June 2016. We used restricted cubic splines to model non-linear relationships between IFN-γ levels and TB, and applied these findings to a competing risk model. The prospective analyses included 50 389 QFT results from 44 875 individuals, of whom 257 developed TB. Overall, 22% (n=9878) of QFT results were positive. TB risk increased with the IFN-γ level until a plateau level, above which further increase was not associated with additional prognostic information. The HRs for TB were 8.8 (95% CI 4.7 to 16.5), 19.2 (95% CI 11.6 to 31.6) and 31.3 (95% CI 19.8 to 49.5) times higher with IFN-γ levels of 0.35 to <1.00, 1.00 to <4.00 and >4.00 IU/mL, respectively, compared with negative tests (<0.35 IU/mL). Consistently, QFT demonstrates increased risk of incident TB with rising IFN-γ concentrations, indicating that IFN-γ levels may be used to guide targeted treatment of LTBI. © 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.

Quantifying risk of early relapse in patients with first demyelinating events: Prediction in clinical practice.

PubMed

Spelman, Tim; Meyniel, Claire; Rojas, Juan Ignacio; Lugaresi, Alessandra; Izquierdo, Guillermo; Grand'Maison, Francois; Boz, Cavit; Alroughani, Raed; Havrdova, Eva; Horakova, Dana; Iuliano, Gerardo; Duquette, Pierre; Terzi, Murat; Grammond, Pierre; Hupperts, Raymond; Lechner-Scott, Jeannette; Oreja-Guevara, Celia; Pucci, Eugenio; Verheul, Freek; Fiol, Marcela; Van Pesch, Vincent; Cristiano, Edgardo; Petersen, Thor; Moore, Fraser; Kalincik, Tomas; Jokubaitis, Vilija; Trojano, Maria; Butzkueven, Helmut

2017-09-01

Characteristics at clinically isolated syndrome (CIS) examination assist in identification of patient at highest risk of early second attack and could benefit the most from early disease-modifying drugs (DMDs). To examine determinants of second attack and validate a prognostic nomogram for individualised risk assessment of clinical conversion. Patients with CIS were prospectively followed up in the MSBase Incident Study. Predictors of clinical conversion were analysed using Cox proportional hazards regression. Prognostic nomograms were derived to calculate conversion probability and validated using concordance indices. A total of 3296 patients from 50 clinics in 22 countries were followed up for a median (inter-quartile range (IQR)) of 1.92 years (0.90, 3.71). In all, 1953 (59.3%) patients recorded a second attack. Higher Expanded Disability Status Scale (EDSS) at baseline, first symptom location, oligoclonal bands and various brain and spinal magnetic resonance imaging (MRI) metrics were all predictors of conversion. Conversely, older age and DMD exposure post-CIS were associated with reduced rates. Prognostic nomograms demonstrated high concordance between estimated and observed conversion probabilities. This multinational study shows that age at CIS onset, DMD exposure, EDSS, multiple brain and spinal MRI criteria and oligoclonal bands are associated with shorter time to relapse. Nomogram assessment may be useful in clinical practice for estimating future clinical conversion.

Three-Gene Immunohistochemical Panel Adds to Clinical Staging Algorithms to Predict Prognosis for Patients With Esophageal Adenocarcinoma

PubMed Central

Ong, Chin-Ann J.; Shapiro, Joel; Nason, Katie S.; Davison, Jon M.; Liu, Xinxue; Ross-Innes, Caryn; O'Donovan, Maria; Dinjens, Winand N.M.; Biermann, Katharina; Shannon, Nicholas; Worster, Susannah; Schulz, Laura K.E.; Luketich, James D.; Wijnhoven, Bas P.L.; Hardwick, Richard H.; Fitzgerald, Rebecca C.

2013-01-01

Purpose Esophageal adenocarcinoma (EAC) is a highly aggressive disease with poor long-term survival. Despite growing knowledge of its biology, no molecular biomarkers are currently used in routine clinical practice to determine prognosis or aid clinical decision making. Hence, this study set out to identify and validate a small, clinically applicable immunohistochemistry (IHC) panel for prognostication in patients with EAC. Patients and Methods We recently identified eight molecular prognostic biomarkers using two different genomic platforms. IHC scores of these biomarkers from a UK multicenter cohort (N = 374) were used in univariate Cox regression analysis to determine the smallest biomarker panel with the greatest prognostic power with potential therapeutic relevance. This new panel was validated in two independent cohorts of patients with EAC who had undergone curative esophagectomy from the United States and Europe (N = 666). Results Three of the eight previously identified prognostic molecular biomarkers (epidermal growth factor receptor [EGFR], tripartite motif-containing 44 [TRIM44], and sirtuin 2 [SIRT2]) had the strongest correlation with long-term survival in patients with EAC. Applying these three biomarkers as an IHC panel to the validation cohort segregated patients into two different prognostic groups (P < .01). Adjusting for known survival covariates, including clinical staging criteria, the IHC panel remained an independent predictor, with incremental adverse overall survival (OS) for each positive biomarker (hazard ratio, 1.20; 95% CI, 1.03 to 1.40 per biomarker; P = .02). Conclusion We identified and validated a clinically applicable IHC biomarker panel, consisting of EGFR, TRIM44, and SIRT2, that is independently associated with OS and provides additional prognostic information to current survival predictors such as stage. PMID:23509313

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.

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.

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.

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

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.

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

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.

Evaluation of Liver Biomarkers as Prognostic Factors for Outcomes to Yttrium-90 Radioembolization of Primary and Secondary Liver Malignancies.

PubMed

Henrie, Adam M; Wittstrom, Kristina; Delu, Adam; Deming, Paulina

2015-09-01

The objective of this study was to examine indicators of liver function and inflammation for prognostic value in predicting outcomes to yttrium-90 radioembolization (RE). In a retrospective analysis, markers of liver function and inflammation, biomarkers required to stage liver function and inflammation, and data regarding survival, tumor response, and progression after RE were recorded. Univariate regression models were used to investigate the prognostic value of liver biomarkers in predicting outcome to RE as measured by survival, tumor progression, and radiographic and biochemical tumor response. Markers from all malignancy types were analyzed together. A subgroup analysis was performed on markers from patients with metastatic colorectal cancer. A total of 31 patients received RE from 2004 to 2014. Median survival after RE for all malignancies combined was 13.6 months (95% CI: 6.7-17.6 months). Results from an exploratory analysis of patient data suggest that liver biomarkers, including albumin concentrations, international normalized ratio, bilirubin concentrations, and the model for end-stage liver disease score, possess prognostic value in predicting outcomes to RE.

A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines.

PubMed

Sánchez Lasheras, Fernando; García Nieto, Paulino José; de Cos Juez, Francisco Javier; Mayo Bayón, Ricardo; González Suárez, Victor Manuel

2015-03-23

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

PubMed Central

Lasheras, Fernando Sánchez; Nieto, Paulino José García; de Cos Juez, Francisco Javier; Bayón, Ricardo Mayo; Suárez, Victor Manuel González

2015-01-01

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines. PMID:25806876

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.

Cryptochrome-1 expression: a new prognostic marker in B-cell chronic lymphocytic leukemia.

PubMed

Lewintre, Eloisa Jantus; Martín, Cristina Reinoso; Ballesteros, Carlos García; Montaner, David; Rivera, Rosa Farrás; Mayans, José Ramón; García-Conde, Javier

2009-02-01

Chronic lymphocytic leukemia is an adult-onset leukemia with a heterogeneous clinical behavior. When chronic lymphocytic leukemia cases were divided on the basis of IgV(H) mutational status, widely differing clinical courses were revealed. Since IgV(H) sequencing is difficult to perform in a routine diagnostic laboratory, finding a surrogate for IgV(H) mutational status seems an important priority. In the present study, we proposed the use of Cryptochrome-1 as a new prognostic marker in early-stage chronic lymphocytic leukemia. Seventy patients (Binet stage A, without treatment) were included in the study. We correlated Cryptochrome-1 mRNA with well established prognostic markers such as IgV(H) mutations, ZAP70, LPL or CD38 expression and chromosomal abnormalities. High Cryptochrome-1 expression correlated with IgV(H) unmutated samples. In addition, Cryptochrome-1 was a valuable predictor of disease progression in early-stage chronic lymphocytic leukemia, therefore it can be introduced in clinical practice with the advantage of a simplified method of quantification.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

PubMed Central

Aerts, Hugo J. W. L.; Velazquez, Emmanuel Rios; Leijenaar, Ralph T. H.; Parmar, Chintan; Grossmann, Patrick; Cavalho, Sara; Bussink, Johan; Monshouwer, René; Haibe-Kains, Benjamin; Rietveld, Derek; Hoebers, Frank; Rietbergen, Michelle M.; Leemans, C. René; Dekker, Andre; Quackenbush, John; Gillies, Robert J.; Lambin, Philippe

2014-01-01

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. PMID:24892406

Remaining Useful Life Estimation in Prognosis: An Uncertainty Propagation Problem

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2013-01-01

The estimation of remaining useful life is significant in the context of prognostics and health monitoring, and the prediction of remaining useful life is essential for online operations and decision-making. However, it is challenging to accurately predict the remaining useful life in practical aerospace applications due to the presence of various uncertainties that affect prognostic calculations, and in turn, render the remaining useful life prediction uncertain. It is challenging to identify and characterize the various sources of uncertainty in prognosis, understand how each of these sources of uncertainty affect the uncertainty in the remaining useful life prediction, and thereby compute the overall uncertainty in the remaining useful life prediction. In order to achieve these goals, this paper proposes that the task of estimating the remaining useful life must be approached as an uncertainty propagation problem. In this context, uncertainty propagation methods which are available in the literature are reviewed, and their applicability to prognostics and health monitoring are discussed.

Prognostic nomogram for previously untreated adult patients with acute myeloid leukemia

PubMed Central

Zheng, Zhuojun; Li, Xiaodong; Zhu, Yuandong; Gu, Weiying; Xie, Xiaobao; Jiang, Jingting

2016-01-01

This study was designed to perform an acceptable prognostic nomogram for acute myeloid leukemia. The clinical data from 311 patients from our institution and 165 patients generated with Cancer Genome Atlas Research Network were reviewed. A prognostic nomogram was designed according to the Cox's proportional hazard model to predict overall survival (OS). To compare the capacity of the nomogram with that of the current prognostic system, the concordance index (C-index) was used to validate the accuracy as well as the calibration curve. The nomogram included 6 valuable variables: age, risk stratifications based on cytogenetic abnormalities, status of FLT3-ITD mutation, status of NPM1 mutation, expression of CD34, and expression of HLA-DR. The C-indexes were 0.71 and 0.68 in the primary and validation cohort respectively, which were superior to the predictive capacity of the current prognostic systems in both cohorts. The nomogram allowed both patients with acute myeloid leukemia and physicians to make prediction of OS individually prior to treatment. PMID:27689396

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.

A practical review of prognostic correlations of molecular biomarkers in glioblastoma.

PubMed

Karsy, Michael; Neil, Jayson A; Guan, Jian; Mahan, Mark A; Mark, Mahan A; Colman, Howard; Jensen, Randy L

2015-03-01

Despite extensive efforts in research and therapeutics, achieving longer survival for patients with glioblastoma (GBM) remains a formidable challenge. Furthermore, because of rapid advances in the scientific understanding of GBM, communication with patients regarding the explanations and implications of genetic and molecular markers can be difficult. Understanding the important biomarkers that play a role in GBM pathogenesis may also help clinicians in educating patients about prognosis, potential clinical trials, and monitoring response to treatments. This article aims to provide an up-to-date review that can be discussed with patients regarding common molecular markers, namely O-6-methylguanine-DNA methyltransferase (MGMT), isocitrate dehydrogenase 1 and 2 (IDH1/2), p53, epidermal growth factor receptor (EGFR), platelet-derived growth factor receptor (PDGFR), phosphatase and tensin homolog (PTEN), phosphoinositide 3-kinase (PI3K), and 1p/19q. The importance of the distinction between a prognostic and a predictive biomarker as well as clinical trials regarding these markers and their relevance to clinical practice are discussed.

Radiomics: Images Are More than Pictures, They Are Data

PubMed Central

Kinahan, Paul E.; Hricak, Hedvig

2016-01-01

In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer. PMID:26579733

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 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.

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

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.

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.

Amyloid imaging with PET in early Alzheimer disease diagnosis.

PubMed

Rowe, Christopher C; Villemagne, Victor L

2013-05-01

In vivo imaging of amyloid-β (Aβ) with positron emission tomography has moved from the research arena into clinical practice. Clinicians working with cognitive decline and dementia must become familiar with its benefits and limitations. Amyloid imaging allows earlier diagnosis of Alzheimer disease and better differential diagnosis of dementia and provides prognostic information for mild cognitive impairment. It also has an increasingly important role in therapeutic trial recruitment and for evaluation of anti-Aβ treatments. Longitudinal observations are required to elucidate the role of Aβ deposition in the course of Alzheimer disease and provide information needed to fully use the prognostic power of this investigation. Copyright © 2013 Elsevier Inc. All rights reserved.

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

PubMed

Goos, Jeroen A C M; Coupé, Veerle M H; van de Wiel, Mark A; Diosdado, Begoña; Delis-Van Diemen, Pien M; Hiemstra, Annemieke C; de Cuba, Erienne M V; Beliën, Jeroen A M; Menke-van der Houven van Oordt, C Willemien; Geldof, Albert A; Meijer, Gerrit A; Hoekstra, Otto S; Fijneman, Remond J A

2016-01-12

Prognosis of patients with colorectal cancer liver metastasis (CRCLM) is estimated based on clinicopathological models. Stratifying patients based on tumor biology may have additional value. Tissue micro-arrays (TMAs), containing resected CRCLM and corresponding primary tumors from a multi-institutional cohort of 507 patients, were immunohistochemically stained for 18 candidate biomarkers. Cross-validated hazard rate ratios (HRRs) for overall survival (OS) and the proportion of HRRs with opposite effect (P(HRR < 1) or P(HRR > 1)) were calculated. A classifier was constructed by classification and regression tree (CART) analysis and its prognostic value determined by permutation analysis. Correlations between protein expression in primary tumor-CRCLM pairs were calculated. Based on their putative prognostic value, EGFR (P(HRR < 1) = .02), AURKA (P(HRR < 1) = .02), VEGFA (P(HRR < 1) = .02), PTGS2 (P(HRR < 1) = .01), SLC2A1 (P(HRR > 1) < 01), HIF1α (P(HRR > 1) = .06), KCNQ1 (P(HRR > 1) = .09), CEA (P (HRR > 1) = .05) and MMP9 (P(HRR < 1) = .07) were included in the CART analysis (n = 201). The resulting classifier was based on AURKA, PTGS2 and MMP9 expression and was associated with OS (HRR 2.79, p < .001), also after multivariate analysis (HRR 3.57, p < .001). The prognostic value of the biomarker-based classifier was superior to the clinicopathological model (p = .001). Prognostic value was highest for colon cancer patients (HRR 5.71, p < .001) and patients not treated with systemic therapy (HRR 3.48, p < .01). Classification based on protein expression in primary tumors could be based on AURKA expression only (HRR 2.59, p = .04). A classifier was generated for patients with CRCLM with improved prognostic value compared to the standard clinicopathological prognostic parameters, which may aid selection of patients who may benefit from adjuvant systemic therapy.

Prognostic significance of blood-brain barrier disruption in patients with severe nonpenetrating traumatic brain injury requiring decompressive craniectomy.

PubMed

Ho, Kwok M; Honeybul, Stephen; Yip, Cheng B; Silbert, Benjamin I

2014-09-01

The authors assessed the risk factors and outcomes associated with blood-brain barrier (BBB) disruption in patients with severe, nonpenetrating, traumatic brain injury (TBI) requiring decompressive craniectomy. At 2 major neurotrauma centers in Western Australia, a retrospective cohort study was conducted among 97 adult neurotrauma patients who required an external ventricular drain (EVD) and decompressive craniectomy during 2004-2012. Glasgow Outcome Scale scores were used to assess neurological outcomes. Logistic regression was used to identify factors associated with BBB disruption, defined by a ratio of total CSF protein concentrations to total plasma protein concentration > 0.007 in the earliest CSF specimen collected after TBI. Of the 252 patients who required decompressive craniectomy, 97 (39%) required an EVD to control intracranial pressure, and biochemical evidence of BBB disruption was observed in 43 (44%). Presence of disruption was associated with more severe TBI (median predicted risk for unfavorable outcome 75% vs 63%, respectively; p = 0.001) and with worse outcomes at 6, 12, and 18 months than was absence of BBB disruption (72% vs 37% unfavorable outcomes, respectively; p = 0.015). The only risk factor significantly associated with increased risk for BBB disruption was presence of nonevacuated intracerebral hematoma (> 1 cm diameter) (OR 3.03, 95% CI 1.23-7.50; p = 0.016). Although BBB disruption was associated with more severe TBI and worse long-term outcomes, when combined with the prognostic information contained in the Corticosteroid Randomization after Significant Head Injury (CRASH) prognostic model, it did not seem to add significant prognostic value (area under the receiver operating characteristic curve 0.855 vs 0.864, respectively; p = 0.453). Biochemical evidence of BBB disruption after severe nonpenetrating TBI was common, especially among patients with large intracerebral hematomas. Disruption of the BBB was associated with more severe TBI and worse long-term outcomes, but when combined with the prognostic information contained in the CRASH prognostic model, this information did not add significant prognostic value.

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

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

PubMed Central

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

2013-01-01

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

A new prognostic model for chemotherapy-induced febrile neutropenia.

PubMed

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

2016-02-01

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

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

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.

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.

On the influence of biomass burning on the seasonal CO2 signal as observed at monitoring stations

USGS Publications Warehouse

Wittenberg, U.; Heimann, Martin; Esse, G.; McGuire, A.D.; Sauf, W.

1998-01-01

We investigated the role of biomass burning in simulating the seasonal signal in both prognostic and diagnostic analyses. The prognostic anaysis involved the High-Resolution Biosphere Model, a prognostic terrestrial biosphere model, and the coupled vegetation fire module, which together produce a prognostic data set of biomass burning. The diagnostic analysis invovled the Simple Diagnostic Biosphere Model (SDBM) and the Hao and Liu [1994] diagnostic data set of bimass burning, which have been scaled to global 2 and 4 Pg C yr-1, respectively. The monthly carbon exchange fields between the atmosphere and the biosphere with a spatial resolution of 0.5?? ?? 0.5??, the seasonal atmosphere-ocean exchange fields, and the emissions from fossil fuels have been coupled to the three-dimensional atmospheric transport model TM2. We have chosen eight monitoring stations of the National Oceanic and Atmospheric Administration network to compare the predicted seasonal atmospheric CO2 signals with those deduced from atmosphere-biosphere carbon exchange fluxes without any contribution from biomass burning. The prognostic analysis and the diagnostic analysis with global burning emissions of 4 Pg C yr-1 agree with respect to the change in the amplitude of the seasonal CO2 concentration introduced through biomass burning. We find that the seasonal CO2 signal at stations in higher northern latitudes (north of 30??N) is marginally influenced by biomass burning. For stations in tropical regions an increase in the CO2 amplitude of more an 1 oppmv (up to 50% with respect to the observed trough to peak amplitude) has been calculated. Biomass burning at stations farther south accounts for an increase in the CO2 amplitude of up to 59% (0.6 ppmv). A change in the phase of the seasonal CO2 signal at tropical and southern stations has been shown to be strongly influenced by the onset of biomass burning in southern tropical Africa and America. Comparing simulated and observed seasonal CO2 signals, we find higher discrepancies at southern troical stations if biomass burning emissions are included. This is caused by the additional increase in the amplitude in the prognostic analysis and a phase shift in a diagnostic analysis. In contrast, at the northern tropical stations biomass burning tends to improve the estimates of the seasonal CO2 signal in the prognostic analysis because of strengthening of the amplitude. Since the SDCM predicts the seasonal CO2 signal resonably well for the northern hemisphere tropical stations, no general improvement of the fit occurs if biomass burning emissions are considered.

TP53 mutations in myelodysplastic syndrome are strongly correlated with aberrations of chromosome 5, and correlate with adverse prognosis.

PubMed

Kulasekararaj, Austin G; Smith, Alexander E; Mian, Syed A; Mohamedali, Azim M; Krishnamurthy, Pramila; Lea, Nicholas C; Gäken, Joop; Pennaneach, Coralie; Ireland, Robin; Czepulkowski, Barbara; Pomplun, Sabine; Marsh, Judith C; Mufti, Ghulam J

2013-03-01

This study aimed to determine the incidence/prognostic impact of TP53 mutation in 318 myelodysplastic syndrome (MDS) patients, and to correlate the changes to cytogenetics, single nucleotide polymorphism array karyotyping and clinical outcome. The median age was 65 years (17-89 years) and median follow-up was 45 months [95% confidence interval (CI) 27-62 months]. TP53 mutations occurred in 30 (9.4%) patients, exclusively in isolated del5q (19%) and complex karyotype (CK) with -5/5q-(72%), correlated with International Prognostic Scoring System intermediate-2/high, TP53 protein expression, higher blast count and leukaemic progression. Patients with mutant TP53 had a paucity of mutations in other genes implicated in myeloid malignancies. Median overall survival of patients with TP53 mutation was shorter than wild-type (9 versus 66 months, P < 0.001) and it retained significance in multivariable model (Hazard Ratio 3.8, 95%CI 2.3-6.3,P < 0.001). None of the sequentially analysed samples showed a disappearance of the mutant clone or emergence of new clones, suggesting an early occurrence of TP53 mutations. A reduction in mutant clone correlated with response to 5-azacitidine, however clones increased in non-responders and persisted at relapse. The adverse impact of TP53 persists after adjustment for cytogenetic risk and is of practical importance in evaluating prognosis. The relatively common occurrence of these mutations in two different prognostic spectrums of MDS, i.e. isolated 5q- and CK with -5/5q-, possibly implies two different mechanistic roles for TP53 protein. © 2013 Crown copyright. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

Construct and predictive validity of the German Örebro questionnaire short form for psychosocial risk factor screening of patients with low back pain.

PubMed

Schmidt, Carsten Oliver; Kohlmann, T; Pfingsten, M; Lindena, G; Marnitz, U; Pfeifer, K; Chenot, J F

2016-01-01

Recognizing patients at risk of developing chronic low back pain is essential for targeted interventions. One of the best researched screening instruments for this purpose is the Örebro Musculoskeletal Pain Questionnaire (ÖMSPQ). This work addresses psychometric properties of the German ÖMSPQ short form and its construct and prognostic validity. Analyses are based on a cluster-randomized trial assessing a risk tailored intervention for patients consulting for low back pain in 35 general practices. A total of 360 patients consulting for acute and sub-acute back pain, aged 20-60 years, were included. All patients received a 10-item German short version of the ÖMSPQ, and other generic instruments (Graded Chronic Pain Scale, Patient Health Questionnaire-Depression, Hannover Functional Ability Questionnaire, Fear-Avoidance Beliefs Questionnaire). The construct validity was assessed based on the factorial structure of the items and correlations with generic instruments. The area under the curve (AUC), sensitivity and specificity were calculated as measures of prognostic validity. ÖMSPQ items belonging to the same subscale correlated highest among each other. The internal consistency of the ÖMSPQ items was 0.80 (Cronbach's α). The factorial structure corresponds with theoretic expectations. ÖMSPQ subscales on pain related disability, depression, and fear-avoidance beliefs correlated highest with their counterpart generic scales. The AUC for three ÖMSPQ-based prediction models ranged from 0.77 to 0.81. Our results support a satisfactory factorial and prognostic validity of the German short ÖMSPQ. The instrument may guide the provision of targeted interventions. Further research should link it to targeted treatments.

Prognostic Factors for Persistent Leg-Pain in Patients Hospitalized With Acute Sciatica.

PubMed

Fjeld, Olaf; Grotle, Margreth; Siewers, Vibeke; Pedersen, Linda M; Nilsen, Kristian Bernhard; Zwart, John-Anker

2017-03-01

Prospective cohort study. To identify potential prognostic factors for persistent leg-pain at 12 months among patients hospitalized with acute severe sciatica. The long-term outcome for patients admitted to hospital with sciatica is generally unfavorable. Results concerning prognostic factors for persistent sciatica are limited and conflicting. A total of 210 patients acutely admitted to hospital for either surgical or nonsurgical treatment of sciatica were consecutively recruited and received a thorough clinical and radiographic examination in addition to responding to a comprehensive questionnaire. Follow-up assessments were done at 6 weeks, 6 months, and 12 months. Potential prognostic factors were measured at baseline and at 6 weeks. The impact of these factors on leg-pain was analyzed by multiple linear regression modeling. A total of 151 patients completed the entire study, 93 receiving nonrandomized surgical treatment. The final multivariate models showed that the following factors were significantly associated with leg-pain at 12 months: high psychosocial risk according to the Örebro Musculosceletal Pain Questionnaire (unstandardized beta coefficient 1.55, 95% confidence interval [CI] 0.72-2.38, P < 0.001), not receiving surgical treatment (1.11, 95% CI 0.29-1.93, P = 0.01), not actively employed upon admission (1.47, 95% CI 0.63-2.31, P < 0.01), and self-reported leg-pain recorded 6 weeks posthospital admission (0.49, 95% CI 0.34-0.63, P < 0.001). Interaction analysis showed that the Örebro Musculosceletal Pain Questionnaire had significant prognostic value only on the nonsurgically treated patients (3.26, 95% CI 1.89-4.63, P < 0.001). The results suggest that a psychosocial screening tool and the implementation of a 6-week postadmission follow-up has prognostic value in the hospital management of severe sciatica. 2.

Heterogeneity of (18)F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma.

PubMed

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

2016-02-01

The Ang's risk profile (based on p16, smoking and cancer stage) is a well-known prognostic factor in oropharyngeal squamous cell carcinoma (OPSCC). Whether heterogeneity in (18)F-fluorodeoxyglucose (FDG) positron emission tomographic (PET) images and epidermal growth factor receptor (EGFR) expression could provide additional information on clinical outcomes in advanced-stage OPSCC was investigated. Patients with stage III-IV OPSCC who completed primary therapy were eligible. Zone-size nonuniformity (ZSNU) extracted from pretreatment FDG PET scans was used as an index of image heterogeneity. EGFR and p16 expression were examined by immunohistochemistry. Disease-specific survival (DSS) and overall survival (OS) served as outcome measures. Kaplan-Meier estimates and Cox proportional hazards regression models were used for survival analysis. A bootstrap resampling technique was applied to investigate the stability of outcomes. Finally, a recursive partitioning analysis (RPA)-based model was constructed. A total of 113 patients were included, of which 28 were p16-positive. Multivariate analysis identified the Ang's profile, EGFR and ZSNU as independent predictors of both DSS and OS. Using RPA, the three risk factors were used to devise a prognostic scoring system that successfully predicted DSS in both p16-positive and -negative cases. The c-statistic of the prognostic index for DSS was 0.81, a value which was significantly superior to both AJCC stage (0.60) and the Ang's risk profile (0.68). In patients showing an Ang's high-risk profile (N = 77), the use of our scoring system clearly identified three distinct prognostic subgroups. It was concluded that a novel index may improve the prognostic stratification of patients with advanced-stage OPSCC. © 2015 UICC.

Immunoscore encompassing CD3+ and CD8+ T cell densities in distant metastasis is a robust prognostic marker for advanced colorectal cancer

PubMed Central

Kwak, Yoonjin; Koh, Jiwon; Kim, Duck-Woo; Kang, Sung-Bum; Kim, Woo Ho; Lee, Hye Seung

2016-01-01

Background The immunoscore (IS), an index based on the density of CD3+ and CD8+ tumor-infiltrating lymphocytes (TILs) in the tumor center (CT) and invasive margin (IM), has gained considerable attention as a prognostic marker. Tumor-associated macrophages (TAMs) have also been reported to have prognostic value. However, its clinical significance has not been fully clarified in patients with advanced CRC who present with distant metastases. Methods The density of CD3+, CD4+, CD8+, FOXP3+, CD68+, and CD163+ immune cells within CRC tissue procured from three sites–the primary CT, IM, and distant metastasis (DM)–was determined using immunohistochemistry and digital image analyzer (n=196). The IS was obtained by quantifying the densities of CD3+ and CD8+ TILs in the CT and IM. IS-metastatic and IS-macrophage–additional IS models designed in this study–were obtained by adding the score of CD3 and CD8 in DM and the score of CD163 in primary tumors (CT and IM), respectively, to the IS. Result Higher IS, IS-metastatic, and IS-macrophage values were significantly correlated with better prognosis (p=0.020, p≤0.001, and p=0.005, respectively). Multivariate analysis revealed that only IS-metastatic was an independent prognostic marker (p=0.012). No significant correlation was observed between KRAS mutation and three IS models. However, in the subgroup analysis, IS-metastatic showed a prognostic association regardless of the KRAS mutational status. Conclusion IS is a reproducible method for predicting the survival of patients with advanced CRC. Additionally, an IS including the CD3+ and CD8+ TIL densities at DM could be a strong prognostic marker for advanced CRC. PMID:27835889

Comparison of the prognostic value of pretreatment measurements of systemic inflammatory response in patients undergoing curative resection of clear cell renal cell carcinoma.

PubMed

Lucca, Ilaria; de Martino, Michela; Hofbauer, Sebastian L; Zamani, Nura; Shariat, Shahrokh F; Klatte, Tobias

2015-12-01

Pretreatment measurements of systemic inflammatory response, including the Glasgow prognostic score (GPS), the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), the platelet-to-lymphocyte ratio (PLR) and the prognostic nutritional index (PNI) have been recognized as prognostic factors in clear cell renal cell carcinoma (CCRCC), but there is at present no study that compared these markers. We evaluated the pretreatment GPS, NLR, MLR, PLR and PNI in 430 patients, who underwent surgery for clinically localized CCRCC (pT1-3N0M0). Associations with disease-free survival were assessed with Cox models. Discrimination was measured with the C-index, and a decision curve analysis was used to evaluate the clinical net benefit. On multivariable analyses, all measures of systemic inflammatory response were significant prognostic factors. The increase in discrimination compared with the stage, size, grade and necrosis (SSIGN) score alone was 5.8 % for the GPS, 1.1-1.4 % for the NLR, 2.9-3.4 % for the MLR, 2.0-3.3 % for the PLR and 1.4-3.0 % for the PNI. On the simultaneous multivariable analysis of all candidate measures, the final multivariable model contained the SSIGN score (HR 1.40, P < 0.001), the GPS (HR 2.32, P < 0.001) and the MLR (HR 5.78, P = 0.003) as significant variables. Adding both the GPS and the MLR increased the discrimination of the SSIGN score by 6.2 % and improved the clinical net benefit. In patients with clinically localized CCRCC, the GPS and the MLR appear to be the most relevant prognostic measures of systemic inflammatory response. They may be used as an adjunct for patient counseling, tailoring management and clinical trial design.

Hilar fat infiltration: A new prognostic factor in metastatic clear cell renal cell carcinoma with first-line sunitinib treatment.

PubMed

Kammerer-Jacquet, Solène-Florence; Brunot, Angelique; Bensalah, Karim; Campillo-Gimenez, Boris; Lefort, Mathilde; Bayat, Sahar; Ravaud, Alain; Dupuis, Frantz; Yacoub, Mokrane; Verhoest, Gregory; Peyronnet, Benoit; Mathieu, Romain; Lespagnol, Alexandra; Mosser, Jean; Edeline, Julien; Laguerre, Brigitte; Bernhard, Jean-Christophe; Rioux-Leclercq, Nathalie

2017-10-01

The selection of patients with metastatic clear cell renal cell carcinoma (ccRCC) who may benefit from targeted tyrosine kinase inhibitors has been a challenge, even more so now with the advent of new therapies. Hilar fat infiltration (HFI) is a validated prognostic factor in nonmetastatic ccRCC (TNM 2009 staging system) but has never been studied in metastatic patients. We aimed to assess its phenotype and prognostic effect in patients with metastatic ccRCC treated with first-line sunitinib. In a multicentric study, we retrospectively included 90 patients and studied the corresponding ccRCC at the pathological, immunohistochemical, and molecular levels. Patient and tumor characteristics were compared using univariate and multivariate analysis. All the features were then studied by Cox models for prognostic effect. HFI was found in 42 patients (46.7%), who had worse prognosis (Heng criteria) (P = 0.003), liver metastases (P = 0.036), and progressive diseases at first radiological evaluation (P = 0.024). The corresponding ccRCC was associated with poor pathological prognostic factors that are well known in nonmetastatic ccRCC. For these patients, median progression-free survival was 4 months vs. 13 months (P = 0.02), and median overall survival was 14 months vs. 29 months (P = 0.006). In a multivariate Cox model integrating all the variables, only poor prognosis, according to the Heng criteria and HFI, remained independently associated with both progression-free survival and overall survival. HFI was demonstrated for the first time to be an independent poor prognostic factor. Its potential role in predicting resistance to antiangiogenic therapy warrants further investigation. Copyright © 2017 Elsevier Inc. All rights reserved.

Serum prognostic biomarkers in head and neck cancer patients.

PubMed

Lin, Ho-Sheng; Siddiq, Fauzia; Talwar, Harvinder S; Chen, Wei; Voichita, Calin; Draghici, Sorin; Jeyapalan, Gerald; Chatterjee, Madhumita; Fribley, Andrew; Yoo, George H; Sethi, Seema; Kim, Harold; Sukari, Ammar; Folbe, Adam J; Tainsky, Michael A

2014-08-01

A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC). Prospective cohort study. A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient's serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models. Poor overall survival was associated with African Americans (hazard ratio [HR] for death = 2.61; 95% confidence interval [CI]: 1.58-4.33; P = .000), advanced stage (HR = 2.79; 95% CI: 1.40-5.57; P = .004), and recurrent disease (HR = 6.66; 95% CI: 2.54-17.44; P = .000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival. The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Serum Prognostic Biomarkers in Head and Neck Cancer Patients

PubMed Central

Lin, Ho-Sheng; Siddiq, Fauzia; Talwar, Harvinder S.; Chen, Wei; Voichita, Calin; Draghici, Sorin; Jeyapalan, Gerald; Chatterjee, Madhumita; Fribley, Andrew; Yoo, George H.; Sethi, Seema; Kim, Harold; Sukari, Ammar; Folbe, Adam J.; Tainsky, Michael A.

2014-01-01

Objectives/Hypothesis A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC). Study Design Prospective cohort study. Methods A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient’s serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models. Results Poor overall survival was associated with African Americans (hazard ratio [HR] for death =2.61; 95% confidence interval [CI]: 1.58–4.33; P =.000), advanced stage (HR =2.79; 95% CI: 1.40–5.57; P =.004), and recurrent disease (HR =6.66; 95% CI: 2.54–17.44; P =.000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival. Conclusions The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted. PMID:24347532

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.

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.

lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia

PubMed Central

Pan, Jia-Qi; Zhang, Yan-Qing; Wang, Jing-Hua; Xu, Ping; Wang, Wei

2017-01-01

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of lncRNAs as prognostic biomarkers in AML and their detailed mechanisms of action in this disease have not yet been well characterized. For this purpose, in the present study, the expression levels of lncRNAs and mRNAs were calculated using the RNA-seq V2 data for AML, following which a lncRNA-lncRNA co-expression network (LLCN) was constructed. This revealed a total of 8 AML prognosis-related lncRNA modules were identified, which displayed a significant correlation with patient survival (p≤0.05). Subsequently, a prognosis-related lncRNA module pathway network was constructed to interpret the functional mechanism of the prognostic modules in AML. The results indicated that these prognostic modules were involved in the AML pathway, chemokine signaling pathway and WNT signaling pathway, all of which play important roles in AML. Furthermore, the investigation of lncRNAs in these prognostic modules suggested that an lncRNA (ZNF571-AS1) may be involved in AML via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway by regulating KIT and STAT5. The results of the present study not only provide potential lncRNA modules as prognostic biomarkers, but also provide further insight into the molecular mechanisms of action of lncRNAs. PMID:28204819

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

PubMed Central

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

2016-01-01

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

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.

Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients

PubMed Central

Kirilovsky, Amos; Marliot, Florence; El Sissy, Carine; Haicheur, Nacilla; Galon, Jérôme

2016-01-01

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) tumor, nodes, metastasis (TNM) classification system based on tumor features is used for prognosis estimation and treatment recommendations in most cancers. However, the clinical outcome can vary significantly among patients within the same tumor stage and TNM classification does not predict response to therapy. Therefore, many efforts have been focused on the identification of new markers. Multiple tumor cell-based approaches have been proposed but very few have been translated into the clinic. The recent demonstration of the essential role of the immune system in tumor progression has allowed great advances in the understanding of this complex disease and in the design of novel therapies. The analysis of the immune infiltrate by imaging techniques in large patient cohorts highlighted the prognostic impact of the in situ immune cell infiltrate in tumors. Moreover, the characterization of the immune infiltrates (e.g. type, density, distribution within the tumor, phenotype, activation status) in patients treated with checkpoint-blockade strategies could provide information to predict the disease outcome. In colorectal cancer, we have developed a prognostic score (‘Immunoscore’) that takes into account the distribution of the density of both CD3+ lymphocytes and CD8+ cytotoxic T cells in the tumor core and the invasive margin that could outperform TNM staging. Currently, an international retrospective study is under way to validate the Immunoscore prognostic performance in patients with colon cancer. The use of Immunoscore in clinical practice could improve the patients’ prognostic assessment and therapeutic management. PMID:27121213

Prognostic factors for patients with early-stage uterine serous carcinoma without adjuvant therapy.

PubMed

Tate, Keisei; Yoshida, Hiroshi; Ishikawa, Mitsuya; Uehara, Takashi; Ikeda, Shun Ichi; Hiraoka, Nobuyoshi; Kato, Tomoyasu

2018-05-01

Uterine serous carcinoma (USC) is an aggressive type 2 endometrial cancer. Data on prognostic factors for patients with early-stage USC without adjuvant therapy are limited. This study aims to assess the baseline recurrence risk of early-stage USC patients without adjuvant treatment and to identify prognostic factors and patients who need adjuvant therapy. Sixty-eight patients with International Federation of Gynecology and Obstetrics (FIGO) stage I-II USC between 1997 and 2016 were included. All the cases did not undergo adjuvant treatment as institutional practice. Clinicopathological features, recurrence patterns, and survival outcomes were analyzed to determine prognostic factors. FIGO stages IA, IB, and II were observed in 42, 7, and 19 cases, respectively. Median follow-up time was 60 months. Five-year disease-free survival (DFS) and overall survival (OS) rates for all cases were 73.9% and 78.0%, respectively. On multivariate analysis, cervical stromal involvement and positive pelvic cytology were significant predictors of DFS and OS, and ≥1/2 myometrial invasion was also a significant predictor of OS. Of 68 patients, 38 patients had no cervical stromal invasion or positive pelvic cytology and showed 88.8% 5-year DFS and 93.6% 5-year OS. Cervical stromal invasion and positive pelvic cytology are prognostic factors for stage I-II USC. Patients with stage IA or IB USC showing negative pelvic cytology may have an extremely favorable prognosis and need not receive any adjuvant therapies. Copyright © 2018. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology.

Real-world data on Len/Dex combination at second-line therapy of multiple myeloma: treatment at biochemical relapse is a significant prognostic factor for progression-free survival.

PubMed

Katroditou, Eirini; Kyrtsonis, Marie-Christine; Delimpasi, Sosana; Kyriakou, Despoina; Symeonidis, Argiris; Spanoudakis, Emmanouil; Vasilopoulos, Georgios; Anagnostopoulos, Achilles; Kioumi, Anna; Zikos, Panagiotis; Aktypi, Anthi; Briasoulis, Evangelos; Megalakaki, Aikaterini; Repousis, Panayiotis; Adamopoulos, Ioannis; Gogos, Dimitrios; Kotsopoulou, Maria; Pappa, Vassiliki; Papadaki, Eleni; Fotiou, Despoina; Nikolaou, Eftychia; Giannopoulou, Evlambia; Hatzimichael, Eleftheria; Giannakoulas, Nikolaos; Douka, Vassiliki; Kokoviadou, Kyriaki; Timotheatou, Despoina; Terpos, Evangelos

2018-05-13

We evaluated progression-free survival (PFS) rate of patients treated with lenalidomide/dexamethasone (Len/Dex), the efficacy of the combination, and the prognostic significance of treatment at biochemical vs. clinical relapse on PFS in 207 consecutive myeloma patients treated with Len/Dex in second line, according to routine clinical practice in Greece. First-line treatment included bortezomib-based (63.3%) or immunomodulatory drug-based (34.8%) therapies; 25% of patients underwent autologous stem cell transplantation. Overall response rate was 73.4% (17.8% complete response and 23.7% very good partial response); median time to best response was 6.7 months. Overall, median PFS and 12-month PFS rate was 19.2 months and 67.6%, respectively. 67.5% of patients had biochemical relapse and 32.5% had clinical relapse prior to initiation of Len/Dex. Median PFS was 24 months for patients treated at biochemical relapse vs. 13.2 months for those treated at clinical relapse (HR:0.63, p = 0.006) and the difference remained significant after adjustment for other prognostic factors. Type of relapse was the strongest prognostic factor for PFS in multivariate analysis. These real-world data confirm the efficacy of Len/Dex combination at first relapse; more importantly, it is demonstrated for the first time outside a clinical trial setting that starting therapy with Len/Dex at biochemical, rather than at clinical relapse, is a significant prognostic factor for PFS, inducing a 37% reduction of the probability of disease progression or death.

Systematic review of the clinical and economic value of gene expression profiles for invasive early breast cancer available in Europe.

PubMed

Blok, E J; Bastiaannet, E; van den Hout, W B; Liefers, G J; Smit, V T H B M; Kroep, J R; van de Velde, C J H

2018-01-01

Gene expression profiles with prognostic capacities have shown good performance in multiple clinical trials. However, with multiple assays available and numerous types of validation studies performed, the added value for daily clinical practice is still unclear. In Europe, the MammaPrint, OncotypeDX, PAM50/Prosigna and Endopredict assays are commercially available. In this systematic review, we aim to assess these assays on four important criteria: Assay development and methodology, clinical validation, clinical utility and economic value. We performed a literature search covering PubMed, Embase, Web of Science and Cochrane, for studies related to one or more of the four selected assays. We identified 147 papers for inclusion in this review. MammaPrint and OncotypeDX both have evidence available, including level IA clinical trial results for both assays. Both assays provide prognostic information. Predictive value has only been shown for OncotypeDX. In the clinical utility studies, a higher reduction in chemotherapy was achieved by OncotypeDX, although the number of available studies differ considerably between tests. On average, economic evaluations estimate that genomic testing results in a moderate increase in total costs, but that these costs are acceptable in relation to the expected improved patient outcome. PAM50/prosigna and EndoPredict showed comparable prognostic capacities, but with less economical and clinical utility studies. Furthermore, for these assays no level IA trial data are available yet. In summary, all assays have shown excellent prognostic capacities. The differences in the quantity and quality of evidence are discussed. Future studies shall focus on the selection of appropriate subgroups for testing and long-term outcome of validation trials, in order to determine the place of these assays in daily clinical practice. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Establishment of an Adjusted Prognosis Analysis Model for Initially Diagnosed Non-Small-Cell Lung Cancer With Brain Metastases From Sun Yat-Sen University Cancer Center.

PubMed

Dinglin, Xiao-Xiao; Ma, Shu-Xiang; Wang, Fang; Li, De-Lan; Liang, Jian-Zhong; Chen, Xin-Ru; Liu, Qing; Zeng, Yin-Duo; Chen, Li-Kun

2017-05-01

The current published prognosis models for brain metastases (BMs) from cancer have not addressed the issue of either newly diagnosed non-small-cell lung cancer (NSCLC) with BMs or the lung cancer genotype. We sought to build an adjusted prognosis analysis (APA) model, a new prognosis model specifically for NSCLC patients with BMs at the initial diagnosis using adjusted prognosis analysis (APA). The model was derived using data from 1158 consecutive patients, with 837 in the derivation cohort and 321 in the validation cohort. The patients had initially received a diagnosis of BMs from NSCLC at Sun Yat-Sen University Cancer Center from 1994 to 2015. The prognostic factors analyzed included patient characteristics, disease characteristics, and treatments. The APA model was built according to the numerical score derived from the hazard ratio of each independent prognostic variable. The predictive accuracy of the APA model was determined using a concordance index and was compared with current prognosis models. The results were validated using bootstrap resampling and a validation cohort. We established 2 prognostic models (APA 1 and 2) for the whole group of patients and for those with known epidermal growth factor receptor (EGFR) genotype, respectively. Six factors were independently associated with survival time: Karnofsky performance status, age, smoking history (replaced by EGFR mutation in APA 2), local treatment of intracranial metastases, EGFR-tyrosine kinase inhibitor treatment, and chemotherapy. Patients in the derivation cohort were stratified into low- (score, 0-2), moderate- (score, 3-5), and high-risk (score 6-7) groups according to the median survival time (16.6, 10.3, and 5.2 months, respectively; P < .001). The results were further confirmed in the validation cohort. Compared with recursive partition analysis and graded prognostic assessment, APA seems to be more suitable for initially diagnosed NSCLC with BMs. Copyright © 2017 Elsevier Inc. All rights reserved.

Prognostic durability of liver fibrosis tests and improvement in predictive performance for mortality by combining tests.

PubMed

Bertrais, Sandrine; Boursier, Jérôme; Ducancelle, Alexandra; Oberti, Frédéric; Fouchard-Hubert, Isabelle; Moal, Valérie; Calès, Paul

2017-06-01

There is currently no recommended time interval between noninvasive fibrosis measurements for monitoring chronic liver diseases. We determined how long a single liver fibrosis evaluation may accurately predict mortality, and assessed whether combining tests improves prognostic performance. We included 1559 patients with chronic liver disease and available baseline liver stiffness measurement (LSM) by Fibroscan, aspartate aminotransferase to platelet ratio index (APRI), FIB-4, Hepascore, and FibroMeter V2G . Median follow-up was 2.8 years during which 262 (16.8%) patients died, with 115 liver-related deaths. All fibrosis tests were able to predict mortality, although APRI (and FIB-4 for liver-related mortality) showed lower overall discriminative ability than the other tests (differences in Harrell's C-index: P < 0.050). According to time-dependent AUROCs, the time period with optimal predictive performance was 2-3 years in patients with no/mild fibrosis, 1 year in patients with significant fibrosis, and <6 months in cirrhotic patients even in those with a model of end-stage liver disease (MELD) score <15. Patients were then randomly split in training/testing sets. In the training set, blood tests and LSM were independent predictors of all-cause mortality. The best-fit multivariate model included age, sex, LSM, and FibroMeter V2G with C-index = 0.834 (95% confidence interval, 0.803-0.862). The prognostic model for liver-related mortality included the same covariates with C-index = 0.868 (0.831-0.902). In the testing set, the multivariate models had higher prognostic accuracy than FibroMeter V2G or LSM alone for all-cause mortality and FibroMeter V2G alone for liver-related mortality. The prognostic durability of a single baseline fibrosis evaluation depends on the liver fibrosis level. Combining LSM with a blood fibrosis test improves mortality risk assessment. © 2016 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Numerical simulation and prediction of coastal ocean circulation

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

Chen, P.

1992-01-01

Numerical simulation and prediction of coastal ocean circulation have been conducted in three cases. 1. A process-oriented modeling study is conducted to study the interaction of a western boundary current (WBC) with coastal water, and its responses to upstream topographic irregularities. It is hypothesized that the interaction of propagating WBC frontal waves and topographic Rossby waves are responsible for upstream variability. 2. A simulation of meanders and eddies in the Norwegian Coastal Current (NCC) for February and March of 1988 is conducted with a newly developed nested dynamic interactive model. The model employs a coarse-grid, large domain to account formore » non-local forcing and a fine-grid nested domain to resolve meanders and eddies. The model is forced by wind stresses, heat fluxes and atmospheric pressure corresponding Feb/March of 1988, and accounts for river/fjord discharges, open ocean inflow and outflow, and M[sub 2] tides. The simulation reproduced fairly well the observed circulation, tides, and salinity features in the North Sea, Norwegian Trench and NCC region in the large domain and fairly realistic meanders and eddies in the NCC in the nested region. 3. A methodology for practical coastal ocean hindcast/forecast is developed, taking advantage of the disparate time scales of various forcing and considering wind to be the dominant factor in affecting density fluctuation in the time scale of 1 to 10 days. The density field obtained from a prognostic simulation is analyzed by the empirical orthogonal function method (EOF), and correlated with the wind; these information are then used to drive a circulation model which excludes the density calculation. The method is applied to hindcast the circulation in the New York Bight for spring and summer season of 1988. The hindcast fields compare favorably with the results obtained from the prognostic circulation model.« less

Wind laws for shockless initialization. [numerical forecasting model

NASA Technical Reports Server (NTRS)

Ghil, M.; Shkoller, B.

1976-01-01

A system of diagnostic equations for the velocity field, or wind laws, was derived for each of a number of models of large-scale atmospheric flow. The derivation in each case is mathematically exact and does not involve any physical assumptions not already present in the prognostic equations, such as nondivergence or vanishing of derivatives of the divergence. Therefore, initial states computed by solving these diagnostic equations should be compatible with the type of motion described by the prognostic equations of the model and should not generate initialization shocks when inserted into the model. Numerical solutions of the diagnostic system corresponding to a barotropic model are exhibited. Some problems concerning the possibility of implementing such a system in operational numerical weather prediction are discussed.

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.

Next-Generation Pathology.

PubMed

Caie, Peter D; Harrison, David J

2016-01-01

The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.

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.

A Novel Independent Survival Predictor in Pulmonary Embolism: Prognostic Nutritional Index.

PubMed

Hayıroğlu, Mert İlker; Keskin, Muhammed; Keskin, Taha; Uzun, Ahmet Okan; Altay, Servet; Kaya, Adnan; Öz, Ahmet; Çinier, Göksel; Güvenç, Tolga Sinan; Kozan, Ömer

2018-05-01

The prognostic impact of nutritional status in patients with pulmonary embolism (PE) is poorly understood. A well-accepted nutritional status parameter, prognostic nutritional index (PNI), which was first demonstrated to be valuable in patients with cancer and gastrointestinal surgery, was introduced to patients with PE. Our aim was to evaluate the predictive value of PNI in outcomes of patients with PE. We evaluated the in-hospital and long-term (53.8 ± 5.4 months) prognostic impact of PNI on 251 patients with PE. During a median follow-up of 53.8 ± 5.4 months, 27 (11.6%) patients died in hospital course and 31 (13.4%) died in out-of-hospital course. The patients with lower PNI had significantly higher in-hospital and long-term mortality. The Cox proportional hazard analyses showed that PNI was associated with an increased risk of all-cause death for both unadjusted model and adjusted for all covariates. Our study demonstrated that PNI, calculated based on serum albumin level and lymphocyte count, is an independent prognostic factor for mortality in patients with PE.

Prognostic score–based balance measures for propensity score methods in comparative effectiveness research

PubMed Central

Stuart, Elizabeth A.; Lee, Brian K.; Leacy, Finbarr P.

2013-01-01

Objective Examining covariate balance is the prescribed method for determining when propensity score methods are successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (also known as the disease-risk score), to determine which balance measures best correlate with bias in the treatment effect estimate. Study Design and Setting The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only. Results The standardized mean difference in prognostic scores, the mean standardized mean difference, and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification and performed well under a variety of scenarios. Conclusion Researchers should consider using prognostic score–based balance measures for assessing the performance of propensity score methods for reducing bias in non-experimental studies. PMID:23849158

Prognostic, predictive and pharmacogenomic assessments of CDX2 refine stratification of colorectal cancer.

PubMed

Bruun, Jarle; Sveen, Anita; Barros, Rita; Eide, Peter W; Eilertsen, Ina; Kolberg, Matthias; Pellinen, Teijo; David, Leonor; Svindland, Aud; Kallioniemi, Olli; Guren, Marianne G; Nesbakken, Arild; Almeida, Raquel; Lothe, Ragnhild A

2018-06-14

We aimed to refine the value of CDX2 as an independent prognostic and predictive biomarker in colorectal cancer (CRC) according to disease stage and chemotherapy sensitivity in preclinical models. CDX2 expression was evaluated in 1045 stage I-IV primary CRCs by gene expression (n=403) or immunohistochemistry (n=642) and in relation to 5-year relapse-free survival (RFS), overall survival (OS), and chemotherapy. Pharmacogenomic associations between CDX2 expression and 69 chemotherapeutics were assessed by drug screening of 35 CRC cell lines. CDX2 expression was lost in 11.6% of cases and showed independent poor prognostic value in multivariable models. For individual stages, CDX2 was prognostic only in stage IV, independent of chemotherapy. Among stage I-III patients not treated in an adjuvant setting, CDX2 loss was associated with a particularly poor survival in the BRAF-mutated subgroup, but prognostic value was independent of microsatellite instability status and the consensus molecular subtypes In stage III, the 5-year RFS rate was higher among patients with loss of CDX2 who received adjuvant chemotherapy than among patients who did not. The CDX2-negative cell lines were significantly more sensitive to chemotherapeutics than CDX2-positive cells, and the multidrug resistance genes MDR1 and CFTR were significantly downregulated both in CDX2-negative cells and patient tumors. Molecular Oncology (2018) © 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

[Essential thrombocythemia: baseline characteristics and risk factors for survival and thrombosis in a series of 214 patients].

PubMed

Angona, Anna; Alvarez-Larrán, Alberto; Bellosillo, Beatriz; Martínez-Avilés, Luz; Garcia-Pallarols, Francesc; Longarón, Raquel; Ancochea, Àgueda; Besses, Carles

2015-03-15

Two prognostic models to predict overall survival and thrombosis-free survival have been proposed: International Prognostic Score for Essential Thrombocythemia (IPSET) and IPSET-Thrombosis, respectively, based on age, leukocytes count, history of previous thrombosis, the presence of cardiovascular risk factors and the JAK2 mutational status. The aim of the present study was to assess the clinical and biological characteristics at diagnosis and during evolution in essential thrombocythemia (ET) patients as well as the factors associated with survival and thrombosis and the usefulness of these new prognostic models. We have evaluated the clinical data and the mutation status of JAK2, MPL and calreticulin of 214 ET patients diagnosed in a single center between 1985 and 2012, classified according to classical risk stratification, IPSET and IPSET-Thrombosis. With a median follow-up of 6.9 years, overall survival was not associated with any variable by multivariate analysis. Thrombotic history and leukocytes>10×10(9)/l were associated with thrombosis-free survival (TFS). In our series, IPSET prognostic systems of survival and thrombosis did not provide more clinically relevant information regarding the classic risk of thrombosis stratification. Thrombotic history and leukocytosis>10×10(9)/l were significantly associated with lower TFS, while the prognostic IPSET-Thrombosis system did not provide more information than classical thrombotic risk assessment. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

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.

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

Traditional and emerging molecular markers in neuroblastoma prognosis: the good, the bad and the ugly.

PubMed

Poremba, C; Hero, B; Goertz, H G; Scheel, C; Wai, D; Schaefer, K L; Christiansen, H; Berthold, F; Juergens, H; Boecker, W; Dockhorn-Dworniczak, B

2001-01-01

Neuroblastomas (NB) are a heterogeneous group of childhood tumours with a wide range of likelihood for tumour progression. As traditional parameters do not ensure completely accurate prognostic grouping, new molecular markers are needed for assessing the individual patient's prognosis more precisely. 133 NB of all stages were analysed in blind-trial fashion for telomerase activity (TA), expression of surviving, and MYCN status. These data were correlated with other traditional prognostic indicators and disease outcome. TA is a powerful independent prognostic marker for all stages and is capable of differentiating between good and poor outcome in putative "favourable" clinical or biological subgroups of NB patients. High surviving expression is associated with an adverse outcome, but is more difficult to interprete than TA because survivin expression needs to be accurately quantified to be of predictive value. We propose an extended progression model for NB including emerging prognostic markers, with emphasis on telomerase activity.

Bayesian Knowledge Fusion in Prognostics and Health Management—A Case Study

NASA Astrophysics Data System (ADS)

Rabiei, Masoud; Modarres, Mohammad; Mohammad-Djafari, Ali

2011-03-01

In the past few years, a research effort has been in progress at University of Maryland to develop a Bayesian framework based on Physics of Failure (PoF) for risk assessment and fleet management of aging airframes. Despite significant achievements in modelling of crack growth behavior using fracture mechanics, it is still of great interest to find practical techniques for monitoring the crack growth instances using nondestructive inspection and to integrate such inspection results with the fracture mechanics models to improve the predictions. The ultimate goal of this effort is to develop an integrated probabilistic framework for utilizing all of the available information to come up with enhanced (less uncertain) predictions for structural health of the aircraft in future missions. Such information includes material level fatigue models and test data, health monitoring measurements and inspection field data. In this paper, a case study of using Bayesian fusion technique for integrating information from multiple sources in a structural health management problem is presented.

High serum soluble tumor necrosis factor receptor 1 predicts poor treatment response in acute-stage schizophrenia.

PubMed

Nishimon, Shohei; Ohnuma, Tohru; Takebayashi, Yuto; Katsuta, Narimasa; Takeda, Mayu; Nakamura, Toru; Sannohe, Takahiro; Higashiyama, Ryoko; Kimoto, Ayako; Shibata, Nobuto; Gohda, Tomohito; Suzuki, Yusuke; Yamagishi, Sho-Ichi; Tomino, Yasuhiko; Arai, Heii

2017-06-02

Inflammation may be involved in the pathophysiology of schizophrenia. However, few cross-sectional or longitudinal studies have examined changes in biomarker expression to evaluate diagnostic and prognostic efficacy in acute-stage schizophrenia. We compared serum inflammatory biomarker concentrations in 87 patients with acute-stage schizophrenia on admission to 105 age-, sex-, and body mass index (BMI)-matched healthy controls. The measured biomarkers were soluble tumor necrosis factor receptor 1 (sTNFR1) and adiponectin, which are associated with inflammatory responses, and pigment epithelium-derived factor (PEDF), which has anti-inflammatory properties. We then investigated biomarker concentrations and associations with clinical factors in 213 patients (including 42 medication-free patients) and 110 unmatched healthy controls to model conditions typical of clinical practice. Clinical symptoms were assessed using the Brief Psychiatric Rating Scale and Global Assessment of Function. In 121 patients, biomarker levels and clinical status were evaluated at both admission and discharge. Serum sTNFR1 was significantly higher in patients with acute-stage schizophrenia compared to matched controls while no significant group differences were observed for the other markers. Serum sTNFR1 was also significantly higher in the 213 patients compared to unmatched controls. The 42 unmedicated patients had significantly lower PEDF levels compared to controls. Between admission and discharge, sTNFR1 levels decreased significantly; however, biomarker changes did not correlate with clinical symptoms. The discriminant accuracy of sTNFR1 was 93.2% between controls and patients, showing no symptom improvement during care. Inflammation and a low-level anti-inflammatory state may be involved in both schizophrenia pathogenesis and acute-stage onset. High serum sTNFR1 in the acute stage could be a useful prognostic biomarker for treatment response in clinical practice. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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

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

PubMed

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

2017-10-01

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

A Framework for Model-Based Diagnostics and Prognostics of Switched-Mode Power Supplies

DTIC Science & Technology

2014-10-02

system. Some highlights of the work are included but not only limited to the following aspects: first, the methodology is based on electronic ... electronic health management, with the goal of expanding the realm of electronic diagnostics and prognostics. 1. INTRODUCTION Electronic systems such...as electronic controls, onboard computers, communications, navigation and radar perform many critical functions in onboard military and commercial

Army Logistician. Volume 39, Issue 1, January-February 2007

DTIC Science & Technology

2007-02-01

of electronic systems using statistical methods. P& C , however, requires advanced prognostic capabilities not only to detect the early onset of...patterns. Entities operating in a P& C -enabled environment will sense and understand contextual meaning , communicate their state and mission, and act to...accessing of historical and simulation patterns; on- board prognostics capabilities; physics of failure analyses; and predictive modeling. P& C also

Modified TIME-H: a simplified scoring system for chronic wound management.

PubMed

Lim, K; Free, B; Sinha, S

2015-09-01

Chronic wound assessment requires a systematic approach in order to guide management and improve prognostication. Following a pilot study using the original TIME-H scoring system in chronic wound management, modifications were suggested leading to the development of the Modified TIME-H scoring system. This study investigates the feasibility and reliability of chronic wound prognostication applying the Modified TIME-H score. Patients referred to the hospital's outpatient wound clinic over a 9-month period were categorised into one of three predicted outcome categories based on their Modified TIME-H score. This study shows a higher proportion of patients in the certain healing category achieved healed wounds, with a higher rate of reduction in wound size, when compared with the other categories. The three categories defined in this study are certain healing, uncertain healing and difficult healing. The Modified TIME-H score could be a useful tool for assessment, patient-centred management and prognostication of chronic wounds in clinical practice and requires further validation from other institutions. The authors have no conflict of interest to declare.

Value of coronary computed tomography as a prognostic tool.

PubMed

Contractor, Tahmeed; Parekh, Maansi; Ahmed, Shameer; Martinez, Matthew W

2012-08-01

Coronary computed tomography angiography (CCTA) has become an important part of our armamentarium for noninvasive diagnosis of coronary artery disease (CAD). Emerging technologies have produced lower radiation dose, improved spatial and temporal resolution, as well as information about coronary physiology. Although the prognostic role of coronary artery calcium scoring is known, similar evidence for CCTA has only recently emerged. Initial, small studies in various patient populations have indicated that CCTA-identified CAD may have a prognostic value. These findings were confirmed in a recent analysis of the international, prospective Coronary CT Angiography Evaluation For Clinical Outcomes: An International Multicenter (CONFIRM) registry. An incremental increase in mortality was found with a worse severity of CAD on a per-patient, per-vessel, and per-segment basis. In addition, age-, sex-, and ethnicity-based differences in mortality were also found. Whether changing our management algorithms based on these findings will affect outcomes is unclear. Large prospective studies utilizing targeted management strategies for obstructive and nonobstructive CAD are required to incorporate these recent findings into our daily practice. © 2012 Wiley Periodicals, Inc.

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

PubMed

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

2018-06-01

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

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.

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

PubMed

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

2016-08-01

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

Prognostic Significance of Modified Advanced Lung Cancer Inflammation Index (ALI) in Patients with Small Cell Lung Cancer_ Comparison with Original ALI.

PubMed

Kim, Eun Young; Kim, Nambeom; Kim, Young Saing; Seo, Ja-Young; Park, Inkeun; Ahn, Hee Kyung; Jeong, Yu Mi; Kim, Jeong Ho

2016-01-01

Advanced lung cancer inflammation index (ALI, body mass index [BMI] x serum albumin/neutrophil-lymphocyte ratio [NLR]) has been shown to predict overall survival (OS) in small cell lung cancer (SCLC). CT enables skeletal muscle to be quantified, whereas BMI cannot accurately reflect body composition. The purpose was to evaluate prognostic value of modified ALI (mALI) using CT-determined L3 muscle index (L3MI, muscle area at L3/height2) beyond original ALI. L3MIs were calculated using the CT images of 186 consecutive patients with SCLC taken at diagnosis, and mALI was defined as L3MI x serum albumin/NLR. Using chi-squared test determined maximum cut-offs for low ALI and low mALI, the prognostic values of low ALI and low mALI were tested using Kaplan-Meier method and Cox proportional hazards analysis. Finally, deviance statistics was used to test whether the goodness of fit of the prognostic model is improved by adding mALI as an extra variable. Patients with low ALI (cut-off, 31.1, n = 94) had shorter OS than patients with high ALI (median, 6.8 months vs. 15.8 months; p < 0.001), and patients with low mALI (cut-off 67.7, n = 94) had shorter OS than patients with high mALI (median, 6.8 months vs. 16.5 months; p < 0.001). There was no significant difference in estimates of median survival time between low ALI and low mALI (z = 0.000, p = 1.000) and between high ALI and high mALI (z = 0.330, p = 0.740). Multivariable analysis showed that low ALI was an independent prognostic factor for shorter OS (HR, 1.67, p = 0.004), along with advanced age (HR, 1.49, p = 0.045), extensive disease (HR, 2.27, p < 0.001), supportive care only (HR, 7.86, p < 0.001), and elevated LDH (HR, 1.45, p = 0.037). Furthermore, goodness of fit of this prognostic model was not significantly increased by adding mALI as an extra variable (LR difference = 2.220, p = 0.136). The present study confirms mALI using CT-determined L3MI has no additional prognostic value beyond original ALI using BMI. ALI is a simple and useful prognostic indicator in SCLC.

Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa; Jackson, Tom

2017-09-01

This paper investigates the polymer electrolyte membrane (PEM) fuel cell internal behaviour variation at different operating condition, with characterization test data taken at predefined inspection times, and uses the determined internal behaviour evolution to predict the future PEM fuel cell performance. For this purpose, a PEM fuel cell behaviour model is used, which can be related to various fuel cell losses. By matching the model to the collected polarization curves from the PEM fuel cell system, the variation of fuel cell internal behaviour can be obtained through the determined model parameters. From the results, the source of PEM fuel cell degradation during its lifetime at different conditions can be better understood. Moreover, with determined fuel cell internal behaviour, the future fuel cell performance can be obtained by predicting the future model parameters. By comparing with prognostic results using adaptive neuro fuzzy inference system (ANFIS), the proposed prognostic analysis can provide better predictions for PEM fuel cell performance at dynamic condition, and with the understanding of variation in PEM fuel cell internal behaviour, mitigation strategies can be designed to extend the fuel cell performance.

Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma.

PubMed

Tadeo, Irene; Piqueras, Marta; Montaner, David; Villamón, Eva; Berbegall, Ana P; Cañete, Adela; Navarro, Samuel; Noguera, Rosa

2014-02-01

Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma.

[Prognostic model of the space station contamination stage].

PubMed

Zlotovol'skiĭ, V M; Smolenskaia, G S

1998-01-01

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

Comparison of Cox's Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000-2012.

PubMed

Adelian, R; Jamali, J; Zare, N; Ayatollahi, S M T; Pooladfar, G R; Roustaei, N

2015-01-01

Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. To compare Cox's regression model with parametric models for determining the independent factors for predicting adults' and pediatrics' survival after liver transplantation. This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Parametric regression model is a good alternative for the Cox's regression model.

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.

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

NASA Technical Reports Server (NTRS)

Avissar, Roni; Chen, Fei

1993-01-01

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

Heterogeneity of Prognostic Studies of 24-Hour Blood Pressure Variability: Systematic Review and Meta-Analysis

PubMed Central

Taylor, Kathryn S.; Heneghan, Carl J.; Stevens, Richard J.; Adams, Emily C.; Nunan, David; Ward, Alison

2015-01-01

In addition to mean blood pressure, blood pressure variability is hypothesized to have important prognostic value in evaluating cardiovascular risk. We aimed to assess the prognostic value of blood pressure variability within 24 hours. Using MEDLINE, EMBASE and Cochrane Library to April 2013, we conducted a systematic review of prospective studies of adults, with at least one year follow-up and any day, night or 24-hour blood pressure variability measure as a predictor of one or more of the following outcomes: all-cause mortality, cardiovascular mortality, all cardiovascular events, stroke and coronary heart disease. We examined how blood pressure variability is defined and how its prognostic use is reported. We analysed relative risks adjusted for covariates including the appropriate mean blood pressure and considered the potential for meta-analysis. Our analysis of methods included 24 studies and analysis of predictions included 16 studies. There were 36 different measures of blood pressure variability and 13 definitions of night- and day-time periods. Median follow-up was 5.5 years (interquartile range 4.2–7.0). Comparing measures of dispersion, coefficient of variation was less well researched than standard deviation. Night dipping based on percentage change was the most researched measure and the only measure for which data could be meaningfully pooled. Night dipping or lower night-time blood pressure was associated with lower risk of cardiovascular events. The interpretation and use in clinical practice of 24-hour blood pressure variability, as an important prognostic indicator of cardiovascular events, is hampered by insufficient evidence and divergent methodologies. We recommend greater standardisation of methods. PMID:25984791

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

PubMed Central

Roy, Janine; Aust, Daniela; Knösel, Thomas; Rümmele, Petra; Jahnke, Beatrix; Hentrich, Vera; Rückert, Felix; Niedergethmann, Marco; Weichert, Wilko; Bahra, Marcus; Schlitt, Hans J.; Settmacher, Utz; Friess, Helmut; Büchler, Markus; Saeger, Hans-Detlev; Schroeder, Michael; Pilarsky, Christian; Grützmann, Robert

2012-01-01

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. PMID:22615549

Vemurafenib in BRAF-mutant metastatic melanoma patients in real-world clinical practice: prognostic factors associated with clinical outcomes.

PubMed

Schouwenburg, Maartje G; Jochems, Anouk; Leeneman, Brenda; Franken, Margreet G; van den Eertwegh, Alfons J M; Haanen, John B A G; van Zeijl, Michiel C T; Aarts, Maureen J; van Akkooi, Alexander C J; van den Berkmortel, Franchette W P J; Blokx, Willeke A M; de Groot, Jan Willem B; Hospers, Geke A P; Kapiteijn, Ellen; Koornstra, Rutger H; Kruit, Wim H; Louwman, Marieke W J; Piersma, Djura; van Rijn, Rozemarijn S; Suijkerbuijk, Karijn P M; Ten Tije, Albert J; Vreugdenhil, Gerard; Wouters, Michel W J M; van der Hoeven, Jacobus J M

2018-08-01

The aim of this population-based study was to identify the factors associated with clinical outcomes in vemurafenib-treated patients and to evaluate outcomes across subgroups of patients with different risk profiles. Data were retrieved from the Dutch Melanoma Treatment Registry. Time to next treatment (TTNT) and overall survival (OS) of all metastatic melanoma patients who received vemurafenib between 2012 and 2015 were assessed using Kaplan-Meier estimates. A risk score was developed on the basis of all prognostic factors associated with TTNT and OS derived from multivariable Cox regression analyses. Patients were stratified according to the presence of prognostic risk factors by counting the number of factors, ranging from 0 to 6. A total of 626 patients received vemurafenib with a median follow-up of 35.8 months. The median TTNT and OS were 4.7 months [95% confidence intervals (CI): 4.4-5.1] and 7.3 months (95% CI: 6.6-8.0). The strongest prognostic factors were serum lactate dehydrogenase (LDH) level, Eastern Cooperative Oncology Group performance score, number of organ sites involved and brain metastases. Patients with a favourable risk profile (no risk factors) had a median TTNT and OS of 7.1 (95% CI: 5.8-8.5) and 15.4 months (95% CI: 10.0-20.9). The median OS more than halved for patients with greater than or equal to 2 risk factors compared with patients with no risk factors. The clinical outcomes of vemurafenib in metastatic melanoma patients with a favourable risk profile are comparable with the results of the trials. Combining prognostic factors into a risk score could be valuable to stratify patients into favourable and poor-prognosis groups.

Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients.

PubMed

Kirilovsky, Amos; Marliot, Florence; El Sissy, Carine; Haicheur, Nacilla; Galon, Jérôme; Pagès, Franck

2016-08-01

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) tumor, nodes, metastasis (TNM) classification system based on tumor features is used for prognosis estimation and treatment recommendations in most cancers. However, the clinical outcome can vary significantly among patients within the same tumor stage and TNM classification does not predict response to therapy. Therefore, many efforts have been focused on the identification of new markers. Multiple tumor cell-based approaches have been proposed but very few have been translated into the clinic. The recent demonstration of the essential role of the immune system in tumor progression has allowed great advances in the understanding of this complex disease and in the design of novel therapies. The analysis of the immune infiltrate by imaging techniques in large patient cohorts highlighted the prognostic impact of the in situ immune cell infiltrate in tumors. Moreover, the characterization of the immune infiltrates (e.g. type, density, distribution within the tumor, phenotype, activation status) in patients treated with checkpoint-blockade strategies could provide information to predict the disease outcome. In colorectal cancer, we have developed a prognostic score ('Immunoscore') that takes into account the distribution of the density of both CD3(+) lymphocytes and CD8(+) cytotoxic T cells in the tumor core and the invasive margin that could outperform TNM staging. Currently, an international retrospective study is under way to validate the Immunoscore prognostic performance in patients with colon cancer. The use of Immunoscore in clinical practice could improve the patients' prognostic assessment and therapeutic management. © The Japanese Society for Immunology. 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Gender and age related predictive value of walk test in heart failure: do anthropometrics matter in clinical practice?

PubMed

Frankenstein, L; Remppis, A; Graham, J; Schellberg, D; Sigg, C; Nelles, M; Katus, H A; Zugck, C

2008-07-21

The six-minute walk test (6 WT) is a valid and reliable predictor of morbidity and mortality in chronic heart failure (CHF) patients, frequently used as an endpoint or target in clinical trials. As opposed to spiroergometry, improvement of its prognostic accuracy by correction for height, weight, age and gender has not yet been attempted comprehensively despite known influences of these parameters. We recorded the 6 WT of 1035 CHF patients, attending clinic from 1995 to 2005. The 1-year prognostic value of 6 WT was calculated, alone and after correction for height, weight, BMI and/or age. Analysis was performed on the entire cohort, on males and females separately and stratified according to BMI (<25, 25-30 and >30 kg/m(2)). 6 WT weakly correlated with age (r=-0.32; p<0.0001), height (r=0.2; p<0.0001), weight (r=0.11; p<0.001), not with BMI (r=0.01; p=ns). The 6 WT was a strong predictor of 1-year mortality in both genders, both as a single and age corrected parameter. Parameters derived from correction of 6 WT for height, weight or BMI did not improve the prognostic value in univariate analysis for either gender. Comparison of the receiver operated characteristics showed no significant gain in prognostic accuracy from any derived variable, either for males or females. The six-minute walk test is a valid tool for risk prediction in both male and female CHF patients. In both genders, correcting 6 WT distance for height, weight or BMI alone, or adjusting for age, does not increase the prognostic power of this tool.

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.

Switching Kalman filter for failure prognostic

NASA Astrophysics Data System (ADS)

Lim, Chi Keong Reuben; Mba, David

2015-02-01

The use of condition monitoring (CM) data to predict remaining useful life have been growing with increasing use of health and usage monitoring systems on aircraft. There are many data-driven methodologies available for the prediction and popular ones include artificial intelligence and statistical based approach. The drawback of such approaches is that they require a lot of failure data for training which can be scarce in practice. In lieu of this, methods using state-space and regression-based models that extract information from the data history itself have been explored. However, such methods have their own limitations as they utilize a single time-invariant model which does not represent changing degradation path well. This causes most degradation modeling studies to focus only on segments of their CM data that behaves close to the assumed model. In this paper, a state-space based method; the Switching Kalman Filter (SKF), is adopted for model estimation and life prediction. The SKF approach however, uses multiple models from which the most probable model is inferred from the CM data using Bayesian estimation before it is applied for prediction. At the same time, the inference of the degradation model itself can provide maintainers with more information for their planning. This SKF approach is demonstrated with a case study on gearbox bearings that were found defective from the Republic of Singapore Air Force AH64D helicopter. The use of in-service CM data allows the approach to be applied in a practical scenario and results showed that the developed SKF approach is a promising tool to support maintenance decision-making.

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

PubMed Central

Vogenberg, F. Randy

2009-01-01

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

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.

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.

Allogeneic transplantation provides durable remission in a subset of DLBCL patients relapsing after autologous transplantation.

PubMed

Fenske, Timothy S; Ahn, Kwang W; Graff, Tara M; DiGilio, Alyssa; Bashir, Qaiser; Kamble, Rammurti T; Ayala, Ernesto; Bacher, Ulrike; Brammer, Jonathan E; Cairo, Mitchell; Chen, Andy; Chen, Yi-Bin; Chhabra, Saurabh; D'Souza, Anita; Farooq, Umar; Freytes, Cesar; Ganguly, Siddhartha; Hertzberg, Mark; Inwards, David; Jaglowski, Samantha; Kharfan-Dabaja, Mohamed A; Lazarus, Hillard M; Nathan, Sunita; Pawarode, Attaphol; Perales, Miguel-Angel; Reddy, Nishitha; Seo, Sachiko; Sureda, Anna; Smith, Sonali M; Hamadani, Mehdi

2016-07-01

For diffuse large B-cell lymphoma (DLBCL) patients progressing after autologous haematopoietic cell transplantation (autoHCT), allogeneic HCT (alloHCT) is often considered, although limited information is available to guide patient selection. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we identified 503 patients who underwent alloHCT after disease progression/relapse following a prior autoHCT. The 3-year probabilities of non-relapse mortality, progression/relapse, progression-free survival (PFS) and overall survival (OS) were 30, 38, 31 and 37% respectively. Factors associated with inferior PFS on multivariate analysis included Karnofsky performance status (KPS) <80, chemoresistance, autoHCT to alloHCT interval <1-year and myeloablative conditioning. Factors associated with worse OS on multivariate analysis included KPS<80, chemoresistance and myeloablative conditioning. Three adverse prognostic factors were used to construct a prognostic model for PFS, including KPS<80 (4 points), autoHCT to alloHCT interval <1-year (2 points) and chemoresistant disease at alloHCT (5 points). This CIBMTR prognostic model classified patients into four groups: low-risk (0 points), intermediate-risk (2-5 points), high-risk (6-9 points) or very high-risk (11 points), predicting 3-year PFS of 40, 32, 11 and 6%, respectively, with 3-year OS probabilities of 43, 39, 19 and 11% respectively. In conclusion, the CIBMTR prognostic model identifies a subgroup of DLBCL patients experiencing long-term survival with alloHCT after a failed prior autoHCT. © 2016 John Wiley & Sons Ltd.

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

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.

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.

Incremental Prognostic Value of Apparent Diffusion Coefficient Histogram Analysis in Head and Neck Squamous Cell Carcinoma.

PubMed

Li, Xiaoxia; Yuan, Ying; Ren, Jiliang; Shi, Yiqian; Tao, Xiaofeng

2018-03-26

We aimed to investigate the incremental prognostic value of apparent diffusion coefficient (ADC) histogram analysis in patients with head and neck squamous cell carcinoma (HNSCC) and integrate it into a multivariate prognostic model. A retrospective review of magnetic resonance imaging findings was conducted in patients with pathologically confirmed HNSCC between June 2012 and December 2015. For each tumor, six histogram parameters were derived: the 10th, 50th, and 90th percentiles of ADC (ADC 10 , ADC 50 , and ADC 90 ); mean ADC values (ADC mean ); kurtosis; and skewness. The clinical variables included age, sex, smoking status, tumor volume, and tumor node metastasis stage. The association of these histogram and clinical variables with overall survival (OS) was determined. Further validation of the histogram parameters as independent biomarkers was performed using multivariate Cox proportional hazard models combined with clinical variables, which was compared to the clinical model. Models were assessed with C index and receiver operating characteristic curve analyses for the 12- and 36-month OS. Ninety-six patients were eligible for analysis. Median follow-up was 877 days (range, 54-1516 days). A total of 29 patients died during follow-up (30%). Patients with higher ADC values (ADC 10  > 0.958 × 10 -3 mm 2 /s, ADC 50  > 1.089 × 10 -3 mm 2 /s, ADC 90  > 1.152 × 10 -3 mm 2 /s, ADC mean  > 1.047 × 10 -3 mm 2 /s) and lower kurtosis (≤0.967) were significant predictors of poor OS (P < .100 for all). After adjusting for sex and tumor node metastasis stage, the ADC 90 and kurtosis are both significant predictors of OS with hazard ratios = 1.00 (95% confidence interval: 1.001-1.004) and 0.58 (95% confidence interval: 0.37-0.90), respectively. By adding the ADC parameters into the clinical model, the C index and diagnostic accuracies for the 12- and 36-month OS showed significant improvement. ADC histogram analysis has incremental prognostic value in patients with HNSCC and increases the performance of a multivariable prognostic model in addition to clinical variables. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Prognostic value of proliferation in pleomorphic soft tissue sarcomas: a new look at an old measure.

PubMed

Seinen, Jojanneke M; Jönsson, Mats; Bendahl, Pär-Ola O; Baldetorp, Bo; Rambech, Eva; Åkerman, Måns; Rydholm, Anders; Nilbert, Mef; Carneiro, Ana

2012-12-01

Though proliferation has repeatedly shown a prognostic role in sarcomas, it has not reached clinical application. We performed a comprehensive evaluation of the prognostic role of 5 proliferation measures in a large series of soft tissue sarcomas of the extremities and the trunk wall. One hundred ninety-six primary soft tissue sarcomas of the extremities and the trunk wall were subjected to DNA flow cytometry for quantification of S-phase fraction and to immunohistochemical evaluation of Ki-67, Top2a, p21, and p27Kip1. In univariate analysis, positive expression of Ki-67 (hazard ratio = 4.5, CI = 1.6-12.1), Top2a (hazard ratio = 2.2, CI = 1.2-3.5) and high S-phase fraction (hazard ratio = 1.8, CI = 1.2-3.7) significantly correlated with risk for metastasis. When combined with currently used prognostic factors, Ki-67, S-phase fraction and Top2a fraction contributed to refined identification of prognostic risk groups. Proliferation, as assessed by expression of Ki-67 and Top2a and evaluation of S-phase fraction and applied to statistical decision-tree models, provides prognostic information in soft tissue sarcomas of the extremity and trunk wall. Though proliferation contributes independently to currently applied prognosticators, its role is particularly strong when few other factors are available, which suggests a role in preoperative decision-making related to identification of high-risk individuals who would benefit from neoadjuvant therapy. Copyright © 2012 Elsevier Inc. All rights reserved.

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.

[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.

Professional satisfaction of women in surgery: results of a national study.

PubMed

End, Adelheid; Mittlboeck, Martina; Piza-Katzer, Hildegunde

2004-11-01

Individual, group, and organizational factors influence the professional satisfaction of women surgeons in Austria. Survey on professional and private issues sent out by mail in 2000 and 2001. Women surgeons working in hospitals and/or in private practices and those who were retired or on maternity leave. All 351 Austrian women surgeons of all core surgical specialties (general, trauma, pediatric, plastic, thoracic, and cardiovascular), certified or in training, were addressed. Proportional odds regression models were used to correlate professional satisfaction with objectively measurable prognostic factors such as age, surgical subspecialty, status of training, type of hospital, location of work (federal states vs the capital), status of activity (active vs on maternity leave), profession of private partner, number of children, and subjectively assessed prognostic factors such as operative volume and departmental organization. The response rate was 58.7% (206/351). One hundred eighty-seven surgeons-active or on maternity leave-were included in the analysis. Higher satisfaction was reported by active surgeons in subspecialties, certified surgeons, comparatively younger and older surgeons, surgeons working in hospitals outside the capital, and surgeons with a physician as a partner. When entering subjectively assessed variables into the model, the quality of departmental organization and operative volume (P<.001), as well as the status of activity (P<.001), had the strongest effect. Women surgeons' professional satisfaction highly depends on departmental organization and status of activity. Inadequate leadership, low operative volume, and being on maternity leave have a negative effect on job satisfaction. Private factors seem to be of little influence. Optimal departmental organization would help women to reconcile their professional and their private lives.

An Extended Eddy-Diffusivity Mass-Flux Scheme for Unified Representation of Subgrid-Scale Turbulence and Convection

NASA Astrophysics Data System (ADS)

Tan, Zhihong; Kaul, Colleen M.; Pressel, Kyle G.; Cohen, Yair; Schneider, Tapio; Teixeira, João.

2018-03-01

Large-scale weather forecasting and climate models are beginning to reach horizontal resolutions of kilometers, at which common assumptions made in existing parameterization schemes of subgrid-scale turbulence and convection—such as that they adjust instantaneously to changes in resolved-scale dynamics—cease to be justifiable. Additionally, the common practice of representing boundary-layer turbulence, shallow convection, and deep convection by discontinuously different parameterizations schemes, each with its own set of parameters, has contributed to the proliferation of adjustable parameters in large-scale models. Here we lay the theoretical foundations for an extended eddy-diffusivity mass-flux (EDMF) scheme that has explicit time-dependence and memory of subgrid-scale variables and is designed to represent all subgrid-scale turbulence and convection, from boundary layer dynamics to deep convection, in a unified manner. Coherent up and downdrafts in the scheme are represented as prognostic plumes that interact with their environment and potentially with each other through entrainment and detrainment. The more isotropic turbulence in their environment is represented through diffusive fluxes, with diffusivities obtained from a turbulence kinetic energy budget that consistently partitions turbulence kinetic energy between plumes and environment. The cross-sectional area of up and downdrafts satisfies a prognostic continuity equation, which allows the plumes to cover variable and arbitrarily large fractions of a large-scale grid box and to have life cycles governed by their own internal dynamics. Relatively simple preliminary proposals for closure parameters are presented and are shown to lead to a successful simulation of shallow convection, including a time-dependent life cycle.

A clinical tool for predicting survival in ALS.

PubMed

Knibb, Jonathan A; Keren, Noa; Kulka, Anna; Leigh, P Nigel; Martin, Sarah; Shaw, Christopher E; Tsuda, Miho; Al-Chalabi, Ammar

2016-12-01

Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic 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/.

An Extended Eddy‐Diffusivity Mass‐Flux Scheme for Unified Representation of Subgrid‐Scale Turbulence and Convection

PubMed Central

Tan, Zhihong; Kaul, Colleen M.; Pressel, Kyle G.; Cohen, Yair; Teixeira, João

2018-01-01

Abstract Large‐scale weather forecasting and climate models are beginning to reach horizontal resolutions of kilometers, at which common assumptions made in existing parameterization schemes of subgrid‐scale turbulence and convection—such as that they adjust instantaneously to changes in resolved‐scale dynamics—cease to be justifiable. Additionally, the common practice of representing boundary‐layer turbulence, shallow convection, and deep convection by discontinuously different parameterizations schemes, each with its own set of parameters, has contributed to the proliferation of adjustable parameters in large‐scale models. Here we lay the theoretical foundations for an extended eddy‐diffusivity mass‐flux (EDMF) scheme that has explicit time‐dependence and memory of subgrid‐scale variables and is designed to represent all subgrid‐scale turbulence and convection, from boundary layer dynamics to deep convection, in a unified manner. Coherent up and downdrafts in the scheme are represented as prognostic plumes that interact with their environment and potentially with each other through entrainment and detrainment. The more isotropic turbulence in their environment is represented through diffusive fluxes, with diffusivities obtained from a turbulence kinetic energy budget that consistently partitions turbulence kinetic energy between plumes and environment. The cross‐sectional area of up and downdrafts satisfies a prognostic continuity equation, which allows the plumes to cover variable and arbitrarily large fractions of a large‐scale grid box and to have life cycles governed by their own internal dynamics. Relatively simple preliminary proposals for closure parameters are presented and are shown to lead to a successful simulation of shallow convection, including a time‐dependent life cycle. PMID:29780442

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.

Prognostic impact of sarcopenia in patients with diffuse large B-cell lymphoma treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone.

PubMed

Go, Se-Il; Park, Mi Jung; Song, Haa-Na; Kim, Hoon-Gu; Kang, Myoung Hee; Lee, Hyang Rae; Kim, Yire; Kim, Rock Bum; Lee, Soon Il; Lee, Gyeong-Won

2016-12-01

Sarcopenia is known to be related to an increased risk of chemotherapy toxicity and to a poor prognosis in patients with malignancy. We assessed the prognostic role of sarcopenia in patients with diffuse large B-cell lymphoma (DLBCL). In total, 187 consecutive patients with DLBCL treated with induction rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) immunochemotherapy were reviewed. Sarcopenia was defined as the lowest sex-specific quartile of the skeletal muscle index, calculated by dividing the pectoralis muscle area by the height. Clinical outcomes were compared between the sarcopenic and non-sarcopenic groups. A nomogram was constructed from the Cox regression model for overall survival (OS). Treatment-related mortality (21.7 vs. 5.0%, P  = 0.002) and early discontinuation of treatment (32.6 vs. 14.9%, P  = 0.008) were more common in the sarcopenic group than in the non-sarcopenic group. The 5 year progression-free survival (PFS) rates were 35.3% in the sarcopenic group and 65.8% in the non-sarcopenic group ( P  < 0.001). The 5 year OS rates were 37.3% in the sarcopenic group and 68.1% in the non-sarcopenic group ( P  < 0.001). Sarcopenia and the five variables of the International Prognostic Index (IPI) were independent prognostic factors in a multivariate analysis for PFS and OS and were used to construct the nomogram. The calibration plot showed good agreement between the nomogram predictions and actual observations. The c index of the nomogram (0.80) was higher than those of other prognostic indices (IPI, 0.77, P  = 0.009; revised-IPI, 0.74, P  < 0.001; National Comprehensive Cancer Network-IPI, 0.77, P  = 0.062). Sarcopenia is associated with intolerance to standard R-CHOP chemotherapy as well as a poor prognosis. Moreover, sarcopenia itself can be included in prognostic models in DLBCL.

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.

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

Breast Cancer Knowledge, Perception and Breast Self- Examination Practices among Yemeni Women: an Application of the Health Belief Model.

PubMed

Al-Sakkaf, Khaled Abdulla; Basaleem, Huda Omer

2016-01-01

The incidence of breast cancer is rapidly increasing in Yemen with recent indications of constituting one-third of female cancers. The main problem in Yemen remains very late presentation of breast cancer, most of which should have been easily recognisable. Since stage of disease at diagnosis is the most important prognostic variable, early diagnosis is an important option to be considered for control of breast cancer in low resourced settings like Yemen. In the present study, we aimed at describing breast cancer knowledge, perceptions and breast self-examination (BSE) practices among a sample of Yemeni women. This cross-sectional study covered 400 women attending four reproductive health centres in Aden, Yemen through face-to-face interview using a structured questionnaire during April - July 2014. We collected data on sociodemographic characteristics, knowledge about breast cancer, and screening practices as well as respondents' perceptions based on the five sub scales of the Health Belief Model (HBM): perceived susceptibility; perceived severity; perceived barriers; perceived benefits; and self-efficacy. The response format was a fivepoint Likert scale. Statistical Package for Social Sciences (SPSS 20) was used for statistical analysis. Statistical significance was set at p<0.05. Logistic regression analysis was conducted with BSE as a dependent variable. The mean age of women was 26.5 (S.D=5.6) years. The majority (89.0%) had never ever performed any screening. Two-thirds of respondents had poor knowledge. Perceived BSE benefits and self-efficacy and lower BSE barriers perception were significant independent predictors of BSE practice. Poor knowledge and inadequate BSE practices are prevailing in Yemen. The need for implementing culturally sensitive targeted education measures is mandatory in the effort to improve early detection and reduce the burden of breast cancer.

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.

The Presence of Vascular Mimicry Predicts High Risk of Clear Cell Renal Cell Carcinoma after Radical Nephrectomy.

PubMed

Zhou, Lin; Chang, Yuan; Xu, Le; Liu, Zheng; Fu, Qiang; Yang, Yuanfeng; Lin, Zongming; Xu, Jiejie

2016-08-01

Vascular mimicry is a type of tumor cell plasticity. The aim of this study was to determine the prognostic value of vascular mimicry in patients with clear cell renal cell carcinoma. We performed a retrospective cohort study in 387 patients with clear cell renal cell carcinoma who underwent radical nephrectomy at Zhongshan Hospital, Fudan University between 2008 and 2009. Pathological features, baseline patient characteristics and followup data were recorded. Vascular mimicry in clear cell renal cell carcinoma tissue was identified by CD31-periodic acid-Schiff double staining. Univariate and multivariate Cox regression models were used to analyze the impact of prognostic factors on recurrence-free survival. The concordance index and the Akaike information criterion were used to assess the predictive accuracy and sufficiency of different models. Positive vascular mimicry staining occurred in 25 of 387 clear cell renal cell carcinoma cases (6.5%) and it was associated with an increased risk of recurrence (log-rank p <0.001). Incorporating vascular mimicry into pT stage, Fuhrman grade and Leibovich score helped refine individual risk stratification. Moreover, vascular mimicry was identified as an independent prognostic factor (p = 0.001). It was entered into a nomogram together with pT stage, Fuhrman grade, tumor size and necrosis. In the primary cohort the Harrell concordance index for the established nomogram to predict recurrence-free survival was slightly higher than that of the Leibovich model (0.850 vs. 0.823), which failed to reach statistical significance (p = 0.158). Vascular mimicry could be a potential prognosticator for recurrence-free survival in patients with clear cell renal cell carcinoma after radical nephrectomy. Further external validation and functional analysis should be pursued to assess its potential prognostic and therapeutic values for clear cell renal cell carcinoma. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

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

Prognostic factors for perceived recovery or functional improvement in non-specific low back pain: secondary analyses of three randomized clinical trials

PubMed Central

Staal, J. Bart; Heymans, Martijn W.; Harts, Chris C.; Hendriks, Erik J. M.; de Bie, Rob A.

2009-01-01

The objective of this study was to report on secondary analyses of a merged trial dataset aimed at exploring the potential importance of patient factors associated with clinically relevant improvements in non-acute, non-specific low back pain (LBP). From 273 predominantly male army workers (mean age 39 ± 10.5 years, range 20–56 years, 4 women) with LBP who were recruited in three randomized clinical trials, baseline individual patient factors, pain-related factors, work-related psychosocial factors, and psychological factors were evaluated as potential prognostic variables in a short-term (post-treatment) and a long-term logistic regression model (6 months after treatment). We found one dominant prognostic factor for improvement directly after treatment as well as 6 months later: baseline functional disability, expressed in Roland–Morris Disability Questionnaire scores. Baseline fear of movement, expressed in Tampa Scale for Kinesiophobia scores, had also significant prognostic value for long-term improvement. Less strongly associated with the outcome, but also included in our final models, were supervisor social support and duration of complaints (short-term model), and co-worker social support and pain radiation (long-term model). Information about initial levels of functional disability and fear-avoidance behaviour can be of value in the treatment of patient populations with characteristics comparable to the current army study population (e.g., predominantly male, physically active, working, moderate but chronic back problems). Individuals at risk for poor long-term LBP recovery, i.e., individuals with high initial level of disability and prominent fear-avoidance behaviour, can be distinguished that may need additional cognitive-behavioural treatment. PMID:20035358

Hepatocellular carcinoma in uremic patients: is there evidence for an increased risk of mortality?

PubMed

Lee, Yun-Hsuan; Hsu, Chia-Yang; Hsia, Cheng-Yuan; Huang, Yi-Hsiang; Su, Chien-Wei; Lin, Han-Chieh; Lee, Rheun-Chuan; Chiou, Yi-You; Huo, Teh-Ia

2013-02-01

The clinical aspects of patients with hepatocellular carcinoma (HCC) undergoing maintenance dialysis are largely unknown. We aimed to investigate the long-term survival and prognostic determinants of dialysis patients with HCC. A total of 2502 HCC patients, including 30 dialysis patients and 90 age, sex, and treatment-matched controls were retrospectively analyzed. Dialysis patients more often had dual viral hepatitis B and C, lower serum α-fetoprotein level, worse performance status, higher model for end-stage liver disease (MELD) score than non-dialysis patients and matched controls (P all < 0.05). There was no significant difference in long-term survival between dialysis and non-dialysis patients and matched controls (P = 0.684 and 0.373, respectively). In the Cox proportional hazards model, duration of dialysis < 40 months (hazard ratio [HR]: 6.67, P = 0.019) and ascites (HR: 5.275, P = 0.019) were independent predictors of poor prognosis for dialysis patients with HCC. Survival analysis disclosed that the Child-Turcotte-Pugh (CTP) provided a better prognostic ability than the MELD system. Among the four currently used staging systems, the Japan Integrated Scoring (JIS) system was a more accurate prognostic model for dialysis patients; a JIS score ≥ 2 significantly predicted a worse survival (P = 0.024). Patients with HCC undergoing maintenance dialysis do not have a worse long-term survival. A longer duration of dialysis and absence of ascites formation are associated with a better outcome in dialysis patients. The CTP classification is a more feasible prognostic marker to indicate the severity of cirrhosis, and the JIS system may be a better staging model for outcome prediction. © 2012 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

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

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.

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.

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.

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

Comparison of Cox’s Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000–2012

PubMed Central

Adelian, R.; Jamali, J.; Zare, N.; Ayatollahi, S. M. T.; Pooladfar, G. R.; Roustaei, N.

2015-01-01

Background: Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. Objective: To compare Cox’s regression model with parametric models for determining the independent factors for predicting adults’ and pediatrics’ survival after liver transplantation. Method: This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Result: Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Conclusion: Parametric regression model is a good alternative for the Cox’s regression model. PMID:26306158

Prognostication in Pulmonary Arterial Hypertension with Submaximal Exercise Testing.

PubMed

Khatri, Vinod; Neal, Jennifer E; Burger, Charles D; Lee, Augustine S

2015-02-06

The submaximal exercise test (SET), which gives both a measure of exercise tolerance, as well as disease severity, should be a more robust functional and prognostic marker than the six-minute walk test (6MWT). This study aimed to determine the prognostic value of SET as predicted by the validated REVEAL (Registry to Evaluate Early and Long-Term Pulmonary Artery Hypertension Disease Management) registry risk score (RRRS). Sixty-five consecutive patients with idiopathic and associated pulmonary arterial hypertension (PAH) underwent right-heart catheterization, echocardiogram, 6MWT and a three-minute SET (Shape-HF™). Analyses explored the association between SET variables and prognosis predicted by the RRRS. Although multiple SET variables correlated with the RRRS on univariate analyses, only V E /V CO2 (r = 0.57, p < 0.0001) remained an independent predictor in multivariate analysis (β = 0.05, p = 0.0371). Additionally, the V E /V CO2 was the most discriminatory (area under receiver operating characteristic curve, 0.84) in identifying the highest-risk category (RRRS ≥ 10), with an optimal cut-off of 40.6, resulting in a high sensitivity (92%) and negative-predictive value (97%), but a lower specificity (67%). SETs, particularly the V E /V CO2 , appear to have prognostic value when compared to the RRRS. If validated in prospective trials, SET should prove superior to the 6MWT or the RRRS, with significant implications for both future clinical trials and clinical practice.

Discrimination measures for survival outcomes: connection between the AUC and the predictiveness curve.

PubMed

Viallon, Vivian; Latouche, Aurélien

2011-03-01

Finding out biomarkers and building risk scores to predict the occurrence of survival outcomes is a major concern of clinical epidemiology, and so is the evaluation of prognostic models. In this paper, we are concerned with the estimation of the time-dependent AUC--area under the receiver-operating curve--which naturally extends standard AUC to the setting of survival outcomes and enables to evaluate the discriminative power of prognostic models. We establish a simple and useful relation between the predictiveness curve and the time-dependent AUC--AUC(t). This relation confirms that the predictiveness curve is the key concept for evaluating calibration and discrimination of prognostic models. It also highlights that accurate estimates of the conditional absolute risk function should yield accurate estimates for AUC(t). From this observation, we derive several estimators for AUC(t) relying on distinct estimators of the conditional absolute risk function. An empirical study was conducted to compare our estimators with the existing ones and assess the effect of model misspecification--when estimating the conditional absolute risk function--on the AUC(t) estimation. We further illustrate the methodology on the Mayo PBC and the VA lung cancer data sets. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Intravascular photoacoustic imaging: a new tool for vulnerable plaque identification.

PubMed

Jansen, Krista; van Soest, Gijs; van der Steen, Antonius F W

2014-06-01

The vulnerable atherosclerotic plaque is believed to be at the root of the majority of acute coronary events. Even though the exact origins of plaque vulnerability remain elusive, the thin-cap fibroatheroma, characterized by a lipid-rich necrotic core covered by a thin fibrous cap, is considered to be the most prominent type of vulnerable plaque. No clinically available imaging technique can characterize atherosclerotic lesions to the extent needed to determine plaque vulnerability prognostically. Intravascular photoacoustic imaging (IVPA) has the potential to take a significant step in that direction by imaging both plaque structure and composition. IVPA is a natural extension of intravascular ultrasound that adds tissue type specificity to the images. IVPA utilizes the optical contrast provided by the differences in the absorption spectra of plaque components to image composition. Its capability to image lipids in human coronary atherosclerosis has been shown extensively ex vivo and has recently been translated to an in vivo animal model. Other disease markers that have been successfully targeted are calcium and inflammatory markers, such as macrophages and matrix metalloproteinase; the latter two through application of exogenous contrast agents. By simultaneously displaying plaque morphology and composition, IVPA can provide a powerful prognostic marker for disease progression, and as such has the potential to transform the current practice in percutaneous coronary intervention. Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Increased body mass index is associated with improved overall survival in extranodal natural killer/T-cell lymphoma, nasal type.

PubMed

Li, Ya-Jun; Yi, Ping-Yong; Li, Ji-Wei; Liu, Xian-Ling; Liu, Xi-Yu; Zhou, Fang; OuYang, Zhou; Sun, Zhong-Yi; Huang, Li-Jun; He, Jun-Qiao; Yao, Yuan; Fan, Zhou; Tang, Tian; Jiang, Wen-Qi

2017-01-17

The role of body mass index (BMI) in lymphoma survival outcomes is controversial. The prognostic significance of BMI in extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is unclear. We evaluated the prognostic role of BMI in patients with ENKTL. We retrospectively analyzed 742 patients with newly diagnosed ENKTL. The prognostic value of BMI was compared between patients with low BMIs (< 20.0 kg/m2) and patients with high BMIs (≥ 20.0 kg/m2). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) was also evaluated and compared with that of the BMI classification. Patients with low BMIs tended to exhibit higher Eastern Cooperative Oncology Group performance status (ECOG PS) scores (≥ 2) (P = 0.001), more frequent B symptoms (P < 0.001), lower albumin levels (P < 0.001), higher KPI scores (P = 0.03), and lower rates of complete remission (P < 0.001) than patients with high BMIs, as well as inferior progression-free survival (PFS, P = 0.003), and inferior overall survival (OS, P = 0.001). Multivariate analysis demonstrated that age > 60 years, mass > 5 cm, stage III/IV, elevated LDH levels, albumin levels < 35 g/L and low BMIs were independent adverse predictors of OS. The BMI classification was found to be superior to the IPI with respect to predicting patient outcomes among low-risk patients and the KPI with respect to distinguishing between intermediate-low- and high-intermediate-risk patients. Higher BMI at the time of diagnosis is associated with improved overall survival in ENKTL. Using the BMI classification may improve the IPI and KPI prognostic models.

Increased body mass index is associated with improved overall survival in extranodal natural killer/T-cell lymphoma, nasal type

PubMed Central

Li, Ya-Jun; Yi, Ping-Yong; Li, Ji-Wei; Liu, Xian-Ling; Liu, Xi-Yu; Zhou, Fang; OuYang, Zhou; Sun, Zhong-Yi; Huang, Li-Jun; He, Jun-Qiao; Yao, Yuan; Fan, Zhou; Tang, Tian; Jiang, Wen-Qi

2017-01-01

Objectives: The role of body mass index (BMI) in lymphoma survival outcomes is controversial. The prognostic significance of BMI in extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is unclear. We evaluated the prognostic role of BMI in patients with ENKTL. Methods: We retrospectively analyzed 742 patients with newly diagnosed ENKTL. The prognostic value of BMI was compared between patients with low BMIs (< 20.0 kg/m2) and patients with high BMIs (≥ 20.0 kg/m2). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) was also evaluated and compared with that of the BMI classification. Results: Patients with low BMIs tended to exhibit higher Eastern Cooperative Oncology Group performance status (ECOG PS) scores (≥ 2) (P = 0.001), more frequent B symptoms (P < 0.001), lower albumin levels (P < 0.001), higher KPI scores (P = 0.03), and lower rates of complete remission (P < 0.001) than patients with high BMIs, as well as inferior progression-free survival (PFS, P = 0.003), and inferior overall survival (OS, P = 0.001). Multivariate analysis demonstrated that age > 60 years, mass > 5 cm, stage III/IV, elevated LDH levels, albumin levels < 35 g/L and low BMIs were independent adverse predictors of OS. The BMI classification was found to be superior to the IPI with respect to predicting patient outcomes among low-risk patients and the KPI with respect to distinguishing between intermediate-low- and high-intermediate-risk patients. Conclusions: Higher BMI at the time of diagnosis is associated with improved overall survival in ENKTL. Using the BMI classification may improve the IPI and KPI prognostic models. PMID:28002803

Prognostic role of ABO blood type in patients with extranodal natural killer/T cell lymphoma, nasal type: a triple-center study.

PubMed

Li, Ya-Jun; Yi, Ping-Yong; Li, Ji-Wei; Liu, Xian-Ling; Tang, Tian; Zhang, Pei-Ying; Jiang, Wen-Qi

2017-07-31

The prognostic significance of ABO blood type for lymphoma is largely unknown. We evaluated the prognostic role of ABO blood type in patients with extranodal natural killer (NK)/T-cell lymphoma (ENKTL). We retrospectively analyzed clinical data of 697 patients with newly diagnosed ENKTL from three cancer centers. The prognostic value of ABO blood type was evaluated using Kaplan-Meier curves and Cox proportional hazard models. The prognostic values of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) were also evaluated. Compared with patients with blood type O, those with blood type non-O tended to display elevated baseline serum C-reactive protein levels (P = 0.038), lower rate of complete remission (P = 0.005), shorter progression-free survival (PFS, P < 0.001), and shorter overall survival (OS, P = 0.001). Patients with blood type O/AB had longer PFS (P < 0.001) and OS (P = 0.001) compared with those with blood type A/B. Multivariate analysis demonstrated that age >60 years (P < 0.001), mass ≥5 cm (P = 0.001), stage III/IV (P < 0.001), elevated serum lactate dehydrogenase (LDH) levels (P = 0.001), and blood type non-O were independent adverse predictors of OS (P = 0.001). ABO blood type 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 intermediate-to-low- and high-to-intermediate-risk groups. ABO blood type was an independent predictor of clinical outcome for patients with ENKTL.

BRAF Mutation is Associated with an Improved Survival in Glioma-a Systematic Review and Meta-analysis.

PubMed

Vuong, Huy Gia; Altibi, Ahmed M A; Duong, Uyen N P; Ngo, Hanh T T; Pham, Thong Quang; Fung, Kar-Ming; Hassell, Lewis

2018-05-01

Newly emerged molecular markers in gliomas provide prognostic values beyond the capabilities of histologic classification. BRAF mutation, especially BRAF V600E, is common in a subset of gliomas and may represent a potential prognostic marker. The aim of our study is to investigate the potential use of BRAF mutations on prognosis of glioma patients. Four electronic databases were searched for potential articles, including PubMed, Scopus, ISI Web of Science, and Virtual Health Library (VHL). Data of hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS) were directly obtained from original papers or indirectly estimated from Kaplan Meier curve (KMC). A random effect model weighted by inverse variance method was used to calculate the pooled HR. From 705 articles, we finally included 11 articles with 1308 glioma patients for the final analysis. The overall estimates showed that BRAF V600E was associated with an improved overall survival (OS) in glioma patients (HR = 0.60; 95% CI = 0.44-0.80). Results for progression-free survival (PFS), however, were not statistically significant (HR = 1.39; 95% CI = 0.82-2.34). In subgroup analyses, BRAF V600E showed its effect in improving survival in pediatric and young adult gliomas (under 35 years) but did not have prognostic value in old adult. Additionally, BRAF V600E was only associated with a favorable prognosis in lower grade glioma. Our meta-analysis provides evidence that BRAF mutation has a favorable prognostic impact in gliomas and its prognostic value might be dependent on patient age and tumor grade. This mutation can be used as a prognostic factor in glioma but additional studies are required to clarify its prognostic value taking into account other confounding factors.

The importance of histopathological and clinical variables in predicting the evolution of colon cancer.

PubMed

Diculescu, Mircea; Iacob, Răzvan; Iacob, Speranţa; Croitoru, Adina; Becheanu, Gabriel; Popeneciu, Valentin

2002-09-01

It has been a consensus that prognostic factors should always be taken into account before planning treatment in colorectal cancer. A 5 year prospective study was conducted, in order to assess the importance of several histopathological and clinical prognostic variables in the prediction of evolution in colon cancer. Some of the factors included in the analysis are still subject to dispute by different authors. 46 of 53 screened patients qualified to enter the study and underwent a potentially curative resection of the tumor, followed, when necessary, by adjuvant chemotherapy. Univariate and multivariate analyses were carried out in order to identify independent prognostic indicators. The endpoint of the study was considered the recurrence of the tumor or the detection of metastases. 65.2% of the patients had a good evolution during the follow up period. Multivariate survival analysis performed by Cox proportional hazard model identified 3 independent prognostic factors: Dukes stage (p = 0.00002), the grade of differentiation (p = 0.0009) and the weight loss index, representing the weight loss of the patient divided by the number of months when it was actually lost (p = 0.02). Age under 40 years, sex, microscopic aspect of the tumor, tumor location, anemia degree were not identified by our analysis as having prognostic importance. Histopathological factors continue to be the most valuable source of information regarding the possible evolution of patients with colorectal cancer. Individual clinical symptoms or biological parameters such as erytrocyte sedimentation rate or hemoglobin level are of little or no prognostic value. More research is required relating to the impact of a performance status index (which could include also weight loss index) as another reliable prognostic variable.

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.

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.

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

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.

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

PubMed

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

2018-05-15

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

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.« less

Validation of serum amyloid α as an independent biomarker for progression-free and overall survival in metastatic renal cell cancer patients.

PubMed

Vermaat, Joost S; Gerritse, Frank L; van der Veldt, Astrid A; Roessingh, Wijnand M; Niers, Tatjana M; Oosting, Sjoukje F; Sleijfer, Stefan; Roodhart, Jeanine M; Beijnen, Jos H; Schellens, Jan H; Gietema, Jourik A; Boven, Epie; Richel, Dick J; Haanen, John B; Voest, Emile E

2012-10-01

We recently identified apolipoprotein A2 (ApoA2) and serum amyloid α (SAA) as independent prognosticators in metastatic renal cell carcinoma (mRCC) patients, thereby improving the accuracy of the Memorial-Sloan Kettering Cancer Center (MSKCC) model. Validate these results prospectively in a separate cohort of mRCC patients treated with tyrosine kinase inhibitors (TKIs). For training we used 114 interferon-treated mRCC patients (inclusion 2001-2006). For validation we studied 151 TKI-treated mRCC patients (inclusion 2003-2009). Using Cox proportional hazards regression analysis, SAA and ApoA2 were associated with progression-free survival (PFS) and overall survival (OS). In 72 TKI-treated patients, SAA levels were analyzed longitudinally as a potential early marker for treatment effect. Baseline ApoA2 and SAA levels significantly predicted PFS and OS in the training and validation cohorts. Multivariate analysis identified SAA in both separate patient sets as a robust and independent prognosticator for PFS and OS. In contrast to our previous findings, ApoA2 interacted with SAA in the validation cohort and did not contribute to a better predictive accuracy than SAA alone and was therefore excluded from further analysis. According to the tertiles of SAA levels, patients were categorized in three risk groups, demonstrating accurate risk prognostication. SAA as a single biomarker showed equal prognostic accuracy when compared with the multifactorial MSKCC risk mode. Using receiver operating characteristic analysis, SAA levels >71 ng/ml were designated as the optimal cut-off value in the training cohort, which was confirmed for its significant sensitivity and specificity in the validation cohort. Applying SAA >71 ng/ml as an additional risk factor significantly improved the predictive accuracy of the MSKCC model in both independent cohorts. Changes in SAA levels after 6-8 wk of TKI treatment had no value in predicting treatment outcome. SAA but not ApoA2 was shown to be a robust and independent prognosticator for PFS and OS in mRCC patients. When incorporated in the MSKCC model, SAA showed additional prognostic value for patient management. Copyright © 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Comparison of the AJCC, MSTS, and Modified Spanier Systems for Clinical and Pathologic Staging of Osteosarcoma.

PubMed

Cates, Justin M M

2017-03-01

The prognostic performance of the 2 most commonly used staging systems for skeletal sarcoma (the American Joint Committee on Cancer [AJCC] and Musculoskeletal Tumor Society [MSTS] systems) have never been compared analytically. Another staging system originally proposed by Spanier has not yet been validated. Given the recent release of the 8th edition of the AJCC Cancer Staging Manual, this study was designed to directly compare these anatomic staging systems in a series of 153 high-grade, intramedullary osteosarcomas. Kaplan-Meier curves were plotted and pairwise comparisons between each stage category were performed. Predictive accuracy of each staging system for determining 5-year disease-free survival was evaluated by comparing areas under receiver-operating characteristic curves generated from logistic regression analysis. Multiple concordance indices were calculated using bootstrapping methods (200 replications). ρk and R were estimated as measures of the variation in survival outcomes explained by the regression models. The AJCC, MSTS, and a modified version of the Spanier staging systems showed similar discriminatory abilities and no significant differences in the levels of contrast between different tumor stages across staging systems. Addition of T-category information from each staging system contributed significant prognostic information compared with a Cox proportional hazard regression model consisting only of the presence or absence of metastatic disease as a measure of disease extent. Concordance indices and predictive accuracy for 5-year disease-free survival were not significantly different among the different staging systems either. Similar findings were observed after accounting for other important prognostic variables. Additional studies are necessary to determine performance parameters of each staging system for other types of skeletal sarcoma. Prognostic performance of osteosarcoma staging systems would also be improved by incorporating nonanatomic prognostic variables into staging algorithms.

Tumor-infiltrating Neutrophils is Prognostic and Predictive for Postoperative Adjuvant Chemotherapy Benefit in Patients With Gastric Cancer.

PubMed

Zhang, Heng; Liu, Hao; Shen, Zhenbin; Lin, Chao; Wang, Xuefei; Qin, Jing; Qin, Xinyu; Xu, Jiejie; Sun, Yihong

2018-02-01

This study was aimed to investigate the prognostic value of tumor-infiltrating neutrophils (TINs) and to generate a predictive model to refine postoperative risk stratification system for patients with gastric cancer. TIN presents in various malignant tumors, but its clinical significance in gastric cancer remains obscure. The study enrolled 3 independent sets of patients with gastric cancer from 2 institutional medical centers of China. TIN was estimated by immunohistochemical staining of CD66b, and its relationship with clinicopathological features and clinical outcomes were evaluated. Prognostic accuracies were evaluated by C-index and Akaike information criterion. TINs in gastric cancer tissues ranged from 0 to 192 cells/high magnification filed (HPF), 0 to 117 cells/HPF, and 0 to 142 cells/HPF in the training, testing, and validation sets, respectively. TINs were negatively correlated with lymph node classification (P = 0.007, P = 0.041, and P = 0.032, respectively) and tumor stage (P = 0.019, P = 0.013, and P = 0.025, respectively) in the 3 sets. Moreover, multivariate analysis identified TINs and tumor node metastasis (TNM) stage as 2 independent prognostic factors for overall survival. Incorporation of TINs into well-established TNM system generated a predictive model that shows better predictive accuracy for overall survival. More importantly, patients with higher TINs were prone to overall survival benefit from postoperative adjuvant chemotherapy. These results were validated in the independent testing and validation sets. TIN in gastric cancer was identified as an independent prognostic factor, which could be incorporated into standard TNM staging system to refine risk stratification and predict for overall survival benefit from postoperative chemotherapy in patients with gastric cancer.

Non-hematologic predictors of mortality improve the prognostic value of the international prognostic scoring system for MDS in older adults†

PubMed Central

Fega, K. Rebecca; Abel, Gregory A.; Motyckova, Gabriela; Sherman, Alexander E.; DeAngelo, Daniel J.; Steensma, David P.; Galinsky, Ilene; Wadleigh, Martha; Stone, Richard M.; Driver, Jane A.

2016-01-01

Objectives The International Prognostic Scoring System (IPSS) is commonly used to predict survival and assign treatment for the myelodysplastic syndromes (MDS). We explored whether self-reported and readily available non-hematologic predictors of survival add independent prognostic information to the IPSS. Materials and Methods Retrospective cohort study of consecutive MDS patients ≥age 65 who presented to Dana-Farber Cancer Institute between 2006 and 2011 and completed a baseline quality of life questionnaire. Questions corresponding to functional status and symptoms and extracted clinical-pathologic data from medical records. Kaplan–Meier and Cox proportional hazards models were used to estimate survival. Results One hundred fourteen patients consented and were available for analysis. Median age was 73 years, and the majority of patients were White, were male, and had a Charlson comorbidity score of <2. Few patients (24%) had an IPSS score consistent with lower-risk disease and the majority received chemotherapy. In addition to IPSS score and history of prior chemotherapy or radiation, significant univariate predictors of survival included low serum albumin, Charlson score, performance status, ability to take a long walk, and interference of physical symptoms in family life. The multivariate model that best predicted mortality included low serum albumin (HR = 2.3; 95% CI: 1.06–5.14), therapy-related MDS (HR = 2.1; 95% CI: 1.16–4.24), IPSS score (HR = 1.7; 95% CI: 1.14–2.49), and ease taking a long walk (HR = 0.44; 95% CI: 0.23–0.90). Conclusions In this study of older adults with MDS, we found that low serum albumin and physical function added important prognostic information to the IPSS score. Self-reported physical function was more predictive than physician-assigned performance status. PMID:26073533

Infused autograft lymphocyte-to-monocyte ratio and survival in T-cell lymphoma post-autologous peripheral blood hematopoietic stem cell transplantation.

PubMed

Porrata, Luis F; Inwards, David J; Ansell, Stephen M; Micallef, Ivana N; Johnston, Patrick B; Hogan, William J; Markovic, Svetomir N

2015-07-03

The infused autograft lymphocyte-to-monocyte ratio (A-LMR) is a prognostic factor for survival in B-cell lymphomas post-autologous peripheral hematopoietic stem cell transplantation (APHSCT). Thus, we set out to investigate if the A-LMR is also a prognostic factor for survival post-APHSCT in T-cell lymphomas. From 1998 to 2014, 109 T-cell lymphoma patients that underwent APHSCT were studied. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to identify the optimal cut-off value of A-LMR for survival analysis and k-fold cross-validation model to validate the A-LMR cut-off value. Univariate and multivariate Cox proportional hazard models were used to assess the prognostic discriminator power of A-LMR. ROC and AUC identified an A-LMR ≥ 1 as the best cut-off value and was validated by k-fold cross-validation. Multivariate analysis showed A-LMR to be an independent prognostic factor for overall survival (OS) and progression-free survival (PFS). Patients with an A-LMR ≥ 1.0 experienced a superior OS and PFS versus patients with an A-LMR < 1.0 [median OS was not reached vs 17.9 months, 5-year OS rates of 87% (95% confidence interval (CI), 75-94%) vs 26% (95% CI, 13-42%), p < 0.0001; median PFS was not reached vs 11.9 months, 5-year PFS rates of 72% (95% CI, 58-83%) vs 16% (95% CI, 6-32%), p < 0.0001]. A-LMR is also a prognostic factor for clinical outcomes in patients with T-cell lymphomas undergoing APHSCT.

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

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

PubMed

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

2018-02-01

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

On the Prognostic Efficiency of Topological Descriptors for Magnetograms of Active Regions

NASA Astrophysics Data System (ADS)

Knyazeva, I. S.; Urtiev, F. A.; Makarenko, N. G.

2017-12-01

Solar flare prediction remains an important practical task of space weather. An increase in the amount and quality of observational data and the development of machine-learning methods has led to an improvement in prediction techniques. Additional information has been retrieved from the vector magnetograms; these have been recently supplemented by traditional line-of-sight (LOS) magnetograms. In this work, the problem of the comparative prognostic efficiency of features obtained on the basis of vector data and LOS magnetograms is discussed. Invariants obtained from a topological analysis of LOS magnetograms are used as complexity characteristics of magnetic patterns. Alternatively, the so-called SHARP parameters were used; they were calculated by the data analysis group of the Stanford University Laboratory on the basis of HMI/SDO vector magnetograms and are available online at the website (http://jsoc.stanford.edu/) with the solar dynamics observatory (SDO) database for the entire history of SDO observations. It has been found that the efficiency of large-flare prediction based on topological descriptors of LOS magnetograms in epignosis mode is at least s no worse than the results of prognostic schemes based on vector features. The advantages of the use of topological invariants based on LOS data are discussed.

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.

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

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

PubMed

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

2018-02-20

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

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.

Supportive Care: Time to Change Our Prognostic Tools and Their Use in CKD

PubMed Central

Hemmelgarn, Brenda; Kotanko, Peter; Germain, Michael J.; Moranne, Olivier; Davison, Sara N.

2016-01-01

In using a patient-centered approach, neither a clinician nor a prognostic score can predict with absolute certainty how well a patient will do or how long he will live; however, validated prognostic scores may improve accuracy of prognostic estimates, thereby enhancing the ability of the clinicians to appreciate the individual burden of disease and the prognosis of their patients and inform them accordingly. They may also facilitate nephrologist’s recommendation of dialysis services to those who may benefit and proposal of alternative care pathways that might better respect patients’ values and goals to those who are unlikely to benefit. The purpose of this article is to discuss the use as well as the limits and deficiencies of currently available prognostic tools. It will describe new predictors that could be integrated in future scores and the role of patients’ priorities in development of new scores. Delivering patient-centered care requires an understanding of patients’ priorities that are important and relevant to them. Because of limits of available scores, the contribution of new prognostic tools with specific markers of the trajectories for patients with CKD and patients’ health reports should be evaluated in relation to their transportability to different clinical and cultural contexts and their potential for integration into the decision-making processes. The benefit of their use then needs to be quantified in clinical practice by outcome studies including health–related quality of life, patient and caregiver satisfaction, or utility for improving clinical management pathways and tailoring individualized patient–centered strategies of care. Future research also needs to incorporate qualitative methods involving patients and their caregivers to better understand the barriers and facilitators to use of these tools in the clinical setting. Information given to patients should be supported by a more realistic approach to what dialysis is likely to entail for the individual patient in terms of likely quality and quantity of life according to the patient’s values and goals and not just the possibility of life prolongation. PMID:27510452

Systematic profiling of alternative splicing signature reveals prognostic predictor for ovarian cancer.

PubMed

Zhu, Junyong; Chen, Zuhua; Yong, Lei

2018-02-01

The majority of genes are alternatively spliced and growing evidence suggests that alternative splicing is modified in cancer and is associated with cancer progression. Systematic analysis of alternative splicing signature in ovarian cancer is lacking and greatly needed. We profiled genome-wide alternative splicing events in 408 ovarian serous cystadenocarcinoma (OV) patients in TCGA. Seven types of alternative splicing events were curated and prognostic analyses were performed with predictive models and splicing network built for OV patients. Among 48,049 mRNA splicing events in 10,582 genes, we detected 2,611 alternative splicing events in 2,036 genes which were significant associated with overall survival of OV patients. Exon skip events were the most powerful prognostic factors among the seven types. The area under the curve of the receiver-operator characteristic curve for prognostic predictor, which was built with top significant alternative splicing events, was 0.937 at 2,000 days of overall survival, indicating powerful efficiency in distinguishing patient outcome. Interestingly, splicing correlation network suggested obvious trends in the role of splicing factors in OV. In summary, we built powerful prognostic predictors for OV patients and uncovered interesting splicing networks which could be underlying mechanisms. Copyright © 2017 Elsevier Inc. All rights reserved.

Percutaneous Endoscopic Gastrostomy Tube Is a Negative Prognostic Factor for Recurrent/Metastatic Head and Neck Cancer.

PubMed

Siano, Marco; Jarisch, Nadine; Joerger, Markus; Espeli, Vittoria

2018-06-01

Recurrent/metastatic head and neck squamous cell cancer (r/mHNSCC) patients often need a percutaneous endoscopic gastrostomy feeding tube (PEG). Among known prognostic factors, PEG could be prognostic as well. We retrospectively analyzed r/mHNSCC patients referred for systemic treatment. Kaplan-Meier and multivariate cox regression models were applied to assess prognostic impact of PEG. One hunderd and ten patients were identified, 42 had a PEG at treatment start. Median survival from start of 1st-line systemic treatment was 8 months (95%CI=6.5-12.0 months), 4.5 months (95%CI=2.5-7.0 months) for patients with PEG and 11.5 months (95%CI=7.5-14.5 months) without PEG (adjusted HR=1.98, p=0.011). Similarly, survival from first recurrence of distant metastases was lower in patients with PEG as compared to patients without (7.5 vs. 15.5 months, adjusted HR=2.60, p<0.001). Presence of PEG feeding tube has an unfavourable prognostic impact on survival in patients with r/mHNSCC. While any causality remains speculative, potential complications should be appreciated before PEG implantation. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects

PubMed Central

Ng, Selina S. Y.; Tse, Peter W.; Tsui, Kwok L.

2014-01-01

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets. PMID:24419162

A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects.

PubMed

Ng, Selina S Y; Tse, Peter W; Tsui, Kwok L

2014-01-13

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

Simplifying the use of prognostic information in traumatic brain injury. Part 2: Graphical presentation of probabilities.

PubMed

Murray, Gordon D; Brennan, Paul M; Teasdale, Graham M

2018-06-01

OBJECTIVE Clinical features such as those included in the Glasgow Coma Scale (GCS) score, pupil reactivity, and patient age, as well as CT findings, have clear established relationships with patient outcomes due to neurotrauma. Nevertheless, predictions made from combining these features in probabilistic models have not found a role in clinical practice. In this study, the authors aimed to develop a method of displaying probabilities graphically that would be simple and easy to use, thus improving the usefulness of prognostic information in neurotrauma. This work builds on a companion paper describing the GCS-Pupils score (GCS-P) as a tool for assessing the clinical severity of neurotrauma. METHODS Information about early GCS score, pupil response, patient age, CT findings, late outcome according to the Glasgow Outcome Scale, and mortality were obtained at the individual adult patient level from the CRASH (Corticosteroid Randomisation After Significant Head Injury; n = 9045) and IMPACT (International Mission for Prognosis and Clinical Trials in TBI; n = 6855) databases. These data were combined into a pooled data set for the main analysis. Logistic regression was first used to model the combined association between the GCS-P and patient age and outcome, following which CT findings were added to the models. The proportion of variability in outcomes "explained" by each model was assessed using Nagelkerke's R 2 . RESULTS The authors observed that patient age and GCS-P have an additive effect on outcome. The probability of mortality 6 months after neurotrauma is greater with increasing age, and for all age groups the probability of death is greater with decreasing GCS-P. Conversely, the probability of favorable recovery becomes lower with increasing age and lessens with decreasing GCS-P. The effect of combining the GCS-P with patient age was substantially more informative than the GCS-P, age, GCS score, or pupil reactivity alone. Two-dimensional charts were produced displaying outcome probabilities, as percentages, for 5-year increments in age between 15 and 85 years, and for GCS-Ps ranging from 1 to 15; it is readily seen that the movement toward combinations at the top right of the charts reflects a decreasing likelihood of mortality and an increasing likelihood of favorable outcome. Analysis of CT findings showed that differences in outcome are very similar between patients with or without a hematoma, absent cisterns, or subarachnoid hemorrhage. Taken in combination, there is a gradation in risk that aligns with increasing numbers of any of these abnormalities. This information provides added value over age and GCS-P alone, supporting a simple extension of the earlier prognostic charts by stratifying the original charts in the following 3 CT groupings: none, only 1, and 2 or more CT abnormalities. CONCLUSIONS The important prognostic features in neurotrauma can be brought together to display graphically their combined effects on risks of death or on prospects for independent recovery. This approach can support decision making and improve communication of risk among health care professionals, patients, and their relatives. These charts will not replace clinical judgment, but they will reduce the risk of influences from biases.

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.

Dynamic and Structural Gas Turbine Engine Modeling

NASA Technical Reports Server (NTRS)

Turso, James A.

2003-01-01

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

Real-life clinical practice results with vinflunine in patients with relapsed platinum-treated metastatic urothelial carcinoma: an Italian multicenter study (MOVIE-GOIRC 01-2014).

PubMed

Passalacqua, Rodolfo; Lazzarelli, Silvia; Donini, Maddalena; Montironi, Rodolfo; Tambaro, Rosa; De Giorgi, Ugo; Pignata, Sandro; Palumbo, Raffaella; Ceresoli, Giovanni Luca; Del Conte, Gianluca; Tonini, Giuseppe; Morelli, Franco; Nolè, Franco; Panni, Stefano; Rondini, Ermanno; Guida, Annalisa; Zucali, Paolo Andrea; Doni, Laura; Iezzi, Elisa; Caminiti, Caterina

2017-07-19

Vinflunine is the only chemotherapeutic agent shown to improve survival in platinum-refractory patients with metastatic transitional cell carcinoma of the urothelium (TCCU) in a phase III clinical trial, which led to product registration for this indication in Europe. The aim of this study was to assess the efficacy of vinflunine and to evaluate the prognostic significance of risk factors in a large, unselected cohort of patients with metastatic TCCU treated according to routine clinical practice. This was a retrospective multicenter study. Italian cancer centers were selected if, according to the Registry of the Italian Medicines Agency (AIFA), at least four patients had been treated with vinflunine between February 2011 and June 2014, after first- or second-line platinum-based chemotherapy. The primary objective was to test whether the efficacy measured by overall survival (OS) in the registration study could be confirmed in routine clinical practice. Multivariate analysis was carried out using Cox proportional hazard model. A total of 217 patients were treated in 28 Italian centers. Median age was 69 years (IQR 62-76) and 84% were male; Eastern Cooperative Oncology Group performance status (ECOG PS) was ≥ 1 in 53% of patients. The median number of cycles was 4 (IQR 2-6); 29%, 35%, and 36% received an initial dose of 320 mg/m 2 , 280 mg/m 2 or a lower dose, respectively. Median progression-free survival (PFS) and OS for the entire population was 3.2 months (2.6-3.7) and 8.1 months (6.3-8.9). A complete response was observed in six patients, partial response in 21, stable disease in 60, progressive disease in 108, with a disease control rate of 40%. Multivariate analysis showed that ECOG PS, number of metastatic sites and liver involvement were unfavorable prognostic factors for OS. Toxicity was mild, and grade 3-4 adverse effects were mainly: neutropenia (9%), anemia (6%), asthenia/fatigue (7%) and constipation (5%). In routine clinical practice the results obtained with VFL seem to be better than the results of the registration trial and reinforce evidence supporting its use after failure of a platinum-based chemotherapy.

[Actual relevance of Pauwels' classification of femoral neck fractures--a critical review].

PubMed

Schwarz, N

2010-03-01

The aim of this study was to evaluate the validity of Pauwels' classification of femoral neck fractures. A study of literature was performed. It has never been proven that the inclination of the fracture plane has a prognostic relevance. A number of papers prove the contrary, there are no publications where Pauwels' classification has been used successfully in selecting treatment modalities. Pauwels' theory of fracture inclination angle has not been transferred into clinical practice. This discrepancy probably goes back to the fact that the angle cannot be determined preoperatively, that in the majority of femoral neck fractures the angle is within the range of 40 to 60 degrees, that the theoretical angle variations do practically not exist, and that the shearing forces are reduced to an unknown amount by friction resistance due to the uneven fracture plane. The mechanical laws of the pseudarthrosis of the femoral neck cannot be extrapolated to acute fractures. The theory of Pauwels has apparently no clinical relevance for the majority of acute fractures, except for the rare transcervical fractures, and should not be considered any longer as a classification of acute femoral neck fractures due to the lack of prognostic and therapeutic relevance.

NEW BIOGENIC VOC EMISSIONS MODEL

EPA Science Inventory

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

Echocardiographic assessment of right ventricular function in routine practice: Which parameters are useful to predict one-year outcome in advanced heart failure patients with dilated cardiomyopathy?

PubMed

Kawata, Takayuki; Daimon, Masao; Kimura, Koichi; Nakao, Tomoko; Lee, Seitetsu L; Hirokawa, Megumi; Kato, Tomoko S; Watanabe, Masafumi; Yatomi, Yutaka; Komuro, Issei

2017-10-01

Right ventricular (RV) function has recently gained attention as a prognostic predictor of outcome even in patients who have left-sided heart failure. Since several conventional echocardiographic parameters of RV systolic function have been proposed, our aim was to determine if any of these parameters (tricuspid annular plane systolic excursion: TAPSE, tissue Doppler derived systolic tricuspid annular motion velocity: S', fractional area change: FAC) are associated with outcome in advanced heart failure patients with dilated cardiomyopathy (DCM). We retrospectively enrolled 68 DCM patients, who were New York Heart Association (NYHA) Class III or IV and had a left ventricular (LV) ejection fraction <35%. All patients were undergoing evaluation for heart transplantation or management of heart failure. Primary outcomes were defined as LV assist device implantation or cardiac death within one year. Thirty-nine events occurred (5 deaths, 32 LV assist devices implanted). Univariate analysis showed that age, systolic blood pressure, heart rate, NYHA functional class IV, plasma brain natriuretic peptide concentration, intravenous inotrope use, left atrial volume index, and FAC were associated with outcome, whereas TAPSE and S' were not. Receiver-operating characteristic curve analysis showed that the optimal FAC cut-off value to identify patients with an event was <26.7% (area under the curve=0.74). The event-free rate determined by Kaplan-Meier analysis was significantly higher in patients with FAC≥26.7% than in those with FAC<26.7% (log-lank, p=0.0003). Moreover, the addition of FAC<26.7% improved the prognostic utility of a model containing clinical variables and conventional echocardiographic indexes. FAC may provide better prognostic information than TAPSE or S' in advanced heart failure patients with DCM. Copyright © 2017 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Selective Genomic Copy Number Imbalances and Probability of Recurrence in Early-Stage Breast Cancer

PubMed Central

Thompson, Patricia A.; Brewster, Abenaa M.; Kim-Anh, Do; Baladandayuthapani, Veerabhadran; Broom, Bradley M.; Edgerton, Mary E.; Hahn, Karin M.; Murray, James L.; Sahin, Aysegul; Tsavachidis, Spyros; Wang, Yuker; Zhang, Li; Hortobagyi, Gabriel N.; Mills, Gordon B.; Bondy, Melissa L.

2011-01-01

A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (n = 728) and test (n = 243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index full model, train[test]  =  0.72[0.71] ± 0.02 vs. C-Index clinical + subtype model, train[test]  =  0.62[0.62] ± 0.02; p<10−6). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER–, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread. PMID:21858162

Tourette Syndrome and Tic Disorders

PubMed Central

Leckman, James F.

2005-01-01

Objective: This is a practical review of Tourette syndrome, including phenomenology, natural history, and state-of-the-art assessment and treatment. Method: Computerized literature searches were conducted under the keywords Tourette syndrome,tics, and children-adolescents. Results: Studies have documented the natural history of Tourette syndrome and its frequent co-occurrence with attention problems, obsessive-compulsive disorder (OCD), and a range of other mood and anxiety disorders, which are often of primary concern to patients and their families. Proper diagnosis and education are often very helpful for patients, parents, siblings, teachers, and peers. When necessary, available anti-tic treatments have proven efficacious. First-line options include the alpha adrenergic agents and the atypical neuroleptics, as well as behavioral interventions such as habit reversal. Conclusions: The study of tics and Tourette symdrome has led to the development of several pathophysiological models and helped in the development of management options. However, fully explanatory models are still needed that would allow for accurate prognostication in the course of illness and the development of improved treatments. PMID:21152158

SILHIL Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines

NASA Technical Reports Server (NTRS)

Bole, Brian; Teubert, Christopher Allen; Cuong Chi, Quach; Hogge, Edward; Vazquez, Sixto; Goebel, Kai; George, Vachtsevanos

2013-01-01

Software-in-the-loop and Hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The creation of an offline framework for verifying and validating supervisory failure prognostics and decision making routines is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.

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.

Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery

PubMed Central

Henriksson, Martin; Palmer, Stephen; Chen, Ruoling; Damant, Jacqueline; Fitzpatrick, Natalie K; Abrams, Keith; Hingorani, Aroon D; Stenestrand, Ulf; Janzon, Magnus; Feder, Gene; Keogh, Bruce; Shipley, Martin J; Kaski, Juan-Carlos; Timmis, Adam; Sculpher, Mark

2010-01-01

Objective To determine the effectiveness and cost effectiveness of using information from circulating biomarkers to inform the prioritisation process of patients with stable angina awaiting coronary artery bypass graft surgery. Design Decision analytical model comparing four prioritisation strategies without biomarkers (no formal prioritisation, two urgency scores, and a risk score) and three strategies based on a risk score using biomarkers: a routinely assessed biomarker (estimated glomerular filtration rate), a novel biomarker (C reactive protein), or both. The order in which to perform coronary artery bypass grafting in a cohort of patients was determined by each prioritisation strategy, and mean lifetime costs and quality adjusted life years (QALYs) were compared. Data sources Swedish Coronary Angiography and Angioplasty Registry (9935 patients with stable angina awaiting coronary artery bypass grafting and then followed up for cardiovascular events after the procedure for 3.8 years), and meta-analyses of prognostic effects (relative risks) of biomarkers. Results The observed risk of cardiovascular events while on the waiting list for coronary artery bypass grafting was 3 per 10 000 patients per day within the first 90 days (184 events in 9935 patients). Using a cost effectiveness threshold of £20 000-£30 000 (€22 000-€33 000; $32 000-$48 000) per additional QALY, a prioritisation strategy using a risk score with estimated glomerular filtration rate was the most cost effective strategy (cost per additional QALY was <£410 compared with the Ontario urgency score). The impact on population health of implementing this strategy was 800 QALYs per 100 000 patients at an additional cost of £245 000 to the National Health Service. The prioritisation strategy using a risk score with C reactive protein was associated with lower QALYs and higher costs compared with a risk score using estimated glomerular filtration rate. Conclusion Evaluating the cost effectiveness of prognostic biomarkers is important even when effects at an individual level are small. Formal prioritisation of patients awaiting coronary artery bypass grafting using a routinely assessed biomarker (estimated glomerular filtration rate) along with simple, routinely collected clinical information was cost effective. Prioritisation strategies based on the prognostic information conferred by C reactive protein, which is not currently measured in this context, or a combination of C reactive protein and estimated glomerular filtration rate, is unlikely to be cost effective. The widespread practice of using only implicit or informal means of clinically ordering the waiting list may be harmful and should be replaced with formal prioritisation approaches. PMID:20085988

Using microRNA profiling in urine samples to develop a non-invasive test for bladder cancer.

PubMed

Mengual, Lourdes; Lozano, Juan José; Ingelmo-Torres, Mercedes; Gazquez, Cristina; Ribal, María José; Alcaraz, Antonio

2013-12-01

Current standard methods used to detect and monitor bladder urothelial cell carcinoma (UCC) are invasive or have low sensitivity. The incorporation into clinical practice of a non-invasive tool for UCC assessment would enormously improve patients' quality of life and outcome. This study aimed to examine the microRNA (miRNA) expression profiles in urines of UCC patients in order to develop a non-invasive accurate and reliable tool to diagnose and provide information on the aggressiveness of the tumor. We performed a global miRNA expression profiling analysis of the urinary cells from 40 UCC patients and controls using TaqMan Human MicroRNA Array followed by validation of 22 selected potentially diagnostic and prognostic miRNAs in a separate cohort of 277 samples using a miRCURY LNA qPCR system. miRNA-based signatures were developed by multivariate logistic regression analysis and internally cross-validated. In the initial cohort of patients, we identified 40 and 30 aberrantly expressed miRNA in UCC compared with control urines and in high compared with low grade tumors, respectively. Quantification of 22 key miRNAs in an independent cohort resulted in the identification of a six miRNA diagnostic signature with a sensitivity of 84.8% and specificity of 86.5% (AUC = 0.92) and a two miRNA prognostic model with a sensitivity of 84.95% and a specificity of 74.14% (AUC = 0.83). Internal cross-validation analysis confirmed the accuracy rates of both models, reinforcing the strength of our findings. Although the data needs to be externally validated, miRNA analysis in urine appears to be a valuable tool for the non-invasive assessment of UCC. Copyright © 2013 UICC.

Clinimetrics and clinical psychometrics: macro- and micro-analysis.

PubMed

Tomba, Elena; Bech, Per

2012-01-01

Clinimetrics was introduced three decades ago to specify the domain of clinical markers in clinical medicine (indexes or rating scales). In this perspective, clinical validity is the platform for selecting the various indexes or rating scales (macro-analysis). Psychometric validation of these indexes or rating scales is the measuring aspect (micro-analysis). Clinical judgment analysis by experienced psychiatrists is included in the macro-analysis and the item response theory models are especially preferred in the micro-analysis when using the total score as a sufficient statistic. Clinical assessment tools covering severity of illness scales, prognostic measures, issues of co-morbidity, longitudinal assessments, recovery, stressors, lifestyle, psychological well-being, and illness behavior have been identified. The constructive dialogue in clinimetrics between clinical judgment and psychometric validation procedures is outlined for generating developments of clinical practice in psychiatry. Copyright © 2012 S. Karger AG, Basel.

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

PubMed

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

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.

The Lagrangian Ensemble metamodel for simulating plankton ecosystems

NASA Astrophysics Data System (ADS)

Woods, J. D.

2005-10-01

This paper presents a detailed account of the Lagrangian Ensemble (LE) metamodel for simulating plankton ecosystems. It uses agent-based modelling to describe the life histories of many thousands of individual plankters. The demography of each plankton population is computed from those life histories. So too is bio-optical and biochemical feedback to the environment. The resulting “virtual ecosystem” is a comprehensive simulation of the plankton ecosystem. It is based on phenotypic equations for individual micro-organisms. LE modelling differs significantly from population-based modelling. The latter uses prognostic equations to compute demography and biofeedback directly. LE modelling diagnoses them from the properties of individual micro-organisms, whose behaviour is computed from prognostic equations. That indirect approach permits the ecosystem to adjust gracefully to changes in exogenous forcing. The paper starts with theory: it defines the Lagrangian Ensemble metamodel and explains how LE code performs a number of computations “behind the curtain”. They include budgeting chemicals, and deriving biofeedback and demography from individuals. The next section describes the practice of LE modelling. It starts with designing a model that complies with the LE metamodel. Then it describes the scenario for exogenous properties that provide the computation with initial and boundary conditions. These procedures differ significantly from those used in population-based modelling. The next section shows how LE modelling is used in research, teaching and planning. The practice depends largely on hindcasting to overcome the limits to predictability of weather forecasting. The scientific method explains observable ecosystem phenomena in terms of finer-grained processes that cannot be observed, but which are controlled by the basic laws of physics, chemistry and biology. What-If? Prediction ( WIP), used for planning, extends hindcasting by adding events that describe natural or man-made hazards and remedial actions. Verification is based on the Ecological Turing Test, which takes account of uncertainties in the observed and simulated versions of a target ecological phenomenon. The rest of the paper is devoted to a case study designed to show what LE modelling offers the biological oceanographer. The case study is presented in two parts. The first documents the WB model (Woods & Barkmann, 1994) and scenario used to simulate the ecosystem in a mesocosm moored in deep water off the Azores. The second part illustrates the emergent properties of that virtual ecosystem. The behaviour and development of an individual plankton lineage are revealed by an audit trail of the agent used in the computation. The fields of environmental properties reveal the impact of biofeedback. The fields of demographic properties show how changes in individuals cumulatively affect the birth and death rates of their population. This case study documents the virtual ecosystem used by Woods, Perilli and Barkmann (2005; hereafter WPB); to investigate the stability of simulations created by the Lagrangian Ensemble metamodel. The Azores virtual ecosystem was created and analysed on the Virtual Ecology Workbench (VEW) which is described briefly in the Appendix.

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.

Associations of prognostic awareness/acceptance with psychological distress, existential suffering, and quality of life in terminally ill cancer patients' last year of life.

PubMed

Tang, Siew Tzuh; Chang, Wen-Cheng; Chen, Jen-Shi; Chou, Wen-Chi; Hsieh, Chia-Hsun; Chen, Chen H

2016-04-01

Whether prognostic awareness benefits terminally ill cancer patients' psychological-existential well-being and quality of life (QOL) is unclear because of lack of well-controlled longitudinal studies. This study longitudinally evaluated the associations of accurate prognostic awareness and prognostic acceptance with psychological distress, existential suffering, and QOL while comprehensively controlling for confounders in Taiwanese terminally ill cancer patients' last year of life. A convenience sample of 325 cancer patients was followed until death. Psychological distress and existential suffering were assessed by severe anxiety and depressive symptoms and high self-perceived sense of burden to others, respectively. Dichotomized and continuous (QOL) outcome variables were evaluated by multivariate logistic and linear regression modeling with the generalized estimating equation, respectively. Accurate prognostic awareness was not associated with the likelihood of severe anxiety or depressive symptoms but significantly increased the likelihood of high self-perceived sense of burden to others and was associated with poorer QOL in participants' last year of life. Participants who knew and highly accepted their prognosis were significantly less likely to experience severe anxiety symptoms than those who were unaware of or knew their prognosis but had difficulty accepting it. Knowing one's poor prognosis and confronting one's impending death without full acceptance and adequate professional psycho-spiritual support may harm more than benefit terminally ill cancer patients' psychological state, existential well-being, and QOL. These findings highlight the importance of tailoring psycho-spiritual support to cancer patients' psychological and existential needs when prognostic information is disclosed. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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.

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

PubMed

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

2018-02-01

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

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

PubMed

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

2008-05-01

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

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

PubMed

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

2018-02-01

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

Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease.

PubMed

Guerra, Beniamino; Haile, Sarah R; Lamprecht, Bernd; Ramírez, Ana S; Martinez-Camblor, Pablo; Kaiser, Bernhard; Alfageme, Inmaculada; Almagro, Pere; Casanova, Ciro; Esteban-González, Cristóbal; Soler-Cataluña, Juan J; de-Torres, Juan P; Miravitlles, Marc; Celli, Bartolome R; Marin, Jose M; Ter Riet, Gerben; Sobradillo, Patricia; Lange, Peter; Garcia-Aymerich, Judith; Antó, Josep M; Turner, Alice M; Han, Meilan K; Langhammer, Arnulf; Leivseth, Linda; Bakke, Per; Johannessen, Ane; Oga, Toru; Cosio, Borja; Ancochea-Bermúdez, Julio; Echazarreta, Andres; Roche, Nicolas; Burgel, Pierre-Régis; Sin, Don D; Soriano, Joan B; Puhan, Milo A

2018-03-02

External validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD. We relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores. Depending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile-3rd quartile = 0.655-0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUC ADO - AUC BODE = 0.015 [95% confidence interval (CI) = -0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUC BODE updated - AUC BODE = 0.008 [95% CI = -0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency. Our analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research.

Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

PubMed

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

2013-05-07

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

Skeletal muscle radiodensity is prognostic for survival in patients with advanced non-small cell lung cancer.

PubMed

Sjøblom, Bjørg; Grønberg, Bjørn H; Wentzel-Larsen, Tore; Baracos, Vickie E; Hjermstad, Marianne J; Aass, Nina; Bremnes, Roy M; Fløtten, Øystein; Bye, Asta; Jordhøy, Marit

2016-12-01

Recent research indicates that severe muscular depletion (sarcopenia) is frequent in cancer patients and linked to cachexia and poor survival. Our aim was to investigate if measures of skeletal muscle hold prognostic information in advanced non-small cell lung cancer (NSCLC). We included NSCLC patients with disease stage IIIB/IV, performance status 0-2, enrolled in three randomised trials of first-line chemotherapy (n = 1305). Computed tomography (CT) images obtained before start of treatment were used for body composition analyses at the level of the third lumbar vertebra (L3). Skeletal muscle mass was assessed by measures of the cross sectional muscle area, from which the skeletal muscle index (SMI) was obtained. Skeletal muscle radiodensity (SMD) was measured as the mean Hounsfield unit (HU) of the measured muscle area. A high level of mean HU indicates a high SMD. Complete data were available for 734 patients, mean age 65 years. Both skeletal muscle index (SMI) and muscle radiodensity (SMD) varied largely. Mean SMI and SMD were 47.7 cm 2 /m 2 and 37.4 HU in men (n = 420), 39.6 cm 2 /m 2 and 37.0 HU in women (n = 314). Multivariable Cox regression analyses, adjusted for established prognostic factors, showed that SMD was independently prognostic for survival (Hazard ratio (HR) 0.98, 95% CI 0.97-0.99, p = 0.001), whereas SMI was not (HR 0.99, 95% CI 0.98-1.01, p = 0.329). Low SMD is associated with poorer survival in advanced NSCLC. Further research is warranted to establish whether muscle measures should be integrated into routine practice to improve prognostic accuracy. Copyright © 2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Pros and cons of prognostic disclosure to Japanese cancer patients and their families from the family's point of view.

PubMed

Yoshida, Saran; Shiozaki, Mariko; Sanjo, Makiko; Morita, Tatsuya; Hirai, Kei; Tsuneto, Satoru; Shima, Yasuo

2012-12-01

The primary goals of this analysis were to explore the pros and cons of prognostic disclosure to patients and their families from the bereaved family's point of view. Semistructured interviews were conducted with 60 bereaved family members of patients with cancer in Japan. There were eight categories of influence related to the disclosure of prognosis to the family, including pros (e.g., "Enabling mental preparedness for the patient's death") and cons (e.g., "Being distressed by acknowledging the patient's prognosis"); and seven categories of influence of not disclosing the prognosis to family, including pros (e.g., "Being able to maintain hope") and cons (e.g., "Being prevented from providing adequate care for the patient"). There were also nine categories of influence related to the disclosure of prognosis to patients (e.g., "Enabling various discussions regarding death with the patient"), and eight categories of influence related to not disclosing the prognosis to patients (e.g., "Maintaining the patient's hope"). Although prognostic disclosure to family members can contribute to psychological distress and hopelessness, at the same time, it has the potential to prepare them for the future both emotionally and practically, and also to make the time until the patient's death as meaningful as possible. It is useful for physicians to introduce pros and cons of prognostic disclosure to family members at the time of decision making, to understand the family members' psychological state, and to provide support considering pros and cons whether or not they disclosed prognosis.

Defining the Scope of Prognosis: Primary Care Clinicians' Perspectives on Predicting the Future Health of Older Adults.

PubMed

Thomas, John M; Fried, Terri R

2018-05-01

Studies examining the attitudes of clinicians toward prognostication for older adults have focused on life expectancy prediction. Little is known about whether clinicians approach prognostication in other ways. To describe how clinicians approach prognostication for older adults, defined broadly as making projections about patients' future health. In five focus groups, 30 primary care clinicians from community-based, academic-affiliated, and Veterans Affairs primary care practices were given open-ended questions about how they make projections about their patients' future health and how this informs the approach to care. Content analysis was used to organize responses into themes. Clinicians spoke about future health in terms of a variety of health outcomes in addition to life expectancy, including independence in activities and decision making, quality of life, avoiding hospitalization, and symptom burden. They described approaches in predicting these health outcomes, including making observations about the overall trajectory of patients to predict health outcomes and recognizing increased risk for adverse health outcomes. Clinicians expressed reservations about using estimates of mortality risk and life expectancy to think about and communicate patients' future health. They discussed ways in which future research might help them in thinking about and discussing patients' future health to guide care decisions, including identifying when and whether interventions might impact future health. The perspectives of primary care clinicians in this study confirm that prognostic considerations can go beyond precise estimates of mortality risk and life expectancy to include a number of outcomes and approaches to predicting those outcomes. Published by Elsevier Inc.

Cell-free DNA detected by "liquid biopsy" as a potential prognostic biomarker in early breast cancer.

PubMed

Maltoni, Roberta; Casadio, Valentina; Ravaioli, Sara; Foca, Flavia; Tumedei, Maria Maddalena; Salvi, Samanta; Martignano, Filippo; Calistri, Daniele; Rocca, Andrea; Schirone, Alessio; Amadori, Dino; Bravaccini, Sara

2017-03-07

As conventional biomarkers for defining breast cancer (BC) subtypes are not always capable of predicting prognosis, search for new biomarkers which can be easily detected by liquid biopsy is ongoing. It has long been known that cell-free DNA (CF-DNA) could be a promising diagnostic and prognostic marker in different tumor types, although its prognostic value in BC is yet to be confirmed. This retrospective study evaluated the prognostic role of CF-DNA quantity and integrity of HER2, MYC, BCAS1 and PI3KCA, which are frequently altered in BC. We collected 79 serum samples before surgery from women at first diagnosis of BC at Forlì Hospital (Italy) from 2002 to 2010. Twenty-one relapsed and 58 non-relapsed patients were matched by subtype and age. Blood samples were also collected from 10 healthy donors. All samples were analyzed by Real Time PCR for CF-DNA quantity and integrity of all oncogenes. Except for MYC, BC patients showed significantly higher median values of CF-DNA quantity (ng) than healthy controls, who had higher integrity and lower apoptotic index. A difference nearing statistical significance was observed for HER2 short CF-DNA (p = 0.078, AUC value: 0.6305). HER2 short CF-DNA showed an odds ratio of 1.39 for disease recurrence with p = 0.056 (95% CI 0.991-1.973). Our study suggests that CF-DNA detected as liquid biopsy could have great potential in clinical practice once demonstration of its clinical validity and utility has been provided by prospective studies with robust assays.

The Spinal Instability Neoplastic Score: Impact on Oncologic Decision-Making.

PubMed

Versteeg, Anne L; Verlaan, Jorrit-Jan; Sahgal, Arjun; Mendel, Ehud; Quraishi, Nasir A; Fourney, Daryl R; Fisher, Charles G

2016-10-15

Systematic literature review. To address the following questions in a systematic literature review: 1. How is spinal neoplastic instability defined or classified in the literature before and after the introduction of the Spinal Instability Neoplastic Score (SINS)? 2. How has SINS affected daily clinical practice? 3. Can SINS be used as a prognostic tool? Spinal neoplastic-related instability was defined in 2010 and simultaneously SINS was introduced as a novel tool with criteria agreed upon by expert consensus to assess the degree of spinal stability. PubMed, Embase, and clinical trial databases were searched with the key words "spinal neoplasm," "spinal instability," "spinal instability neoplastic score," and synonyms. Studies describing spinal neoplastic-related instability were eligible for inclusion. Primary outcomes included studies describing and/or defining neoplastic-related instability, SINS, and studies using SINS as a prognostic factor. The search identified 1414 articles, of which 51 met the inclusion criteria. No precise definition or validated assessment tool was used specific to spinal neoplastic-related instability prior to the introduction of SINS. Since the publication of SINS in 2010, the vast majority of the literature regarding spinal instability has used SINS to assess or describe instability. Twelve studies specifically investigated the prognostic value of SINS in patients who underwent radiotherapy or surgery. No consensus could be determined regarding the definition, assessment, or reporting of neoplastic-related instability before introduction of SINS. Defining spinal neoplastic-related instability and the introduction of SINS have led to improved uniform reporting within the spinal neoplastic literature. Currently, the prognostic value of SINS is controversial. N/A.

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

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

Impact of the revised International Prognostic Scoring System, cytogenetics and monosomal karyotype on outcome after allogeneic stem cell transplantation for myelodysplastic syndromes and secondary acute myeloid leukemia evolving from myelodysplastic syndromes: a retrospective multicenter study of the European Society of Blood and Marrow Transplantation

PubMed Central

Koenecke, Christian; Göhring, Gudrun; de Wreede, Liesbeth C.; van Biezen, Anja; Scheid, Christof; Volin, Liisa; Maertens, Johan; Finke, Jürgen; Schaap, Nicolaas; Robin, Marie; Passweg, Jakob; Cornelissen, Jan; Beelen, Dietrich; Heuser, Michael; de Witte, Theo; Kröger, Nicolaus

2015-01-01

The aim of this study was to determine the impact of the revised 5-group International Prognostic Scoring System cytogenetic classification on outcome after allogeneic stem cell transplantation in patients with myelodysplastic syndromes or secondary acute myeloid leukemia who were reported to the European Society for Blood and Marrow Transplantation database. A total of 903 patients had sufficient cytogenetic information available at stem cell transplantation to be classified according to the 5-group classification. Poor and very poor risk according to this classification was an independent predictor of shorter relapse-free survival (hazard ratio 1.40 and 2.14), overall survival (hazard ratio 1.38 and 2.14), and significantly higher cumulative incidence of relapse (hazard ratio 1.64 and 2.76), compared to patients with very good, good or intermediate risk. When comparing the predictive performance of a series of Cox models both for relapse-free survival and for overall survival, a model with simplified 5-group cytogenetics (merging very good, good and intermediate cytogenetics) performed best. Furthermore, monosomal karyotype is an additional negative predictor for outcome within patients of the poor, but not the very poor risk group of the 5-group classification. The revised International Prognostic Scoring System cytogenetic classification allows patients with myelodysplastic syndromes to be separated into three groups with clearly different outcomes after stem cell transplantation. Poor and very poor risk cytogenetics were strong predictors of poor patient outcome. The new cytogenetic classification added value to prediction of patient outcome compared to prediction models using only traditional risk factors or the 3-group International Prognostic Scoring System cytogenetic classification. PMID:25552702

Long-term prognostic impact of CT-Leaman score in patients with non-obstructive CAD: Results from the COronary CT Angiography EvaluatioN For Clinical Outcomes InteRnational Multicenter (CONFIRM) study.

PubMed

Andreini, Daniele; Pontone, Gianluca; Mushtaq, Saima; Gransar, Heidi; Conte, Edoardo; Bartorelli, Antonio L; Pepi, Mauro; Opolski, Maksymilian P; Ó Hartaigh, Bríain; Berman, Daniel S; Budoff, Matthew J; Achenbach, Stephan; Al-Mallah, Mouaz; Cademartiri, Filippo; Callister, Tracy Q; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J W; Cury, Ricardo; Delago, Augustin; Hadamitzky, Martin; Hausleiter, Joerg; Feuchtner, Gudrun; Kim, Yong-Jin; Kaufmann, Philipp A; Leipsic, Jonathon; Lin, Fay Y; Maffei, Erica; Raff, Gilbert; Shaw, Leslee J; Villines, Todd C; Dunning, Allison; Marques, Hugo; Rubinshtein, Ronen; Hindoyan, Niree; Gomez, Millie; Min, James K

2017-03-15

Non-obstructive coronary artery disease (CAD) identified by coronary computed tomography angiography (CCTA) demonstrated prognostic value. CT-adapted Leaman score (CT-LeSc) showed to improve the prognostic stratification. Aim of the study was to evaluate the capability of CT-LeSc to assess long-term prognosis of patients with non-obstructive (CAD). From 17 centers, we enrolled 2402 patients without prior CAD history who underwent CCTA that showed non-obstructive CAD and provided complete information on plaque composition. Patients were divided into a group without CAD and a group with non-obstructive CAD (<50% stenosis). Segment-involvement score (SIS) and CT-LeSc were calculated. Outcomes were non-fatal myocardial infarction (MI) and the combined end-point of MI and all-cause mortality. Patient mean age was 56±12years. At follow-up (mean 59.8±13.9months), 183 events occurred (53 MI, 99 all-cause deaths and 31 late revascularizations). CT-LeSc was the only multivariate predictor of MI (HRs 2.84 and 2.98 in two models with Framingham and risk factors, respectively) and of MI plus all-cause mortality (HR 2.48 and 1.94 in two models with Framingham and risk factors, respectively). This was confirmed by a net reclassification analysis confirming that the CT-LeSc was able to correctly reclassify a significant proportion of patients (cNRI 0.28 and 0.23 for MI and MI plus all-cause mortality, respectively) vs. baseline model, whereas SIS did not. CT-LeSc is an independent predictor of major acute cardiac events, improving prognostic stratification of patients with non-obstructive CAD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

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

PubMed

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

2017-10-01

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