scholarly journals Assessment of Financial Risk Prediction Models with Multi-criteria Decision Making Methods

2012 ◽  
pp. 60-67 ◽  
Author(s):  
Jose Salvador Sánchez ◽  
Vicente García ◽  
Ana Isabel Marqués
Keyword(s):  
Decision Making ◽  
Risk Prediction ◽  
Financial Risk ◽  
Prediction Models ◽  
Multi Criteria Decision Making ◽  
Risk Prediction Models

scholarly journals The Need for Accurate Risk Prediction Models for Road Mapping, Shared Decision Making and Care Planning for the Elderly with Advanced Chronic Kidney Disease

PRILOZI ◽  
2016 ◽  
Vol 37 (2-3) ◽  
pp. 33-42 ◽  
Author(s):  
Marijke Stryckers ◽  
Evi V Nagler ◽  
Wim Van Biesen
Keyword(s):  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Shared Decision Making ◽  
Risk Prediction ◽  
Prediction Models ◽  
Shared Decision ◽  
End Stage Kidney Disease ◽  
Risk Prediction Models ◽  
End Stage

AbstractAs people age, chronic kidney disease becomes more common, but it rarely leads to end-stage kidney disease. When it does, the choice between dialysis and conservative care can be daunting, as much depends on life expectancy and personal expectations of medical care. Shared decision making implies adequately informing patients about their options, and facilitating deliberation of the available information, such that decisions are tailored to the individual’s values and preferences. Accurate estimations of one’s risk of progression to end-stage kidney disease and death with or without dialysis are essential for shared decision making to be effective. Formal risk prediction models can help, provided they are externally validated, well-calibrated and discriminative; include unambiguous and measureable variables; and come with readily applicable equations or scores. Reliable, externally validated risk prediction models for progression of chronic kidney disease to end-stage kidney disease or mortality in frail elderly with or without chronic kidney disease are scant. Within this paper, we discuss a number of promising models, highlighting both the strengths and limitations physicians should understand for using them judiciously, and emphasize the need for external validation over new development for further advancing the field.

Review of Risk Prediction Approaches for Bipolar Episodes in the Perinatal Period

2017 ◽  
Vol 41 (S1) ◽  
pp. S113-S113
Author(s):  
M. Casanova Dias ◽  
I. Jones ◽  
A. Di Florio ◽  
L. Jones ◽  
N. Craddock
Keyword(s):  
Decision Making ◽  
Bipolar Disorder ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Perinatal Period ◽  
Management Options ◽  
Risk Prediction Models ◽  
Risk Period ◽  
The Impact

IntroductionThe perinatal period is a high-risk period for the development of illness episodes in women with bipolar disorder. Relapse rates vary between 9 and 75% depending on the study. The overall risk of a severe episode is approximately 20%. The impact on women, the relationships with their babies and their families can be devastating. In the UK costs to society are £8.1 billion per year-cohort of births. The advice currently given to women is based of general risk rates. Women's needs of information for decision-making in the perinatal period are not being met.ObjectivesTo review the risk prediction approaches used for women with bipolar disorder in the perinatal period.AimsTo understand the existing risk prediction models and approaches used for prognosis of the risk of recurrence of bipolar disorder for women in the perinatal period.MethodsSystematic literature search of public medical electronic databases and grey literature on risk prediction for bipolar episodes in the perinatal period.ResultsWe will present the existing models and approaches used for risk prediction of illness episodes in the perinatal period.ConclusionsAwareness of existing risk prediction models for recurrence of bipolar disorder in the perinatal period will allow better informed risk-benefit analysis of treatment and management options.This person-centred approach will help women and clinicians in their decision-making at this crucial high-risk period.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Rare Germline Pathogenic Variants Identified by Multigene Panel Testing and the Risk of Aggressive Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1495
Author(s):  
Tú Nguyen-Dumont ◽  
James G. Dowty ◽  
Robert J. MacInnis ◽  
Jason A. Steen ◽  
Moeen Riaz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Aggressive Prostate Cancer ◽  
Panel Testing ◽  
Pathogenic Variants ◽  
Increased Risk ◽  
Predisposition Genes ◽  
Rare Genetic Variants

While gene panel sequencing is becoming widely used for cancer risk prediction, its clinical utility with respect to predicting aggressive prostate cancer (PrCa) is limited by our current understanding of the genetic risk factors associated with predisposition to this potentially lethal disease phenotype. This study included 837 men diagnosed with aggressive PrCa and 7261 controls (unaffected men and men who did not meet criteria for aggressive PrCa). Rare germline pathogenic variants (including likely pathogenic variants) were identified by targeted sequencing of 26 known or putative cancer predisposition genes. We found that 85 (10%) men with aggressive PrCa and 265 (4%) controls carried a pathogenic variant (p < 0.0001). Aggressive PrCa odds ratios (ORs) were estimated using unconditional logistic regression. Increased risk of aggressive PrCa (OR (95% confidence interval)) was identified for pathogenic variants in BRCA2 (5.8 (2.7–12.4)), BRCA1 (5.5 (1.8–16.6)), and ATM (3.8 (1.6–9.1)). Our study provides further evidence that rare germline pathogenic variants in these genes are associated with increased risk of this aggressive, clinically relevant subset of PrCa. These rare genetic variants could be incorporated into risk prediction models to improve their precision to identify men at highest risk of aggressive prostate cancer and be used to identify men with newly diagnosed prostate cancer who require urgent treatment.

scholarly journals Risk Prediction of Barrett’s Esophagus in a Taiwanese Health Examination Center Based on Regression Models

2021 ◽  
Vol 18 (10) ◽  
pp. 5332
Author(s):  
Po-Hsiang Lin ◽  
Jer-Guang Hsieh ◽  
Hsien-Chung Yu ◽  
Jyh-Horng Jeng ◽  
Chiao-Lin Hsu ◽  
...  
Keyword(s):  
Barrett’S Esophagus ◽  
Risk Prediction ◽  
Barrett's Esophagus ◽  
Gastrointestinal Endoscopy ◽  
Prediction Models ◽  
Upper Gastrointestinal Endoscopy ◽  
Health Examination ◽  
Endoscopic Screening ◽  
Risk Prediction Models ◽  
Upper Gastrointestinal

Determining the target population for the screening of Barrett’s esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.

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