Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

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

European Psychiatry ◽

10.1016/j.eurpsy.2017.01.1891 ◽

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.

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The Impact of Pharmacy-specific Predictors on the Performance of 30-Day Readmission Risk Prediction Models

Medical Care ◽

10.1097/mlr.0000000000001075 ◽

2019 ◽

Vol 57 (4) ◽

pp. 295-299

Author(s):

Samuel Kabue ◽

John Greene ◽

Patricia Kipnis ◽

Brian Lawson ◽

Gina Rinetti-Vargas ◽

...

Keyword(s):

Risk Prediction ◽

Prediction Models ◽

Risk Prediction Models ◽

The Impact ◽

Readmission Risk

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The impact of patient age on breast cancer risk prediction models

The Breast Journal ◽

10.1111/tbj.12976 ◽

2018 ◽

Vol 24 (4) ◽

pp. 592-598 ◽

Cited By ~ 4

Author(s):

Suzanne B. Coopey ◽

Ahmet Acar ◽

Molly Griffin ◽

Jessica Cintolo-Gonzalez ◽

Alan Semine ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Prediction ◽

Prediction Models ◽

Patient Age ◽

Risk Prediction Models ◽

Patient Âgé ◽

The Impact

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Abstract 14: Examining the Predictive Power and the Impact of Incorporating Hemoglobin A1c into Existing Diabetes Risk Prediction Models among African American Adults in the Jackson Heart Study

Circulation ◽

10.1161/circ.129.suppl_1.14 ◽

2014 ◽

Vol 129 (suppl_1) ◽

Author(s):

Mary E Lacy ◽

Gregory Wellenius ◽

Charles B Eaton ◽

Eric B Loucks ◽

Adolfo Correa ◽

...

Keyword(s):

Risk Prediction ◽

Predictive Power ◽

Prediction Models ◽

Model Performance ◽

Diabetes Risk ◽

Risk Prediction Models ◽

C Statistic ◽

Heart Study ◽

The Impact

Background: In 2010, the American Diabetes Association (ADA) updated diagnostic criteria for diabetes to include hemoglobin A1c (A1c). However, the appropriateness of these criteria in African Americans (AAs) is unclear as A1c may not reflect glycemic control as accurately in AAs as in whites. Moreover, existing diabetes risk prediction models have been developed in populations composed primarily of whites. Objectives were to (1) examine the predictive power of existing diabetes risk prediction models in the Jackson Heart Study (JHS), a prospective cohort of 5,301 AA adults and (2) explore the impact of incorporating A1c into these models. Methods: We selected 3 widely-used diabetes risk prediction models and examined their ability to predict 5-year diabetes risk among 3,185 JHS participants free of diabetes at baseline and who returned for the 5 year follow-up visit. Incident diabetes was identified at follow-up based on current antidiabetic medications, fasting glucose ≥126 mg/dl or A1c ≥6.5%. We evaluated model performance using model discrimination (C-statistic) and reclassification (net reclassification index (NRI) and integrated discrimination improvement (IDI)). For each of the 3 models, model performance in JHS was evaluated using (1) covariates identified in the original published model and (2) published covariates plus A1c. Results: Of 3,185 participants (mean age 53.7; 64.0% female), 9.8% (n=311) developed diabetes over 5 years of follow-up. Each diabetes prediction model suffered a drop in predictive power when applied to JHS using ADA 2010 criteria (Table 1). The performance of all 3 models improved significantly with the addition of A1c, as evidenced by the increase in C-statistic and improvement in reclassification. Conclusion: Despite evidence that A1c may not accurately reflect glycemic control in AAs as well as in whites, adding A1c to existing diabetes risk prediction models developed in primarily white populations significantly improved 5-year predictive power of all 3 models among AAs in the JHS.

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