Dataset Information

Validation of Risk Equations for Complications of Type 2 Diabetes (RECODe) Using Individual Participant Data From Diverse Longitudinal Cohorts in the U.S.

ABSTRACT: OBJECTIVE:We sought to validate Risk Equations for Complications of Type 2 Diabetes (RECODe) among diverse populations. RESEARCH DESIGN AND METHODS:We compared risk predictions from RECODe equations and from two alternative risk models (UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2] and American College of Cardiology/American Heart Association Pooled Cohort Equations) to observed outcomes in two studies: the Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,555 adults with type 2 diabetes, median follow-up 9.1 years) and the Jackson Heart Study (JHS, n = 1,746 adults with type 2 diabetes, median follow-up 8.0 years). Outcomes included nephropathy by multiple measures (microalbuminuria, macroalbuminuria, renal failure, end-stage renal disease, and reduction in glomerular filtration rate), moderate to severe diabetic retinopathy by Airlie House classification, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, congestive heart failure, and all-cause mortality. RESULTS:RECODe equations for microvascular and cardiovascular outcomes had C-statistics for discrimination ranging from 0.71 to 0.85 in MESA and 0.64 to 0.91 in JHS for alternative outcomes. Calibration slopes in MESA ranged from 0.62 for a composite nephropathy outcome, 0.83-1.04 for individual nephropathy outcomes, 1.07 for retinopathy, 1.00-1.05 for cardiovascular outcomes, and 1.03 for all-cause mortality. Slopes in JHS ranged from 0.47 for retinopathy, 0.97-1.16 for nephropathy, 0.72-1.05 for cardiovascular outcomes, and 1.01 for all-cause mortality. The alternative models had C-statistics 0.50-0.72 and calibration slopes 0.07-0.60. CONCLUSIONS:RECODe equations improved risk estimation for diverse patients with type 2 diabetes, as compared with two commonly used alternatives.

SUBMITTER: Basu S

PROVIDER: S-EPMC5829967 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Validation of Risk Equations for Complications of Type 2 Diabetes (RECODe) Using Individual Participant Data From Diverse Longitudinal Cohorts in the U.S.

Basu Sanjay S Sussman Jeremy B JB Berkowitz Seth A SA Hayward Rodney A RA Bertoni Alain G AG Correa Adolfo A Mwasongwe Stanford S Yudkin John S JS

Diabetes care 20171221 3

<h4>Objective</h4>We sought to validate Risk Equations for Complications of Type 2 Diabetes (RECODe) among diverse populations.<h4>Research design and methods</h4>We compared risk predictions from RECODe equations and from two alternative risk models (UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2] and American College of Cardiology/American Heart Association Pooled Cohort Equations) to observed outcomes in two studies: the Multi-Ethnic Study of Atherosclerosis (MESA, <i>n</i> = 1,555 ...[more]

PMID: 29269511

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Project description:BACKGROUND:In view of substantial mis-estimation of risks of diabetes complications using existing equations, we sought to develop updated Risk Equations for Complications Of type 2 Diabetes (RECODe). METHODS:To develop and validate these risk equations, we used data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD, n=9635; 2001-09) and validated the equations for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS, n=1018; 1996-2001), and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD, n=4760; 2001-12). Microvascular outcomes were nephropathy, retinopathy, and neuropathy. Cardiovascular outcomes were myocardial infarction, stroke, congestive heart failure, and cardiovascular mortality. We also included all-cause mortality as an outcome. We used a cross-validating machine learning method to select predictor variables from demographic characteristics, clinical variables, comorbidities, medications, and biomarkers into Cox proportional hazards models for each outcome. The new equations were compared to older risk equations by assessing model discrimination, calibration, and the net reclassification index. FINDINGS:All equations had moderate internal and external discrimination (C-statistics 0·55-0·84 internally, 0·57-0·79 externally) and high internal and external calibration (slopes 0·71-1·31 between observed and estimated risk). Our equations had better discrimination and calibration than the UK Prospective Diabetes Study Outcomes Model 2 (for microvascular and cardiovascular outcomes, C-statistics 0·54-0·62, slopes 0·06-1·12) and the American College of Cardiology/American Heart Association Pooled Cohort Equations (for fatal or non-fatal myocardial infarction or stroke, C-statistics 0·61-0·66, slopes 0·30-0·39). INTERPRETATION:RECODe might improve estimation of risk of complications for patients with type 2 diabetes. FUNDING:National Institute for Diabetes and Digestive and Kidney Disease, National Heart, Lung and Blood Institute, and National Institute on Minority Health and Health Disparities, National Institutes of Health, and US Department of Veterans Affairs.

Project description:BACKGROUND:Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS:IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS:Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. CONCLUSIONS:The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. TRIAL REGISTRATION:PROSPERO ID: CRD42015029349 .

Project description:BackgroundPredicted maturity offset and age at peak height velocity are increasingly used with youth athletes, although validation studies of the equations indicated major limitations. The equations have since been modified and simplified.ObjectiveThe objective of this study was to validate the new maturity offset prediction equations in independent longitudinal samples of boys and girls.MethodsTwo new equations for boys with chronological age and sitting height and chronological age and stature as predictors, and one equation for girls with chronological age and stature as predictors were evaluated in serial data from the Wroc?aw Growth Study, 193 boys (aged 8-18 years) and 198 girls (aged 8-16 years). Observed age at peak height velocity for each youth was estimated with the Preece-Baines Model 1. The original prediction equations were included for comparison. Predicted age at peak height velocity was the difference between chronological age at prediction and maturity offset.ResultsPredicted ages at peak height velocity with the new equations approximated observed ages at peak height velocity in average maturing boys near the time of peak height velocity; a corresponding window for average maturing girls was not apparent. Compared with observed age at peak height velocity, predicted ages at peak height velocity with the new and original equations were consistently later in early maturing youth and earlier in late maturing youth of both sexes. Predicted ages at peak height velocity with the new equations had reduced variation compared with the original equations and especially observed ages at peak height velocity. Intra-individual variation in predicted ages at peak height velocity with all equations was considerable.ConclusionThe new equations are useful for average maturing boys close to the time of peak height velocity; there does not appear to be a clear window for average maturing girls. The new and original equations have major limitations with early and late maturing boys and girls.

Dataset Information

Validation of Risk Equations for Complications of Type 2 Diabetes (RECODe) Using Individual Participant Data From Diverse Longitudinal Cohorts in the U.S.

Publications

Validation of Risk Equations for Complications of Type 2 Diabetes (RECODe) Using Individual Participant Data From Diverse Longitudinal Cohorts in the U.S.

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