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Cardiovascular Risk Prediction Scores in Type 1 Diabetes: A Systematic Review and Meta-Analysis
Sebhat Erqou1,2, Ahmed Shahab1, Fayez H Fayad1
1Department of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Background:
The extent of the performance and utility of scores for the risk of cardiovascular disease (CVD) in persons with type 1 diabetes (T1DM) largely remains unclear.
Objective:
The purpose of this study was to synthesize data on the performance of CVD risk scores in people living with T1DM.
Methods:
This study is a systematic review and meta-analysis. PubMed and EMBASE were searched through December 31, 2023. The included studies: 1) were retrospective, prospective, or cross-sectional in design; 2) included persons with T1DM; 3) assessed CVD outcomes; and 4) had data on at least on CVD risk score. Measures of calibration and discrimination qualitatively summarized. Measures of discrimination were combined using random-effects models stratified by type of risk model.
Results:
In a meta-analysis of observational studies of CVD risk scores in T1DM individuals, including 11 studies and 73,664 participants (mean age of 34 years, mainly White individuals and male [55%]), we evaluated 12 CVD risk prediction models (7 T1DM-specific, 1 type 2 diabetes-specific, and 4 general population models). Most risk scores had a moderate to excellent discrimination (C-statistic: 0.73-0.85) and predicted CVD risk well when compared to actual clinical events. CVD risk scores specifically developed in T1DM individuals exhibited a higher discriminative performance-pooled C-statistic of 0.81 vs 0.75 for risk scores developed in the general population or those with type 2 diabetes and also showed a better calibration.
Conclusions:
Among individuals with T1DM, CVD risk models had a moderate to excellent discrimination, with a better discrimination and accuracy for T1DM-specific scores.
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