Cardiovascular Risk Prediction Scores in Type 1 Diabetes: A Systematic Review and Meta-Analysis
Erqou S., Shahab A., Fayad FH., Haji M., Yuyun MF., Joseph J., Wu WC., Adler AI., Orchard TJ., Echouffo-Tcheugui JB.
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.