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Validation and comparison of cardiovascular risk prediction equations in Chinese patients with Type 2 diabetes
Jingyuan Liang1, Qianqian Li1, Zhangping Fu1
1Department of Epidemiology and Biostatistics, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China.
Insights
Diabetes-specific cardiovascular disease (CVD) risk models perform better than general population models for predicting CVD in diabetic patients. Different models and age-specific cut-offs significantly vary high-risk patient selection, particularly impacting women.
Area of Science:
- Cardiovascular Disease Epidemiology
- Diabetes Mellitus Management
- Risk Prediction Modeling
Background:
- European guidelines recommend diabetes-specific cardiovascular disease (CVD) risk prediction models with age-specific cut-offs.
- American guidelines suggest using models derived from the general population for patients with diabetes.
- Discrepancies in risk prediction models necessitate a comparison of their performance in diabetic populations.
Purpose of the Study:
- To compare the performance of four cardiovascular risk models in predicting CVD events in a Chinese diabetes population.
- To evaluate diabetes-specific models (ADVANCE, HK) against general population models (PCE, China-PAR).
- To assess the impact of age-specific versus fixed cut-offs on high-risk patient identification.
Main Methods:
- Utilized electronic health records from the CHERRY study cohort in China, including patients with diabetes.
- Calculated 5-year CVD risk using original and recalibrated ADVANCE, HK, PCE, and China-PAR models.
- Followed patients for a median of 5.8 years, recording 2605 CVD events.
Main Results:
- Diabetes-specific models (ADVANCE, HK) demonstrated better discrimination (higher C-statistics) than general population models (PCE, China-PAR).
- Recalibrated ADVANCE and PCE models showed varying degrees of risk underestimation.
- Age-specific cut-offs resulted in significant variation in high-risk patient selection, identifying fewer high-risk women compared to fixed cut-offs.
Conclusions:
- Diabetes-specific CVD risk prediction models offer superior discrimination for diabetic patients.
- Significant variability exists in identifying high-risk individuals across different models and cut-off strategies.
- Age-specific cut-offs may lead to under-selection of high-risk patients, especially in women.
Aims:
For patients with diabetes, the European guidelines updated the cardiovascular disease (CVD) risk prediction recommendations using diabetes-specific models with age-specific cut-offs, whereas American guidelines still advise models derived from the general population. We aimed to compare the performance of four cardiovascular risk models in diabetes populations.
Methods And Results:
Patients with diabetes from the CHERRY study, an electronic health records-based cohort study in China, were identified. Five-year CVD risk was calculated using original and recalibrated diabetes-specific models [Action in Diabetes and Vascular disease: PreterAx and diamicroN-MR Controlled Evaluation (ADVANCE) and the Hong Kong cardiovascular risk model (HK)] and general population-based models [Pooled Cohort Equations (PCE) and Prediction for Atherosclerotic cardiovascular disease Risk in China (China-PAR)]. During a median 5.8-year follow-up, 46 558 patients had 2605 CVD events. C-statistics were 0.711 [95% confidence interval: 0.693-0.729] for ADVANCE and 0.701 (0.683-0.719) for HK in men, and 0.742 (0.725-0.759) and 0.732 (0.718-0.747) in women. C-statistics were worse in two general population-based models. Recalibrated ADVANCE underestimated risk by 1.2% and 16.8% in men and women, whereas PCE underestimated risk by 41.9% and 24.2% in men and women. With the age-specific cut-offs, the overlap of the high-risk patients selected by every model pair ranged from only 22.6% to 51.2%. When utilizing the fixed cut-off at 5%, the recalibrated ADVANCE selected similar high-risk patients in men (7400) as compared to the age-specific cut-offs (7102), whereas age-specific cut-offs exhibited a reduction in the selection of high-risk patients in women (2646 under age-specific cut-offs vs. 3647 under fixed cut-off).
Conclusion:
Diabetes-specific CVD risk prediction models showed better discrimination for patients with diabetes. High-risk patients selected by different models varied significantly. Age-specific cut-offs selected fewer patients at high CVD risk especially in women.
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