Enguita Germán, Mónica
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Enguita Germán
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Mónica
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Estadística, Informática y Matemáticas
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Publication Open Access External validation of cardiovascular risk scores in patients with Type 2 diabetes using the Spanish population-based CARDIANA cohort(Oxford University Press, 2025-05-29) Enguita Germán, Mónica; Ballesteros-Domínguez, Asier; Tamayo Rodríguez, Ibai; Librero, Julián; Oscoz-Villanueva, Ignacio; Forga, Lluís; Goñi-Iriarte, María José; Lafita, Javier; Lecea, Óscar; Parraza, Naiara ; Ibáñez Beroiz, Berta; Ciencias de la Salud; Osasun ZientziakAims. There is an overabundance of cardiovascular disease (CVD) risk-prediction models applicable to patients with Type 2 diabetes (T2D), but most of them still require external validation. Our aim was to assess the performance of 18 CVD risk scores in a Spanish cohort of patients with T2D. Methods and results. The CARdiovascular Risk in patients with DIAbetes in Navarra (CARDIANA) cohort, which includes 20 793 individuals with T2D and no history of CVD, was used to externally validate 13 models developed in patients with T2D [Action in Diabetes and Vascular Disease (ADVANCE), Atherosclerosis Risk in Communities, Basque Country Prospective Complications and Mortality Study risk engine, Cardiovascular Healthy Study, Diabetes Cohort Study, DIAL2, DIAL2-extended, Fremantle, Kaasenbrood, Swedish National Diabetes Register (NDR), PREDICT1-diabetes, SCORE2-diabetes, and Wan] and 5 models developed in the general population (ASCVD, PREVENT-basic, PREVENT-full, QRISK2, and SCORE2). Harrell's C-statistic and calibration plots were used as measures of discrimination and calibration, respectively. There were 991 incident CVD events within 5 years of follow-up, resulting in a cumulative incidence of 5.0% (95% confidence interval 4.7-5.3). Discrimination ability was moderate for all the models, with SCORE2-diabetes, NDR, PREDICT1-diabetes, PREVENT-full, Wan, ADVANCE, and both DIAL2 models showing the highest C-index values. All models showed good calibration, although most of them required recalibration, with the exception of ADVANCE-, DIAL2-, and SCORE2-related models. Conclusion. In our context, models derived for or adapted to diabetes patients, as well as models derived in the general population but incorporating diabetes-related metabolic measures (such as Hb1Ac) as predictors, demonstrated better performance than the others. DIAL2, DIAL2-extended, SCORE2-diabetes, and ADVANCE showed optimal calibration even without recalibration, which implies greater applicability, especially for SCORE2-diabetes and ADVANCE because of their simplicity. Lay summary. There are many tools to predict cardiovascular risk for patients with Type 2 diabetes (T2D), but most of them need to be validated in other contexts. This study evaluates the performance of 18 risk-prediction tools in a Spanish cohort of patients with T2D (CARDIANA). Five models (DIAL2, DIAL2-extended, SCORE2-diabetes, PREDICT1-diabetes, and Action in Diabetes and Vascular Disease (ADVANCE)], specially designed for people with diabetes, together with the PREVENT-full model, designed for the general population but including diabetes-related metabolic control measures, such as Hb1Ac as a predictor, demonstrated the best accuracy in predicting cardiovascular risk for Spanish patients with T2D, with the DIAL2-extended model performing best, closely followed by the SCORE2-diabetes and ADVANCE models, which are easier to use in clinical settings for their simplicity.This research provides valuable and updated information on CVD risk-prediction models for patients with T2D. These results may help clinicians to choose the most suitable risk-prediction tool for their specific clinical setting and patient population and may help healthcare providers make better decisions regarding preventive interventions and patient care, improving healthcare quality and efficiency.