Open Access
Cardiovascular risk in patients with type 2 diabetes: a systematic review of prediction models
(Elsevier, 2022) Galbete Jiménez, Arkaitz; Tamayo Rodríguez, Ibai; Librero, Julián; Enguita Germán, Mónica; Cambra Contin, Koldo; Ibáñez Beroiz, Berta; Ciencias de la Salud; Osasun Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Aims: to identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability. Methods: a systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations. Results: the titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated. Conclusions: there exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.
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 Zientziak
Aims. 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.
Open Access
Effect of physical activity on cardiovascular event risk in a population-based cohort of patients with type 2 diabetes
(MDPI, 2021) Enguita Germán, Mónica; Tamayo Rodríguez, Ibai; Galbete Jiménez, Arkaitz; Librero, Julián; Cambra Contin, Koldo; Ibáñez Beroiz, Berta; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Cardiovascular disease (CVD) is the most common cause of morbidity and mortality among patients with type 2 diabetes (T2D). Physical activity (PA) is one of the few modifiable factors that can reduce this risk. The aim of this study was to estimate to what extent PA can contribute to reducing CVD risk and all-cause mortality in patients with T2D. Information from a population-based cohort including 26,587 patients with T2D from the Navarre Health System who were fol-lowed for five years was gathered from electronic clinical records. Multivariate Cox regression models were fitted to estimate the effect of PA on CVD risk and all-cause mortality, and the approach was complemented using conditional logistic regression models within a matched nested case–con-trol design. A total of 5111 (19.2%) patients died during follow-up, which corresponds to 37.8% of the inactive group, 23.9% of the partially active group and 12.4% of the active group. CVD events occurred in 2362 (8.9%) patients, which corresponds to 11.6%, 10.1% and 7.6% of these groups. Compared with patients in the inactive group, and after matching and adjusting for confounders, the OR of having a CVD event was 0.84 (95% CI: 0.66–1.07) for the partially active group and 0.71 (95% CI: 0.56–0.91) for the active group. A slightly more pronounced gradient was obtained when focused on all-cause mortality, with ORs equal to 0.72 (95% CI: 0.61–0.85) and 0.50 (95% CI: 0.42–0.59), respectively. This study provides further evidence that physically active patients with T2D may have a reduced risk of CVD-related complications and all-cause mortality.

Enguita Germán, Mónica

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