Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis

Abstract

Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular disease (PVD), significantly contribute to morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). The aim of this study was to evaluate the performance of artificial intelligence (AI) models in predicting these complications, emphasizing applicability in diverse healthcare settings. Following PRISMA guidelines, a systematic search of six databases was conducted, yielding 46 eligible studies with 184 AI models. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC). Subgroup analyses examined model performance by outcome type, predictor data (lab-only, non-lab, mixed), and algorithm type. Heterogeneity was evaluated using I² statistics, and sensitivity analyses addressed outliers and study biases. The pooled AUROC for all AI models was 0.753 (95%CI: 0.740–0.766; I²=99.99%). Models predicting PVD achieved the highest AUROC (0.794), followed by cerebrovascular diseases (0.770) and CVD (0.741). Gradient-boosting algorithms outperformed others (AUROC: 0.789). Models with lab-only predictors had superior performance (AUROC: 0.837) compared to mixed (0.759) and non-lab predictors (0.714). External validations reported reduced AUROC (0.725), underscoring limitations in generalizability. AI models show moderate predictive accuracy for T2DM macrovascular complications, with laboratory-based predictors being key to performance. However, the limited external validation and reliance on high-resource data restrict implementation in low-resource settings. Future efforts should focus on non-lab predictors, external validation, and context-appropriate AI solutions to enhance global applicability.