Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension

Advanced preventive strategies are needed to decrease the burden of cardiovascular disease (CVD). We aimed to develop a predictive tool to identify individuals at higher CVD risk and facilitate proactive interventions to improve clinical outcomes. This single-center retrospective study enrolled consecutive hypertensive subjects free of CVD at baseline and followed them up for a mean of 8.3 years. The primary outcome was new-onset CVD (ischemic heart disease, stroke or hospitalization due to heart failure). The 155-variable dataset was enriched by creating trend variables using statistical measures, Principal Component Analysis (PCA) and Latent Class Analysis (LCA). Then, an artificial intelligence (AI) XGBoost prediction algorithm was trained on 70% of the dataset and validated on the remaining 30%. XGBoost-based risk stratification was compared with risk stratification according to SCORE2. The 3,588 consecutive patients enrolled had a mean age of 54.2 ± 14 years, 53% were women. The incidence rate of new-onset CVD was 1.93 (95% CI: 1.78-2.09) per 100 patient-years. The XGBoost model incorporated 30 variables and achieved 86% ROC AUC, 81% sensitivity, and 78% specificity for predicting CVD. The number of antihypertensive drugs had the strongest predictive power within the model. SCORE2 classified at baseline only 32% of participants with a CV event in the follow-up as high or very-high risk, whereas the XGBoost model correctly identified 81% of them. AI-based modeling outperformed SCORE2 in predicting new-onset CVD in patients with hypertension, identifying the number of antihypertensive drugs as a key predictor and supporting the role of AI risk stratification in clinical practice to implement precision medicine.