Beyond Atrial Fibrillation: Machine Learning Algorithm Predicts Stroke in Adult Patients With Congenital Heart Disease.

Objective: To develop and validate a robust risk prediction model for stroke and systemic embolism (SSE) in adult patients with congenital heart disease (ACHD), using artificial intelligence. Patients and Methods: Deidentified insurance claims from the Optum Labs Data Warehouse, including enrollment records and medical and pharmacy claims for commercial and Medicare Advantage enrollees, were used to identify 49,276 patients with ACHD, followed between January 1, 2009, and December 31, 2014. The group was randomly divided into development (70%) and validation (30%) cohorts. The development cohort was used to train 2 machine learning (ML) algorithms, regularized Cox regression (RegCox), and extreme gradient boosting (XGBoost) to predict SSE at 1, 2, and 5 years. The Shapley additive explanations (SHAP) model was used to identify the variables particularly driving the SSE risk. Results: Within this large and diverse cohort of patients with ACHD (mean age, 59 ± 19 years; 25,390 (51.5%) female, 35,766 [77.6%]) white), 1756 (3.6%) patients experienced SSE during follow-up. In the Validation cohort, CHA Conclusion: ML models significantly outperformed the clinical risk scores in patients with ACHD.