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

Abstract

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, CHA2DS2-VASC had an area under the receiver operating characteristics curve (AUC) of 0.66 for predicting SSE at 1-,2, and 5-years. RegCox had the best predictive performance, with AUCs of 0.82,.81, and.80 at 1-, 2, and 5-years. XGBoost had AUCs of 0.81, 0.80, and 0.79 respectively. Atrial septal defect (ASD) emerged as an important predictor for SSE uncovered by the unbiased ML algorithms. A new clinical risk score, the CHA2DS2-VASC-ASD2 score, provides improved SSE prediction in ACHD. Yet, the ML models still outperformed this. Conclusion: ML models significantly outperformed the clinical risk scores in patients with ACHD.