Abstract Body: Background: Sudden Cardiac Death (SCD) in young adults is a significant public health issue, with survival rates often less than 15%. Effective prevention is critical as many SCD cases go undetected due to nonspecific symptoms and ineffective risk stratification methods, especially among the youth.
Objective: To explore risk markers specific to SCD in younger populations using Artificial Intelligence (AI) trained on systematically collected Electronic Health Records (EHR) data, and to compare these markers with those in older populations.
Methods: We conducted a retrospective cohort study using data from 46,060 participants in Paris and its inner suburbs, spanning 2011 to 2020. The cohort included 1,100 SCD cases and 1,100 controls aged 40 years or younger, and 21,930 SCD cases and 21,930 controls aged over 40. A machine learning model was developed, trained, and validated on these cohorts. We used the Catboost algorithm and evaluated model performance using AUC, sensitivity, and specificity. The SHAP algorithm was employed to elucidate the relationship between variables and predicted risk.
Results: The model achieved on validation sets an AUC of 0.75 (95% CI 0.67-0.83) for the younger cohort and AUC of 0.82 (95% CI: 0.70-0.83), for the older cohort. Neuropsychiatric drugs were the most influential predictors for the younger cohort, whereas age and cardiovascular drugs were most predictive for the older cohort. The results indicate that traditional cardiovascular risk factors play a secondary role in predicting SCD among younger individuals.
Conclusion: In analyzing predictors of sudden death in individuals under 40, we developed a personalized SCD prediction model using EHR data tailored to this demographic. Psychiatric medications, and possibly underlying psychiatric conditions, emerged as significant predictors. Our findings emphasize the need for a multidisciplinary approach to SCD prevention, integrating neuropsychiatric and cardiovascular considerations.