Development and validation of a moderate aortic stenosis disease progression model

Understanding the multifactorial determinants of rapid progression in patients with aortic stenosis (AS) remains limited. We aimed to develop and validate a machine learning model (ML) for predicting rapid progression from moderate to severe AS within one year. 8,746 patients were identified with moderate AS across seven healthcare organizations. Three ML models were trained using demographic, and echocardiographic variables, namely Random Forest, XGBoost and causal discovery-logistic regression. An ensemble model was developed integrating the aforementioned three. A total of 3,355 patients formed the training and internal validation cohort. External validation was performed on 171 patients from one institution. An ensemble model was selected due to its superior F1 score and precision in internal validation (0.382 and 0.301, respectively). Its performance on the external validation cohort was modest (F1 score=0.626, precision=0.532). An ensemble model comprising only demographic and echocardiographic variables was shown to have modest performance in predicting one-year progression from moderate to severe AS. Further validation in larger populations, along with integration of comprehensive clinical data, is crucial for broader applicability. • Machine Learning Model Development: We developed and validated an ensemble machine learning model to predict the rapid progression of moderate aortic stenosis (AS) to severe AS within one year. • Large-Scale Data Utilization: The model was trained on data from 8,746 patients across seven healthcare organizations, ensuring a robust and diverse dataset. • Model Performance: The ensemble model demonstrated modest performance with an F1-score of 0.626 and precision of 0.532 in the external validation cohort. • Clinical Implications: Our findings suggest that machine learning can enhance risk stratification and personalize the management of patients with moderate AS, potentially leading to more timely interventions and improved patient outcomes. • Future Directions: Further validation in larger populations and integration of comprehensive clinical data are crucial for broader applicability and improved model performance.