Abstract
INTRODUCTION: Acute myocardial infarction complicated by cardiogenic shock (AMI-CS) is a heterogeneous clinical syndrome associated with substantial morbidity and mortality. We developed a machine learning-based mortality model to identify features and patient subgroups associated with the largest change in mortality risk when evaluating treatment with Impella devices or intra-aortic Balloon Pump (IABP). METHODS: Our cohort comprised 369 sites and 15,796 patient visits to the cardiac catheterization laboratory from the National Cardiovascular Data Registry. We developed XGBoost decision tree models to model mortality with and without treatment variable included (Impella or IABP). RESULTS: The estimated population mean excess mortality effect of treatment with Impella devices vs IABP was 10.4 ± 0.8%. However, we identified clinical subgroups of 282 patients for whom a decreased risk of mortality was associated with use of Impella as compared with IABP. Those patients were on average younger, presented with higher systolic blood pressure, higher rate of salvage percutaneous coronary intervention, higher initial creatinine, and lower hemoglobin. DISCUSSION: While Impella devices were associated with higher mortality risk overall, certain clinical profiles were associated with lower risk, illustrating heterogeneity of treatment effects.