1149 MACHINE-LEARNING BASED PREDICTION OF IN-HOSPITAL DEATH FOR PATIENTS WITH TAKOTSUBO SYNDROME: THE INTERTAK-ML MODEL


 
 
 Takotsubo syndrome (TTS) is burdened by a not negligible rate of an impaired short-term prognosis. Current existing models, based on classical statistical methods, showed only moderate accuracy to predict the risk of in-hospital adverse events following admission for TTS. We sought to design a machine-learning (ML) based model to predict the risk of in-hospital death among patients admitted for TTS, and to provide clusters of TTS patients associated with different risks of adverse short-term prognosis.
 
 
 
 A Penalized Logistic Regression-based ML model for predicting in-hospital death was trained and tested on a cohort of 3482 patients with TTS from the international, multicenter, InterTAK Registry. 33 clinically relevant variables were selected to be included in the prediction model. Model performance was assessed according to area under the receiver operating characteristic curve (AUC). A K-Means clustering algorithm was designed to stratify patients into phenotypic groups based on the most relevant features emerging from the main model.
 
 
 
 The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.88 (95%CI 0.87-0.90) and 0.87 (95%CI 0.83-0.91) with respect to in-hospital death prediction in the train and test cohorts, respectively. By exploiting the 5 variables showing the highest feature importance (use of catecholamines, type of triggering factor, left ventricular ejection fraction, white blood cell count, heart rate), TTS patients were clustered into five groups associated with different risks of in-hospital death (29.4% vs 3.9% vs 1.6% vs 1.3% vs 0.7%).
 
 
 
 A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed accurate discriminative capability for the prediction of in-hospital death. To support clinical decision-making, TTS patients can be clustered into groups entailing different risks of death based on routinely collected variables.
