Machine‐learning based prediction of in‐hospital death for patients with takotsubo syndrome: the InterTAK‐ML model

The study developed a machine-learning model using a Ridge Logistic Regression algorithm to predict the risk of in-hospital death among Takotsubo syndrome (TTS) patients. The model, called InterTAK-ML, was trained on 3482 patients from the International Takotsubo Registry and validated on both internal and external datasets. Additionally, a clustering algorithm identified six patient groups with varying risks of in-hospital death (28.8% to 0.5%). This machine-learning approach demonstrates the potential to effectively predict adverse outcomes in TTS patients.