A New Risk Score Based on Machine Learning in Patients with Acute Heart Failure: The ML-HF score.

BACKGROUND: Conventional prognostic assessment scores often fall short in performance to predict mortality in patients with acute heart failure (AHF). OBJECTIVE: To develop and validate a machine learning-based prognostic score to predict in-hospital death in patients with AHF and compare its performance with the main traditional scores. METHODS: Patients admitted for AHF in Brazilian hospitals from the "Best Practices in Cardiology Program" from 2016 - 2022 were included. Clinical data, laboratory results, and the World Health Organization Quality of Life (WHOQOL-Bref) at hospital admission were collected. The outcome was in-hospital death. The model was trained using 70% of admissions (training set) and validated with the remaining 30% (test set). The ML-HF score area under the ROC curve (AUC) was compared with the AUC of the traditional scores Acute Decompensated Heart Failure National Registry (ADHERE) and Get With the Guidelines-Heart Failure (GWTG-HF). The level of significance was p