Multicenter Prospective Blind External Validation of a Machine Learning Model for Predicting Heart Failure Decompensation: A 3-Hospital Validation Study

This paper presents the results of a multicenter prospective blind external validation study aimed at validating a machine learning model, HFPred, for predicting heart failure decompensation. The model, initially developed using self-reported daily questionnaires and health monitoring data, was trained on a cohort of 242 patients from Basurto Hospital, Bilbao. The validation study spanned three European cohorts, each with distinct objectives and patient demographics, providing a comprehensive assessment of the model's applicability. While the model accurately identified instances of decompensation, it also generated false alarms, primarily attributed to measurement errors and uncontrolled external factors. Despite these challenges, patient compliance was commendable, underscoring the potential benefits of the model. Future improvements include incorporating personalized alert thresholds and conducting non-blind pilot studies for enhanced predictive capabilities.