HOME PREDICT HF is single center, prospective, unblinded, randomly assigned training and validation observational cohorts to develop machine learning algorithms from an in-home suite of sensors in order to predict 3-month heart failure hospitalization and/or emergency department visits. Study population includes adults presenting with a diagnosis of reduced ejection fraction (LVEF \<= 40%), NYHA class II-IV) who have had a hospitalization for HF in the previous 6 months. The study objectives include (1) To collect observational data from multiple sensors, patient-reported outcomes, and medical record data to develop (train) machine-learning algorithms (2) To validate trained algorithms in a separate validation cohort (3) To collect data to inform the design of a future intervention study. The primary outcome is Ninety-day heart failure hospitalization/emergency department visit for heart failure.
