Abstract
Abstract Background Outpatient monitoring of patients with chronic heart failure (HF) may result in HF exacerbation and subsequent hospitalizations that may be avoided with an early recognition. The HeartLogic (HL) algorithm automatically calculates a daily HF index based on values including heart sounds, markers of ventilation, thoracic impedance, activity and heart rate and recognize an active alert state relative to a configurable threshold. Methods We included 35 patients with HF in optimized medical therapy; the investigational chronic ambulatory data collection was performed via implanted Boston pace–maker, defibrillator or cardiac resynchronization therapy and we analyzed these data since February 2021, ongoing. We established a cut off of HL index of 16 due to the confirmatory trial and we contacted the patients 2 weeks after the threshold exceeding to evaluate their clinical state. Results Preliminary data (until December 2021) showed us 24 alarms in total, of whom 7 were truly heart failure related alarms. These are independent from the values of the HL index reached and the best parameter resulted to be the chest impedance. Measurement of third heart sound was another sensitive but not very specific parameter. The main confounding factor was given by infections, especially related to Covid–19. The therapeutic strategy was predominantly increasing the doses of diuretics. There were no heart failure events in patients without the warning HL index. Discussion The preliminary data showed us how high is the negative predictor factor of the HL index based on our center parameters. We aim to analyze further data to be more accurate and to find the best way to detect heart failure.
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