Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report

Abstract

Aims In the Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients study, a novel algorithm for heart failure (HF) monitoring was implemented. The HeartLogic (Boston Scientific) index combines data from multiple implantable cardioverter defibrillator (ICD)‐based sensors and has proved to be a sensitive and timely predictor of impending HF decompensation. The remote monitoring of HF patients by means of HeartLogic has never been described in clinical practice. We report post‐implantation data collected from sensors, the combined index, and their association with clinical events during follow‐up in a group of patients who received a HeartLogic‐enabled device in clinical practice. Methods and results Patients with ICD and cardiac resynchronization therapy ICD were remotely monitored. In December 2017, the HeartLogic feature was activated on the remote monitoring platform, and multiple ICD‐based sensor data collected since device implantation were made available: HeartLogic index, heart rate, heart sounds, thoracic impedance, respiration, and activity. Their association with clinical events was retrospectively analysed. Data from 58 patients were analysed. During a mean follow‐up of 5 ± 3 months, the HeartLogic index crossed the threshold value (set by default to 16) 24 times (over 24 person‐years, 0.99 alerts/patient‐year) in 16 patients. HeartLogic alerts preceded five HF hospitalizations and five unplanned in‐office visits for HF. Symptoms or signs of HF were also reported at the time of five scheduled visits. The median early warning time and the time spent in alert were longer in the case of hospitalizations than in the case of minor events of clinical deterioration of HF. HeartLogic contributing sensors detected changes in heart sound amplitude (increased third sound and decreased first sound) in all cases of alerts. Patients with HeartLogic alerts during the observation period had higher New York Heart Association class ( P = 0.025) and lower ejection fraction ( P = 0.016) at the time of activation. Conclusions Our retrospective analysis indicates that the HeartLogic algorithm might be useful to detect gradual worsening of HF and to stratify risk of HF decompensation.

Use of a novel implantable cardioverter‐defibrillator multisensor algorithm for heart failure monitoring in a COVID‐19 patient: A case report

“…This seems to suggest that in this case, the clinical event had a cardiac origin. As previously described, 12,13 the HeartLogic alert diagnosed an impending HF event before signs or symptoms occurred, it early detected discontinuation of the HF therapy and allowed to reestablish it promptly.…”

Influence of decompensated heart failure on cardiac acoustic biomarkers: impact on early readmissions

“…The authors concluded that this algorithm might be able to detect clinical worsening in patients with HF early before a critical clinical deterioration. 28 When comparing patients with HF events versus patients without HF events, our results show that patients with HF events had a significantly lower EF at the end of WCD use compared with patients without HF events (31 ± 10% vs. 35% ± 10%; P = 0.005), and patients with HF events more often reported symptoms related to decompensated HF (P < 0.001). In our CABs and HR algorithm, we used a dual threshold strategy to stratify patients into low-risk and high-risk group categories for an HF event.…”

“…Each of the high‐risk alerts of the HeartLogic algorithm had contributions from changes in S1 and/or S3 amplitudes providing additional evidence that CABs are useful in early detection of an impending HF event. The authors concluded that this algorithm might be able to detect clinical worsening in patients with HF early before a critical clinical deterioration 28 …”

Real‐world outcomes in cardiac resynchronization therapy patients: design and baseline demographics of the SMART‐ Registry

“…Finally, the HeartLogic ™ algorithm combines novel sensor parameters such as heart sounds and markers of ventilation, along with other measurements like thoracic impedance, heart rate, and activity into an index for the early detection of worsening HF. 10 Following recent publications on this new and unique feature, [11][12][13][14][15][16] the SMART Registry will provide data on relevant sample size in real-life scenario and may help in evaluating the relationship between sensor data and CRT response.…”