Background The HeartLogic algorithm measures data from multiple implantable cardioverter‐defibrillator‐based sensors and combines them into a single index. The associated alert has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation. Hypothesis We describe a multicenter experience of remote HF management by means of HeartLogic and appraise the value of an alert‐based follow‐up strategy. Methods The alert was activated in 104 patients. All patients were followed up according to a standardized protocol that included remote data reviews and patient phone contacts every month and at the time of alerts. In‐office examinations were performed every 6 months or when deemed necessary. Results During a median follow‐up of 13 (10–16) months, the overall number of HF hospitalizations was 16 (rate 0.15 hospitalizations/patient‐year) and 100 alerts were reported in 53 patients. Sixty alerts were judged clinically meaningful, and were associated with multiple HF‐related conditions. In 48 of the 60 alerts, the clinician was not previously aware of the condition. Of these 48 alerts, 43 triggered clinical actions. The rate of alerts judged nonclinically meaningful was 0.37/patient‐year, and the rate of hospitalizations not associated with an alert was 0.05/patient‐year. Centers performed remote follow‐up assessments of 1113 scheduled monthly transmissions (10.3/patient‐year) and 100 alerts (0.93/patient‐year). Monthly remote data review allowed to detect 11 (1%) HF events requiring clinical actions (vs 43% actionable alerts, P < .001). Conclusions HeartLogic allowed relevant HF‐related clinical conditions to be identified remotely and enabled effective clinical actions to be taken; the rates of unexplained alerts and undetected HF events were low. An alert‐based management strategy seemed more efficient than a scheduled monthly remote follow‐up scheme.

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.

At present, the few studies regarding genetic polymorphisms of congenital thrombophilic factors in SSNHL are not conclusive. According to our data, factor V G1691A, prothrombin G20210A, and MTHFR C677T variants should be not considered risk factors for SSNHL. Further large prospective studies are needed to provide currently lacking information and to improve our knowledge in the field before we recommend the determination of genetic polymorphism in SSNHL as routine practice.

Background: The HeartLogic algorithm combines multiple implantable cardioverter-defibrillator sensors to identify patients at risk of heart failure (HF) events. We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events. Methods: The HeartLogic feature was activated in 366 implantable cardioverter-defibrillator and cardiac resynchronization therapy implantable cardioverter-defibrillator patients at 22 centers. The median follow-up was 11 months [25th–75th percentile: 6–16]. The HeartLogic algorithm calculates a daily HF index and identifies periods IN alert state on the basis of a configurable threshold. Results: The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients. The time IN alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations, and 8 patients died of HF during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (hazard ratio, 24.53 [95% CI, 8.55–70.38], P <0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with less HF events (hazard ratio, 0.37 [95% CI, 0.14–0.99], P =0.047). No differences in event rates were observed between in-office and remote alert management. Conclusions: This multiparametric algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required to effectively manage HeartLogic alerts. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02275637.

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