Search

Aims: The HeartLogic Index combines data from multiple implantable cardioverter defibrillators (ICDs) sensors and has been shown to accurately stratify patients at risk of heart failure (HF) events. We evaluated and compared the performance of this algorithm during sinus rhythm and during long-lasting atrial fibrillation (AF)., Methods and Results: HeartLogic was activated in 568 ICD patients from 26 centres. We found periods of ≥30 consecutive days with an atrial high-rate episode (AHRE) burden <1 h/day and periods with an AHRE burden ≥20 h/day. We then identified patients who met both criteria during the follow-up (AHRE group, n = 53), to allow pairwise comparison of periods. For control purposes, we identified patients with an AHRE burden <1 h throughout their follow-up and implemented 2:1 propensity score matching vs. the AHRE group (matched non-AHRE group, n = 106). In the AHRE group, the rate of alerts was 1.2 [95% confidence interval (CI): 1.0-1.5]/patient-year during periods with an AHRE burden <1 h/day and 2.0 (95% CI: 1.5-2.6)/patient-year during periods with an AHRE-burden ≥20 h/day (P = 0.004). The rate of HF hospitalizations was 0.34 (95% CI: 0.15-0.69)/patient-year during IN-alert periods and 0.06 (95% CI: 0.02-0.14)/patient-year during OUT-of-alert periods (P < 0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95% CI: 1.67-55.31) during periods with an AHRE burden <1 h/day and 2.70 (95% CI: 1.01-28.33) during periods with an AHRE burden ≥20 h/day. In the matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95% CI: 0.12-0.60)/patient-year during IN-alert periods and 0.04 (95% CI: 0.02-0.08)/patient-year during OUT-of-alert periods (P < 0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44)., Conclusion: Patients received more alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio in comparison with non-AF periods and non-AF patients., Clinical Trial Registration: http://clinicaltrials.gov/Identifier: NCT02275637., Competing Interests: Conflict of interest: G.B. reported small speaker fees from Bayer, Boehringer Ingelheim, Boston, Daiichi Sankyo, Janssen, and Sanofi outside of the submitted work. L.S. is a consultant for Boston Scientific, Abbott, and Medtronic and reported small speaker fees from Zoll. M.Z. reported small speaker fees from Abbott, Boston Scientific, and Biotronik. M.C. and S.V. are employees of Boston Scientific, Inc. The other authors did not report other conflicts of interest to disclose., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: The HeartLogic algorithm combines multiple implantable defibrillator (ICD) sensor data and has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation in cardiac resynchronization therapy (CRT-D) patients. We evaluated the performance of this algorithm in non-CRT ICD patients and in the presence of co-morbidities., Methods and Results: The HeartLogic feature was activated in 568 ICD patients (410 with CRT-D) from 26 centres. The median follow-up was 26 months [25th-75th percentile: 16-37]. During follow-up, 97 hospitalizations were reported (53 cardiovascular) and 55 patients died. We recorded 1200 HeartLogic alerts in 370 patients. Overall, the time IN the alert state was 13% of the total observation period. The rate of cardiovascular hospitalizations or death was 0.48/patient-year (95% CI: 0.37-0.60) with the HeartLogic IN the alert state and 0.04/patient-year (95% CI: 0.03-0.05) OUT of the alert state, with an incidence rate ratio of 13.35 (95% CI: 8.83-20.51, P < 0.001). Among patient characteristics, atrial fibrillation (AF) on implantation (HR: 1.62, 95% CI: 1.27-2.07, P < 0.001) and chronic kidney disease (CKD) (HR: 1.53, 95% CI: 1.21-1.93, P < 0.001) independently predicted alerts. HeartLogic alerts were not associated with CRT-D versus ICD implantation (HR: 1.03, 95% CI: 0.82-1.30, P = 0.775). Comparisons of the clinical event rates in the IN alert state with those in the OUT of alert state yielded incidence rate ratios ranging from 9.72 to 14.54 (all P < 0.001) in all groups of patients stratified by: CRT-D/ICD, AF/non-AF, and CKD/non-CKD. After multivariate correction, the occurrence of alerts was associated with cardiovascular hospitalization or death (HR: 1.92, 95% CI: 1.05-3.51, P = 0.036)., Conclusions: The burden of HeartLogic alerts was similar between CRT-D and ICD patients, while patients with AF and CKD seemed more exposed to alerts. Nonetheless, the ability of the HeartLogic algorithm to identify periods of significantly increased risk of clinical events was confirmed, regardless of the type of device and the presence of AF or CKD., (© 2023 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Background: The HeartLogic algorithm (Boston Scientific) has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation., Objective: The purpose of this study was to determine whether remotely monitored data from this algorithm could be used to identify patients at high risk for mortality., Methods: The algorithm combines implantable cardioverter-defibrillator (ICD)-measured accelerometer-based heart sounds, intrathoracic impedance, respiration rate, ratio of respiration rate to tidal volume, night heart rate, and patient activity into a single index. An alert is issued when the index crosses a programmable threshold. The feature was activated in 568 ICD patients from 26 centers., Results: During median follow-up of 26 months [25th-75th percentile 16-37], 1200 alerts were recorded in 370 patients (65%). Overall, the time IN-alert state was 13% of the total observation period (151/1159 years) and 20% of the follow-up period of the 370 patients with alerts. During follow-up, 55 patients died (46 in the group with alerts). The rate of death was 0.25 per patient-year (95% confidence interval [CI] 0.17-0.34) IN-alert state and 0.02 per patient-year (95% CI 0.01-0.03) OUT of the alert state, with an incidence rate ratio of 13.72 (95% CI 7.62-25.60; P <.001). After multivariate correction for baseline confounders (age, ischemic cardiomyopathy, kidney disease, atrial fibrillation), the IN-alert state remained significantly associated with the occurrence of death (hazard ratio 9.18; 95% CI 5.27-15.99; P <.001)., Conclusion: The HeartLogic algorithm provides an index that can be used to identify patients at higher risk for all-cause mortality. The index state identifies periods of significantly increased risk of death., (Copyright © 2023. Published by Elsevier Inc.)

Introduction: The prediction of ventricular tachyarrhythmias among patients with implantable cardioverter defibrillators is difficult with available clinical tools. We sought to assess whether in patients with heart failure (HF) and reduced ejection fraction with defibrillators, physiological sensor-based HF status, as summarized by the HeartLogic index, could predict appropriate device therapies., Methods: Five hundred and sixty-eight consecutive HF patients with defibrillators (n = 158, 28%) or cardiac resynchronization therapy-defibrillators (n = 410, 72%) were included in this prospective observational multicenter analysis. The association of both HeartLogic index and its physiological components with defibrillator shocks and overall appropriate therapies was assessed in regression and time-dependent Cox models., Results: Over a follow-up of 25 (15-35) months, 122 (21%) patients received an appropriate device therapy (shock, n = 74, 13%), while the HeartLogic index crossed the threshold value (alert, HeartLogic ≥ 16) 1200 times (0.71 alerts/patient-year) in 370 (65%) subjects. The occurrence of ≥1 HeartLogic alert was significantly associated with both appropriate shocks (Hazard ratios [HR]: 2.44, 95% confidence interval [CI]: 1.49-3.97, p = .003), and any appropriate defibrillator therapies. In multivariable time-dependent Cox models, weekly IN-alert state was the strongest predictor of appropriate defibrillator shocks (HR: 2.94, 95% CI: 1.73-5.01, p < .001) and overall therapies. Compared with stable patients, patients with appropriate shocks had significantly higher values of HeartLogic index, third heart sound amplitude, and resting heart rate 30-60 days before device therapy., Conclusion: The HeartLogic index is an independent dynamic predictor of appropriate defibrillator therapies. The combined index and its individual physiological components change before the arrhythmic event occurs., (© 2023 The Authors. Journal of Cardiovascular Electrophysiology published by Wiley Periodicals LLC.)

Aims: Patients with atrial fibrillation frequently experience sleep disorder breathing, and both conditions are highly prevalent in presence of heart failure (HF). We explored the association between the combination of an HF and a sleep apnoea (SA) index and the incidence of atrial high-rate events (AHRE) in patients with implantable defibrillators (ICDs)., Methods and Results: Data were prospectively collected from 411 consecutive HF patients with ICD. The IN-alert HF state was measured by the multi-sensor HeartLogic Index (>16), and the ICD-measured Respiratory Disturbance Index (RDI) was computed to identify severe SA. The endpoints were as follows: daily AHRE burden of ≥5 min, ≥6 h, and ≥23 h. During a median follow-up of 26 months, the time IN-alert HF state was 13% of the total observation period. The RDI value was ≥30 episodes/h (severe SA) during 58% of the observation period. An AHRE burden of ≥5 min/day was documented in 139 (34%) patients, ≥6 h/day in 89 (22%) patients, and ≥23 h/day in 68 (17%) patients. The IN-alert HF state was independently associated with AHRE regardless of the daily burden threshold: hazard ratios from 2.17 for ≥5 min/day to 3.43 for ≥23 h/day (P < 0.01). An RDI ≥ 30 episodes/h was associated only with AHRE burden ≥5 min/day [hazard ratio 1.55 (95% confidence interval: 1.11-2.16), P = 0.001]. The combination of IN-alert HF state and RDI ≥ 30 episodes/h accounted for only 6% of the follow-up period and was associated with high rates of AHRE occurrence (from 28 events/100 patient-years for AHRE burden ≥5 min/day to 22 events/100 patient-years for AHRE burden ≥23 h/day)., Conclusions: In HF patients, the occurrence of AHRE is independently associated with the ICD-measured IN-alert HF state and RDI ≥ 30 episodes/h. The coexistence of these two conditions occurs rarely but is associated with a very high rate of AHRE occurrence., Clinical Trial Registration: URL: http://clinicaltrials.gov/Identifier: NCT02275637., Competing Interests: Conflict of interest: M. Campari and S. Valsecchi are employees of Boston Scientific, Inc. G. Boriani reports receiving small speaker's fees from Bayer, Boston, Janssen, and Sanofi. No other conflicts of interest exist., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Sleep-disordered breathing is highly prevalent in heart failure (HF) and has been suggested as a risk factor for malignant ventricular arrhythmias. The Respiratory Disturbance Index (RDI) computed by an implantable cardioverter-defibrillator (ICD) algorithm accurately identifies severe sleep apnea., Objectives: In the present analysis, the authors evaluated the association between ICD-detected sleep apnea and the incidence of appropriate ICD therapies in patients with HF., Methods: We enrolled 411 HF patients who had received an ICD endowed with an algorithm that calculates the RDI each night. In this analysis, the weekly mean RDI value was considered. The endpoint was the first appropriate ICD shock., Results: The median follow-up was 26 months (25th to 75th percentile: 16-35 months). During follow-up, 1 or more ICD shocks were documented in 58 (14%) patients. Patients with shocks were younger (age 66 ± 13 years vs 70 ± 10 years; P = 0.038), and had more frequently undergone implantation for secondary prevention (21% vs 10%; P = 0.026). The maximum RDI value calculated during the entire follow-up period did not differ between patients with and without shocks (55 ± 15 episodes/h vs 54 ± 14 episodes/h; P = 0.539). However, the ICD-detected RDI showed considerable variability during follow-up. The overall median of the weekly RDI was 33 episodes/h (25th to 75th percentile: 24-45 episodes/h). A time-dependent Cox regression model revealed that a continuously measured weekly mean RDI of ≥45 episodes/h was independently associated with shock occurrence (HR: 4.63; 95% CI: 2.54-8.43; P &lt; 0.001), after correction for baseline confounders (age, secondary prevention)., Conclusions: In HF patients, appropriate ICD shocks were more likely to be delivered during periods when patients exhibited more sleep-disordered breathing. (Arrhythmias Detection in a Real World Population [RHYTHM DETECT]; NCT02275637)., Competing Interests: Funding Support and Author Disclosures Drs Campari and Valsecchi are employees of Boston Scientific, Inc. Dr Boriani has received small speaker fees from Boston, Biotronik, and Medtronic. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: The remote device management (RM) is recommended for patients with cardiac implantable electronic devices (CIEDs). RM underutilization is frequently driven by the lack of correct system activation. The MyLATITUDE Patient App (Boston Scientific) has been developed to encourage patient compliance with RM by providing information on communicator setup, troubleshooting, and connection status of the communicator., Methods: At 14 centers, patients with CIEDs were invited to download and install the App on a mobile device. After 3 months, patients were asked to complete an ad hoc questionnaire to evaluate their experience., Results: The App was proposed to 242 consecutive patients: 81 before RM activation, and 161 during follow-up. The App was successfully installed by 177 (73%) patients. The time required for activation of the communicator and the need for additional support were similar between patients who followed the indications provided by the App and those who underwent standard in-clinic training. During follow-up, notifications of lack of connection were received by 20 (11%) patients and missed transmission by 22 (12%). The median time from notification to resolution was 2 days. After 3 months, 175 (99%) communicators of the 177 patients who installed the App were in "Monitored" status versus 113 (94%) of 120 patients without the App installed (p=0.033). The use of the app made 84% of patients feel reassured., Conclusions: The App was well accepted by CIED patients and offered support for communicator management and installation. Its use enabled patients to remain connected with greater continuity during follow-up., (© 2022. The Author(s).)

Aims: HeartLogic algorithm combines data from multiple implantable defibrillators (ICD)-based sensors to predict impending heart failure (HF) decompensation. A treatment protocol to manage algorithm alerts is not yet known, although decongestive treatment adjustments are the most frequent alert-triggered actions reported in clinical practice. We describe the implementation of HeartLogic for remote monitoring of HF patients, and we evaluate the approach to diuretic dosing and timing of the intervention in patients with device alerts., Methods: The algorithm was activated in 229 ICD patients at eight centers. The median follow-up was 17 months (25th-75th percentile: 11-24). Remote data reviews and patient phone contacts were undertaken at the time of HeartLogic alerts, to assess the patient's status and to prevent HF worsening. We analyzed alert-triggered augmented HF treatments, consisting of isolated increases in diuretics dosage., Results: We reported 242 alerts (0.8 alerts/patient-year) in 123 patients, 137 (56%) alerts triggered clinical actions to treat HF. The HeartLogic index decreased after the 56 actions consisting of diuretics increase. Specifically, alerts resolved more quickly when the increases in dosing of diuretics were early rather than late: 28 days versus 62 days, p < .001. The need of hospitalization for further treatments to resolve the alert condition was associated with higher HeartLogic index values on the day of the diuretics increase (odds ratio: 1.11, 95% CI: 1.02-1.20, p = .013) and with late interventions (odds ratio: 5.11, 95% CI: 1.09-24.48, p = .041). No complications were reported after drug adjustments., Conclusions: Decongestive treatment adjustments triggered by alerts seem safe and effective. The early use of decongestive treatment and the use of high doses of diuretics seem to be associated with more favorable outcomes., (© 2022 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.)

Background: In heart failure (HF) patients, atrial fibrillation (AF) is associated with a worse prognosis. Implantable cardioverter-defibrillator (ICD) diagnostics allow continuous monitoring of AF and are equipped with algorithms for HF monitoring., Objective: We evaluated the association between the values of the multisensor HF HeartLogic index and the incidence of AF, and assessed the performance of the index in detecting follow-up periods of significantly increased AF risk., Methods: The HeartLogic feature was activated in 568 ICD patients. Median follow-up was 25 months [25th-75th percentile (15-35)]. The HeartLogic algorithm calculates a daily HF index and identifies periods of IN-alert state on the basis of a configurable threshold. The endpoints were daily AF burden ≥5 minutes, ≥6 hours, and ≥23 hours., Results: The HeartLogic index crossed the threshold value 1200 times. AF burden ≥5 minutes/day was documented in 183 patients (32%), ≥6 hours/day in 118 patients (21%), and ≥23 hours/day in 89 patients (16%). The weekly time of IN-alert state was independently associated with AF burden ≥5 minutes/day (hazard ratio [HR] 1.95; 95% confidence interval [CI] 1.22-3.13; P = .005), ≥6 hours/day (HR 2.66; 95% CI 1.60-4.44; P <.001), and ≥23 hours/day (HR 3.32; 95% CI 1.83-6.02; P <.001), after correction for baseline confounders. Comparison of the episode rates in the IN-alert state with those in the OUT-of-alert state yielded HR ranging from 1.57 to 3.11 for AF burden from ≥5 minutes to ≥23 hours., Conclusions: The HeartLogic alert state was independently associated with AF occurrence. The intervals of time defined by the algorithm as periods of increased risk of HF allow risk stratification of AF according to various thresholds of daily burden., (Copyright © 2022 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)

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.

Background: During the COVID-19 pandemic in-person visits for patients with cardiac implantable electronic devices should be replaced by remote monitoring (RM), in order to prevent viral transmission. A direct home-delivery service of the RM communicator has been implemented at 49 Italian arrhythmia centers., Methods: According to individual patient preference or the organizational decision of the center, patients were assigned to the home-delivery group or the standard in-clinic delivery group. In the former case, patients received telephone training on the activation process and use of the communicator. In June 2020, the centers were asked to reply to an ad hoc questionnaire to describe and evaluate their experience in the previous 3 months., Results: RM was activated in 1324 patients: 821 (62%) received the communicator at home and the communicator was activated remotely. Activation required one additional call in 49% of cases, and the median time needed to complete the activation process was 15 min [25th-75th percentile: 10-20]. 753 (92%) patients were able to complete the correct activation of the system. At the time when the questionnaire was completed, 743 (90%) communicators were regularly transmitting data. The service was generally deemed useful (96% of respondents) in facilitating the activation of RM during the COVID-19 pandemic and possibly beyond., Conclusions: Home delivery of the communicator proved to be a successful approach to system activation, and received positive feedback from clinicians. The increased use of a RM protocol will reduce risks for both providers and patients, while maintaining high-quality care., (© 2021 The Authors. Pacing and Clinical Electrophysiology published by Wiley Periodicals LLC.)

Introduction: A substantial number of heart failure patients undergoing implantation of implantable cardioverter defibrillators (ICDs) fail to receive beta-blockers, or receive them at a suboptimal dose. Remote monitoring (RM) is becoming the standard means of following up recipients of ICDs. However, the impact of this shift toward remote ICD follow-up on the quality of drug therapy management in current clinical practice is unknown. The present analysis was aimed at investigating the impact of RM on the dose of beta-blockers achieved, and its association with clinical outcome at 12 months., Methods: Altogether 987 consecutive patients were enrolled and followed up for at least 12 months in 25 Italian centres. RM was adopted by 499 patients., Results: The number of patients receiving beta-blockers at any dose decreased after 12 months (from 403 (81%) to 370 (74%) for the remote arm and from 389 (80%) to 342 (70%) for the standard arm, both p < 0.02). Nonetheless, the number of patients on beta-blockers at the effective dose increased in both arms (from 60 (12%) to 82 (16%) for remote and from 63 (13%) to 98 (20%) for standard arms respectively, both p < 0.05). At multivariate analysis, RM was not associated with an effective dose of beta-blockers at the follow-up evaluation. However, the adoption of RM (p = 0.003) and the achievement of the effective dose of beta-blockers (p = 0.006) were independently and positively associated with an improved outcome., Discussion: In a 'real-world' setting, we did not find an association between RM and the achieved dose of beta-blockers. However, we reported outcome benefits in achieving the effective dose of beta-blockers during follow-up and in adopting RM., (© The Author(s) 2015.)