Differentiating Sensor Changes in a Composite Heart Failure ICD Monitoring Index: Clinical Correlates and Implications.

BACKGROUND: The HeartLogic algorithm integrates data from implantable defibrillator(ICD) sensors to predict heart failure(HF) decompensation: first(S1) and third(S3) heart sounds, intrathoracic impedance, respiration rate, ratio of respiration rate to tidal volume(RSBI), and night heart rate. OBJECTIVE: This study assessed the relative changes in ICD sensors at the onset of HeartLogic alerts, their association with patient characteristics, and outcomes. METHODS: The study included 568 HF patients carrying ICDs(CRT-D,n=410) across 26 centers, with a median follow-up of 26 months. HeartLogic alerts triggered patient contact and potential treatment. RESULTS: A total of 1200 HeartLogic alerts were recorded in 370 patients. The sensor with the highest change at the alert's onset was S3 in 27% of alerts, followed by S3/S1(25%). Patients with atrial fibrillation(AF) and chronic kidney disease(CKD) at implantation had higher alert prevalence(AF,84% vs. no-AF,58%; CKD,72% vs. no-CKD,59%; p <0.05) and rate (AF,1.51/patient-year vs. no-AF,0.88/patient-year; CKD,1.30/patient-year vs. no-CKD,0.89/patient-year; p<0.05). During follow-up, 247 patients experienced more than one alert; in 85%, the sensor with the highest change varied between successive alerts. Of the 88(7%) alerts associated with HF hospitalization or death, respiration rate or RSBI(11%, p=0.007 vs. S3/S1) and night heart rate(11%, p=0.031 vs. S3/S1) were more commonly the sensors showing the highest change. Clinical events were more common with the first alert(12.6%) than subsequent alerts(5.2%,p <0.001). CONCLUSION: HeartLogic alerts are mostly triggered by changes in heart sounds, but clinical events are more linked to respiration rate, RSBI, and night heart rate. Recurrent alerts often involve different sensors, indicating diverse mechanisms of HF progression.

Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm.

Background: Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable cardioverter-defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation. Objective: This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm. Methods: The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th-75th percentile: 15-37). Results: During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02-9.72; P = .045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74-9.06; P=.001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74%-98%]) and 55% specificity (95% CI [51%-60%]), while BiV% ≤96% predicted alerts with 74% sensitivity (95% CI [55%-88%]) and 81% specificity (95% CI [77%-85%]). Conclusion: A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.

Clinical impact of remote heart failure management using the multiparameter ICD HeartLogic alert.

INTRODUCTION AND OBJECTIVES: The multiparametric implantable cardioverter-defibrillator HeartLogic index has proven to be a sensitive and timely predictor of impending heart failure (HF) decompensation. We evaluated the impact of a standardized follow-up protocol implemented by nursing staff and based on remote management of alerts. METHODS: The algorithm was activated in HF patients at 19 Spanish centers. Transmitted data were analyzed remotely, and patients were contacted by telephone if alerts were issued. Clinical actions were implemented remotely or through outpatient visits. The primary endpoint consisted of HF hospitalizations or death. Secondary endpoints were HF outpatient visits. We compared the 12-month periods before and after the adoption of the protocol. RESULTS: We analyzed 392 patients (aged 69±10 years, 76% male, 50% ischemic cardiomyopathy) with implantable cardioverter-defibrillators (20%) or cardiac resynchronization therapy defibrillators (80%). The primary endpoint occurred 151 times in 86 (22%) patients during the 12 months before the adoption of the protocol, and 69 times in 45 (11%) patients (P<.001) during the 12 months after its adoption. The mean number of hospitalizations per patient was 0.39±0.89 pre- and 0.18±0.57 postadoption (P<.001). There were 185 outpatient visits for HF in 96 (24%) patients before adoption and 64 in 48 (12%) patients after adoption (P<.001). The mean number of visits per patient was 0.47±1.11 pre- and 0.16±0.51 postadoption (P<.001). CONCLUSIONS: A standardized follow-up protocol based on remote management of HeartLogic alerts enabled effective remote management of HF patients. After its adoption, we observed a significant reduction in HF hospitalizations and outpatient visits.

Performance of a multi-sensor implantable defibrillator algorithm for heart failure monitoring in the presence of atrial fibrillation.

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.

Performance of a multisensor implantable defibrillator algorithm for heart failure monitoring related to co-morbidities.

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.

Predicting all-cause mortality by means of a multisensor implantable defibrillator algorithm for heart failure monitoring.

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.

Combination of an implantable defibrillator multi-sensor heart failure index and an apnea index for the prediction of atrial high-rate events.

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.

Implantable defibrillator-detected heart failure status predicts ventricular tachyarrhythmias.

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.

Association Between Device-Detected Sleep-Disordered Breathing and Implantable Defibrillator Therapy in Patients With Heart Failure.

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 < 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).

Decongestive treatment adjustments in heart failure patients remotely monitored with a multiparametric implantable defibrillators algorithm.

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.

A mobile app for improving the compliance with remote management of patients with cardiac implantable devices: a multicenter evaluation in clinical practice.

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.

Implantable defibrillator-detected heart failure status predicts atrial fibrillation occurrence.

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.

Implantable Cardioverter Defibrillator Multisensor Monitoring during Home Confinement Caused by the COVID-19 Pandemic.

AIMS: The utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic index combines multiple implantable cardioverter defibrillator (ICD) sensors and has proved to be a predictor of impending heart failure (HF) decompensation. We examined how multiple ICD sensors behave in the periods of anticipated restrictions pertaining to physical activity. METHODS: The HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from 1 January to 19 July 2020, was divided into three phases: pre-lockdown (weeks 1-11), lockdown (weeks 12-20), post-lockdown (weeks 21-29). RESULTS: Immediately after the implementation of stay-at-home orders (week 12), we observed a significant drop in median activity level whereas there was no difference in the other contributing parameters. The median composite HeartLogic index increased at the end of the Lockdown. The weekly rate of alerts was significantly higher during the lockdown (1.56 alerts/week/100 pts, 95%CI: 1.15-2.06; IRR = 1.71, p = 0.014) and post-lockdown (1.37 alerts/week/100 pts, 95%CI: 0.99-1.84; IRR = 1.50, p = 0.072) than that reported in pre-lockdown (0.91 alerts/week/100 pts, 95%CI: 0.64-1.27). However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers and the proportion of alerts fully managed remotely. CONCLUSIONS: During the lockdown, the system detected a significant drop in the median activity level and generated a higher rate of alerts suggestive of worsening of the HF status.

Multiparametric Implantable Cardioverter-Defibrillator Algorithm for Heart Failure Risk Stratification and Management: An Analysis in Clinical 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.

Home delivery of the communicator for remote monitoring of cardiac implantable devices: A multicenter experience during the covid-19 lockdown.

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.

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

We report the case of a patient implanted with an implantable defibrillator endowed with a multisensor algorithm for heart failure monitoring. Automatic measurement of multiple clinical variables allowed to detect impending heart failure decompensation and showed its ability to facilitate differential diagnosis in the context of the current COVID-19 pandemic.

Single- and multi-site pacing strategies for optimal cardiac resynchronization therapy: impact on device longevity and therapy cost.

BACKGROUND: Multiple left ventricular pacing strategies have been suggested for improving response to cardiac resynchronization therapy (CRT). However, these programming strategies may sometimes entail accepting configurations with high pacing threshold and accelerated battery drain. We assessed the feasibility of predefined pacing programming protocols, and we evaluated their impact on device longevity and their cost-impact. METHODS: We estimated battery longevity in 167 CRT-D patients based on measured pacing parameters according to multiple alternative programming strategies: single-site pacing associated with lowest threshold, non-apical location, longest interventricular delay, and pacing from two electrodes. To determine the economic impact of each programming strategy, we applied the results of a model-based cost analysis using a 15-year time horizon. RESULTS: Selecting the electrode with the lowest threshold resulted in a median device longevity of 11.5 years. Non-apical pacing and interventricular delay maximization were feasible in most patients and were obtained at the price of a few months of battery life. Device longevity of > 10 years was preserved in 87% of cases of non-apical pacing and in 77% on pacing at the longest interventricular delay. The mean reduction in battery life when the second electrode was activated was 1.5 years. Single-site pacing strategies increased the therapy cost by 4-6%, and multi-site pacing by 12-13%, in comparison with the lowest-cost scenario. CONCLUSIONS: Modern CRT-D systems ensure effective pacing and allow multiple optimization strategies for maximizing service life or for enhancing effectiveness. Single- or multi-site pacing strategies can be implemented without compromising device service life and at an acceptable increase in therapy cost.

Prospective evaluation of the multisensor HeartLogic algorithm for heart failure monitoring.

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.

ICD-measured heart sounds and their correlation with echocardiographic indexes of systolic and diastolic function.

BACKGROUND: Novel implantable defibrillators (ICDs) allow first (S1) and third (S3) heart sounds to be measured by means of an embedded accelerometer. ICD-measured S1 and S3 have been shown to significantly correlate with hemodynamic changes in acute animal models. The HeartLogic algorithm (Boston Scientific) measures and combines multiple parameters, including S3 and S1, into a single index to predict impending heart failure decompensation. We evaluated the echocardiographic correlates of ICD-measured S1 and S3 in patients with ICD and cardiac resynchronization therapy ICD. METHODS: The HeartLogic feature was activated in 104 patients. During in-office visits, patients underwent echocardiographic evaluation, and parameters of systolic and diastolic function were correlated with S3 and S1 amplitude measured on the same day as the visit. RESULTS: S3 amplitude inversely correlated with deceleration time of the E-wave (r = -0.32; 95% CI -0.46 - -0.17; P < 0.001), and S1 amplitude significantly correlated with left ventricular ejection fraction (r = 0.17; 95% CI 0.03-0.30; P = 0.021). S3 > 0.9 mG detected a restrictive filling pattern with 85% (95% CI 72%-93%) sensitivity and 82% (95% CI 75%-88%) specificity, while S1 < 1.5 mG detected ejection fraction < 35% with 28% (95% CI 19%-40%) sensitivity and 88% (95% CI 80%-93%) specificity. CONCLUSION: ICD-measured heart sound parameters are significantly correlated with echocardiographic indexes of systolic and diastolic function. This confirms their utility for remote patient monitoring when used as single sensors and their potential relevance when considered in combination with other physiological ICD sensors that evaluate various aspects of heart failure physiology.