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

CIED-based remote monitoring in heart failure using the HeartLogic™ algorithm: Which patients benefit most?

BACKGROUND & AIMS

Early identification of worsening HF enables timely adjustments to prevent hospitalization. Recent studies show the HeartLogic™ algorithm detects congestion and reduces HF events. However, it is unclear which patients benefit most. Therefore, this study aims to identify and characterize HF patients who benefit most from CIED-based remote monitoring with HeartLogic™.

METHODS

In this multicenter retrospective study, patients with a CIED and HeartLogic™ algorithm under structured follow-up were included. Patients were classified as having "substantial benefit" or "no benefit" from monitoring.

RESULTS

In total, 242 patients were included (male n = 190, 79%, median age 61 years [IQR 61-77]). Median follow-up was 1.2 years [IQR 1.1-2.7]. Among 378 alerts, 266 were true positive (70%) and 112 false positive (30%). Of the 242 patients, 69 (29%) were classified as having "substantial benefit", while 173 (71%) had "no benefit" from HeartLogic™ monitoring. Univariate and multivariate analysis showed that patients with "substantial benefit" had higher NYHA functional class (OR 2.64, P = 0.004), higher NT-ProBNP (OR 1.02, P = 0.003), higher serum creatinine (OR 1.10, P < 0.001), lower LVEF (OR 1.19, P = 0.004), more severe mitral regurgitation (OR 2.16, P = 0.006), higher right ventricular end diastolic volume (OR 1.05, P = 0.040), higher pulmonary artery pressures (OR 1.19, P = 0.003), and were more likely to use loop diuretics (OR 2.79, P = 0.001). Among patients with "substantial benefit," the positive predictive value (PPV) of HeartLogic™ to detect congestion was 92%.

CONCLUSION

The utilization of CIED-based HeartLogic™ driven HF care demonstrated pronounced efficacy, predominantly in patients exhibiting characteristics of HF at a more advanced disease stage.

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.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

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.

Pre-emptive treatment of heart failure exacerbations in patients managed with the HeartLogic™ algorithm

AIMS

Heart failure (HF) is a chronic disease affecting 64 million people worldwide and places a severe burden on society because of its mortality, numerous re-hospitalizations and associated costs. HeartLogic™ is an algorithm programmed into implanted devices incorporating several biometric parameters which aims to predict HF episodes. It provides an index which can be monitored remotely, allowing pre-emptive treatment of congestion to prevent acute decompensation. We aim to assess the impact and security of pre-emptive HF management, guided by the HeartLogic™ index.

METHODS AND RESULTS

The HeartLogic™ France Cohort Study is an investigator-initiated, prospective, multi-centre, non-randomized study. Three hundred ten patients with a history of HF (left ventricular ejection fraction ≤40%; or at least one episode of clinical HF with elevated NT-proBNP ≥450 ng/L) and implanted with a cardioverter defibrillator enabling HeartLogic™ index calculation will be included across 10 French centres. The HeartLogic™ index will be monitored remotely for 12 months and in the event of a HeartLogic™ index ≥16, the local investigator will contact the patient for assessment and adjust HF treatment as necessary. The primary endpoint is unscheduled hospitalization for HF. Secondary endpoints are all-cause mortality, cardiovascular death, HF-related death, unscheduled hospitalizations for ventricular or atrial arrhythmia and HeartLogic™ index evolution over time. Blood samples will be collected for biobanking, and quality of life will be assessed. Finally, the safety of a HeartLogic™-triggered strategy for initiating or increasing diuretic therapy will be assessed. A blind and independent committee will adjudicate the events.

CONCLUSIONS

The HeartLogic™ France Cohort Study will provide robust real-world data in a cohort of HF patients managed with the HeartLogic™ algorithm allowing pre-emptive treatment of heart failure exacerbations.

The Potential of the HeartLogicTM Algorithm in Patients with a Left Ventricular Assist Device, an Initial Report

BACKGROUND

Survival and quality-of-life of left ventricular assist device (LVAD) recipients improved significantly because of growing experience and technological advances. However, LVAD-related complication rates, including recurrent episodes of congestion, remain high. Early detection of fluid retention to provide a time-window for medical intervention is the pillar in preventing hospitalizations. The multisensory HeartLogicTM algorithm accurately detected impending congestion in ambulant heart failure patients. The aim of the current study is to investigate the feasibility of HeartLogicTM-driven care in LVAD patients.

METHODS

Consecutive LVAD destination therapy patients were followed-up according the structured HeartLogicTM-based heart failure carepath. An alert triggered a device check-up, and the heart failure team contacted the patient to evaluate for signs and symptoms of impending congestion. An alert was adjudicated as true positive or unexplained. An episode of congestion not preceded by an alert was deemed as a false negative.

RESULTS

Data from 7 patients were included: the median age was 67 years [IQR 61-71], 71% were male and 71% had a non-ischemic aetiology. Total follow-up entailed 12 patient-years. All patients experienced at least one alert. In total, 33 alerts were observed. Majority of alerts (70%, n = 23) were driven by congestion and one alerts (15%) were clinically meaningful but not primarily fluid-retention-related (e.g., altered hemodynamic triggered by a pump thrombosis). Of all the alerts, five (15%) were classified as an unexplained alert, and during follow-up, four false negative episodes were documented.

CONCLUSIONS

HeartLogicTM-driven care with continuous monitoring to detect impending fluid retention in LVAD patients was feasible and deserves further prospective validation.

PRecision Event Monitoring for PatienTs with Heart Failure using HeartLogic (PREEMPT-HF) study design and enrolment

AIMS

The HeartLogic multisensor index has been found to be a sensitive predictor of worsening heart failure (HF). However, there is limited data on this index's association and its constituent sensors with HF readmissions.

METHODS AND RESULTS

The PREEMPT-HF study is a global, multicentre, prospective, observational, single-arm, post-market study. HF patients with an implantable defibrillator device or cardiac resynchronization therapy with defibrillator with HeartLogic capabilities were eligible if sensor data collection was turned on and the HeartLogic feature was not enabled. Thus, the HeartLogic Index/alert and heart sounds sensor trends were unavailable via the LATITUDE remote monitoring system to clinicians (blinded). Evaluation of subject medical records at 6 months and a final in-clinic visit at 12 months was required for collection of all-cause hospitalizations and HF outpatient visits. The purpose of this study is exploratory, no formal hypothesis tests are planned, and no adjustment for multiple testing will be performed. A total of 2183 patients were enrolled at 103 sites between June 2018 and June 2020. A significant proportion of the patients were implanted with implantable defibrillator devices (39%) versus cardiac resynchronization therapy with defibrillator (61%); were female (27%); over 65 (61%); New York Heart Association class I (13%), II (53%), and III (33%); ejection fraction < 25% (21%); ischaemic (50%); and with a history of renal dysfunction (23%).

CONCLUSIONS

The PREEMPT study will provide clinical data and blinded sensor trends for the characterization of sensor changes with HF readmission, tachyarrhythmias, and event subgroups. These data may help to refine the clinical use of HeartLogic and to improve patient outcomes.

Fewer Worsening Heart Failure Events With HeartLogic on top of Standard Care: a Propensity-Matched Cohort Analysis

BACKGROUND

The implantable cardiac defibrillator-based HeartLogic algorithm aims to detect impending fluid retention in patients with heart failure (HF). Studies show that HeartLogic is safe to integrate into clinical practice. The current study investigates whether HeartLogic provides clinical benefit on top of standard care and device telemonitoring in patients with HF.

METHODS

A multicenter, retrospective, propensity-matched cohort analysis was performed in patients with HF and implantable cardiac defibrillators, and it compared HeartLogic to conventional telemonitoring. The primary endpoint was the number of worsening HF events. Hospitalizations and ambulatory visits due to HF were also evaluated.

RESULTS

Propensity score matching yielded 127 pairs (median age 68 years, 80% male). Worsening HF events occurred more frequently in the control group (2; IQR 0-4) compared to the HeartLogic group (1; IQR 0-3; P = 0.004). The number of HF hospitalization days was higher in controls than in the HeartLogic group (8; IQR 5-12 vs 5; IQR 2-7; P = 0.023), and ambulatory visits for diuretic escalation were more frequent in the control group than in the HeartLogic group (2; IQR 0-3 vs 1; IQR 0-2; P = 0.0001).

CONCLUSION

Integrating the HeartLogic algorithm in a well-equipped HF care path on top of standard care is associated with fewer worsening HF events and shorter duration of fluid retention-related hospitalizations.

Telemedicine for the treatment of heart failure: new opportunities after COVID-19

ABSTRACT

During the Coronavirus Disease 2019 (COVID-19) pandemic, the epidemiology of heart failure significantly changed with reduced access to health system resources and a worsening of patients' outcome. Understanding the causes of these phenomena could be important to refine the management of heart failure during and after the pandemic. Telemedicine was associated with an improvement in heart failure outcomes in several studies; therefore, it may help in refining the out-of-hospital care of heart failure. In this review, the authors describe the changes in heart failure epidemiology during the COVID-19 pandemic; analyse available evidence on use and benefit of telemedicine during the pandemic and prepandemic periods; and discuss approaches to optimize the home-based or outpatient heart failure management in the future, beyond the pandemic.

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.

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.

Heart failure and telemedicine: where are we and where are we going? Opportunities and critical issues

Heart failure (HF) is one of the main causes of morbidity in the world and is responsible for an enormous amount of health costs, mostly due to hospitalizations. The remote control techniques of vital signs and health status have the potential to help prevent factors leading to HF instability by stimulating early therapeutic interventions. The goal of telemedicine is to change the intervention strategy from a 'reactive' type, in which therapy is optimized in response to the worsening of symptoms, to a 'pro-active' type, in which therapeutic changes are undertaken based on changes in the monitored parameters during the sub-clinical phase. This article is aimed at exploring the major results obtained by telemedicine application in HF patients with and without cardiac electronic devices or in those with haemodynamic sensors and to analyse the critical issues and the opportunities of its use.

HeartLogic™: real-world data-efficiency, resource consumption, and workflow optimization

Heart failure (HF) is a major and still growing medical problem and is characterized by episodes of acute decompensation that are associated with a negative prognosis and a significant burden on the patients, doctors, and healthcare resources. Early detection of incipient HF may allow outpatient treatment before patients severely decompensate, thus reducing HF hospitalizations and related costs. The HeartLogic™ algorithm is an automatic, remotely managed system combining data directly related to HF pathophysiology into a single score, the HeartLogic™ index. This index proved to be effective in predicting the risk of incipient HF decompensation, allowing to redistribute resources from low-risk to high-risk patients in a timely and cost-saving manner. The alert-based remote management system seems more efficient than the one based on scheduled remote transmission in terms of caregivers' workload and alert detection timing. The widespread application of the HeartLogic™ algorithm requires the resolution of logistical and financial issues and the adoption of a pre-defined, functional workflow. In this paper, we reviewed general aspects of remote monitoring in HF patients, the functioning and pathophysiological basis of the HeartLogic index, its efficiency in the management of HF patients, and the economic effects and the organizational revolution associated with its use.

The patient perspective on remote monitoring of implantable cardiac devices

Aims

Remote monitoring for patients with cardiac implantable electronic devices (CIEDs) is well established in clinical routine and recommended by current guidelines. Nevertheless, data regarding patients' perceptions are limited. Therefore, this study aims to analyze the patient perspectives on the remote monitoring of cardiac devices in Germany.

Methods and results

Patients with CIEDs and remote monitoring of all current manufacturers from three German centers were asked to participate. The questionnaire consisted of 37 questions regarding the patients' individual use and perspectives on remote monitoring. Survey participation was anonymous and on a voluntary basis. A total of 617 patients (71.6% men) participated. Most patients reported feeling well informed (69.3%) and reported having unchanged or improved coping (98.8%) since the start of remote monitoring. At least 39.7% of patients experienced technical problems regarding the transmitter, whereas most patients (60.3%) reported that they never noted technical issues. Older patients had significantly less interest than younger patients in using their own smartphones for data transfer (p < 0.001).

Conclusion

Patients with remote follow-up of CIED reported that they felt well informed about the remote monitoring approach. Remote monitoring can support coping with their disease. With remote monitoring, patients experienced a prolongation of intervals of in-person follow-up visits, and especially younger patients would appreciate smartphone-based data transfer of their CIEDs.

The Feasibility, Effectiveness and Acceptance of Virtual Visits as Compared to In-Person Visits among Clinical Electrophysiology Patients during the COVID-19 Pandemic

The feasibility and effectiveness of virtual visits (VVs) for cardiac electrophysiology patients are still unknown. We aimed to assess the feasibility and effectiveness of VVs as compared to in-person visits, and to describe patient experience with virtual care in clinical electrophysiology. We prospectively enrolled patients scheduled to receive a clinical electrophysiology evaluation, dividing them in two groups: a VV group and an in-person visit group. Outcomes of interest were: (1) improvement in symptoms after the index visit, (2) disappearance of remote monitoring (RM) alerts at follow-up, (3) necessity of urgent hospitalization and (4) patient satisfaction measured by the Patient Satisfaction Questionnaire-18 (PSQ-18). This study included 162 patients in the VV group and 185 in the in-office visit group. As compared to in-person visits, VVs resulted in a similar reduction in RM alerts (51.5% vs. 43.2%, p-value 0.527) and in symptomatic patient rates (73.6% vs. 56.9%, p-value 0.073) at follow-up, without differences in urgent hospitalization rates (p-value 0.849). Patient satisfaction with VVs was higher than with in-person evaluation (p-value < 0.012). VVs proved to be as feasible and as effective as in-person visits, with high patient satisfaction. A hybrid model of care including VVs and in-person visits may become the new standard of care after the COVID-19 pandemic is over.

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.

Digital Technologies to Support Better Outcome and Experience of Care in Patients with Heart Failure

PURPOSE OF REVIEW

In this article, we review a range of digital technologies for possible application in heart failure patients, with a focus on lessons learned. We also discuss a future model of heart failure management, as digital technologies continue to become part of standard care.

RECENT FINDINGS

Digital technologies are increasingly used by healthcare professionals and those living with heart failure to support more personalised and timely shared decision-making, earlier identification of problems, and an improved experience of care. The COVID-19 pandemic has accelerated the acceptability and implementation of a range of digital technologies, including remote monitoring and health tracking, mobile health (wearable technology and smartphone-based applications), and the use of machine learning to augment data interpretation and decision-making. Much has been learned over recent decades on the challenges and opportunities of technology development, including how best to evaluate the impact of digital health interventions on health and healthcare, the human factors involved in implementation and how best to integrate dataflows into the clinical pathway. Supporting patients with heart failure as well as healthcare professionals (both with a broad range of health and digital literacy skills) is crucial to success. Access to digital technologies and the internet remains a challenge for some patients. The aim should be to identify the right technology for the right patient at the right time, in a process of co-design and co-implementation with patients.

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.

Implantable cardioverter defibrillators and devices for cardiac resynchronization therapy: what perspective for patients' apps combined with remote monitoring?

INTRODUCTION

Remote monitoring (RM) of cardiac implantable electronic devices (CIED) allows rapid detection of clinical and electrical events. Recently, several smartphone applications have been developed with the aim of improving patient compliance and better interpreting and integrating data deriving from remote control for the management of heart failure (HF).

AREAS COVERED

Studies investigating the role of CIEDs' RM in HF patients to predict and early treat acute decompensation. The importance of new technologies and applications developed to provide crucial information to clinicians, to better manage HF patients.

EXPERT OPINION

New medical technologies and smartphone applications for CIEDs' RM were developed to help clinicians in the management of CIED carriers. Indeed, the accessibility of technological devices (e.g. smartphones) and the improvements in medical technology provide the opportunity to optimize HF patients' monitoring by the transmission of device-related data, and with direct involvement of patients themselves. Thanks to these advancements, physicians have the possibility to recognize worsening signs of HF and promptly optimize treatments to potentially avoid hospitalization. The great value of this approach is its potential of reducing scheduled in-office visits or unnecessary medical contacts and optimizing healthcare resources management.

HeartLogicTM: Ready for Prime Time?

HEARTLOGICTM

READY FOR PRIME TIME?

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

Simple Summary

During the COVID-19 pandemic, utilization of remote monitoring platforms was recommended. The HeartLogic algorithm identifies patients at risk of heart failure events, combining multiple sensors available on implantable cardioverter defibrillators. This analysis examined how multiple CIED sensors behave in periods of anticipated restrictions pertaining to physical activity. We demonstrated a significant drop in median activity level immediately after the implementation of stay-at-home orders, whereas there was no difference in the other contributing sensors. The weekly rate of heart failure alerts was significantly higher during the lockdown and post-lockdown than that reported in the pre-lockdown.

Abstract

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.

Clinical Utility of HeartLogic, a Multiparametric Telemonitoring System, in Heart Failure

Telemonitoring through multiple variables measured on cardiac devices has the potential to improve the follow-up of patients with heart failure. The HeartLogic algorithm (Boston Scientific), implemented in some implantable cardiac defibrillators and cardiac resynchronisation therapy, allows monitoring of the nocturnal heart rate, respiratory movements, thoracic impedance, physical activity and the intensity of heart tones, with the aim of predicting major clinical events. Although HeartLogic has demonstrated high sensitivity for the detection of heart failure decompensations, its effects on hospitalisation and mortality in randomised clinical trials has not yet been corroborated. This review details how the HeartLogic algorithm works, compiles available evidence from clinical studies, and discusses its application in daily clinical practice.