Clinical and economic impact of HeartLogic™ compared with standard care in heart failure patients

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.

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.

Patient Outcomes Associated with the First Remote Monitoring Experience of Cardiac Implantable Electronic Devices in South Korea

Purpose

Remote monitoring of cardiac devices reduces unnecessary outpatient visits and increases patient satisfaction. We aimed to evaluate remote monitoring in terms of patient satisfaction, economic efficiency, and safety.

Patients and Methods

This was a single university hospital survey. The time/medical cost efficacy and satisfaction index were evaluated using a questionnaire to investigate patient satisfaction before and after remote monitoring in patients using Biotronik implantable cardiac devices. The questionnaire was adopted and modified from Hwang's 2020 Survey on Telehealth Patient Experience.

Results

Remote monitoring was associated with a decrease in total outpatient visits. Of 1270 remote monitoring-related alerts clinicians received during the study period, more than 95% were from patients with pacemakers. Still, the severity of alerts was higher for implantable cardioverter defibrillators and cardiac resynchronization therapy with defibrillators. The post-RM survey results demonstrated that patients were generally satisfied with RM, perceived it as cost-effective, and found that RM facilitated health management without disrupting their daily routines.

Conclusion

The study participants were satisfied with their first remote monitoring experience and reported having time- and cost-savings by using remote monitoring. Remote monitoring-related alerts from high-voltage devices were more severe and required medical intervention.

Telemonitoring for heart failure: a meta-analysis

AIMS

Telemonitoring modalities in heart failure (HF) have been proposed as being essential for future organization and transition of HF care, however, efficacy has not been proven. A comprehensive meta-analysis of studies on home telemonitoring systems (hTMS) in HF and the effect on clinical outcomes are provided.

METHODS AND RESULTS

A systematic literature search was performed in four bibliographic databases, including randomized trials and observational studies that were published during January 1996-July 2022. A random-effects meta-analysis was carried out comparing hTMS with standard of care. All-cause mortality, first HF hospitalization, and total HF hospitalizations were evaluated as study endpoints. Sixty-five non-invasive hTMS studies and 27 invasive hTMS studies enrolled 36 549 HF patients, with a mean follow-up of 11.5 months. In patients using hTMS compared with standard of care, a significant 16% reduction in all-cause mortality was observed [pooled odds ratio (OR): 0.84, 95% confidence interval (CI): 0.77-0.93, I2: 24%], as well as a significant 19% reduction in first HF hospitalization (OR: 0.81, 95% CI 0.74-0.88, I2: 22%) and a 15% reduction in total HF hospitalizations (pooled incidence rate ratio: 0.85, 95% CI 0.76-0.96, I2: 70%).

CONCLUSION

These results are an advocacy for the use of hTMS in HF patients to reduce all-cause mortality and HF-related hospitalizations. Still, the methods of hTMS remain diverse, so future research should strive to standardize modes of effective hTMS.

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.

Cardiovascular Implantable Electronic Devices Enabled Remote Heart Failure Monitoring; What We Have Learned and Where to Go Next

Despite recent developments, heart failure (HF) remains to be a great burden to the individual patient, entailing major morbidity and mortality. Moreover, HF is a great burden to overall healthcare, mainly because of frequent hospitalizations. Timely diagnosis of HF deterioration and implementation of appropriate therapy may prevent hospitalization and eventually improve a patient’s prognosis; however, depending on the patient’s presentation, the signs and symptoms of HF often offer too little therapeutic window to prevent hospitalizations. Cardiovascular implantable electronic devices (CIEDs) can provide real-time physiologic parameters and remote monitoring of these parameters can potentially help to identify patients at high risk. However, routine implementation of remote monitoring of CIEDs has still not been widely used in daily patient care. This review gives a detailed description of available metrics for remote HF monitoring, the studies that provide evidence of their efficacy, ways to implement them in clinical HF practice, as well as lessons learned on where to go on from where we currently are.

Performance of a HeartLogicTM Based Care Path in the Management of a Real-World Chronic Heart Failure Population

Aim

Early detection of impending fluid retention and timely adjustment of (medical) therapy can prevent heart failure related hospitalizations. The multisensory cardiac implantable electronic device (CIED) based algorithm HeartLogicTM aims to alert in case of impending fluid retention. The aim of the current analysis is to evaluate the performance of the HeartLogicTM guided heart failure care path in a real-world heart failure population and to investigate whether the height of the index and the duration of the alert state are indicative of the degree of fluid retention.

Methods

Consecutive adult heart failure patients with a CIED and an activated HeartLogicTM algorithm were eligible for inclusion. Patients were followed up according to the hospital's heart failure care path. The device technician reviewed alerts for a technical CIED checkup. Afterwards, the heart failure nurse contacted the patient to identify impending fluid retention. An alert was either true positive or false positive. Without an alert a patient was true negative or false negative.

Results

Among 107 patients, [82 male, 70 (IQR 60–77) years, left ventricular ejection fraction 37 ± 11%] 130 HeartLogicTM alerts were available for analysis. Median follow up was 14 months [IQR 8–23]. The sensitivity to detect impending fluid retention was 79%, the specificity 88%. The positive predictive was value 71% and the negative predictive value 91%. The unexplained alert rate was 0.23 alerts/patient year and the false negative rate 0.17 alerts/patient year. True positive alerts [42 days (IQR 28–63)] lasted longer than false positive alerts [28 days (IQR 21–44)], p = 0.02. The maximal HeartLogicTM index was higher in true positive alerts [26 (IQR 21–34)] compared to false positive alerts [19 (IQR 17–24)], p < 0.01. Patients with higher HeartLogicTM indexes required more intense treatment (index height in outpatient setting 25 [IQR 20–32], day clinic treatment 28 [IQR 24–36] and hospitalized patients 45 [IQR 35–58], respectively), p < 0.01.

Conclusion

The CIED-based HeartLogicTM algorithm facilitates early detection of impending fluid retention and thereby enables clinical action to prevent this at early stage. The current analysis illustrates that higher and persistent alerts are indicative for true positive alerts and higher index values are indicative for more severe fluid retention.

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.

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.

No substantial excess all-cause mortality among cardiac implantable electronic device patients during the first COVID‑19 lockdown in the Leiden area

In the Netherlands, the coronavirus disease 2019 (COVID‑19) pandemic has resulted in excess mortality nationwide. Chronic heart disease patients are at risk for a complicated COVID‑19 course. The current study investigates all-cause mortality among cardiac implantable electronic device (CIED) patients during the first peak of the pandemic and compares the data to the statistics for the corresponding period in the two previous years. Data of adult CIED patients undergoing follow-up at the Leiden University Medical Centre were analysed. All-cause mortality between 1 March and 31 May 2020 was evaluated and compared to the data for the same period in 2019 and 2018. At the beginning of the first peak of the pandemic, 3,171 CIED patients (median age 70 years; 68% male; 41% ischaemic aetiology) were alive. Baseline characteristics of the 2019 (n = 3,216) and 2018 (n = 3,169) cohorts were comparable. All-cause mortality during the peak of the pandemic was 1.4% compared to 1.6% and 1.4% in the same period in 2019 and 2018, respectively (p = 0.84). During the first peak of the COVID‑19 pandemic, there was no substantial excess mortality among CIED patients in the Leiden area, despite the fact that this is group at high risk for a complicated course of a COVID‑19 infection. Strict adherence to the preventive measures may have prevented substantial excess mortality in these vulnerable patients.

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.

Remote heart failure management using the HeartLogic algorithm. RE-HEART Registry

INTRODUCTION AND OBJECTIVES

HeartLogic is a multiparametric algorithm incorporated into implantable cardioverter-defibrillators (ICD). The associated alerts predict impending heart failure (HF) decompensations. Our objective was to analyze the association between alerts and clinical events and to describe the implementation of a protocol for remote management in a multicenter registry.

METHODS

We evaluated study phase 1 (the investigators were blinded to the alert state) and phases 2 and 3 (after HeartLogic activation, managed as per local practice and with a standardized protocol, respectively).

RESULTS

We included 288 patients from 15 centers. In phase 1, the median observation period was 10 months and there were 73 alerts (0.72 alerts/patient-y), with 8 hospitalizations and 2 emergency room admissions for HF (0.10 events/patient-y). There were no HF hospitalizations outside the alert period. In the active phases, the median follow-up was 16 (95%CI, 15-22) months and there were 277 alerts (0.89 alerts/patient-y); 33 were associated with HF hospitalizations or HF death (n=6), 46 with minor decompensations, and 78 with other events. The unexplained alert rate was 0.39 alerts/patient-y. Outside the alert state, there was only 1 HF hospitalization and 1 minor HF decompensation. Most alerts (82% in phase 2 and 81% in phase 3; P=.861) were remotely managed. The median NT-proBNP value was higher within than outside the alert state (7378 vs 1210 pg/mL; P <.001).

CONCLUSIONS

The HeartLogic index was frequently associated with HF-related events and other clinically relevant situations, with a low rate of unexplained events. A standardized protocol allowed alerts to be safely and remotely detected and appropriate action to be taken on them.

In Search of Clinical Impact: Advanced Monitoring Technologies in Daily Heart Failure Care

Despite significant advances in the management of heart failure (HF), further improvement in the outcome of this chronic and progressive disease is still considered a major unmet need. Recurrent hospitalizations due to decompensated HF frequently occur, resulting in increased morbidity and mortality rates. Past attempts at early detection of clinical deterioration were mainly based on monitoring of signs and symptoms of HF exacerbation, which have mostly given disappointing results. Extensive research of the pathophysiology of HF decompensation has indicated that hemodynamic alterations start days prior to clinical manifestation. Novel technologies aim to monitor these minute hemodynamic changes, allowing time for therapeutic interventions to prevent hemodynamic derangement and HF exacerbation. The latest noticeable advancements include assessment of lung fluid volume, wearable devices with integrated sensors, and microelectromechanical systems-based implantable devices for continuous measurement of cardiac filling pressures. This manuscript will review the rationale for monitoring HF patients and discuss previous and ongoing attempts to develop clinically meaningful monitoring devices to improve daily HF health care, with particular emphasis on the recent advances and clinical trials relevant to this evolving field.

Clinical and economic impact of HeartLogic™ compared with standard care in heart failure patients

Aims

The implantable cardiac defibrillator/cardiac resynchronization therapy with defibrillator‐based HeartLogic™ algorithm has recently been developed for early detection of impending decompensation in heart failure (HF) patients; but whether this novel algorithm can reduce HF hospitalizations has not been evaluated. We investigated if activation of the HeartLogic algorithm reduces the number of hospital admissions for decompensated HF in a 1 year post‐activation period as compared with a 1 year pre‐activation period.

Methods and results

Heart failure patients with an implantable cardiac defibrillator/cardiac resynchronization therapy with defibrillator with the ability to activate HeartLogic and willingness to have remote device monitoring were included in this multicentre non‐blinded single‐arm trial with historical comparison. After a HeartLogic alert, the presence of HF symptoms and signs was evaluated. If there were two or more symptoms and signs apart from the HeartLogic alert, lifestyle advices were given and/or medication was adjusted. After activation of the algorithm, patients were followed for 1 year. HF events occurring in the 1 year prior to activation and in the 1 year after activation were compared. Of the 74 eligible patients (67.2 ± 10.3 years, 84% male), 68 patients completed the 1 year follow‐up period. The total number of HF hospitalizations reduced from 27 in the pre‐activation period to 7 in the post‐activation period (P = 0.003). The number of patients hospitalized for HF declined from 21 to 7 (P = 0.005), and the hospitalization length of stay diminished from average 16 to 7 days (P = 0.079). Subgroup analysis showed similar results (P = 0.888) for patients receiving cardiac resynchronization therapy during the pre‐activation period or not receiving cardiac resynchronization therapy, meaning that the effect of hospitalizations cannot solely be attributed to reverse remodelling. Subanalysis of a single‐centre Belgian subpopulation showed important reductions in overall health economic costs (P = 0.025).

Conclusion

Activation of the HeartLogic algorithm enables remote monitoring of HF patients, coincides with a significant reduction in hospitalizations for decompensated HF, and results in health economic benefits.