Multiparameter diagnostic sensor measurements during clinically stable periods and worsening heart failure in ambulatory patients

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.

The role of cardiac acoustic biomarkers in monitoring patients with heart failure: A systematic literature review

Heart failure (HF) creates a considerable clinical, humanistic and economic burden on patients and caregivers as well as on healthcare systems. To attenuate the significant burden of HF, there is a need for enhanced management of patients with HF. The use of digital tools for remote non-invasive monitoring of heart parameters is gaining traction, and cardiac acoustic biomarkers (CABs) have been proposed as a complementary set of measures to assess heart function alongside traditional methods such as electrocardiogram and echocardiography. We conducted a systematic literature review to evaluate associations between CABs and HF outcomes. Embase and MEDLINE databases were searched for recent studies published between 2013 and 2023 that evaluated CABs in patients with HF. Additional grey literature (i.e., conference, congress and pre-print publications from January 2021 to May 2023) searches were included. Two reviewers independently examined all articles; a third resolved conflicts. Data were extracted from articles meeting inclusion criteria. Extracted studies underwent quality and bias assessments using the Joanna Briggs Institute (JBI) critical appraisal tools. In total, 3074 records were screened, 73 full-text articles were assessed for eligibility and 27 publications were included. Third heart sound (S3) and electromechanical activation time (EMAT) were the CABs most often reported in the literature for monitoring HF. Fifteen publications discussed changes in S3 characteristics and its role in HF detection or outcomes: six studies highlighted S3 assessment among various groups of patients with HF; four studies evaluated the strength or amplitude of S3 with clinical outcomes; five studies assessed the relationship between S3 presence and clinical outcomes; and one study assessed both S3 presence and amplitude in relation to HF clinical outcomes. Eleven publications reported on EMAT and its derivatives: five studies on the relationship between EMAT and HF and six studies on the association of EMAT and HF clinical outcomes. Studies reporting the first and fourth heart sound, left ventricular ejection time and systolic dysfunction index were limited. Published literature supported S3 and EMAT as robust CAB measures in HF that may have value in remote clinical monitoring and management of patients with HF. Additional studies designed to test the predictive power of these CABs, and others less well-characterized, are needed. This work was funded by Astellas Pharma Inc.

Myocardial Recovery in the Systemic Context: A Philosophic Shift for the Heart Failure Subspecialty to Optimize Patient Care

Heart failure poses a significant challenge to healthcare systems and society at large, mainly due to its increasing prevalence among the aging population and its association with frequent hospitalizations with high mortality rates. At its core, heart failure management seeks to emphasize myocardial recovery across the spectrum of disease, from acute cardiogenic shock to ambulatory heart failure, with care ranging from consideration of mechanical circulatory support to medication optimization. In this review, we propose a definition of "recovery" that extends beyond the restoration of normal myocardial dynamics to the entire human organism, ultimately improving functional capacity and clinical outcomes. Prioritizing this more holistic definition of "recovery" allows a broader representation of the spectrum of disease and corresponding management that falls under the "heart failure" umbrella. In so doing, a more synchronized delivery of care across settings and disciplines may be feasible for the modern patient living with heart failure.

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 Predictive Modeling of Heart Failure: Domain Description, Models' Characteristics and Literature Review

This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.

Predicting worsening heart failure hospitalizations in patients with implantable cardioverter defibrillators: is it all about alerts? A pooled analysis of nine trials

AIMS

To predict worsening heart failure hospitalizations (WHFHs) in patients with implantable defibrillators and remote monitoring, the HeartInsight algorithm (Biotronik, Berlin, Germany) calculates a heart failure (HF) score combining seven physiologic parameters: 24 h heart rate (HR), nocturnal HR, HR variability, atrial tachyarrhythmia, ventricular extrasystoles, patient activity, and thoracic impedance. We compared temporal trends of the HF score and its components 12 weeks before a WHFH with 12-week trends in patients without WHFH, to assess whether trends indicate deteriorating HF regardless of alert status.

METHODS AND RESULTS

Data from nine clinical trials were pooled, including 2050 patients with a defibrillator capable of atrial sensing, ejection fraction ≤ 35%, NYHA class II/III, no long-standing atrial fibrillation, and 369 WHFH from 259 patients. The mean HF score was higher in the WHFH group than in the no WHFH group (42.3 ± 26.1 vs. 30.7 ± 20.6, P < 0.001) already at the beginning of 12 weeks. The mean HF score further increased to 51.6 ± 26.8 until WHFH (+22% vs. no WHFH group, P = 0.003). As compared to the no WHFH group, the algorithm components either were already higher 12 weeks before WHFH (24 h HR, HR variability, thoracic impedance) or significantly increased until WHFH (nocturnal HR, atrial tachyarrhythmia, ventricular extrasystoles, patient activity).

CONCLUSION

The HF score was significantly higher at, and further increased during 12 weeks before WHFH, as compared to the no WHFH group, with seven components showing different behaviour and contribution. Temporal trends of HF score may serve as a quantitative estimate of HF condition and evolution prior to WHFH.

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.

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.

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.

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 &lt; 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 &lt; 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?

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.

Multiparameter diagnostic sensor measurements during clinically stable periods and worsening heart failure in ambulatory patients

Aims

This study aims to characterize the range of implantable device‐based sensor values including heart sounds, markers of ventilation, thoracic impedance, activity, and heart rate for patients with heart failure (HF) when patients were deemed to be in clinically stable periods against the time course of acute decompensation and recovery from HF events.

Methods and results

The MultiSENSE trial followed 900 patients implanted with a COGNIS CRT‐D for up to 1 year. Chronic, ambulatory diagnostic sensor data were collected and evaluated during clinically stable periods (CSP: unchanged NYHA classification, no adverse events, and weight change ≤2.27 kg), and in the timeframe leading up to and following HF events (HF admissions or unscheduled visits with intravenous HF treatment). Physiologic sensor data from 1667 CSPs occurring in 676 patients were compared with those data leading up to and following 192 HF events in 106 patients. Overall, the mean age was 66.6 years, and the population were predominantly male (73%). Patients were primarily in NYHA II (67%), with a mean LVEF of 29.6% and median NT‐proBNP of 754.5 pg/mL. Sensor values during CSP were poorer in patients who had HF events during the study period than those without HF events, including first heart sound (S1: 2.18 ± 0.84 mG vs. 2.62 ± 0.95 mG, P = 0.002), third heart sound (S3: 1.13 ± 0.36 mG vs. 0.91 ± 0.30 mG, P < 0.001), thoracic impedance (45.66 ± 8.78 Ohm vs. 50.33 ± 8.43 Ohm, P < 0.001), respiratory rate (19.09 ± 3.10 br/min vs. 17.66 ± 2.39 br/min, P = 0.002), night time heart rate (73.39 ± 8.36 b.p.m. vs. 69.56 ± 8.09 b.p.m., P = 0.001), patient activity (1.69 ± 1.84 h vs. 2.56 ± 2.20 h, P = 0.006), and HeartLogic index (11.07 ± 12.14 vs. 5.31 ± 5.13, P = 0.001). Sensor parameters measured worsening status leading up to HF events with recovery of values following treatment.

Conclusions

Device‐based physiologic sensors not only revealed progressive worsening leading up to HF events but also differentiated patients at increased risk of HF events when presumed to be clinically stable.