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.

Concurrent Assessment of the CardioMEMS HF System and HeartLogic HF Diagnostic: A Retrospective Case Series.

BACKGROUND: Heart failure (HF) causes high morbidity and mortality despite advances in medical therapy. Remote patient monitoring for HF allows for the optimization of medical therapy and prevention of HF hospitalizations. This study is the first to assess pulmonary artery diastolic pressures (PADP) using the CardioMEMS HF System (CMEMS) and cardiac implantable electronic device-based multisensor indexes (HeartLogic index [HLI]) using the HeartLogic HF Diagnostic (HL) in a small, retrospective cohort of patients with HF at a single center. METHODS AND RESULTS: Any hospitalization, HF hospitalization, HF-related outpatient visit, and pulmonary artery pressure action were recorded in 7 patients with concurrent CMEMS and HL measurements for at least 1 year. The median time before both platforms were implanted and present in the same participant was 3.12 months. The median study period was 1.44 years per participant. Data availability for HL was significantly higher at 99.6% compared with 64.1% adherence for CMEMS (P = .016). Overall, PADP was only weakly correlated to HLI (r = 0.098), but there was a 2.87 mm Hg (P = .014) estimated increase in PADP during HLI alert periods versus nonalert periods. Similarly, the estimated odds of being above a PADP goal was 4.7 times higher (95% confidence interval 3.0-7.2, P < .001) in HLI alert vs nonalert periods. CONCLUSIONS: Concurrent analysis of patients with CMEMS and HL showed an association between PADP and HLI, but the correlation was weak. However, there was a significant increase in PADP during HLI alert periods versus nonalert periods.

Evaluation of Medicare Claims for the Development of Heart Failure Diagnostics.

BACKGROUND: Although claims data provide a large and efficient source of clinical events, validation is needed prior to use in heart failure (HF) diagnostic development. METHODS AND RESULTS: Data from the Multisensor Chronic Evaluations in Ambulatory Heart Failure Patients (MultiSENSE) study, used to create the HeartLogic HF diagnostic, were linked with fee-for-service (FFS) Medicare claims. Events were matched by patient ID and date, and agreement was calculated between claims primary HF diagnosis codes and study event adjudication. HF events (HFEs) were defined as inpatient visits, or outpatient visits with intravenous decongestive therapy. Diagnostic performance was measured as HFE-detection sensitivity and false-positive rate (FPR). Linkage of 791 MultiSENSE subjects returned 320 FFS patients with an average follow-up duration of 0.94 years. Although study and claims deaths matched exactly (n = 14), matching was imperfect between study hospitalizations and acute inpatient claims events. Of 239 total events, 165 study hospitalizations (69%) matched inpatient claims events, 28 hospitalizations matched outpatient claims events (12%), 14 hospitalizations were study-unique (6%), and 32 inpatient events were claims-unique (13%). Inpatient HF classification had substantial agreement with study adjudication (κ = 0.823). Diagnostic performance was not different between claims and study events (sensitivity = 75.6% vs 77.6% and FPR = 1.539 vs 1.528 alerts/patient-year). HeartLogic-detected events contributed to > 90% of the HFE costs used for evaluation. CONCLUSIONS: Acceptable event matching, good agreement of claims diagnostic codes with adjudication, and equivalent diagnostic performance support the validity of using claims for HF diagnostic development.

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.

A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study.

OBJECTIVES: The aim of this study was to develop and validate a device-based diagnostic algorithm to predict heart failure (HF) events. BACKGROUND: HF involves costly hospitalizations with adverse impact on patient outcomes. The authors hypothesized that an algorithm combining a diverse set of implanted device-based sensors chosen to target HF pathophysiology could detect worsening HF. METHODS: The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) study enrolled patients with investigational chronic ambulatory data collection via implanted cardiac resynchronization therapy defibrillators. HF events (HFEs), defined as HF admissions or unscheduled visits with intravenous treatment, were independently adjudicated. The development cohort of patients was used to construct a composite index and alert algorithm (HeartLogic) combining heart sounds, respiration, thoracic impedance, heart rate, and activity; the test cohort was sequestered for independent validation. The 2 coprimary endpoints were sensitivity to detect HFE >40% and unexplained alert rate <2 alerts per patient-year. RESULTS: Overall, 900 patients (development cohort, n = 500; test cohort, n = 400) were followed for up to 1 year. Coprimary endpoints were evaluated using 320 patient-years of follow-up data and 50 HFEs in the test cohort (72% men; mean age 66.8 ± 10.3 years; New York Heart Association functional class at enrollment: 69% in class II, 25% in class III; mean left ventricular ejection fraction 30.0 ± 11.4%). Both endpoints were significantly exceeded, with sensitivity of 70% (95% confidence interval [CI]: 55.4% to 82.1%) and an unexplained alert rate of 1.47 per patient-year (95% CI: 1.32 to 1.65). The median lead time before HFE was 34.0 days (interquartile range: 19.0 to 66.3 days). CONCLUSIONS: The HeartLogic multisensor index and alert algorithm provides a sensitive and timely predictor of impending HF decompensation. (Evaluation of Multisensor Data in Heart Failure Patients With Implanted Devices [MultiSENSE]; NCT01128166).

Heart sounds based measures of cardiac status for heart failure patient management.

Heart sounds based measurements such as S3 amplitude and systolic timing intervals (STIs) are known to be indicative of cardiac dysfunction. In this paper we investigate the correlation of these measurements from pacemaker device implant locations to echocardiographic hemodynamic metrics of E wave deceleration time (EDT), Ejection Fraction (EF) and Stroke Volume (SV). Simultaneous heart sounds and echocardiography measurements were made in 6 heart failure patients with cardiac resynchronization therapy (CRT) devices prior to CRT activation (baseline) and at 3 and 6 months post-CRT. In N=17 datasets, S3 amplitude was correlated to EDT (r=-0.86, p<0.05). Similarly STIs such as heart sounds based pre-ejection period (HSPEP) was correlated with EF (r = -0.81, p<0.01) and ejection time (HSET) with SV (r = 0.65, p=0.01). Longitudinal analysis in each patient showed consistent changes between % increase in S3 vs. % decrease in EDT (r= -0.83, p<0.01), % change in HSPEP vs. % change in EF (r= -0.80, p=0.03) and % change in SV (r= -0.72, p< 0.01). These results show proof of concept for using pacemaker derived measurements for monitoring HF patients. Further study is needed to determine their efficacy for chronic monitoring of HF patients.