Multiple cArdiac seNsors for mAnaGEment of Heart Failure (MANAGE-HF) - Phase I Evaluation of the Integration and Safety of the HeartLogic Multisensor Algorithm in Patients With Heart Failure.

BACKGROUND: Patients with heart failure (HF) and reduced ejection fraction suffer from a relapsing and remitting disease course, where early treatment changes may improve outcomes. We assessed the clinical integration and safety of the HeartLogic multisensor index and alerts in HF care. METHODS: The Multiple cArdiac seNsors for mAnaGEment of Heart Failure (MANAGE-HF) study enrolled 200 patients with HF and reduced ejection fraction (<35%), New York Heart Association functional class II-III symptoms, implanted with a cardiac resynchronization therapy-defibrillator or and implantable cardioverter defibrillator, who had either a hospitalization for HF within 12 months or unscheduled visit for HF exacerbation within 90 days or an elevated natriuretic peptide concentration (brain natriuretic peptide [BNP] of ≥150 pg/mL or N-terminal pro-BNP [NT-proBNP] of ≥600 pg/mL). This phase included the development of an alert management guide and evaluated changes in medical treatment, natriuretic peptide levels, and safety. RESULTS: The mean age of participants was 67 years, 68% were men, 81% were White, and 61% had a HF hospitalization in prior 12 months. During follow-up, there were 585 alert cases with an average of 1.76 alert cases per patient-year. HF medications were augmented during 74% of the alert cases. HF treatment augmentation within 2 weeks from an initial alert was associated with more rapid recovery of the HeartLogic Index. Five serious adverse events (0.015 per patient-year) occurred in relation to alert-prompted medication change. NTproBNP levels decreased from median of 1316 pg/mL at baseline to 743 pg/mL at 12 months (P < .001). CONCLUSIONS: HeartLogic alert management was safely implemented in HF care and may optimize HF management. This phase supports further evaluation in larger studies. TRIAL REGISTRATION: ClinicalTrials.gov (NCT03237858).

Multiparameter diagnostic sensor measurements in heart failure patients presenting with SARS-CoV-2 infection.

AIMS: Implantable device-based sensor measurements including heart sounds, markers of ventilation, and thoracic impedance have been shown to predict heart failure (HF) hospitalizations. We sought to assess how these parameters changed prior to COVID-19 (Cov-19) and how these compared with those presenting with decompensated HF or pneumonia. METHODS AND RESULTS: This retrospective analysis explores patterns of changes in daily measurements by implantable sensors in 10 patients with Cov-19 and compares these findings with those observed prior to HF (n = 88) and pneumonia (n = 12) hospitalizations from the MultiSENSE, PREEMPT-HF, and MANAGE-HF trials. The earliest sensor changes prior to Cov-19 were observed in respiratory rate (6 days) and temperature (5 days). There was a three-fold to four-fold greater increase in respiratory rate, rapid shallow breathing index, and night heart rate compared with those presenting with HF or pneumonia. Furthermore, activity levels fell more in those presenting with Cov-19, a change that was often sustained for some time. In contrast, there were no significant changes in 1st or 3rd heart sound (S1 and S3 ) amplitude in those presenting with Cov-19 or pneumonia compared with the known changes that occur in HF decompensation. CONCLUSIONS: Multi-sensor device diagnostics may provide early detection of Cov-19, distinguishable from worsening HF by an extreme and fast rise in respiratory rate along with no changes in S3.

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).

A mathematical model of salt-sensitive hypertension: the neurogenic hypothesis.

Salt sensitivity of arterial pressure (salt-sensitive hypertension) is a serious global health issue. The causes of salt-sensitive hypertension are extremely complex and mathematical models can elucidate potential mechanisms that are experimentally inaccessible. Until recently, the only mathematical model for long-term control of arterial pressure was the model of Guyton and Coleman; referred to as the G-C model. The core of this model is the assumption that sodium excretion is driven by renal perfusion pressure, the so-called 'renal function curve'. Thus, the G-C model dictates that all forms of hypertension are due to a primary shift of the renal function curve to a higher operating pressure. However, several recent experimental studies in a model of hypertension produced by the combination of a high salt intake and administration of angiotensin II, the AngII-salt model, are inconsistent with the G-C model. We developed a new mathematical model that does not limit the cause of salt-sensitive hypertension solely to primary renal dysfunction. The model is the first known mathematical counterexample to the assumption that all salt-sensitive forms of hypertension require a primary shift of renal function: we show that in at least one salt-sensitive form of hypertension the requirement is not necessary. We will refer to this computational model as the 'neurogenic model'. In this Symposium Review we discuss how, despite fundamental differences between the G-C model and the neurogenic model regarding mechanisms regulating sodium excretion and vascular resistance, they generate similar haemodynamic profiles of AngII-salt hypertension. In addition, the steady-state relationships between arterial pressure and sodium excretion, a correlation that is often erroneously presented as the 'renal function curve', are also similar in both models. Our findings suggest that salt-sensitive hypertension is not due solely to renal dysfunction, as predicted by the G-C model, but may also result from neurogenic dysfunction.

Ambulatory respiratory rate trends identify patients at higher risk of worsening heart failure in implantable cardioverter defibrillator and biventricular device recipients: a novel ambulatory parameter to optimize heart failure management.

PURPOSE: Respiratory distress is the primary driver for heart failure (HF) hospitalization. Implantable pacemakers and defibrillators are capable of monitoring respiratory rate (RR) in ambulatory HF patients. We investigated changes in RR prior to HF hospitalizations and its near-term risk stratification power. METHODS: NOTICE-HF was an international multi-center study. Patients were implanted with an implantable cardioverter defibrillator or cardiac resynchronization therapy defibrillator, capable of trending daily maximum, median, and minimum RR (maxRR, medRR, minRR). RR from 120 patients with 9 months of follow-up was analyzed. One-tailed Student's t test was used to compare RR values prior to HF events to baseline defined as 4 weeks prior to the events. A Cox regression model was used to calculate the hazard ratios (HR) for the 30-day HF hospitalization risk based on RR values in the preceding month. RESULTS: Daily maxRR, medRR, and minRR were significantly elevated prior to HF events compared to baseline (ΔmaxRR 1.8 ± 3.0; p = 0.02; ΔmedRR, 2.1 ± 2.8; p = 0.007; ΔminRR, 1.5 ± 2.1, p = 0.008). Risk of experiencing HF events within 30-days was increased if the standard deviation of medRR over the preceding month was above 1.0 br/min (HR = 12.3, 95 % confidence interval (CI) 2.57-59, p = 0.002). The risk remained high after adjusting for clinical variables that differed at enrollment. CONCLUSION: Ambulatory daily respiratory rate trends may be a valuable addition to standard management for HF patients.

A new conceptual paradigm for the haemodynamics of salt-sensitive hypertension: a mathematical modelling approach.

A conceptually novel mathematical model of neurogenic angiotensin II-salt hypertension is developed and analysed. The model consists of a lumped parameter circulatory model with two parallel vascular beds; two distinct control mechanisms for both natriuresis and arterial resistances can be implemented, resulting in four versions of the model. In contrast with the classical Guyton-Coleman model (GC model) of hypertension, in the standard version of our new model natriuresis is assumed to be independent of arterial pressure and instead driven solely by sodium intake; arterial resistances are driven by increased sympathetic nervous system activity in response to the elevated plasma angiotensin II and increased salt intake (AngII-salt). We compare the standard version of our new model against a simplified Guyton-Coleman model in which natriuresis is a function of arterial pressure via the pressure-natriuresis mechanism, and arterial resistances are controlled via the whole-body autoregulation mechanism. We show that the simplified GC model and the new model correctly predict haemodynamic and renal excretory responses to induced changes in angiotensin II and sodium inputs. Importantly, the new model reproduces the pressure-natriuresis relationship--the correlation between arterial pressure and sodium excretion--despite the assumption of pressure-independent natriuresis. These results show that our model provides a conceptually new alternative to Guyton's theory without contradicting observed haemodynamic changes or pressure-natriuresis relationships. Furthermore, the new model supports the view that hypertension need not necessarily have a renal aetiology and that long-term arterial pressure could be determined by sympathetic nervous system activity without involving the renal sympathetic nerves.

Current computational models do not reveal the importance of the nervous system in long-term control of arterial pressure.

Arterial pressure is regulated over long periods of time by neural, hormonal and local control mechanisms, which ultimately determine the total blood volume and how it is distributed between the various vascular compartments of the circulation. A full understanding of the complex interplay of these mechanisms can be greatly facilitated by the use of mathematical models. In 1967, Guyton and Coleman published a model for long-term control of arterial pressure that focused on renal control of body sodium and water and thus total blood volume. The central point of their model is that the long-term level of arterial pressure is determined exclusively by the 'renal function curve', which relates arterial pressure to urinary excretion of salt and water. The contribution of the sympathetic nervous system to setting the long-term level of arterial pressure in the model is limited. In light of the overwhelming evidence for a major role of the sympathetic nervous system in long-term control of arterial pressure and the pathogenesis of hypertension, new mathematical models for long-term control of arterial pressure may be necessary. Despite the prominence and general acceptance of the Guyton-Coleman model in the field of hypertension research, we argue here that it overestimates the importance of renal control of body fluids and total blood volume in blood pressure regulation. Furthermore, we suggest that it is possible to construct an alternative model in which sympathetic nervous system activity plays an important role in long-term control of arterial pressure independent of its effects on total blood volume.