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

New models for heart failure care delivery

Heart failure (HF) is a common disease with increasing prevalence around the world. There is high morbidity and mortality associated with poorly controlled HF along with increasing costs and strain on healthcare systems due to a high rate of rehospitalization and resource utilization. Despite the establishment of clear evidence-based guideline directed medical therapies (GDMT) proven to improve HF morbidity and mortality, there remains significant clinical inertia to optimizing HF patients on GDMT. Only a minority of HF patients are prescribed on all four classes of GDMT. To bridge the gap between the vulnerable population of HF patients and lifesaving GDMT, HF implementation is of increasing importance. HF implementation involves strategies and techniques to improve GDMT optimization along with other modalities to improve HF management. HF implementation meets patients where they are, including at the time of acute decompensation in the inpatient setting, at the vulnerable discharge stage, and at the chronic management stage in the outpatient setting. Inpatient HF implementation strategies include protocolized rapid titration of GDMT, site-level audit-and-feedback, virtual GDMT optimization teams, and electronic health record notifications and alerts. Discharge HF implementation strategies include education at patient and provider levels, discharge summaries, and HF transitional programs. Outpatient HF implementation strategies include digital innovations such as electronic health record utilization and mobile applications, population level strategies such as registries and clinical dashboards), changes in HF team structure and member roles, remote monitoring with implanted devices and telemonitoring, and hospital at home care model. With a growing population of HF patients, there is an increasing need for novel and creative HF implementation and monitoring methods.

Continuous Monitoring of Blood Pressure and Vascular Hemodynamic Properties With Miniature Extravascular Hall-Based Magnetic Sensor

Continuous measurement of vascular and hemodynamic parameters could improve monitoring of disease progression and enable timely clinical decision making and therapy surveillance in patients suffering from cardiovascular diseases. However, no reliable extravascular implantable sensor technology is currently available. Here, we report the design, characterization, and validation of an extravascular, magnetic flux sensing device capable of capturing the waveforms of the arterial wall diameter, arterial circumferential strain, and arterial pressure without restricting the arterial wall. The implantable sensing device, comprising a magnet and a magnetic flux sensing assembly, both encapsulated in biocompatible structures, has shown to be robust, with temperature and cyclic-loading stability. Continuous and accurate monitoring of arterial blood pressure and vascular properties was demonstrated with the proposed sensor in vitro with a silicone artery model and validated in vivo in a porcine model mimicking physiologic and pathologic hemodynamic conditions. The captured waveforms were further used to deduce the respiration frequency, the duration of the cardiac systolic phase, and the pulse wave velocity. The findings of this study not only suggest that the proposed sensing technology is a promising platform for accurate monitoring of arterial blood pressure and vascular properties, but also highlight the necessary changes in the technology and the implantation procedure to allow the translation of the sensing device in the clinical setting.

Back to basics with newer technology: Should we focus on reducing work of breathing earlier?

The typical approach to management of respiratory distress is focused on oxygen supplementation. However, additional oxygen alone does not improve outcomes, particularly in critically ill patients. Instead, supplemental oxygen can be associated with increased morbidities. We present the hypothesis that clinicians should focus on reducing the work of breathing early in the course of critical illness. Rather than simply supplementing oxygen, newer technologies including high flow nasal oxygen, may be utilized to increase the efficiency of gas exchange. By reducing the work of breathing, the cardiac workload can be reduced, thus relieving some excess physiologic stress and supporting the critically ill patient. To illustrate this point, we provided three clinical cases of respiratory failure from non-pulmonary origins; all cases displayed hemodynamic improvement due to reducing the work of breathing through high-velocity therapy prior to receiving definitive therapy for underlying pathologies.

Next-generation Mobile Cardiac Telemetry: Clinical Value of Combining Electrocardiographic and Physiologic Parameters

The ZOLL Arrhythmia Monitoring System, a mobile cardiac telemetry (MCT) device from ZOLL Corporation (Chelmsford, MA, USA), records single-channel electrocardiogram (ECG) signals, heart rate, activity, respiratory rate, and posture. Comprehensive reporting from these multiple biometrics may provide a global evaluation of arrhythmic or other cardiovascular risks in individual patients and insights into the patient's overall wellness and health status. The objective of the study was to evaluate the physician-perceived utility of adding biometric data to the traditional ECG-only-based assessment and subject-reported symptoms. This prospective study recruited candidates for MCT. Independent event and end-of-use (EOU) reports based on ECG and biometrics data were provided to physicians. To document whether the biometric data affected treatment plan decisions or added value over the ECG-alone data, physicians completed a questionnaire for each report. Additionally, they completed the questionnaire to understand the utility of the subject wellness information provided in the EOU report. From December 2020 to July 2021, 583 patients were enrolled by 27 physicians from 18 cardiology practices in the United States. When using biometrics data compared to the ECG alone, this study found that 96% of the physicians made changes to the treatment plan that initially was based on the ECG alone. The biometrics-based changes involved 64% of all patients (n = 535), and included modifications to medications, follow-up, and lifestyle in 18%, 19%, and 63% of the subjects, respectively. In this largest MCT study conducted to date, next-generation MCT, by providing multiple biometric parameters along with ECG data, improves physicians' ability to make patient management decisions. This added functionality and clarity may replace traditional "ECG with diary"-based monitoring.

Nocturnal Respiratory Rate Dynamics Enable Early Recognition of Impending Hospitalizations

The days and weeks preceding hospitalization are poorly understood because they transpire before patients are seen in conventional clinical care settings. Home health sensors offer opportunities to learn signatures of impending hospitalizations and facilitate early interventions, however the relevant biomarkers are unknown. Nocturnal respiratory rate (NRR) is an activity-independent biomarker that can be measured by adherence-independent sensors in the home bed. Here, we report automated longitudinal monitoring of NRR dynamics in a cohort of high-risk recently hospitalized patients using non-contact mechanical sensors under patients' home beds. Since the distribution of nocturnal respiratory rates in populations is not well defined, we first quantified it in 2,000 overnight sleep studies from the NHLBI Sleep Heart Health Study. This revealed that interpatient variability was significantly greater than intrapatient variability (NRR variances of 11.7 brpm2 and 5.2 brpm2 respectively, n=1,844,110 epochs), which motivated the use of patient-specific references when monitoring longitudinally. We then performed adherence-independent longitudinal monitoring in the home beds of 34 high-risk patients and collected raw waveforms (sampled at 80 Hz) and derived quantitative NRR statistics and dynamics across 3,403 patient-nights (n= 4,326,167 epochs). We observed 23 hospitalizations for diverse causes (a 30-day hospitalization rate of 20%). Hospitalized patients had significantly greater NRR deviations from baseline compared to those who were not hospitalized (NRR variances of 3.78 brpm2 and 0.84 brpm2 respectively, n= 2,920 nights). These deviations were concentrated prior to the clinical event, suggesting that NRR can identify impending hospitalizations. We analyzed alarm threshold tradeoffs and demonstrated that nominal values would detect 11 of the 23 clinical events while only alarming 2 times in non-hospitalized patients. Taken together, our data demonstrate that NRR dynamics change days to weeks in advance of hospitalizations, with longer prodromes associating with volume overload and heart failure, and shorter prodromes associating with acute infections (pneumonia, septic shock, and covid-19), inflammation (diverticulitis), and GI bleeding. In summary, adherence-independent longitudinal NRR monitoring has potential to facilitate early recognition and management of pre-symptomatic disease.

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers

Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.

Influential Factors in Remote Monitoring of Heart Failure Patients: A Review of the Literature and Direction for Future Research

With new advances in technology, remote monitoring of heart failure (HF) patients has become increasingly prevalent and has the potential to greatly enhance the outcome of care. Many studies have focused on implementing systems for the management of HF by analyzing physiological signals for the early detection of HF decompensation. This paper reviews recent literature exploring significant physiological variables, compares their reliability in predicting HF-related events, and examines the findings according to the monitored variables used such as body weight, bio-impedance, blood pressure, heart rate, and respiration rate. The reviewed studies identified correlations between the monitored variables and the number of alarms, HF-related events, and/or readmission rates. It was observed that the most promising results came from studies that used a combination of multiple parameters, compared to using an individual variable. The main challenges discussed include inaccurate data collection leading to contradictory outcomes from different studies, compliance with daily monitoring, and consideration of additional factors such as physical activity and diet. The findings demonstrate the need for a shared remote monitoring platform which can lead to a significant reduction of false alarms and help in collecting reliable data from the patients for clinical use especially for the prevention of cardiac events.

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.

Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy

Despite clinical advances in its treatment, heart failure (HF) is associated with significant adverse clinical outcomes and is among the greatest drivers of healthcare utilization. Outpatient management of HF remains suboptimal, with gaps in the provision of evidence-based therapies, and difficulties in predicting and managing clinical decompensation. Remote patient monitoring (RPM) has the potential to address these issues, and thus has been of increasing interest to HF clinicians and health systems. Economic incentives, including increasing RPM reimbursement and HF readmission penalties, are also spurring increased interest in RPM. This review establishes a framework for evaluating RPM based on its various components: 1) patient data collection, 2) data transmission, analysis, and presentation, and 3) care team review and clinical action. The existing evidence regarding RPM in HF management is also reviewed. Based on the data, we identify RPM features associated with clinical efficacy and describe emerging digital tools that have the promise of addressing current needs.

Sensors for rate-adaptive pacing: How they work, strengths, and limitations

Chronotropic incompetence is the inability of the sinus node to increase heart rate commensurate with increased metabolic demand. Cardiac pacing alone may be insufficient to address exercise intolerance, fatigue, dyspnea on exertion, and other symptoms of chronotropic incompetence. Rate-responsive (adaptive) pacing employs sensors to detect physical or physiological indices and mimic the response of the normal sinus node. This review describes the development, strengths, and limitations of a variety of sensors that have been employed to address chronotropic incompetence. A mini-tutorial on programming rate-adaptive parameters is included along with emphasis that patients' lifestyles and underlying medical conditions require careful consideration. In addition, special sensor applications used to respond prophylactically to physiologic signals are detailed and an in-depth discussion of sensors as a potential aid in heart failure management is provided.

Is plasma renin activity associated with worse outcomes in acute heart failure? A secondary analysis from the BLAST-AHF trial

AIMS

Neurohormonal activation characterizes chronic heart failure (HF) and is a well-established therapeutic target. Neurohormonal activation may also play a key role in acute HF (AHF). We aim to describe the association between plasma renin activity (PRA) and three AHF outcomes: (i) worsening HF or death through day 5 of hospitalization; (ii) HF rehospitalization or death through day 30; and (iii) all-cause death through day 30.

METHODS AND RESULTS

A secondary analysis of the BLAST-AHF trial was performed. Eligible patients had a history of HF, elevated natriuretic peptides, signs and symptoms of HF, systolic blood pressure >120 mmHg, and an estimated glomerular filtration rate between 20-75 mL/min/1.73 m² . The primary trial was neutral, with no differential effect of study drug by PRA levels. Baseline PRA levels were grouped into tertiles. Adjusted Cox proportional hazard model determined the association of PRA levels with outcomes (α set at P < 0.05). Of 618 randomized patients, 578 (93.5%) had a baseline PRA. PRA was modestly, but significantly, associated with each outcome without adjustment [worsening HF or death through day 5: hazard ratio (HR) 1.11, 95% confidence interval (CI) 1.01-1.23, P = 0.04; HF rehospitalization or death through day 30: HR 1.13, 95% CI 1.02-1.26, P = 0.02; all-cause death through day 30: HR 1.18, 95% CI 1.02-1.37, P = 0.03]. After multivariable adjustment, PRA was only significantly associated with HF rehospitalization or death through day 30 (HR 1.15, 95% CI 1.01-1.32, P = 0.04).

CONCLUSION

Baseline PRA levels are associated with increased risk for the composite of 30-day HF rehospitalization or death in patients with AHF.

Bidirectional Cardio-Respiratory Interactions in Heart Failure

We investigated cardio-respiratory coupling in patients with heart failure by quantification of bidirectional interactions between cardiac (RR intervals) and respiratory signals with complementary measures of time series analysis. Heart failure patients were divided into three groups of twenty, age and gender matched, subjects: with sinus rhythm (HF-Sin), with sinus rhythm and ventricular extrasystoles (HF-VES), and with permanent atrial fibrillation (HF-AF). We included patients with indication for implantation of implantable cardioverter defibrillator or cardiac resynchronization therapy device. ECG and respiratory signals were simultaneously acquired during 20 min in supine position at spontaneous breathing frequency in 20 healthy control subjects and in patients before device implantation. We used coherence, Granger causality and cross-sample entropy analysis as complementary measures of bidirectional interactions between RR intervals and respiratory rhythm. In heart failure patients with arrhythmias (HF-VES and HF-AF) there is no coherence between signals (p < 0.01), while in HF-Sin it is reduced (p < 0.05), compared with control subjects. In all heart failure groups causality between signals is diminished, but with significantly stronger causality of RR signal in respiratory signal in HF-VES. Cross-sample entropy analysis revealed the strongest synchrony between respiratory and RR signal in HF-VES group. Beside respiratory sinus arrhythmia there is another type of cardio-respiratory interaction based on the synchrony between cardiac and respiratory rhythm. Both of them are altered in heart failure patients. Respiratory sinus arrhythmia is reduced in HF-Sin patients and vanished in heart failure patients with arrhythmias. Contrary, in HF-Sin and HF-VES groups, synchrony increased, probably as consequence of some dominant neural compensatory mechanisms. The coupling of cardiac and respiratory rhythm in heart failure patients varies depending on the presence of atrial/ventricular arrhythmias and it could be revealed by complementary methods of time series analysis.

Monitoring of nocturnal central sleep apnea in Heart failure patients using noncontact respiratory differences

Monitoring of respiration patterns allows the early detection of various breathing disorders and may better identify those at risk for adverse acute outcomes in a variety of clinical settings. In this paper, we report on the use of SleepMinder (SM), a bedside non-contact Doppler-based biomotion recording sensor, to monitor remotely the nocturnal respiration patterns of 50 patients with systolic Heart failure (HF) while undergoing a lab based Polysomnography (PSG) test. A new respiration rate (RR) monitoring algorithm was developed based on the collected overnight radar signals. Two schemes of RR scoring were utilized: respiratory rate count (RRC) and instantaneous respiratory rates (IRR). Analysis of SM vs. PSG revealed that the mean/median IRR scored by SM is highly correlated with that scored on the nasal flow/effort signals from the corresponding PSG studies on all patients, with a significant correlation coefficient of 0.98 (average absolute difference of 0.31 breaths/min), and 0.97 (p<;0.01, average absolute difference of 0.38 breaths/min) for the median and mean of RR respectively. Our experimental results also show that the difference between the RR estimations from IRR and RRC schemes can be utilized to identify central sleep apnea (CSA)/Cheyne-Stokes respiration (CSR) sections without additional apnea detection modules. As a result, with a sensitivity and specificity of 71% and 88% respectively, and an accuracy of 86%, our CSA/CSR screener, plugged with our RR estimation, can play an important role in the remote management of HF patients.

Cardiac resynchronization therapy update: evolving indications, expanding benefit?

Cardiac resynchronisation therapy (CRT) is an effective intervention for appropriately selected patients with heart failure, but exactly how it works is uncertain. Recent data suggest that much, or perhaps most, of the benefits of CRT are not delivered by re-coordinating left ventricular dyssynchrony. Atrio-ventricular resynchronization, reduction in mitral regurgitation and prevention of bradycardia are other potential mechanisms of benefit that will vary from one patient to the next and over time. Because there is no single therapeutic target, it is unlikely that any single measure will accurately predict benefit. The only clinical characteristic that appears to be a useful predictor of the benefits of CRT is a QRS duration of >140 ms. Many new approaches are being developed to try to improve the effectiveness of and extend the indications for CRT. These include smart pacing algorithms, better pacing-site targeting, new sensors, multipoint pacing, remote device monitoring and leadless endocardial pacing. Whether CRT is effective in patients with atrial fibrillation or whether adding a defibrillator function to CRT improves prognosis awaits further evidence.