A current and future outlook on upcoming technologies in remote monitoring of patients with heart failure

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.

Surveillance and Alert-Based Multiparameter Monitoring to reduce Worsening Heart Failure Events: Results from SCALE-HF 1

BACKGROUND

There is a need for better noninvasive remote monitoring solutions that prevent hospitalizations through the early prediction and management of heart failure (HF). SCALE-HF 1 evaluated the performance of a novel Congestion Index that alerts to fluid accumulation preceding HF events.

METHODS

SCALE-HF 1 was a multicenter, prospective, observational study investigating HF event prediction using data from the Cardiac Scale. Participants with HF took measurements at home by standing barefoot on the scale for approximately 20 seconds each day. The Congestion Index was applied retrospectively, and an alert was generated when the index exceeded a fixed threshold established in prior studies. HF events, defined as unplanned administration of IV diuretics or admissions with a primary diagnosis of HF. Sensitivity was defined as the ratio of correctly identified HF events to the total number of HF events.

RESULTS

329 participants were enrolled (mean age 64±14 years; 43% women; 32% black; 56% with reduced ejection fraction) across 8 sites with 238 participant-years of follow-up and 69 usable HF events. The Congestion Index predicted 48 of the 69 HF events (70%) at 2.58 alerts per participant-year. In contrast, the standard weight rule (weight gain exceeds 3lb in 1 day or 5lb in 7 days) predicted only 24 of the 69 HF events (35%) at 4.18 alerts per participant-year. The Congestion Index alerts had a significantly higher sensitivity (p<0.01) at a lower alert rate than the standard weight rule.

CONCLUSIONS

The Congestion Index alerts demonstrated sensitive prediction of HF events at a low alert rate, significantly exceeding the performance of weight-based monitoring.

CLINICALTRIALS

GOV IDENTIFIER

NCT04882449.

Non-invasive heart failure monitoring: leveraging smart scales and digital biomarkers to improve heart failure outcomes

Heart failure (HF) is a significant global concern, impacting patient morbidity, mortality, and healthcare costs. Guideline-directed medical therapy and various preventive measures have proven effective in improving clinical outcomes and reducing HF hospitalizations. Recent data indicates that remote HF monitoring facilitates early detection of HF decompensation by observing upstream events and parameters before clinical signs and symptoms manifest. Moreover, these innovative devices have been shown to decrease unnecessary HF hospitalizations and, in some cases, provide predictive insights before an actual HF incident. In this review, we aim to explore the data regarding smart scales and digital biomarkers and summarize both FDA-approved devices and emerging technologies by assessing their clinical utility, mechanism of HF decompensation detection, and ongoing trials. Furthermore, we also discuss the future trend of integrating these devices into routine clinical practice to improve patient clinical outcomes.

Implementation and evaluation of pharmacist-led heart failure diagnostic and guideline directed medication therapies clinic

BACKGROUND

Timely diagnosis of heart failure (HF) and rapid optimisation of guideline-directed medication therapy (GDMT) improves patients qualities of life, reducing mortality and morbidity. Previous papers describe the role of pharmacists in medication optimisation, but not in the diagnosis of HF.

AIM

To describe the development, implementation, and evaluation of pharmacist-led heart failure clinics with respect to time from referral to diagnosis, time from diagnosis to first review with a specialist, and the proportion receiving optimal GDMT 180 days after diagnosis.

SETTING

Community outpatient clinics in rural west Wales, United Kingdom.

DEVELOPMENT

Two experienced non-medical prescribing pharmacists, one of whom had additional diagnostic qualifications in cardiology, delivered the clinic.

IMPLEMENTATION

Patients referred with suspected HF were risk-stratified to urgent (within 14 days of referral) or routine (within 42 days) review, based on natriuretic peptide levels. Patients attended the clinic for assessment, including physical examination, electrocardiogram, and echocardiogram. Those with HF with reduced ejection fraction were initiated on drug treatment and referred to the follow-up pharmacist-led GDMT clinic.

EVALUATION

A sample of 100 patients was evaluated (50 from pre-existing and 50 from new service). Median time from referral to diagnosis reduced from 61 days (IQR 47-115) to 16 days (IQR 10.5-27.5) for urgent and 19 days (IQR 11.5-33) for routine. Median time to first appointment following diagnosis reduced from 54 days (IQR 36-60.5) to 14 days (IQR 9.75-28.75) (p value < 0.0001), and proportion of patients achieving GDMT at 180 days following diagnosis improved from 24 to 86% (p value < 0.0001).

CONCLUSION

This pharmacist HF diagnostic clinic and medication optimisation clinic improved time to diagnosis, time to first specialist review, and proportion of patients' achieving GDMT optimisation in a rural healthcare setting.

Pulmonary artery pressure monitoring in chronic heart failure: effects across clinically relevant subgroups in the MONITOR-HF trial

BACKGROUND AND AIMS

In patients with chronic heart failure (HF), the MONITOR-HF trial demonstrated the efficacy of pulmonary artery (PA)-guided HF therapy over standard of care in improving quality of life and reducing HF hospitalizations and mean PA pressure. This study aimed to evaluate the consistency of these benefits in relation to clinically relevant subgroups.

METHODS

The effect of PA-guided HF therapy was evaluated in the MONITOR-HF trial among predefined subgroups based on age, sex, atrial fibrillation, diabetes mellitus, left ventricular ejection fraction, HF aetiology, cardiac resynchronization therapy, and implantable cardioverter defibrillator. Outcome measures were based upon significance in the main trial and included quality of life-, clinical-, and PA pressure endpoints, and were assessed for each subgroup. Differential effects in relation to the subgroups were assessed with interaction terms. Both unadjusted and multiple testing adjusted interaction terms were presented.

RESULTS

The effects of PA monitoring on quality of life, clinical events, and PA pressure were consistent in the predefined subgroups, without any clinically relevant heterogeneity within or across all endpoint categories (all adjusted interaction P-values were non-significant). In the unadjusted analysis of the primary endpoint quality-of-life change, weak trends towards a less pronounced effect in older patients (Pinteraction = .03; adjusted Pinteraction = .33) and diabetics (Pinteraction = .01; adjusted Pinteraction = .06) were observed. However, these interaction effects did not persist after adjusting for multiple testing.

CONCLUSIONS

This subgroup analysis confirmed the consistent benefits of PA-guided HF therapy observed in the MONITOR-HF trial across clinically relevant subgroups, highlighting its efficacy in improving quality of life, clinical, and PA pressure endpoints in chronic HF patients.

Enhancing Heart Failure Diagnosis Accuracy and Distinguishing It From Other Pulmonary Conditions: A Retrospective Case Series Study Leveraging the HeartLogic Parameters

Introduction Heart failure (HF) poses a substantial and escalating medical and economic challenge, marked by significant morbidity and mortality. It stands as the primary cause of hospital admissions among the elderly, contributing significantly to healthcare expenditures in developed nations. Evaluating cardiac and pulmonary function remains challenging, necessitating careful interpretation to mitigate misdiagnosis and inappropriate treatment. Remote monitoring has emerged as a preventive strategy to curb HF-related hospitalizations, emphasizing the importance of early detection of impending acute HF decompensation. Implantable cardiac defibrillators (ICDs) capture various parameters, including heart rhythm, pacing percentages, thoracic impedance, and physical activity. Objective In this study, we aim to investigate the effectiveness of HeartLogic (Boston Scientific, Marlborough, Massachusetts) parameters in accurately distinguishing HF patients from individuals with alternative diagnoses. Methods This cross-sectional study was conducted at Cabell Huntington Hospital, St. Mary's Medical Center in Huntington, West Virginia, between 2021 and 2022. The study involved a retrospective chart review of electronic medical records, approved by the institutional review board, encompassing patients aged >18 admitted with Heartlogic-capable devices. The analysis included demographic variables, admission and discharge diagnoses, length of hospital stays, health literacy index, and thoracic impedance. Results Of the initially included 26 patients, 19 met all inclusion criteria. The demographic profile highlighted a predominantly older population with a male preponderance and a notable incidence of congestive heart failure (CHF). Physiological changes, particularly in thoracic impedance and the HeartLogic Index, demonstrated significant alterations. Logistic regression analysis revealed that changes in health literacy index and thoracic impedance significantly contributed to predicting the change in CHF diagnosis. Conclusion This study, conducted in a rural setting, demonstrates the capability of the HeartLogic algorithm in predicting HF events, providing valuable insights into its utility in diverse clinical environments. The findings emphasize the potential of this technology to enhance diagnostic accuracy and improve patient outcomes. Despite inherent limitations, this analysis contributes unique perspectives, particularly in the context of a specific and underexplored rural population in West Virginia.

Self-care challenges of patients with heart failure from the perspectives of patients and caregivers: A qualitative study

OBJECTIVE

self-care is critically important for the long-term management of heart failure (HF) patients, with caregivers playing an important role in promoting self-care. However, adherence to self-care is typically low among HF patients worldwide.

METHODS

In-depth qualitative interviews were conducted with individuals diagnosed with HF. To structure the interview guide and underpin the analysis, two established behavioral science frameworks, the Behavior Change Wheel (BCW) and the Theoretical Domains Framework (TDF), were used in this study.

RESULTS

A total of 32 participants were included (n = 16 patients, n = 16 caregivers), with themes involving: barriers included: "Self-care with Limited Capability," "Insufficient External Support," "Lack of Motivation for Self-Care." Facilitators included: "Striving to Adapt to Disease Demands," "Adequate External Support," "Positive Health Behaviors and Experiences."

CONCLUSIONS

Providing positive support to heart failure patients and their caregivers, along with cultivating intrinsic motivation for behavioral change, can enhance self-care ability.

How to tackle therapeutic inertia in heart failure with reduced ejection fraction. A scientific statement of the Heart Failure Association of the ESC

Guideline-directed medical therapy (GDMT) in patients with heart failure and reduced ejection fraction (HFrEF) reduces morbidity and mortality, but its implementation is often poor in daily clinical practice. Barriers to implementation include clinical and organizational factors that might contribute to clinical inertia, i.e. avoidance/delay of recommended treatment initiation/optimization. The spectrum of strategies that might be applied to foster GDMT implementation is wide, and involves the organizational set-up of heart failure care pathways, tailored drug initiation/optimization strategies increasing the chance of successful implementation, digital tools/telehealth interventions, educational activities and strategies targeting patient/physician awareness, and use of quality registries. This scientific statement by the Heart Failure Association of the ESC provides an overview of the current state of GDMT implementation in HFrEF, clinical and organizational barriers to implementation, and aims at suggesting a comprehensive framework on how to overcome clinical inertia and ultimately improve implementation of GDMT in HFrEF based on up-to-date evidence.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts

Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.

An Era of Digital Healthcare-A Comprehensive Review of Sensor Technologies and Telehealth Advancements in Chronic Heart Failure Management

Heart failure (HF) is a multi-faceted, complex clinical syndrome characterized by significant morbidity, high mortality rate, reduced quality of life, and rapidly increasing healthcare costs. A larger proportion of these costs comprise both ambulatory and emergency department visits, as well as hospital admissions. Despite the methods used by telehealth (TH) to improve self-care and quality of life, patient outcomes remain poor. HF management is associated with numerous challenges, such as conflicting evidence from clinical trials, heterogeneity of TH devices, variability in patient inclusion and exclusion criteria, and discrepancies between healthcare systems. A growing body of evidence suggests there is an unmet need for increased individualization of in-hospital management, continuous remote monitoring of patients pre and post-hospital admission, and continuation of treatment post-discharge in order to reduce re-hospitalizations and improve long-term outcomes. This review summarizes the current state-of-the-art for HF and associated novel technologies and advancements in the most frequently used types of TH (implantable sensors), categorizing devices in their preclinical and clinical stage, bench-to-bedside implementation challenges, and future perspectives on remote HF management to improve long-term outcomes of HF patients. The Review also highlights recent advancements in non-invasive remote monitoring technologies demonstrated by a few pilot observational prospective cohort studies.

Virtual healthcare solutions in heart failure: a literature review

The widespread adoption of mobile technologies offers an opportunity for a new approach to post-discharge care for patients with heart failure (HF). By enabling non-invasive remote monitoring and two-way, real-time communication between the clinic and home-based patients, as well as a host of other capabilities, mobile technologies have a potential to significantly improve remote patient care. This literature review summarizes clinical evidence related to virtual healthcare (VHC), defined as a care team + connected devices + a digital solution in post-release care of patients with HF. Searches were conducted on Embase (06/12/2020). A total of 171 studies were included for data extraction and evidence synthesis: 96 studies related to VHC efficacy, and 75 studies related to AI in HF. In addition, 15 publications were included from the search on studies scaling up VHC solutions in HF within the real-world setting. The most successful VHC interventions, as measured by the number of reported significant results, were those targeting reduction in rehospitalization rates. In terms of relative success rate, the two most effective interventions targeted patient self-care and all-cause hospital visits in their primary endpoint. Among the three categories of VHC identified in this review (telemonitoring, remote patient management, and patient self-empowerment) the integrated approach in remote patient management solutions performs the best in decreasing HF patients' re-admission rates and overall hospital visits. Given the increased amount of data generated by VHC technologies, artificial intelligence (AI) is being investigated as a tool to aid decision making in the context of primary diagnostics, identifying disease phenotypes, and predicting treatment outcomes. Currently, most AI algorithms are developed using data gathered in clinic and only a few studies deploy AI in the context of VHC. Most successes have been reported in predicting HF outcomes. Since the field of VHC in HF is relatively new and still in flux, this is not a typical systematic review capturing all published studies within this domain. Although the standard methodology for this type of reviews was followed, the nature of this review is qualitative. The main objective was to summarize the most promising results and identify potential research directions.

Promoting Self-Management and Independent Living of Older Individuals with Chronic Diseases through Technology: A Study of Self-Reported Needs, Priorities, and Preferences

Background and Objectives: Older patients' needs are rarely examined beforehand, and thus, although technology-based tools can enhance self-management, acceptability rates are still low. This study aimed to examine and compare self-reported needs, priorities, and preferences of older patients with heart failure (HF), diabetes mellitus type II (DM2), and chronic obstructive pulmonary disease (COPD) toward technology use to enhance self-management. Materials and Methods: A convenience sample of 473 participants over 60 s (60.5% females), diagnosed with HF (n= 156), DM2 (n = 164), or COPD (n = 153) was recruited. They were administered a questionnaire about the usefulness of technology in general and in specific areas of disease management. Results: Most participants (84.7%) admitted that technology is needed for better disease management. This was equally recognized across the three groups both for the overall and specific areas of disease management (in order of priority: "Information", "Communication with Physicians and Caregivers", and "Quality of Life and Wellbeing"). Sociodemographic differences were found. Cell phones and PCs were the devices of preference. The four common features prioritized by all three groups were related to 'information about disease management' (i.e., monitoring symptoms, reminders for medication intake, management and prevention of complications), whereas the fifth one was related to 'communication with physicians and caregivers (i.e., in case of abnormal or critical signs). The top disease-specific feature was also monitoring systems (of respiratory rate or blood sugar or blood pressure, and oxygen), whereas other disease-specific features followed (i.e., maintaining normal weight for HF patients, adjusting insulin dose for DM2 patients, and training on breathing exercises for COPD patients). Conclusions: Older individuals in these three groups seem receptive to technology in disease management. mHealth tools, incorporating both common and disease-specific features and addressing different chronic patients, and also being personalized at the same time, could be cost-saving and useful adjuncts in routine clinical care to improve self-management.

Cardiology in a Digital Age: Opportunities and Challenges for e-Health: A Literature Review

To date, mortality rates associated with heart diseases are dangerously increasing, making them the leading cause of death globally. From this point of view, digital technologies can provide health systems with the necessary support to increase prevention and monitoring, and improve care delivery. The present study proposes a review of the literature to understand the state of the art and the outcomes of international experiences. A reference framework is defined to develop reflections to optimize the use of resources and technologies, favoring the development of new organizational models and intervention strategies. Findings highlight the potential significance of e-health and telemedicine in supporting novel solutions and organizational models for cardiac illnesses as a response to the requirements and restrictions of patients and health systems. While privacy concerns and technology-acceptance-related issues arise, new avenues for research and clinical practice emerge, with the need to study ad hoc managerial models according to the type of patient and disease.

Sensor-Controlled Digital Game for Heart Failure Self-management: Protocol for a Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is the leading cause of hospitalization among older adults in the United States. There are substantial racial and geographic disparities in HF outcomes, with patients living in southern US states having a mortality rate 69% higher than the national average. Self-management behaviors, particularly daily weight monitoring and physical activity, are extremely important in improving HF outcomes; however, patients typically have particularly low adherence to these behaviors. With the rise of digital technologies to improve health outcomes and motivate health behaviors, sensor-controlled digital games (SCDGs) have become a promising approach. SCDGs, which leverage sensor-connected technologies, offer the benefits of being portable and scalable and allowing for continuous observation and motivation of health behaviors in their real-world contexts. They are also becoming increasingly popular among older adults and offer an immersive and accessible way to measure self-management behaviors and improve adherence. No SCDGs have been designed for older adults or evaluated to test their outcomes.

OBJECTIVE

This randomized clinical trial aims to assess the efficacy of a SCDG in integrating the behavioral data of participants with HF from weight scale and activity tracker sensors to activate game progress, rewards, and feedback and, ultimately, to improve adherence to important self-management behaviors.

METHODS

A total of 200 participants with HF, aged ≥45 years, will be recruited and randomized into 2 groups: the SCDG playing group (intervention group) and sensor-only group (control group). Both groups will receive a weight scale, physical activity tracker, and accompanying app, whereas only the intervention group will play the SCDG. This design, thereby, assesses the contributions of the game. All participants will complete a baseline survey as well as posttests at 6 and 12 weeks to assess the immediate effect of the intervention. They will also complete a third posttest at 24 weeks to assess the maintenance of behavioral changes. Efficacy and benefits will be assessed by measuring improvements in HF-related proximal outcomes (self-management behaviors of daily weight monitoring and physical activity) and distal outcomes (HF hospitalization, quality of life, and functional status) between baseline and weeks 6, 12, and 24. The primary outcome measured will be days with weight monitoring, for which this design provides at least 80% power to detect differences between the 2 groups.

RESULTS

Recruitment began in the fall of 2022, and the first patient was enrolled in the study on November 7, 2022. Recruitment of the last participant is expected in quarter 1 of 2025. Publication of complete results and data from this study is expected in 2026.

CONCLUSIONS

This project will generate insight and guidance for scalable and easy-to-use digital gaming solutions to motivate persistent adherence to HF self-management behaviors and improve health outcomes among individuals with HF.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05056129; https://clinicaltrials.gov/ct2/show/NCT05056129.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45801.

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.

Use of a Cardiac Scale to Predict Heart Failure Events: Design of SCALE-HF 1

BACKGROUND

There is a need for simple, noninvasive solutions to remotely monitor and predict worsening heart failure (HF) events. SCALE-HF 1 (Surveillance and Alert-Based Multiparameter Monitoring to Reduce Worsening Heart Failure Events) is a prospective, multicenter study that will develop and assess the accuracy of the heart function index-a composite algorithm of noninvasive hemodynamic biomarkers from a cardiac scale-in predicting worsening HF events.

METHODS

Approximately 300 patients with chronic HF and recent decompensation will be enrolled in this observational study for model development. Patients will be encouraged to take daily cardiac scale measurements.

RESULTS

Approximately 50 HF events, defined as an urgent, unscheduled clinic, emergency department, or hospitalization for worsening HF will be used for model development. The composite index will be developed from hemodynamic biomarkers derived from ECG, ballistocardiogram, and impedance plethysmogram signals measured from the cardiac scale. Biomarkers of interest include weight, peripheral impedance, pulse rate and variability, and estimates of stroke volume, cardiac output, and blood pressure captured through the cardiac scale. The sensitivity, unexplained alert rate, and alerting time of the index in predicting worsening HF events will be evaluated and compared with the performance of simple weight-based rule-of-thumb algorithms (eg, weight increase of 3 lbs in 1 day or 5 lbs in 7 days) that are often used in practice.

CONCLUSIONS

SCALE-HF 1 is the first study to develop and evaluate the performance of a composite index derived from noninvasive hemodynamic biomarkers measured from a cardiac scale in predicting worsening HF events. Subsequent studies will validate the heart function index and assess its ability to improve patient outcomes.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT04882449.

Remote consultations: review of guiding themes for equitable and effective delivery

The global coronavirus disease (COVID) -19 pandemic has led to a rapid transformation in the ways in which outpatient care is delivered. The need to minimise the risk of viral infection and transmission through social distancing resulted in the widespread adoption of remote consultations, traditional face-to-face appointments ceasing almost overnight in many specialties. The transition to remote consultations had taken place far faster than anticipated and under crisis conditions. As we work towards the 'new normal', remote consultations have become an integral part of outpatient provision in secondary care. Adapting to this change in clinical practice requires a judicious approach to ongoing service development to ensure safe, effective, and equitable care for all patients. Medical societies have provided some initial guidance around effective delivery. In this article we discuss the potential benefits, limitations, types of remote consultations, and factors that require consideration when deciding on patient suitability for remote consultation in a hospital setting. We use cardiology as a specialty exemplar, although many of the principles will be equally applicable to other medical specialities.

Proteomic profiling of epicardial fat in heart failure with preserved versus reduced and mildly reduced ejection fraction

In order to explore the proteomic signatures of epicardial adipose tissue (EAT) related to the mechanism of heart failure with reduced and mildly reduced ejection fraction (HFrEF/HFmrEF) and heart failure (HF) with preserved ejection fraction (HFpEF), a comprehensive proteomic analysis of EAT was made in HFrEF/HFmrEF (n = 5) and HFpEF (n = 5) patients with liquid chromatography-tandem mass spectrometry experiments. The selected differential proteins were verified between HFrEF/HFmrEF (n = 20) and HFpEF (n = 40) by ELISA (enzyme-linked immunosorbent assay). A total of 599 EAT proteins were significantly different in expression between HFrEF/HFmrEF and HFpEF. Among the 599 proteins, 58 proteins increased in HFrEF/HFmrEF compared to HFpEF, whereas 541 proteins decreased in HFrEF/HFmrEF. Of these proteins, TGM2 in EAT was down-regulated in HFrEF/HFmrEF patients and was confirmed to decrease in circulating plasma of the HFrEF/HFmrEF group (p = 0.019). Multivariate logistic regression analysis confirmed plasma TGM2 could be an independent predictor of HFrEF/HFmrEF (p = 0.033). Receiver operating curve analysis indicated that the combination of TGM2 and Gensini score improved the diagnostic value of HFrEF/HFmrEF (p = 0.002). In summary, for the first time, we described the proteome in EAT in both HFpEF and HFrEF/HFmrEF and identified a comprehensive dimension of potential targets for the mechanism behind the EF spectrum. Exploring the role of EAT may offer potential targets for preventive intervention of HF.

Longer-Term Effects of Remote Patient Management Following Hospital Discharge After Acute Systolic Heart Failure: The Randomized E-INH Trial

BACKGROUND

The randomized INH (Interdisciplinary Network Heart Failure) trial (N = 715) reported that 6 months' remote patient management (RPM) (HeartNetCare-HF) did not reduce the primary outcome (time to all-cause death/rehospitalization) vs usual care (UC) in patients discharged after admission for acute heart failure, but suggested lower mortality and better quality of life in the RPM group.

OBJECTIVES

The Extended (E)-INH trial investigated the effects of 18 months' HeartNetCare-HF on the same primary outcome in an expanded population (N = 1,022) and followed survivors up to 60 months (primary outcome events) or up to 120 months (mortality) after RPM termination.

METHODS

Eligible patients aged ≥18 years, hospitalized for acute heart failure, and with predischarge ejection fraction ≤40% were randomized to RPM (RPM+UC; n = 509) or control (UC; n = 513). Follow-up visits were every 6 months during RPM, and then at 36, 60, and 120 months.

RESULTS

The primary outcome did not differ between groups at 18 months (60.7% [95% CI: 56.5%-65.0%] vs 61.2% [95% CI: 57.0%-65.4%]) or 60 months (78.1% [95% CI: 74.4%-81.6%] vs 82.8% [95% CI: 79.5%-86.0%]). At 60 and 120 months, all-cause mortality was lower in patients previously undergoing RPM (41.1% [95% CI: 37.0%-45.5%] vs 47.4% [95% CI: 43.2%-51.8%]; P = 0.040 and 64.0% [95% CI: 59.8%-68.2%] vs 69.6% [95% CI: 65.6%-73.5%]; P = 0.019). At all visits, health-related quality of life was better in patients exposed to HeartNetCare-HF vs UC.

CONCLUSIONS

Although 18 months' HeartNetCare-HF did not significantly reduce the primary outcome of death or rehospitalization at 60 months, lower 120-month mortality in patients previously undergoing HeartNetCare-HF suggested beneficial longer-term effects, although the possibility of a chance finding remains.

Heart failure: an update from the last years and a look at the near future

In the last years, major progress occurred in heart failure (HF) management. Quadruple therapy is now mandatory for all the patients with HF with reduced ejection fraction. Whilst verciguat is becoming available across several countries, omecamtiv mecarbil is waiting to be released for clinical use. Concurrent use of potassium-lowering agents may counteract hyperkalaemia and facilitate renin-angiotensin-aldosterone system inhibitor implementations. The results of the EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trial were confirmed by the Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction (DELIVER) trial, and we now have, for the first time, evidence for treatment of also patients with HF with preserved ejection fraction. In a pre-specified meta-analysis of major randomized controlled trials, sodium-glucose co-transporter-2 inhibitors reduced all-cause mortality, cardiovascular (CV) mortality, and HF hospitalization in the patients with HF regardless of left ventricular ejection fraction. Other steps forward have occurred in the treatment of decompensated HF. Acetazolamide in Acute Decompensated Heart Failure with Volume Overload (ADVOR) trial showed that the addition of intravenous acetazolamide to loop diuretics leads to greater decongestion vs. placebo. The addition of hydrochlorothiazide to loop diuretics was evaluated in the CLOROTIC trial. Torasemide did not change outcomes, compared with furosemide, in TRANSFORM-HF. Ferric derisomaltose had an effect on the primary outcome of CV mortality or HF rehospitalizations in IRONMAN (rate ratio 0.82; 95% confidence interval 0.66-1.02; P = 0.070). Further options for the treatment of HF, including device therapies, cardiac contractility modulation, and percutaneous treatment of valvulopathies, are summarized in this article.

Patient Perception When Transitioning from Classic to Remote Assisted Cardiac Rehabilitation

Cardiac rehabilitation is an individualized outpatient program of physical exercises and medical education designed to accelerate recovery and improve health status in heart disease patients. In this study, we aimed for assessment of patients’ perception of the involvement of technology and remote monitoring devices in cardiac recovery. During the Living Lab Phase of the Virtual Coaching Activities for Rehabilitation in Elderly (vCare) project, we evaluated eleven patients (five heart failure patients and six ischemic heart disease patients). Patient admission in the UMFCD cardiology clinical department served as a shared inclusion criterion for both study groups. In addition, the presence of II or III heart failure NYHA stage status was considered an inclusion criterion for the heart failure study group and patients diagnosed with ischemic heart disease for the second one. We conducted a system usability survey to assess the patients’ perception of the system’s technical and medical functions. The survey had excellent preliminary results in the heart failure study group and good results in the ischemic heart disease group. The limited access of patients to cardiac rehabilitation in Romania has led to increased interest and motivation in this study. The final version of the product is designed to adapt to patient needs and necessities; therefore, patient perception is necessary.

The crystallin alpha B (HSPB5)-tripartite motif containing 33 (TRIM33) axis mediates myocardial fibrosis induced by angiotensinogen II through transforming growth factor-β (TGF-β1)-Smad3/4 signaling

Myocardial fibrosis, a common pathological manifestation of cardiac remodeling (CR), often leads to heart failure (HF) and even death. The underlying molecular mechanism of the role of TRIM33 in Ang II–induced myocardial fibrosis is not fully understood. We found that TRIM33 was specifically upregulated in CFs and myocardial tissue after Ang II stimulation. Adult mice induced by Ang II were used as in vivo models, and Ang II–induced neonatal mouse primary cardiac fibroblasts (CFs) were used as in vitro models. The level of CF fibrosis in vitro was assessed by CF proliferation, migration, activation and extracellular matrix (ECM) synthesis. In addition, Masson staining, the heart weight/body weight (HW/BW) ratio and echocardiography were used to evaluate the in vivo effect of TRIM33. TRIM33 expression was specifically upregulated in CFs and myocardial tissue after Ang II stimulation. In in vitro experiments, we found that TRIM33 knockdown promoted Ang II–induced CF proliferation, while TRIM33 overexpression weakened Ang II–induced CF proliferation, migration, activation and collagen synthesis. Mechanistically, we showed that TRIM33, negatively regulated by HSPB5, mediated its antifibrotic effect by inhibiting the activation of TGF-β1 and its downstream genes, Smad3 and Smad4. Finally, TRIM33 overexpression suppressed fibrosis and promoted cardiac repair and functional recovery in Ang II–induced mice. Our results clearly establish that TRIM33 limits cardiac fibrosis by hindering CF proliferation, migration, activation and collagen synthesis. Enhancing these beneficial functions of TRIM33 by a targeting vector might be a novel therapeutic strategy for CR.

The year in cardiovascular medicine 2021: heart failure and cardiomyopathies

In the year 2021, the universal definition and classification of heart failure (HF) was published that defines HF as a clinical syndrome with symptoms and/or signs caused by a cardiac abnormality and corroborated by elevated natriuretic peptide levels or objective evidence of cardiogenic congestion. This definition and the classification of HF with reduced ejection fraction (HFrEF), mildly reduced, and HF with preserved ejection fraction (HFpEF) is consistent with the 2021 ESC Guidelines on HF. Among several other new recommendations, these guidelines give a Class I indication for the use of the sodium-glucose co-transporter 2 (SGLT2) inhibitors dapagliflozin and empagliflozin in HFrEF patients. As the first evidence-based treatment for HFpEF, in the EMPEROR-Preserved trial, empagliflozin reduced the composite endpoint of cardiovascular death and HF hospitalizations. Several reports in 2021 have provided novel and detailed analyses of device and medical therapy in HF, especially regarding sacubitril/valsartan, SGLT2 inhibitors, mineralocorticoid receptor antagonists, ferric carboxymaltose, soluble guanylate cyclase activators, and cardiac myosin activators. In patients hospitalized with COVID-19, acute HF and myocardial injury is quite frequent, whereas myocarditis and long-term damage to the heart are rather uncommon.

Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch

OBJECTIVE

We report on our experience of deploying a continuous remote patient monitoring (CRPM) study soft launch with structured cascading and escalation pathways on heart failure (HF) patients post-discharge. The lessons learned from the soft launch are used to modify and fine-tune the workflow process and study protocol.

METHODS

This soft launch was conducted at NorthShore University HealthSystem's Evanston Hospital from December 2020 to March 2021. Patients were provided with non-invasive wearable biosensors that continuously collect ambulatory physiological data, and a study phone that collects patient-reported outcomes. The physiological data are analyzed by machine learning algorithms, potentially identifying physiological perturbation in HF patients. Alerts from this algorithm may be cascaded with other patient status data to inform home health nurses' (HHNs') management via a structured protocol. HHNs review the monitoring platform daily. If the patient's status meets specific criteria, HHNs perform assessments and escalate patient cases to the HF team for further guidance on early intervention.

RESULTS

We enrolled five patients into the soft launch. Four participants adhered to study activities. Two out of five patients were readmitted, one due to HF, one due to infection. Observed miscommunication and protocol gaps were noted for protocol amendment. The study team adopted an organizational development method from change management theory to reconfigure the study protocol.

CONCLUSION

We sought to automate the monitoring aspects of post-discharge care by aligning a new technology that generates streaming data from a wearable device with a complex, multi-provider workflow into a novel protocol using iterative design, implementation, and evaluation methods to monitor post-discharge HF patients. CRPM with structured escalation and telemonitoring protocol shows potential to maintain patients in their home environment and reduce HF-related readmissions. Our results suggest that further education to engage and empower frontline workers using advanced technology is essential to scale up the approach.

The unmet need of evidence-based therapy for patients with advanced chronic kidney disease and heart failure: Position paper from the Cardiorenal Working Groups of the Spanish Society of Nephrology and the Spanish Society of Cardiology

Despite the high prevalence of chronic kidney disease (CKD) and its high cardiovascular risk, patients with CKD, especially those with advanced CKD (stages 4-5 and patients on kidney replacement therapy), are excluded from most cardiovascular clinical trials. It is particularly relevant in patients with advanced CKD and heart failure (HF) who have been underrepresented in many pivotal randomized trials that have modified the management of HF. For this reason, there is little or no direct evidence for HF therapies in patients with advanced CKD and treatment is extrapolated from patients without CKD or patients with earlier CKD stages. The major consequence of the lack of direct evidence is the under-prescription of HF drugs to this patient population. As patients with advanced CKD and HF represent probably the highest cardiovascular risk population, the exclusion of these patients from HF trials is a serious deontological fault that must be solved. There is an urgent need to generate evidence on how to treat HF in patients with advanced CKD. This article briefly reviews the management challenges posed by HF in patients with CKD and proposes a road map to address 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.

Effects of an outpatient intervention comprising nurse-led non-invasive assessments, telemedicine support and remote cardiologists' decisions in patients with heart failure (AMULET study): a randomised controlled trial

Aim

Prevention of heart failure (HF) hospitalisations and deaths constitutes a major therapeutic aim in patients with HF. The role of telemedicine in this context remains equivocal. We investigated whether an outpatient telecare based on nurse‐led non‐invasive assessments supporting remote therapeutic decisions (AMULET telecare) could improve clinical outcomes in patients after an episode of acute HF during 12‐month follow‐up.

Methods and results

In this prospective randomised controlled trial, patients with HF and left ventricular ejection fraction (LVEF) ≤49%, after an episode of acute HF within the last 6 months, were randomly assigned to receive either an outpatient telecare based on nurse‐led non‐invasive assessments (n = 300) (AMULET model) or standard care (n = 305). The primary composite outcome of unplanned HF hospitalisation or cardiovascular death occurred in 51 (17.1%) patients in the telecare group and 73 (23.9%) patients in the standard care group up to 12 months after randomization [hazard ratio (HR) 0.69, 95% confidence interval (CI) 0.48–0.99; P = 0.044]. The implementation of AMULET telecare, as compared to standard care, reduced the risk of first unplanned HF hospitalisation (HR 0.62, 95% CI 0.42–0.91; P = 0.015) as well as the risk of total unplanned HF hospitalisations (HR 0.64, 95% CI 0.41–0.99; P = 0.044).There was no difference in cardiovascular mortality between the study groups (HR 1.03, 95% CI 0.54–1.67; P = 0.930).

Conclusions

AMULET telecare as compared to standard care significantly reduced the risk of HF hospitalisation or cardiovascular death during 12‐month follow‐up among patients with HF and LVEF ≤49% after an episode of acute HF.

Clinical value of pulmonary congestion detection by lung ultrasound in patients with chronic heart failure

Chronic heart failure is one of the common causes of hospitalization and death. Pulmonary congestion is the common disease feature of patients with chronic heart failure, which could be correctly diagnosed by lung ultrasound. Efficacy of lung ultrasound‐guided pulmonary congestion management for patients with acute heart failure is well documented, however, more evidence is needed to establish the clinical value of pulmonary congestion detection by lung ultrasound examination in patients with chronic heart failure. This review summarized current evidence related to the use and clinical value of pulmonary congestion assessment by lung ultrasound in patients with chronic heart failure, aiming to provide new suggestions on promoting the widespread use of lung ultrasound in patients with chronic heart failure to improve the quality of life and outcome of patients with chronic heart failure.

A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations

Abstract

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care.

Lay summary

Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12265-021-10151-7.

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 (RPM) for HF allows for optimization of medical therapy and prevention of HF hospitalizations. This is the first study to assess pulmonary artery diastolic pressures (PADP) using the CardioMEMS™ HF System (CMEMS) and cardiac implantable electronic device (CIED)-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 (HSP), HF hospitalization (HFH), HF-related outpatient visit (OPV), and pulmonary artery pressure action (PACT) were recorded in 7 patients with concurrent CMEMS and HL measurements for at least 1-year. Median time before both platforms were implanted and present in the same participant was 3.12 months. Median study period was 1.44 years per participant. Data availability for HL was significantly higher at 99.6% compared to 64.1% adherence for CMEMS (P = 0.016). Overall, PADP was only weakly correlated to HLI (r = 0.098), but there was a 2.87 mm Hg (P = 0.014) estimated increase in PADP during HLI alert periods versus non-alert periods. Similarly, the estimated odds of being above a PADP goal was 4.7 times higher (95% CI: [3.0, 7.2], P < 0.001) in HLI alert vs non-alert 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 non-alert periods.

Ambulatory Management of Worsening Heart Failure: Current Strategies and Future Directions

Heart failure (HF) is a highly prevalent and morbid disease in the USA. The chronic, progressive course of HF is defined by periodic exacerbations of symptoms, described as 'worsening heart failure' (WHF). Previously, episodes of WHF have required hospitalization for intravenous diuretics; however, recent innovations in care delivery models for patients with HF have allowed a transition from the acute care setting to the ambulatory setting. The development of remote monitoring strategies, including device-based algorithms and implantable haemodynamic monitoring systems, has facilitated more advanced surveillance of patients, aiming to prevent the clinical deterioration that leads to hospitalization. Additionally, the establishment of multidisciplinary HF clinics has provided the setting and resources for the outpatient treatment of WHF, specifically the administration of intravenous diuretics. Here we review the current state of ambulatory HF management, including mechanisms for patient monitoring and treatment, and outline future opportunities for outpatient management of this patient population.

Smart Wearables for Cardiac Monitoring-Real-World Use beyond Atrial Fibrillation

The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.

The Future of Telemedicine in the Management of Heart Failure Patients

Telemedicine (TM) is potentially a way of escalating heart failure (HF) multidisciplinary integrated care. Despite the initial efforts to implement TM in HF management, we are still at an early stage of its implementation. The coronavirus disease 2019 pandemic led to an increased utilisation of TM. This tendency will probably remain after the resolution of this threat. Face-to-face medical interventions are gradually transitioning to the virtual setting by using TM. TM can improve healthcare accessibility and overcome geographic inequalities. It promotes healthcare system efficiency gains, and improves patient self-management and empowerment. In cooperation with human intervention, artificial intelligence can enhance TM by helping to deal with the complexities of multicomorbidity management in HF, and will play a relevant role towards a personalised HF patient approach. Artificial intelligence-powered/telemedical/heart team/multidisciplinary integrated care may be the next step of HF management. In this review, the authors analyse TM trends in the management of HF patients and foresee its future challenges within the scope of HF multidisciplinary integrated care.