Telemedicine and Emerging Trends in Heart Failure Management: Comparison
Please note this is a comparison between Version 2 by Fanny Huang and Version 1 by Ramprakash Devadoss.

Heart failure is a cardiovascular condition, leading to fatigue, breathlessness, and fluid retention. It affects around 56 million people globally and is a leading cause of hospitalization and mortality. Its prevalence is rising due to aging populations and lifestyle factors. Managing heart failure demands a multidisciplinary approach, encompassing medications, lifestyle modifications, and often medical devices or surgeries. The treatment burden is substantial, impacting patients’ daily lives and straining healthcare systems. Improving early detection, novel therapies, and patient education are crucial for alleviating the burden and enhancing the quality of life. There are notable advancements in the field of heart failure treatment and prevention. 

  • heart failure
  • artificial intelligence
  • telemedicine
  • Gene therapy

1. Introduction

Heart failure is caused by left ventricular dysfunction, resulting in clinical symptoms such as shortness of breath, tiredness, and the limitation of exercise capacity. It is a significant public health concern with an estimated prevalence of over 56 million globally, with an age-standardized rate (ASR) of 711.90 per 100,000 population [1]. Though there was an improvement in ASR from 1990 to 2019, ASR prevalence has increased at an annual percentage change of 0.6% from 2017 to 2019 [1]. A nationwide survey from the American Heart Association in 2013 reported that direct and indirect costs attributed to HF will significantly increase from USD 30.7 billion in 2012 to USD 69.7 billion in 2030 [2]. In attempts to reduce the morbidity and mortality of heart failure patients, multiple new therapeutic interventions have surfaced in the last decade. 

2. Telemedicine and Emerging Trends in Heart Failure Management

2.1. Remote Pulmonary Pressure Monitoring

CardioMems, an implantable pressure sensor placed in the pulmonary artery, reduced heart failure-related hospitalizations due to its ability to track patients’ filling pressures and guide management [46][3]. The initial study included all heart failure patients in NYHA class III, irrespective of the left ventricular ejection fraction and previous hospital admission for heart failure. The benefit was again reproducible in the GUIDE-HF trial, reducing heart failure hospitalizations across the spectrum of LVEF, but was more prominent in the HFrEF subgroup [47][4]. Post-FDA approval, the real-world observational study also showed significantly lower heart failure and all-cause hospitalization post-device placement [48][5]. This technology greatly benefits heart failure patients with its ability to monitor and intervene remotely to prevent exacerbation, warranting hospitalization.

2.2. Telerehabilitation

Exercise-based interventions have consistently demonstrated a significant, clinically meaningful improvement in symptoms, objectively determining exercise capacity and quality of life in heart failure patients [49][6]. Telerehabilitation is a home-based program with devices to monitor vitals and an online platform for structured exercise regimens. This field is still in its infancy, but with the advent of new technology, it is evolving rapidly. Telerehabilitation will improve access to many patients with rehabilitative needs with travel limitations.

2.3. Artificial Intelligence in Heart Failure

Artificial intelligence (AI) has seen a rapid increase in utility in medicine in recent years. AI is increasingly used to revolutionize risk assessment, screening, diagnosis, treatment and drug discovery in cardiovascular medicine [50][7]. EAGLE trial is testing AI-guided ECG screening for low ejection fraction, which will significantly impact the heart failure field with the early identification of at-risk populations [51][8]. Multiple supervised and semi-supervised machine learning (ML) algorithms have predicted the onset of heart failure based on large labeled and unlabeled datasets from electronic health records. However, it is essential to compare the performance of these ML-developed risk models to known or conventional approaches to determine their clinical utility [52][9]. Some ML algorithms also incorporate imaging data, which helps to track change longitudinally and predict disease progression.
Heart Failure Association (HFA) of the European Society of Cardiology (ESC) recently provided guidelines for developing HFpEF models through a stepwise approach of comprehensive cardiac and extra-cardiac phenotyping. There were three leading phenogroups based on aging, cardiometabolic stress, and chronic hypertension [53][10]. ML algorithms can be beneficial in identifying phenotypes and provide a hypothesis-generating framework for designing future clinical trials.
AI models have assessed heterogeneity in response to HF pharmacotherapies [54][11] and cardiac resynchronization therapy (CRT). At least six trials have studied machine learning algorithms for predicting responses to CRT [52][9]. Studies have identified several predictors, such as gender, etiology, severity of HF, renal function, and comorbidity burden, for determining the response to CRT. ML analytics demonstrated predicting rehospitalization for heart failure with 87.5% sensitivity and 85% specificity based on non-invasive remote monitoring in the LINK-HF trial [55][12].
The incorporation of AI technologies into heart failure also faces several regulatory concerns. New privacy and data management principles are necessary that can allow for the training of algorithms in these datasets while also maintaining individual privacy [52][9]. The algorithms should undergo rigorous testing and validation to ensure proper performance. The US FDA has issued guidance emphasizing the prospective validation of AI algorithms before their implementation in clinical practice [56][13].

2.4. Gene Therapies for Advanced Heart Failure

There is a dysregulation of the excitation–contraction coupling at multiple levels in HF. Targets for gene therapy so far have involved various ways to restore contractile function, angiogenesis, cytoprotection, and stem cell homing.
The critical regulator in cardiac contractility is the β-adrenergic system. It is downregulated and desensitized in HF because the critical protein G protein-coupled receptor kinase 2 (GRK2) is upregulated. In rodents and preclinical large animal heart failure models, the inhibition of GRK2 via βARKct (carboxyl-terminus of the β-adrenergic receptor kinase) expression has shown positive results, including an improvement of the left ventricular systolic dysfunction [57][14]. Ca2+-handling proteins involved in the excitation–contraction coupling has also been assessed as targets for heart failure management. SERCA2a gene transfer improved cardiac contractility in the swine volume-overload model of HF [58][15] and decreased arrhythmias and mortality [59][16]. Other studies have demonstrated the increase in the small ubiquitin-like modifier type 1 (SUMO1) with the help of the adenovirus vector leads to the increased levels of the SERCA2a gene, which results in improved cardiac contractility, decreased arrhythmias, and decreased mortality [60][17].
However, the same results as in animal models have been hard to reproduce in human trials. The initial Calcium Upregulation by Percutaneous Administration of Gene Therapy in Cardiac Disease (CUPID) [61][18] trial did demonstrate some benefits. However, the larger CUPID2 trial failed to demonstrate any significant benefit in the recurrence of heart failure or mortality [62][19]. AGENT-HF trial [63][20] showed similar results. Adenylyl Cyclase VI (AC VI) is another target that has been studied [64][21] and is awaiting phase III study [65][22]. Learning from the trials conducted so far, there is a focus for identifying new targets and improving the vector with high cardiac tropism that de-targets the liver [66][23].
Gene-editing technology has also evolved in recent years, leading to fundamental upgrades of the biomedical research model with the achievement of falling off-target incidence, improving editing efficiency, and expanding application scope. Current third-generation gene-editing technology clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system functions through protein–nucleic acid complex [67][24]. A milestone advancement in genetic therapy leading to an effective and sustained improvement in patients with heart failure can be anticipated soon.


  1. Yan, T.; Zhu, S.; Yin, X.; Xie, C.; Xue, J.; Zhu, M.; Weng, F.; Zhu, S.; Xiang, B.; Zhou, X.; et al. Burden, Trends, and Inequalities of Heart Failure Globally, 1990 to 2019: A Secondary Analysis Based on the Global Burden of Disease 2019 Study. J. Am. Heart Assoc. 2023, 12, e027852.
  2. Heidenreich, P.A.; Albert, N.M.; Allen, L.A.; Bluemke, D.A.; Butler, J.; Fonarow, G.C.; Ikonomidis, J.S.; Konstam, M.A.; Maddox, T.M.; Nichol, G.; et al. Forecasting the Impact of Heart Failure in the United States. Circ. Heart Fail. 2013, 6, 606–619.
  3. William, T.; Abraham, M.D.; Bourge, R.C.; Aaron, M.F.; Costanzo, M.R.; Stevenson, L.W.; Strickland, W.; Neelagaru, S.; Raval, N.; Krueger, S.; et al. Wireless pulmonary artery hemodynamic monitoring in chronic heart failure: A randomized controlled trial. Lancet 2011, 377, 658–666.
  4. Zile, M.R.; Mehra, M.R.; Ducharme, A.; Sears, S.F.; Desai, A.S.; Maisel, A.; Paul, S.; Smart, F.; Grafton, G.; Kumar, S.; et al. Hemodynamically Guided Management of Heart Failure Across the Ejection Fraction Spectrum: The Guide-HF trial. J. Am. Coll. Cardiol. Heart Fail. 2022, 10, 931–944.
  5. Shavelle, D.M.; Desai, A.S.; Abraham, W.T.; Bourge, R.C.; Raval, N.; Rathman, L.D.; Heywood, J.T.; Jermyn, R.A.; Pelzel, J.; Jonsson, O.T.; et al. Lower Rates of Heart Failure and All-Cause Hospitalizations During Pulmonary Artery Pressure-Guided Therapy for Ambulatory Heart Failure. Circ. Heart Fail. 2020, 13, e006863.
  6. Sachdev, V.; Sharma, K.; Keteyian, S.J.; Alcain, C.F.; Desvigne-Nickens, P.; Fleg, J.L.; Florea, V.G.; Franklin, B.A.; Guglin, M.; Halle, M.; et al. Supervised Exercise Training for Chronic Heart Failure With Preserved Ejection Fraction: A Scientific Statement From Amerian Heart Association and American College of Cardiology. Circulation 2023, 147, e699–e715.
  7. Yasmin, F.; Shah, S.M.I.; Naeem, A.; Shujauddin, S.M.; Jabeen, A.; Kazmi, S.; Siddiqui, S.A.; Kumar, P.; Salman, S.; Hassan, S.A.; et al. Artificial intelligence in the diagnosis and detection of heart failure: The past, present, and future. Rev. Cardiovasc. Med. 2021, 22, 1095–1113.
  8. Yao, X.; McCoy, R.G.; Friedman, P.A.; Shah, N.D.; Barry, B.A.; Behnken, E.M.; Inselman, J.W.; Attia, Z.I.; Noseworthy, P.A. ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial. Am. Heart J. 2019, 219, 31–36.
  9. Khan, M.S.; Arshad, M.S.; Greene, S.J.; Van Spall, H.G.C.; Pandey, A.; Vemulapalli, S.; Perakslis, E.; Butler, J. Artificial intelligence and heart failure: A state-of-the-art review. Eur. J. Heart Fail. 2023, 25, 1507–1525.
  10. Roh, J.; Hill, J.A.; Singh, A.; Valero-Muñoz, M.; Sam, F. Heart Failure With Preserved Ejection Fraction: Heterogeneous Syndrome, Diverse Preclinical Models. Circ. Res. 2022, 130, 1906–1925.
  11. Ahmad, T.; Lund, L.H.; Rao, P.; Ghosh, R.; Warier, P.; Vaccaro, B.; Dahlström, U.; O’Connor, C.M.; Felker, G.M.; Desai, N.R. Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. J. Am. Heart Assoc. 2018, 7, e008081.
  12. Stehlik, J.; Schmalfuss, C.; Bozkurt, B.; Nativi-Nicolau, J.; Wohlfahrt, P.; Wegerich, S.; Rose, K.; Ray, R.; Schofield, R.; Deswal, A.; et al. Continuous wearable monitoring analytics predict heart failure hospitalization: The LINK-HF multicenter study. Circ. Heart Fail. 2020, 13, e006513.
  13. Ebrahimian, S.; Kalra, M.K.; Agarwal, S.; Bizzo, B.C.; Elkholy, M.; Wald, C.; Allen, B.; Dreyer, K.J. FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies. Acad. Radiol. 2021, 29, 559–566.
  14. Reinkober, J.; Tscheschner, H.; Pleger, S.T.; Most, P.; Katus, H.A.; Koch, W.J.; Raake, P.W.J. Targeting GRK2 by gene therapy for heart failure: Benefits above β-blockade. Gene Ther. 2012, 19, 686–693.
  15. Kawase, Y.; Ly, H.Q.; Prunier, F.; Lebeche, D.; Shi, Y.; Jin, H.; Hadri, L.; Yoneyama, R.; Hoshino, K.; Takewa, Y.; et al. Reversal of Cardiac Dysfunction After Long-Term Expression of SERCA2a by Gene Transfer in a Pre-Clinical Model of Heart Failure. J. Am. Coll. Cardiol. 2008, 51, 1112–1119.
  16. Prunier, F.; Kawase, Y.; Gianni, D.; Scapin, C.; Danik, S.B.; Ellinor, P.T.; Hajjar, R.J.; del Monte, F. Prevention of Ventricular Arrhythmias With Sarcoplasmic Reticulum Ca 2+ ATPase Pump Overexpression in a Porcine Model of Ischemia Reperfusion. Circulation 2008, 118, 614–624.
  17. Kho, C.; Lee, A.; Jeong, D.; Oh, J.G.; Chaanine, A.H.; Kizana, E.; Park, W.J.; Hajjar, R.J. SUMO1-dependent modulation of SERCA2a in heart failure. Nature 2011, 477, 601–605.
  18. Jessup, M.; Greenberg, B.; Mancini, D.; Cappola, T.; Pauly, D.F.; Jaski, B.; Yaroshinsky, A.; Zsebo, K.M.; Dittrich, H.; Hajjar, R.J. Calcium Upregulation by Percutaneous Administration of Gene Therapy in Cardiac Disease (CUPID): A phase 2 trial of intracoronary gene therapy of sarcoplasmic reticulum Ca2+-ATPase in patients with advanced heart failure. Circulation 2011, 124, 304–313.
  19. Greenberg, B.; Butler, J.; Felker, G.M.; Ponikowski, P.; Voors, A.A.; Desai, A.S.; Barnard, D.; Bouchard, A.; Jaski, B.; Lyon, A.R.; et al. Calcium upregulation by percutaneous administration of gene therapy in patients with cardiac disease (CUPID 2): A randomized, multinational, double-blind, placebo-controlled phase 2b trial. Lancet 2016, 387, 1178–1186.
  20. Hulot, J.-S.; Salem, J.-E.; Redheuil, A.; Collet, J.-P.; Varnous, S.; Jourdain, P.; Logeart, D.; Gandjbakhch, E.; Bernard, C.; Hatem, S.N.; et al. Effect of intracoronary administration of AAV1/SERCA2a on ventricular remodeling in patients with advanced systolic heart failure: Results from the AGENT-HF randomized phase 2 trial. Eur. J. Heart Fail. 2017, 19, 1534–1541.
  21. Hammond, H.K.; Penny, W.F.; Traverse, J.H.; Henry, T.D.; Watkins, M.W.; Yancy, C.W.; Sweis, R.N.; Adler, E.D.; Patel, A.N.; Murray, D.R.; et al. Intracoronary gene transfer of adenylyl cyclase 6 in patients with heart failure: A randomized clinical trial. JAMA Cardiol. 2016, 1, 163–171.
  22. Penny, W.F.; Henry, T.D.; Watkins, M.W.; Patel, A.N.; Hammond, H.K. Design of a Phase 3 trial of intracoronary administration of human adenovirus 5 encoding human adenylyl cyclase type 6 (RT-100) gene transfer in patients with heart failure with reduced left ventricular ejection fraction: The FLOURISH Clinical Trial. Am. Heart J. 2018, 201, 111–116.
  23. Hulot, J.-S.; Ishikawa, K.; Hajjar, R.J. Gene therapy for the treatment of heart failure: Promise postponed. Eur. Heart J. 2016, 37, 1651–1658.
  24. Cao, G.; Xuan, X.; Zhang, R.; Hu, J.; Dong, H. Gene Therapy for Cardiovascular Disease: Basic Research and Clinical Prospects. Front. Cardiovasc. Med. 2021, 8, 760140.
Video Production Service