Cohort Study of Chronic Heart Failure

Overview

The goal of this observational study is to learn about the influencing factors of chronic heart failure prognosis in heart failure patients. The main question it aims to answer is that what are the influencing factors of clinical outcomes in chronic heart failure. Participants will be collected multiple omics data such as phenotype group, environmental exposure group, intestinal microbiome, genome, metabolome, and noninvasive biomarkers.

Interventions

• Behavioral Remote monitoring assisted by community physicians
  Patients upload data such as wristband data, blood pressure, weight through their mobile phones, while physicians can check the data on the platform. Upon detecting alerts, community physicians review participant-related medical information, conduct structured symptom assessments via "StarNet Home" platform/phone within 2 hours, and initiate protocol-guided interventions (e.g., diuretic adjustment for fluid overload).

Primary outcome measures

• incidence of composite event (cardiovascular mortality, unplanned HF hospitalization, emergency HF intervention) [Time frame: 2 years]

Secondary outcome measures (1)

• all-cause mortality [Time frame: 2 years]

Eligibility criteria

Inclusion criteria

• Age ≥18 years;
• Fulfill at least one of the following criteria：①History of chronic heart failure;②New-onset heart failure with LVEF <50%；③New-onset heart failure with LVEF≧50%，E/e'≧15 and BNP>400 pg/mL/NT-proBNP(<50 years, >450 pg/mL; 50\~75 years, >900 pg/mL; >75 years, >1800 pg/mL)
• Etiology: dilated cardiomyopathy, hypertensive heart disease, ischaemic cardiomyopathy, Non-severe valvular heart disease；
• Primary post-discharge therapy: oral medication;
• Sign the informed consent form.

Exclusion criteria

• Patients with renal failure (Ccr<30ml/min) or patients on dialysis therapy;
• Various correctable secondary causes of cardiac insufficiency including hyperthyroid heart disease, anaemic heart disease, uncorrected congenital heart disease; severe valvular heart disease (including severe stenosis or closure of organic aortic and mitral valves);
• Patients with indications for pacemaker implantation but have not received a pacemaker implant;
• Patients with chronic obstructive pulmonary disease accompanied by type II respiratory failure.
• Patients who cannot understand and sign an informed consent form.
• Patients with mental illness, pregnant women, and other special populations.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Non-randomized

Model

Parallel assignment

Masking

Single blind

Primary purpose

Health services research

Study locations

China · 1 center

• Taizhou People's Hospital affiliated to Nanjing Medical University — Taizhou

Publications

• Zhang Y, Zhang J, Butler J, Yang X, Xie P, Guo D, Wei T, Yu J, Wu Z, Gao Y, Han X, Zhang X, Wen S, Anker SD, Filippatos G, Fonarow GC, Gan T, Zhang R; China-HF Investigators. Contemporary Epidemiology, Management, and Outcomes of Patients Hospitalized for Heart Failure in China: Results From the China Heart Failure (China-HF) Registry. J Card Fail. 2017 Dec;23(12):868-875. doi: 10.1016/j.cardfail. PMID 29029965
• Olivier B, Verdonck M, Caseleijn D. Digital technologies in undergraduate and postgraduate education in occupational therapy and physiotherapy: a scoping review. JBI Evid Synth. 2020 May;18(5):863-892. doi: 10.11124/JBISRIR-D-19-00210. PMID 32813350
• Sverdlov O, van Dam J, Hannesdottir K, Thornton-Wells T. Digital Therapeutics: An Integral Component of Digital Innovation in Drug Development. Clin Pharmacol Ther. 2018 Jul;104(1):72-80. doi: 10.1002/cpt.1036. Epub 2018 Feb 23. PMID 29377057
• Urtnasan E, Joo EY, Lee KH. AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram. Diagnostics (Basel). 2021 Nov 5;11(11):2054. doi: 10.3390/diagnostics11112054. PMID 34829400
• Yang Y, Yuan Y, Zhang G, Wang H, Chen YC, Liu Y, Tarolli CG, Crepeau D, Bukartyk J, Junna MR, Videnovic A, Ellis TD, Lipford MC, Dorsey R, Katabi D. Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals. Nat Med. 2022 Oct;28(10):2207-2215. doi: 10.1038/s41591-022-01932-x. Epub 2022 Aug 22. PMID 35995955
• Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7. PMID 30617318
• Koehler F, Koehler K, Deckwart O, Prescher S, Wegscheider K, Kirwan BA, Winkler S, Vettorazzi E, Bruch L, Oeff M, Zugck C, Doerr G, Naegele H, Stork S, Butter C, Sechtem U, Angermann C, Gola G, Prondzinsky R, Edelmann F, Spethmann S, Schellong SM, Schulze PC, Bauersachs J, Wellge B, Schoebel C, Tajsic M, Dreger H, Anker SD, Stangl K. Efficacy of telemedical interventional management in patients wi PMID 30153985
• Wu Y, Lin J, Gong B, Wang L, Ruan Z, Xu K. Cardiac Rehabilitation in Atrial Fibrillation Patients With Left Atrial Appendage Occlusion: A RANDOMIZED TRIAL. J Cardiopulm Rehabil Prev. 2022 Jul 1;42(4):266-271. doi: 10.1097/HCR.0000000000000693. Epub 2022 Apr 13. PMID 35428035

Identifiers

NCT: NCT05960890 · IIT202305

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗