A Multicenter, Cluster-randomized, Controlled Study Evaluating the Effectiveness and Safety of Artificial Intelligence(AI)-Assisted Medical Treatment Decision Support System Compared to Conventional Care in HFrEF Patients
Phase: NA
Conditions: Heart Failure With Reduced Ejection Fraction (HF-rEF)
Sponsor: Shanghai Zhongshan Hospital

Title: A Multicenter, Cluster-Randomized, Controlled Study Evaluating 
the Effectiveness and Safety of an Artificial Intelligence (AI)-Assisted 
Medical Treatment Decision Support System Compared to Conventional 
Care in Patients with Heart Failure with Reduced Ejection Fraction 
(HFrEF) 
Sponsor: Zhongshan Hospital, Fudan University 
• 
Principal Investigator: Zhou Jingmin 
• 
Protocol Version: 1.3 
• 
Version Date: September 8, 2025  
 
 

2. Version History 
Document Version 
Date 
Summary of Changes 
Protocol 
V1.1 
July 25, 2025 Revisions after initial review. Mainly modified background, AI 
system introduction, and remote intelligent platform content. 
Protocol 
V1.2 
September 8, 
2025 
Revisions after secondary review. Modified background, AI-
guided medication strategy. Submitted AI system test report. 
3. Investigator's Statement 
I will diligently fulfill the investigator's responsibilities in accordance with Chinese 
GCP regulations, personally participating in or directly supervising this clinical 
research. I have read and confirmed this protocol, agreeing with its scientific and 
ethical soundness. I will perform relevant duties according to Chinese laws and 
regulations, the Declaration of Helsinki, Chinese GCP, and this study protocol, and 
will only implement it after approval by the Academic Committee and Ethics 
Committee. I will keep this study protocol confidential unless disclosure is necessary 
to protect the safety, rights, and interests of the subjects. 
• 
Research Unit: Zhongshan Hospital, Fudan University 
• 
Principal Investigator: Zhou Jingmin 
4. Protocol Synopsis 
Item 
Details 
Protocol Title 
A multicenter, cluster-randomized, controlled study evaluating the 
effectiveness and safety of Artificial Intelligence (AI)-assisted medical 
treatment decision support system compared to conventional care in 
HFrEF patients 
Study Objective 
To evaluate the effectiveness and safety of AI-assisted decision support 
system-guided pharmacotherapy compared to conventional care in HFrEF 
patients. 
Primary Endpoint 
Composite endpoint of first rehospitalization for heart failure or 
cardiovascular death. 
Overall Design 
A multicenter, open-label, cluster-randomized, controlled study. 
Sample Size 
40 centers, 1200 patients. 
Study Groups 
Cluster randomization. 
Inclusion/Exclusion 
Criteria 
Center Inclusion: Capable of randomly accepting the study interventions 
and completing the study.  
Patient Inclusion: Hospitalized patients diagnosed with HF and 
discharged as planned; LVEF ≤40% measured by echocardiography within 
1 month prior to enrollment; Age ≥18 years; Signed informed consent.  
Exclusion: ① Patients unable or unsuitable for follow-up; ② Poor 
compliance, risk of switching groups. 

Item 
Details 
Intervention 
Pharmacotherapy guided by the AI-assisted decision support system. 
Withdrawal Criteria 
Subjects may withdraw if: ① Subject requests to withdraw consent for any 
reason; ② Investigator considers continuation contrary to the subject's best 
interests. 
Statistical Analysis 
Plan 
1) Primary Endpoint: Cumulative incidence will be estimated using 
Kalbfleisch-Prentice's method. A stratified Cox proportional hazards model 
will estimate Hazard Ratio (HR) and 95% Confidence Interval (CI), 
accounting for cluster effects.  
2) Secondary Endpoints: Analyzed using Fine-Gray's competing risks 
model, accounting for cluster effects.  
3) Subgroup Analysis: Predefined subgroups include hospital level, age, 
sex, BP<100mmHg, diabetes, chronic kidney disease (serum 
creatinine>1.5mg/dL), baseline treatments, frail patients (baseline NYHA, 
NT-proBNP). Interaction effects will be assessed. No adjustment for 
multiple comparisons.  
4) Sensitivity Analyses: Excluding early events (within 10 days); using 
patient-reported GDMT as treatment variable; Per-Protocol analysis; 
Tipping-point analysis for potential underreporting. 
5. Abbreviations 
ACEI: Angiotensin-Converting Enzyme Inhibitor 
AI: Artificial Intelligence 
ARB: Angiotensin II Receptor Blocker 
ARNI: Angiotensin Receptor-Neprilysin Inhibitor 
eCRF: electronic Case Report Form 
EDC: Electronic Data Capture 
GDMT: Guideline-Directed Medical Therapy 
HFrEF: Heart Failure with reduced Ejection Fraction 
LVEDD: Left Ventricular End-Diastolic Diameter 
LVEF: Left Ventricular Ejection Fraction 
NT-proBNP: N-terminal pro-B-type Natriuretic Peptide 
NYHA: New York Heart Association 

SGLT-2i: Sodium-Glucose Cotransporter-2 Inhibitor 
6. Study Details 
6.1 Background 
Heart Failure (HF) is a major global health burden. In China, the prevalence of HF in 
adults aged ≥25 is 1.1%, affecting approximately 12.1 million people, with high 
annual mortality (13.7%). HFrEF has the worst prognosis among HF types. While 
GDMT (including ACEI/ARB/ARNI, Beta-blockers, MRAs, SGLT2i) improves 
outcomes, real-world implementation faces challenges. This study aims to validate an 
AI-assisted decision support system designed to optimize GDMT use and dosing in 
HFrEF patients post-discharge, potentially improving outcomes and standardizing 
care across healthcare levels. 
6.2 Objectives 
• 
Primary: To validate if AI-guided pharmacotherapy reduces the composite 
endpoint of first HF rehospitalization or cardiovascular death compared to 
conventional care in HFrEF patients. 
• 
Secondary: To evaluate the AI system's effect on dose adjustment, adherence, 
and guideline compliance; explore its applicability across subgroups; conduct 
an economic evaluation. 
6.3 Study Design 
• 
Design: Multicenter, Open-label, Cluster-Randomized, Controlled Trial. 
• 
Duration: 6-month enrollment, 12-month follow-up. Planned end date: 
December 2026. 
• 
Randomization: Centers (20 tertiary, 20 secondary) stratified by level and 
randomized 1:1 to Intervention or Control arm using a central system. 
• 
Blinding: Subjects are blinded to group assignment. 
 

6.4 Population 
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Sample Size: 1200 patients (600 per arm), 30 patients per center. 
• 
Inclusion Criteria: As per Synopsis. 
• 
Exclusion Criteria: As per Synopsis. 
• 
Withdrawal Criteria: As per Synopsis. 
6.5 Endpoints 
• 
Primary Endpoint: Time to first occurrence of HF rehospitalization or 
cardiovascular death. 
• 
Secondary Endpoints: Time to first HF rehospitalization; all-cause 
rehospitalization; cardiovascular death; all-cause death; number of HF 
rehospitalizations; change from baseline in NT-proBNP, LVEF, LVEDD, EQ-
5D score. 
• 
Safety Endpoints: Adverse events including hypotension, renal dysfunction, 
hyperkalemia, hypokalemia, arrhythmia. 
• 
Exploratory Endpoints: Compare effectiveness/safety of AI-guided vs. 
conventional dose adjustment for diuretics, beta-blockers, ACEI/ARB/ARNI, 
MRAs, SGLT2i; GDMT effects in specific subgroups. 
6.6 Study Procedures 
• 
Baseline: Informed consent, demographic/clinical/lab data collection, EQ-5D. 
• 
Follow-up: Hybrid model (remote + site visits). 
o Remote Visits (Monthly, V1, V2, V4, V5, V7, V8, V9, V10, V11): 
Via WeChat mini-program/EDC system. Collects symptoms, signs, 
medications, lab data (e.g., electrolytes, NT-proBNP), endpoints, AEs. 
o Site Visits (V3-3mo, V6-6mo, V12-12mo): Physical exam, ECG, 
echocardiography, lab tests (including NT-proBNP), EQ-5D, 
endpoints, AEs. 
• 
Intervention Group: AI system generates medication recommendations 
based on uploaded patient data, reviewed by investigator. 
• 
Control Group: Investigator provides medication recommendations per 
conventional care based on uploaded data. 

 
• 
AI Medication Strategy (Based on GDMT guidelines): 
o Dose Increase Logic (if met): Diuretics: double dose. Other GDMT: 
if <50% target -> increase to 50%; if ≥50% & <100% -> increase to 
100%; if at 100% -> maintain. 
o Dose Decrease/Pause Logic (if met): Diuretics: halve dose. MRA: 
discontinue. Other GDMT: halve dose. 
o Note: The AI system applies pre-set rules without self-
learning/optimization. 
6.7 Data Management 
Electronic Data Capture (EDC) system will be used. Database locking procedures will 
be followed. 
6.8 Statistical Analysis 
• 
Sample Size Justification: Based on estimated 15.3% event rate in control vs. 
9.3% in intervention arm, ICC=0.005, alpha=0.05, power=80%, accounting 
for 5-10% dropout. Total 40 centers, 1200 patients. 
• 
Analysis Sets: Full Analysis Set (FAS, Intent-to-Treat), Per-Protocol Set 
(PPS). 
• 
Analysis Methods: As per Synopsis. 
7. Ethics and Confidentiality 

• 
Ethics Approval: The protocol and informed consent form must be approved 
by the Institutional Ethics Committee before study initiation. Amendments 
require prior approval unless for immediate subject safety. 
• 
Informed Consent: Written informed consent must be obtained from each 
subject prior to any study procedures. 
• 
Confidentiality: Subject confidentiality will be maintained in accordance with 
legal requirements. Results may be published without disclosing personal 
identifiers. 
8. References 
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a nationwide Cardiovascular Association Database-Heart Failure Centre Registry cohort study. 
Lancet Glob Health, 2024. 12(4):e611-e622. 
3. McDonagh TA, Metra M, Adamo M, et al; ESC Scientific Document Group. 2023 Focused 
Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. 
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4. Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA Guideline for the 
Management of Heart Failure: A Report of the American College of Cardiology/American Heart 
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Circulation. 2023 Apr 4;147(14):e674. doi: 10.1161/CIR.0000000000001142. PMID: 35363499. 
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6. Mebazaa A, Davison B, Chioncel O, et al. Safety, tolerability and efficacy of up-titration of 
guideline-directed medical therapies for acute heart failure (STRONG-HF): a multinational, open-
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