LLM-Based Intelligent Health Management Assistant in Life-Cycle Health Management of Cardiac Surgery Patients

Summary

This is a single-center, prospective, randomized, open-label, parallel-controlled clinical study to evaluate the effectiveness of a large language model (LLM)-based intelligent health management assistant in the life-cycle health management of patients after cardiac surgery. A total of 500 adult patients who undergo cardiac surgery (including coronary artery bypass grafting, heart valve surgery, great vessel surgery, congenital heart disease correction, and other cardiac procedures) at Beijing Anzhen Hospital will be randomly assigned in a 1:1 ratio to an intervention group or a control group, stratified by age (\<65 vs ≥65 years) and surgery type. The intervention group will use the LLM-based mobile health management application in addition to standard postoperative care, while the control group will receive standard postoperative care alone. The application integrates multimodal clinical data into a personalized health profile and provides surgery-type-specific postoperative management recommendations, medication adherence reminders, complication early warning, and cardiac rehabilitation guidance. The primary outcome is the composite endpoint of major adverse cardiac and cerebrovascular events (MACCE), defined as all-cause death, non-fatal myocardial infarction, non-fatal stroke, or unplanned cardiovascular reoperation/reintervention, within 12 months after randomization. Secondary outcomes include health-related quality of life (EQ-5D-5L), cardiovascular rehospitalization rate, medication adherence (MMAS-8), postoperative complication rate, and cardiac rehabilitation achievement rate. Follow-up visits are scheduled at 1, 3, 6, 9, and 12 months post-randomization.

Conditions

• Coronary Artery Bypass Grafting
• Heart Valve Disease
• Aortic Aneurysm
• Aortic Dissection
• Congenital Heart Disease
• Cardiac Surgical Procedures

Interventions

OTHER

LLM-Based Intelligent Health Management Assistant

A mobile application integrating large language model technology with individual health records. Participants upload multimodal clinical data including medical records, laboratory results, imaging data, and treatment histories to build a structured personal health profile. The assistant periodically incorporates the latest clinical practice guidelines and provides personalized lifestyle intervention recommendations, medication adherence reminders, and early warnings for potential health-critical events, in addition to standard clinical care.

Sponsors & Collaborators

• Beijing Anzhen Hospital

  lead OTHER

Principal Investigators

• Ming Gong, MD · Beijing Anzhen Hospital

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-05-01

Primary Completion

2027-12-31

Completion

2027-12-31

Countries

• China

Study Locations