Acute Myocardial Infarction, Heart Failure
Conditions
Brief summary
Myocardial infarction (MI) remains a major threat to human health. Although interventional treatment techniques have advanced rapidly, many patients still experience major adverse cardiovascular events (MACE) and require hospital readmission after discharge. Artificial intelligence (AI) based on wearable device data has shown great potential in the diagnosis and management of cardiovascular diseases. This study aims to explore the clinical value of wearable device-based data analysis and AI-driven risk stratification models in post-discharge management of acute myocardial infarction (AMI) patients.
Detailed description
This prospective, open-label, randomized controlled study aims to evaluate the clinical benefits of wearable device-based AI risk models in post-discharge management of AMI patients. A total of 200 patients who have undergone PCI and provided informed consent will be enrolled, including those with both preserved and reduced left ventricular ejection fraction (LVEF). Participants will be randomly assigned to either the control group or the intervention group in a 1:1 ratio. All patients will be equipped with a wearable smartwatch and continuously monitored for 3 months after discharge. Data collected will include physiological signals, sleep and activity parameters. In both groups, patients will receive weekly telephone follow-ups and monthly office visits to record symptoms, medication use, and adverse events. In the intervention group, wearable data and AI analytical results will be made available to both patients and their physicians. These insights will be discussed during follow-ups and used to support lifestyle modification, medication adjustment, and clinical decision-making. In the control group, AI data will be collected but not shared or used for clinical management during the study period. The primary study endpoint is the time to first unplanned hospital readmission within 3 months, including readmissions due to chest pain, heart failure, arrhythmia, recurrent myocardial infarction, or death. The secondary endpoints include: Change in Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) score from baseline to 3 months; change in left ventricular ejection fraction (LVEF) measured by echocardiography between baseline and 3 months. The investigators hypothesize that AI-assisted, wearable-based monitoring and feedback will improve early detection of adverse cardiovascular events, reduce unplanned hospitalizations, increase LVEF in patients with reduced LVEF at discharge, and enhance quality of life compared with standard post-discharge care.
Interventions
COMBINATION_PRODUCTOptimized Integrated Management Based on AI-Guided Wearable Data
The collected data will be shared with both patients and their treating physicians during follow-up visits. Based on these insights, the clinical team will offer personalized recommendations regarding medication adjustment, lifestyle modification, diet optimization, and physical activity guidance.
Sponsors
RenJi Hospital
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* Adults aged 18 to 75 years. * Confirmed diagnosis of acute myocardial infarction (AMI), including both ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). * Underwent successful percutaneous coronary intervention (PCI) during index hospitalization. * Hemodynamically stable at the time of hospital discharge. * Willing and able to wear a smartwatch continuously for the study period. * Compatible with the data collection application and have stable internet access.
Exclusion criteria
* Planned staged or elective PCI or any coronary revascularization scheduled within 3 months after discharge. * Unable to tolerate or contraindicated for wearing metal or electronic monitoring devices. * Pregnant or breastfeeding women. * Residence in an area without stable network connectivity or inability to use a smartphone for data upload and communication. * Severe comorbidities that limit 3-month survival or follow-up.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Time to First Unplanned Re-hospitalization event | From the date of hospital discharge to 3 months post-discharge (90 days). | The primary study endpoint is the time to first unplanned hospital readmission within 3 months, including readmissions due to chest pain, heart failure, arrhythmia, recurrent myocardial infarction, or death. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in LVEF | At baseline and at 3 months post-discharge | LVEF will be assessed by transthoracic echocardiography at discharge (baseline) and at 3 months post-discharge follow-up. The change in LVEF will be calculated as the difference between the two measurements. |
| Change in the score of Kansas City Cardiomyopathy Questionnaire-12 | At baseline and at 3 months post-discharge. | The KCCQ-12, a validated patient-reported outcome measure, will be administered during the index hospitalization (prior to discharge) and again at 3 months post-discharge follow-up. |
Contacts
Primary ContactZHIGUO ZOU, MD, PhD
Outcome results
None listed