Acute Myocardial Infarction With ST Elevation, Early Warning, Intelligent Management Platform
Conditions
Brief summary
The goal of this observational study is to learn about the effectiveness of an artificial intelligence-based early warning system for predicting adverse events in patients with acute myocardial infarction (AMI). The main question it aims to answer is: Does an AI-based early warning system improve the assessment and prediction of adverse events across the full course of AMI care (from prevention to diagnosis, treatment, and rehabilitation)? Participants who are receiving routine medical care for AMI in tertiary hospitals will have their multimodal medical data (clinical records, diagnostic tests, imaging, treatment pathways) collected and analyzed. Data will be integrated using innovative cross-modal representation methods and predictive models. The study will follow patients during their hospital stay and subsequent clinical follow-up to evaluate the feasibility, accuracy, and clinical value of the AI-based early warning system.
Interventions
None listed
Sponsors
Hui Chen
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Inclusion criteria
* 1\. Hospitalized patients who meet the diagnostic criteria for acute myocardial infarction. 2. Patients who agree to participate and sign the informed consent form.
Exclusion criteria
* 1\. Patients with terminal malignant tumors and an expected survival time of less than 3 months. 2. Patients with complete disability and inability to communicate. 3. Patients unable to comply with follow-up.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| MACCE | 1-3 years | Cardiac death All-cause mortality Malignant arrhythmia Non-fatal recurrent myocardial infarction Non-fatal stroke Unplanned repeat revascularization Rehospitalization for heart failure |
Countries
China
Contacts
Primary ContactHui Chen
Outcome results
None listed