Development and Validation of a Cardiac Magnetic Resonance-Based Multimodal Deep Learning Model for Long-Term Outcome Prediction in ST-Segment Elevation Myocardial Infarction
Trial ID: NCT07277400
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-31
Completion date: 2026-06-30
Sponsor: Qian geng
Collaborators: Xi'an Jiaotong University; Beijing Anzhen Hospital; Second Affiliated Hospital of Nanchang University; First People's Hospital of Yulin; Guizhou Provincial People's Hospital; Jiangxi Provincial People's Hopital; First Affiliated Hospital of Xinjiang Medical University; People's Hospital of Xinjiang Uygur Autonomous Region; Beijing Chao Yang Hospital; Wuhan Asia Heart Hospital
Conditions: ST Elevation (STEMI) Myocardial Infarction
Locations: Chinese PLA General Hospital
Countries: China
Enrollment: 800
Age range: 18 Years - None
Sex: ALL

Summary:
Objective: This study aims to develop and test a novel artificial intelligence-based prediction model. This model will integrate cardiac magnetic resonance imaging and clinical data to predict the long-term risk of major adverse cardiovascular events in patients who have undergone emergency percutaneous coronary intervention for ST-segment elevation myocardial infarction.

Description: This study plans to enroll patients with STEMI who have received primary PCI. Approximately one week after the procedure, patients will undergo a cardiac magnetic resonance scan. Concurrently, we will collect patients' basic information, blood test results during treatment, and procedural records. Thereafter, patients will be followed up regularly (every six months) to record the occurrence of any major adverse cardiac events, such as cardiovascular death, recurrent myocardial infarction, hospitalization for heart failure, or unplanned repeat revascularization.All collected data, including clinical data and analyzed cardiac MR images, will be used to construct a multimodal deep learning model. The model will learn to identify features associated with future cardiac problems. The accuracy of the model will be tested and validated in different patient groups.

Potential Impact: If successful, this prediction tool could assist physicians in identifying high-risk patients earlier and more accurately, enabling closer monitoring and more timely interventions, ultimately improving the long-term prognosis for these patients.

Eligibility criteria:
Inclusion Criteria:

(1)diagnosis of STEMI , elevated cardiac biomarkers (troponin) above the 99th percentile upper reference limit together with at least one of the following: chest pain lasting \>30 minutes; ST-segment elevation ≥0.1 mV in two or more contiguous limb leads or ≥0.2 mV in two or more contiguous precordial leads on a 12-lead ECG; (2) age \>18 years, first episode of STEMI treated with primary PCI; (3)diagnostic and therapeutic management consistent with current clinical standards and guideline recommendations; (4) Killip class≤III at presentation; (5) provided informed consent for CMR imaging and clinical follow-up.

Exclusion Criteria:

(1) contraindications to CMR (e.g., cardiac pacemakers, severe claustrophobia, known allergy to gadolinium-based contrast agents, or impaired renal function defined as eGFR\<30mL/min/1.73m²). (2) structural heart disease (e.g., significant valvular or congenital disease); (3) documented history of prior myocardial infarction, PCI, or coronary artery bypass grafting (CABG)； (4) comorbidities severely compromising overall prognosis or the ability to undergo the study procedures, such as serious hematological disorders, active systemic infections, or active malignancy; (5) cognitive impairment or psychiatric conditions precluding adequate cooperation with the CMR examination or clinical follow-up; (6) Poor-quality CMR images or missing essential sequences precluding accurate quantitative analysis.

Outcomes:
major adverse cardiac events