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Development of a prediction model for the risk of MACCE after discharge in patients with frist acute myocardial infarction using survival analysis-based machine learning: a retrospective cohort study

Development of a prediction model for the risk of MACCE after discharge in patients with frist acute myocardial infarction using survival analysis-based machine learning: a retrospective cohort study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0012176
Enrollment
3000
Registered
2026-06-25
Start date
2025-07-01
Completion date
Unknown
Last updated
2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

Chung-Ang University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Adults aged 19 years or older who were hospitalized with a diagnosis of acute myocardial infarction (ICD-10 codes: I21.0–I21.4, I21.9, I22.0, I22.1, I22.8, I22.9, I23.0, I23.1–I23.6, and I23.8) between April 1, 2016, and March 31, 2025, and who were discharged alive after receiving medical therapy together with successful percutaneous coronary intervention (PCI) and/or coronary artery bypass grafting (CABG).

Exclusion criteria

Exclusion criteria: Patients with no follow-up records after discharge or with missing MACCE-related data will be excluded.

Design outcomes

Primary

MeasureTime frame
Major adverse cardiovascular and cerebrovascular events (MACCE)

Secondary

MeasureTime frame
Individual incidence and cumulative incidence rates at each time point for each MACCE component event, including recurrent myocardial infarction, stroke, cardiovascular death, all-cause death, rehospitalization for unstable angina, rehospitalization for heart failure, stent thrombosis, repeat revascularization by percutaneous coronary intervention (PCI), and repeat revascularization by coronary artery bypass grafting (CABG).

Countries

Korea, Republic of

Contacts

Public ContactMin-Young Yu

Chung-Ang University

minyoung0910@google.com+82-2-820-5198

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Jul 3, 2026