Acute myocardial infarction (AMI) remains a leading cause of mortality worldwide. Although early revascularization has markedly improved short-term outcomes, the incidence of major adverse cardiovascular events after the index event remains unacceptably high, posing a formidable clinical challenge. Contemporary risk-stratification instruments rely predominantly on a restricted set of conventional clinical variables and therefore fail to capture the full spectrum of individual pathophysiological complexity. To overcome these limitations, the present investigation aims to develop a post-AMI prognostic model that integrates comprehensive multimodal data.
Study Type
OBSERVATIONAL
Enrollment
1,544
Percutaneous coronary intervention
The Second Affiliated Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang, China
Recurrent myocardial infarction or cardiac death
Patients received recurrent myocardial infarction or cardiac death
Time frame: At 24-month Follow-up
Diagnostic performance
Diagnostic performance of the AI-driven multimodal predictive model: area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, overall accuracy, positive predictive value (PPV), and negative predictive value (NPV).
Time frame: At 24-month follow-up
MACE at 24 months
Major adverse cardiovascular events, defined as a compisite of cardiac death, myocardial infarction, target vessel revascularization.
Time frame: At 24-month follow-up
MACE at 60 months
Major adverse cardiovascular events, defined as a compisite of cardiac death, myocardial infarction, target vessel revascularization.
Time frame: At 60-month follow-up
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