Prediction Model of Major Cardiovascular Events in Patients with Acute Myocardial Infarction Based on Clinical Characteristics, Electrocardiographic Findings, and Biomarkers

Adam Rizky Mildsi

Authors

Adam Rizky Mildsi Fakultas Kedokteran, Program Studi S1 Kedokteran, Departemen Penyakit Jantung dan Pembuluh Darah, Universitas Sumatera Utara,

Keywords:

Acute Myocardial Infarction, Major Adverse Cardiovascular Events (MACE), Haji Adam Malik General Hospital, Prediction Model, Biomarkers, Electrocardiography

Abstract

Background: Acute Myocardial Infarction (AMI) is a life-threatening medical emergency and remains one of the leading causes of cardiovascular morbidity and mortality worldwide, including in Indonesia. As the main referral center in Northern Sumatra, Haji Adam Malik General Hospital, Medan, manages a substantial number of AMI cases, where survivors of the initial event remain at high risk for developing Major Adverse Cardiovascular Events (MACE). This study aimed to develop a 30-day MACE prediction model by integrating clinical characteristics, electrocardiographic findings, and cardiac biomarkers among AMI patients treated at Haji Adam Malik General Hospital. Methods: A retrospective cohort study was conducted involving 450 AMI patients admitted to the Cardiovascular Care Unit (CVCU) of Haji Adam Malik General Hospital, Medan, between January 2023 and December 2024. Variables analyzed included clinical parameters (Killip class, blood pressure, and age), electrocardiographic findings (location of ST-segment elevation myocardial infarction and bundle branch block), and cardiac biomarkers (high-sensitivity cardiac troponin T [hs-cTnT] and N-terminal pro–B-type natriuretic peptide [NT-proBNP]). Multivariate logistic regression analysis was performed to develop the prediction model, and its discriminatory performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC). Results: Thirty-day MACE occurred in 85 patients (18.8%). Multivariate analysis identified five independent predictors of MACE: age >65 years (OR = 2.1), Killip class >II (OR = 3.5), anterior STEMI (OR = 2.4), hs-cTnT level >1000 ng/L (OR = 2.8), and NT-proBNP level >2000 pg/mL (OR = 3.1). The prediction model incorporating these five variables demonstrated excellent discriminatory performance, with an AUC of 0.86 (95% CI: 0.82–0.90). Conclusion: The integration of clinical characteristics, electrocardiographic findings, and dual biomarkers (hs-cTnT and NT-proBNP) provides an accurate prediction model for 30-day MACE in AMI patients treated at Haji Adam Malik General Hospital, Medan. This model may serve as a practical tool for emergency department triage and risk stratification, particularly for referral patients who frequently present late after symptom onset.

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