Predictors of Myocardial Infarction Among Patients Presenting with Chest Pain to the Emergency Department of a Tertiary Care Hospital in Peshawar, Pakistan

Background: Chest pain is one of the most common complaints among patients presenting to emergency departments and represents a major diagnostic challenge for healthcare providers. Myocardial infarction (MI) is a serious cause of chest pain that requires rapid diagnosis and timely management to reduce mortality and complications. Objective: To determine predictors of myocardial infarction among patients presenting with chest pain in the emergency department. Methods: A prospective cohort study with 72-hour follow-up was conducted among 138 patients presenting with chest pain to the emergency department of Lady Reading Hospital, Peshawar, using non-probability convenience sampling. Data were collected through a structured, pre-tested proforma covering demographic characteristics, clinical symptoms, cardiovascular risk factors, ECG findings, troponin levels, and final diagnosis, and analyzed using SPSS version 26. Frequencies, percentages, the chi-square test, and binary logistic regression analysis were applied, with p ≤ 0.05 considered statistically significant. Results: Increasing age, sweating, smoking status, abnormal ECG findings, and elevated troponin levels were significantly associated with myocardial infarction. On binary logistic regression, patients with sweating had 4.7 times higher odds of MI (AOR = 4.707, 95% CI: 1.466–15.110, p = 0.009), ST-segment depression on ECG was associated with markedly increased odds of MI (AOR = 25.829, 95% CI: 3.483–191.524, p = 0.001), and elevated troponin was associated with 9.2 times higher odds of MI (AOR = 9.236, 95% CI: 1.913–44.582, p = 0.006). The overall model was statistically significant (Omnibus χ² = 84.006, df = 15, p < 0.001). Conclusion: Demographic factors, sweating, abnormal ECG findings, and elevated troponin levels are important predictors of myocardial infarction among chest pain patients presenting to the emergency department. Early identification of these predictors may improve triage accuracy and patient outcomes.