Abstract

Introduction: It is well known that ST-Segment Elevation Myocardial Infarction (STEMI) is a significant contributor to both illness and death on a global scale. An Electrocardiogram (ECG) is an easily accessible bedside tool for diagnosing acute myocardial infarction. The T wave is usually negative in lead aVR (augmented unipolar right arm lead). However, a positive T wave in lead aVR has been shown to be associated with adverse in-hospital outcomes in patients with Acute Coronary Syndrome (ACS). Aim: To examine whether a positive T wave in lead aVR can be used as an indicator to predict Major Adverse Cardiac Events (MACE) during the hospital stay in patients with STEMI. Materials and Methods: A cohort study was performed at Shri BM Patil Medical College, Hospital and Research Centre, Vijayapura, involving patients admitted with STEMI. A total of 98 newly diagnosed ST-segment elevation patients were classified into two groups: Group A (positive T wave) in lead aVR with an amplitude of ≥ 0 mV, and Group B (negative T wave) in lead aVR with an amplitude of ≤0 mV. The hospital stays of STEMI patients were evaluated for adverse cardiac events. Chi-square test was used to assess relationships between categorical variables. Results: A total of 98 patients were evaluated, among which two were excluded. Hence, among 96 patients considered, 25 were females and 71 were male, with average ages of 57 years in Group A and 55 years in Group B. Among the 96 patients, 34 had positive T waves (35.4%) and 62 had negative T waves (64.5%) in lead aVR. The study revealed significantly higher rates of in-hospital MACE (heart failure, pulmonary oedema, and arrhythmias) in patients with positive T waves (Group A) in lead aVR, with p-values <0.05, which were statistically significant. Conclusion: The present study showed that a positive T wave in lead aVR is a valuable and cost-effective tool for predicting inhospital MACE in patients with STEMI. Utilising this simple and readily available ECG measurement could support clinicians in detecting high-risk patients who require closer monitoring and more aggressive interventions, potentially leading to improved patient outcomes and resource allocation.

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