Clinical Significance of ST Elevation in Lead aVR in Acute Pulmonary Embolism. Issue 2 (25th May 2016)

• Record Type: Journal Article
• Authors: Pourafkari, Leili; Ghaffari, Samad; Tajlil, Arezou; Akbarzadeh, Fariborz; Jamali, Farin; Nader, Nader D.
• Is Part Of: Annals of noninvasive electrocardiology. Volume 22:Issue 2(2017:Mar.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-05-25
• Subjects: pulmonary embolism -- electrocardiography -- hospital mortality; Electrocardiography -- Periodicals; Arrhythmia -- Periodicals; 616.1207547
• DOI: 10.1111/anec.12368
• Languages: English
• ISSNs: 1082-720X

Abstract

Abstract : Background: Prognostic stratification of patients with acute pulmonary embolism (PTE) is crucial in identifying patients who would benefit from more aggressive treatment. We aimed to examine the value of ST elevation in lead aVR (STEaVR ) in predicting hospital mortality following PTE. Materials: Two hundred patients with a diagnosis of PTE were allocated into two groups based on the presence or absence of STEaVR . Multivariate logistic regression analysis was used to investigate the role of "STEaVR " in relation to the other risk factors in predicting prognosis of PTE. Results: Out of 200 patients, 24 (12.0%) had STEaVR . Patients with STEaVR were more likely to present with hypotension and tachycardia than those who did not have this electrocardiographic finding. A total of 33.3% of patients with STEaVR and 13.1% of those without STEaVR died during hospitalization. STEaVR had a low sensitivity of 25.8% but a high specificity of 90.5% for predicting hospital mortality. Odds ratio for hospital mortality was 3.32 for STEaVR with 95% confidence interval of 1.28–8.64 (P = 0.017) in univariate analysis. In multivariate analysis shock was the strongest predictor of hospital mortality. Conclusion: The presence of STEaVR is indicative of hemodynamic instability, thereby having the ability to predict poor outcome. However, its impact on hospital mortality disappears when the presence of shock on admission is factored in the prediction model.