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Original Research ArticleInternational Journal of Research in Medical Sciences

Publication:
Electrocardiographic findings in COVID-19 patients

Int J Res Med Sci. 2021;9(2):378-385.DOI10.18203/2320-6012.ijrms20210410

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Abstract

Background Severe acute respiratory syndrome coronavirus 2 (SARS CoV2) has caused the global pandemic, COVID-19. Though predominantly a respiratory illness, cardiac manifestations of COVID-19 significantly contribute to mortality. We wanted to determine whether admission electrocardiographic (ECG) characteristics provide prognostic information in COVID-19.

Methods We performed a retrospective, cross-sectional observational study in a designated District COVID hospital. COVID-19 patient’s medical records were converted into an electronic database which included demographic data, clinical characteristics and electrocardiogram recorded at/near the time of admission. Primary outcome assessed was the occurrence of severe COVID-19.

Results Of 180 patients, the majority were males (67.8%) and aged 31-50 years (38.9%). The predominant comorbidity among patients who were discharged (non-severe COVID-19 disease) and those who got referred (severe COVID-19 disease) was hypertension (56.5% vs 43.3%), followed by diabetes mellitus (37.7% vs 36.7%). Fatigue (41.9%) and cough (18.5%) were the most frequent symptoms among non-severe cases. Of 32.8% of the patients with abnormal ECG, abnormal axis (25.5%), poor R wave progression (23.5%), T inversion (15.3%), left ventricular hypertrophy (12.2%) followed by ST segment depression (8.3%) were the frequent findings. Logistic regression analysis revealed that elderly patients (>60 years) (β=2.276, OR=9.737, p=0.002), Heart rate (β=0.191, OR=1.211, p=0.045) and ST segment depression (β=9.986, OR=21725.39, p=0.022) showed statistically significant positive association with Severe COVID-19.

Conclusions ST segment abnormalities on admission ECG are markers of cardiac injury and may assist in prognostication of COVID-19. Early identification of these findings might play a crucial role in identifying patients likely to progress to severe COVID-19.

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Affiliations

  1. Department of General Medicine, Gayatri Vidya Parishad Institute of Healthcare and Medical Technology, Visakhapatnam, Andhra Pradesh, India
  2. Department of General Medicine, Government Medical College, Srikakulam, Andhra Pradesh, India

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