The electrocardiographic manifestations and derangements of 2019 novel coronavirus disease (COVID-19)

Electrocardiographic (ECG) findings in patients admitted with COVID-19 and a decision tree to predict their survival were assessed. 145 consecutive patients with severe COVID-19 infection were selected. Patient demographics, ECG variables, peak troponins, use of standard medications, and clinical outcomes were analyzed using descriptive and inferential statistics, and a predictive model of survival was developed using classification tree analysis. Of the 145 admitted patients, 38 (26%) died. Deceased patients were more likely to have a significantly higher incidence of poor R-Wave progression [6 of 37 (16.2%) Vs. 0 of 104 (0%), p < 0.001] as well as prolonged QTc values [24 of 37 (64.9%) Vs. 38 of 99 (38.4%), p 0.006]. Significant ST segment depressions were found in 5 of 37 (13.5%) of the deceased category compared to 0% in the non-deceased (p < 0.01). Right and/or left atrial enlargement was more prevalent in the deceased cohort [7 of 37 (18.9%) Vs. 4 of 104 (3.8%), p = 0.03]. Bundle branch blocks were more prevalent in the deceased group [9 of 35 (25.8%) Vs. 7 of 104 (6.7%), p 0.002]. Peak troponins were significantly higher in the deceased group (1.0 Vs 0.07 ng/ml, p < 0.001) A prediction tree built utilizing age, PACs, troponins and QTc had an accuracy of 85.5%. 65 of 74 patients (87.8%) were correctly predicted to survive, while 23 of 29 (79.3%) were correctly predicted to become deceased. Among patients hospitalized with Covid-19, the parameters of age, QT interval, troponin and PACs are useful for prognostication and help predict survival with reasonable accuracy.

Abstract licence: CC BY-NC-ND

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