Electrocardiogram abnormalities and higher body mass index as clinically applicable factors for predicting poor outcome in patients with coronavirus disease 2019

Abstract

Abstract Background Patients with coronavirus disease 2019 (COVID-19) have high resource utilization. Identifying the causes of severe COVID-19 is helpful for early intervention to reduce the consumption of medical resources. Methods We included 103 patients with COVID-19 in this single-center observational study. To evaluate the incidence, predictors, and effects of COVID-19, we analyzed demographic information, laboratory results, comorbidities, and vital signs as factors for association with severe COVID-19. Results The incidence of severe COVID-19 was 16.5% and the percent poor outcome (including mortality, entering in ICU or transferred to a superior hospital) was 6.8%. The majority of severe COVID-19 patients had abnormal electrocardiogram (ECG) (82.35%), hypertension (76.47%) and other cardiac diseases (58.82%). Multivariate logistic regression was used to determine the predictors of severe illness. Abnormal body mass index (BMI) and ECG (P < 0.05) were independent predictors of severe COVID-19. ECG abnormality was associated with increased odds of poor outcome (area under the receiver operating characteristic curves [AUC], 0.793; P = 0.010) and severe COVID-19 (AUC, 0.807; P < 0.0001). Overweight was also associated with increased odds of poor outcome (AUC, 0.728; P = 0.045) and severe illness COVID-19 (AUC, 0.816; P < 0.0001). Conclusion Overweight and electrophysiological disorders on admission are important predictors of prognosis of patients with COVID-19.