Cardiopulmonary predictors of mortality in patients with COVID-19: What are the findings?

Background. - Since 2019, coronavirus disease 2019 (COVID-19) has been the leading cause of mortality worldwide. Aims. - To determine independent predictors of mortality in COVID-19, and identify any asso-ciations between pulmonary disease severity and cardiac involvement. Methods. - Clinical, laboratory, electrocardiography and computed tomography (CT) imaging data were collected from 389 consecutive patients with COVID-19. Patients were divided into alive and deceased groups. Independent predictors of mortality were identified. Kaplan-Meier analysis was performed, based on patients having a troponin concentration > 99th percentile (cardiac injury) and a CT severity score >= 18. Results. - The mortality rate was 29.3%. Cardiac injury (odds ratio [OR] 2.19, 95% confidence interval [CI] 1.14-4.18; P = 0.018), CT score >= 18 (OR 2.24, 95% CI 1.15-4.34; P = 0.017), localized ST depression (OR 3.77, 95% CI 1.33-10.67; P = 0.012), hemiblocks (OR 3.09, 95% CI 1.47-6.48; P = 0.003) and history of leukaemia/lymphoma (OR 3.76, 95% CI 1.37-10.29; P = 0.010) were identified as independent predictors of mortality. Additionally, patients with car-diac injury and CT score >= 18 were identified to have a significantly shorter survival time (mean 14.21 days, 95% CI 10.45-17.98 days) than all other subgroups. There were no associations between CT severity score and electrocardiogram or cardiac injury in our results. Conclusions. - Our findings suggest that using CT imaging and electrocardiogram characteris-tics together can provide a better means of predicting mortality in patients with COVID-19. We identified cardiac injury, CT score >= 18, presence of left or right hemiblocks on initial electro-cardiogram, localized ST depression and history of haematological malignancies as independent predictors of mortality in patients with COVID-19. (c) 2022 Published by Elsevier Masson SAS.