OBJECTIVES: Coronavirus disease 2019 (Covid‐19) is outbreaking globally. We aimed to analyse the clinical characteristics, cardiac injury, electrocardiogram and computed tomography (CT) features of patients confirmed Covid‐19 and explored the prediction models for the severity of Covid‐19. METHODS: A retrospective and single‐centre study enrolled 98 laboratory‐confirmed Covid‐19 patients. Clinical data, electrocardiogram and CT features were collected and analysed using Statistical Package for the Social Sciences software. RESULTS: There were 46 males and 52 females, with a median age of 44 years, categorised into three groups, including mild, moderate and severe/critical Covid‐19. The rate of abnormal electrocardiograms in severe/critical group (79%) was significantly higher than that in the mild group (17%) (P = .027), which (r = 0.392, P = .005) positively related to the severity of Covid‐19 (OR: 5.71, 95% CI: 0.45‐3.04, P = .008). Age older than 60 years old, comorbidities, whether had symptoms on admission, fatigue, CT features, laboratory test results such as platelet count, lymphocyte cell count, eosinophil cell count, CD3+ cell count, CD4+ cell count, CD8+ cell count, the ratio of albumin/globulin decreased and D‐dimer, C‐reactive protein (CRP), B‐type natriuretic peptide (BNP), cardiac troponin I (cTnI) elevated were the risk factors for the increased severity of Covid‐19. The logistic model, adjusted by age, lobular involvement score and lymphocyte cell count, could be applied for assessing the severity of Covid‐19 (AUC, 0.903; Sensitivity, 90.9%; Specificity, 78.1%). CONCLUSIONS: Age >60 years old, chronic comorbidities, lymphocytopoenia and lobular involvement score were associated with the Covid‐19 severity. The inflammation induced by Covid‐19 caused myocardial injury with elevated BNP and cTnI level and abnormal electrocardiograms.
Indicators and prediction models for the severity of Covid‐19