Indicators and prediction models for the severity of Covid-19

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.

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.

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%).

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.