BACKGROUND: Limited research has been conducted on early laboratory biomarkers to identify patients with severe coronavirus disease (COVID‐19). This study fills this gap to ensure appropriate treatment delivery and optimal resource utilization. METHODS: In this retrospective, multicentre, cohort study, 52 and 64 participants with severe and mild cases of COVID‐19, respectively, were enrolled during January‐March 2020. Least absolute shrinkage and selection operator and binary forward stepwise logistic regression were used to construct a predictive risk score. A prediction model was then developed and verified using data from four hospitals. RESULTS: Of the 50 variables assessed, eight were independent predictors of COVID‐19 and used to calculate risk scores for severe COVID‐19: age (odds ratio (OR = 14.01, 95% confidence interval (CI) 2.1–22.7), number of comorbidities (OR = 7.8, 95% CI 1.4–15.5), abnormal bilateral chest computed tomography images (OR = 8.5, 95% CI 4.5–10), neutrophil count (OR = 10.1, 95% CI 1.88–21.1), lactate dehydrogenase (OR = 4.6, 95% CI 1.2–19.2), C‐reactive protein OR = 16.7, 95% CI 2.9–18.9), haemoglobin (OR = 16.8, 95% CI 2.4–19.1) and D‐dimer levels (OR = 5.2, 95% CI 1.2–23.1). The model was effective, with an area under the receiver‐operating characteristic curve of 0.944 (95% CI 0.89–0.99, p < 0.001) in the derived cohort and 0.8152 (95% CI 0.803–0.97; p < 0.001) in the validation cohort. CONCLUSION: Predictors based on the characteristics of patients with COVID‐19 at hospital admission may help predict the risk of subsequent critical illness.
Early predictors of severe COVID‐19 among hospitalized patients