Abstract
BACKGROUND: Novel coronavirus disease 2019 (COVID-19) poses a huge threat to the global public health. This study aimed to identify predictive indicators of severe COVID-19. METHODS: We retrospectively collected clinical data on hospital admission of all patients with severe COVID-19 and a control cohort (1:1) of gender- and hospital-matched patients with mild disease from 13 designated hospitals in the Hebei Province between 22 January and 15 April 2020. RESULTS: A total of 104 patients (52 with severe COVID-19 and 52 with mild disease) were included. Only age, fever, duration from symptom onset to confirmation, respiratory rate, percutaneous oxygen saturation (SpO(2) ) and neutrophilic percentage were independent predictors of severe COVID-19. Age and neutrophilic percentage performed best in predicting severe COVID-19, followed by SpO(2) . 'Age + neutrophilic percentage' (the sum of age and neutrophilic percentage) (area under the curve [AUC] 0.900, 95% confidence interval [CI] 0.825-0.950, P < .001) and 'age and neutrophilic percentage' (the prediction probability of age and neutrophilic percentage for severe type obtained by logistic regression analysis) (AUC 0.899, 95% CI 0.824-0.949, P < .001) had excellent predictive performance for severe type. The optimal cut-off for 'age + neutrophilic percentage' was >119.1 (sensitivity, 86.5%; specificity, 84.6%; Youden index, 0.712). CONCLUSION: The combination of age and neutrophil percentage could effectively predict severe COVID-19. The sum of age and neutrophil percentage was recommended for clinical application because of its excellent predictive value and practicability. TRAIL REGISTRATION: China Clinical Trial Registry, number ChiCTR2000030226. Registered 26 February 2020-Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=49855.