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- W4200167002 abstract "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.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.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.Predictors based on the characteristics of patients with COVID-19 at hospital admission may help predict the risk of subsequent critical illness." @default.
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- W4200167002 date "2021-12-23" @default.
- W4200167002 modified "2023-09-27" @default.
- W4200167002 title "Early predictors of severe COVID‐19 among hospitalized patients" @default.
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- W4200167002 doi "https://doi.org/10.1002/jcla.24177" @default.
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