Author:
Sun Zhuoyu,Guo Yi’an,He Wei,Chen Shiyue,Sun Changqing,Zhu Hong,Li Jing,Chen Yongjie,Du Yue,Wang Guangshun,Yang Xilin,Su Hongjun
Abstract
Objectives: To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks.Methods: Medical records were extracted for a retrospective cohort of 336 suspected patients admitted to Baodi hospital between 27 January to 20 February 2020. Multivariate logistic regression was applied to develop the risk-scoring models, which were internally validated using a 5-fold cross-validation method and Hosmer-Lemeshow (H-L) tests.Results: Fifty-six cases were diagnosed from the cohort. The first model was developed based on seven significant predictors, including age, close contact with confirmed/suspected cases, same location of exposure, temperature, leukocyte counts, radiological findings of pneumonia and bilateral involvement (the mean area under the receiver operating characteristic curve [AUC]:0.88, 95% CI: 0.84–0.93). The second model had the same predictors except leukocyte and radiological findings (AUC: 0.84, 95% CI: 0.78–0.89, Z = 2.56, p = 0.01). Both were internally validated using H-L tests and showed good calibration (both p > 0.10).Conclusion: Two clinical risk scores to detect COVID-19 in local outbreaks were developed with excellent predictive performances, using commonly measured clinical variables. Further external validations in new outbreaks are warranted.
Funder
National Natural Science Foundation of China
National Social Science Fund of China
Subject
Public Health, Environmental and Occupational Health,Health (social science)
Cited by
2 articles.
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