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.
Keywords: COVID-19; clinical variables; local outbreaks; retrospective cohort study; risk score.
【저자키워드】 COVID-19, Risk Score., retrospective cohort study, clinical variables, local outbreaks, 【초록키워드】 coronavirus disease, Pneumonia, hospital, risk, Local, predictors, Cohort, outbreak, Logistic regression, age, temperature, predictor, characteristic, close contact, external validation, Predictive, medical record, leukocyte, multivariate, retrospective cohort, clinical risk, radiological finding, Seven, detect, develop, diagnosed, applied, the mean, local outbreak, suspected patient, 【제목키워드】 detection, clinical, development, score, Suspected,