Development of Clinical Risk Scores for Detection of COVID-19 in Suspected Patients During a Local Outbreak in China: A Retrospective Cohort Study

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.