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 

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

JOURNAL=International Journal of Public Health

VOLUME=67

YEAR=2022

URL=https://www.ssph-journal.org/journals/international-journal-of-public-health/articles/10.3389/ijph.2022.1604794

DOI=10.3389/ijph.2022.1604794

ISSN=1661-8564

ABSTRACT=<p><bold>Objectives:</bold> To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> 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, <italic>p</italic> = 0.01). Both were internally validated using H-L tests and showed good calibration (both <italic>p</italic> &gt; 0.10).</p><p><bold>Conclusion:</bold> 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.</p>