BACKGROUND: Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid-based reverse transcription polymerase chain reaction (PCR) testing. METHODS: This retrospective case-control study involves subjects (7 to 98 years) presenting at the designated national outbreak screening centre and tertiary care hospital in Singapore for SARS-CoV-2 testing from January 26 to February 16, 2020. COVID-19 status was confirmed by PCR testing of sputum, nasopharyngeal swabs or throat swabs. Demographic, clinical, laboratory and exposure-risk variables ascertainable at presentation were analyzed to develop an algorithm for estimating the risk of COVID-19. Model development used Akaike's information criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction scores. Performance was measured using receiver operating characteristics curves, adjusting for over-confidence using leave-out-one cross validation. RESULTS: The study population included 788 subjects, of whom 54 (6.9%) were SARS-CoV-2 positive and 734 (93.1%) were SARS-CoV-2 negative. The median age was 34 years and 407 (51.7%) were female. Using leave-out-one cross validation, all the models incorporating clinical tests (Models 1, 2 and 3) performed well with areas under the receiver operating characteristics curve (AUC) of 0.91, 0.88 and 0.88 respectively. In comparison, Model 4 had an AUC of 0.65. CONCLUSIONS: Rapidly ascertainable clinical and laboratory data could identify individuals at high risk of COVID-19 and enable prioritization of PCR-testing and containment efforts. Basic laboratory test results were crucial to prediction models.
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