Epidemiological and Clinical Predictors of COVID-19
Yinxiaohe Sun, Vanessa Koh, Kalisvar Marimuthu, Oon Tek Ng, Barnaby Edward Young, Shawn Vasoo, Mónica Chan, Vernon J. M. Lee, Partha Pratim De, Timothy Barkham, Raymond Tzer Pin Lin, Alex R. Cook, Yee‐Sin Leo, Poh Lian Lim, Brenda Ang, Cheng Chuan Lee, David Chien Lye, Li Min Ling, Lawrence Soon-U Lee, Sapna P. Sadarangani, Chen Seong Wong, Tau Hong Lee, Ray Junhao Lin, Po Ying Chia, Mucheli Sharavan Sadasiv, Deborah Hee Ling Ng, Chiaw Yee Choy, Tsin Wen Yeo, Glorijoy Shi En Tan, Yu Kit Chan, Jun Yang Tay, Pei Hua Lee, Sean Wei Xiang Ong, Stephanie Sutjipto, Ian L.E. Wee, Frederico Dimatatac, Chi Jong Go, Florante Santo Isais
Abstract
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 (7-98 years) presenting at the designated national outbreak screening center and tertiary care hospital in Singapore for SARS-CoV-2 testing from 26 January to 16 February 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 characteristic curves, adjusting for overconfidence using leave-one-out 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-one-out cross-validation, all the models incorporating clinical tests (models 1, 2, and 3) performed well with areas under the receiver operating characteristic curve (AUCs) 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.