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dc.titleIdentifying COVID-19 cases in outpatient settings
dc.contributor.authorMao, Yinan
dc.contributor.authorTan, Yi-Roe
dc.contributor.authorThein, Tun Linn
dc.contributor.authorChai, Yi Ann Louis
dc.contributor.authorCook, Alex R
dc.contributor.authorDickens, Borame L
dc.contributor.authorLew, Yii Jen
dc.contributor.authorLim, Fong Seng
dc.contributor.authorLim, Jue Tao
dc.contributor.authorSun, Yinxiaohe
dc.contributor.authorSundaram, Meena
dc.contributor.authorSoh, Alexius
dc.contributor.authorTan, Glorijoy Shi En
dc.contributor.authorWong, Franco Pey Gein
dc.contributor.authorYoung, Barnaby
dc.contributor.authorZeng, Kangwei
dc.contributor.authorChen, Mark
dc.contributor.authorOng, Desmond Luan Seng
dc.identifier.citationMao, Yinan, Tan, Yi-Roe, Thein, Tun Linn, Chai, Yi Ann Louis, Cook, Alex R, Dickens, Borame L, Lew, Yii Jen, Lim, Fong Seng, Lim, Jue Tao, Sun, Yinxiaohe, Sundaram, Meena, Soh, Alexius, Tan, Glorijoy Shi En, Wong, Franco Pey Gein, Young, Barnaby, Zeng, Kangwei, Chen, Mark, Ong, Desmond Luan Seng (2021-04-05). Identifying COVID-19 cases in outpatient settings. EPIDEMIOLOGY AND INFECTION 149. ScholarBank@NUS Repository.
dc.description.abstractCase identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritize for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 SARS-CoV-2 positive cases and 564 controls, accounting for the time course of illness using generalized multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5°C and 37.9°C, and temperature above 38°C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectPublic, Environmental & Occupational Health
dc.subjectInfectious Diseases
dc.subjectdiagnosis model
dc.subjectonline tool
dc.subjectrespiratory symptoms
dc.contributor.departmentDEAN'S OFFICE (SSH SCH OF PUBLIC HEALTH)
dc.contributor.departmentDEPT OF MEDICINE
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.sourcetitleEPIDEMIOLOGY AND INFECTION
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