Please use this identifier to cite or link to this item: https://doi.org/10.1017/S0950268821000704
Title: Identifying COVID-19 cases in outpatient settings
Authors: Mao, 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 
Keywords: Science & Technology
Life Sciences & Biomedicine
Public, Environmental & Occupational Health
Infectious Diseases
Classification
COVID-19
diagnosis model
online tool
respiratory symptoms
Issue Date: 5-Apr-2021
Publisher: CAMBRIDGE UNIV PRESS
Citation: Mao, 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. https://doi.org/10.1017/S0950268821000704
Abstract: Case 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.
Source Title: EPIDEMIOLOGY AND INFECTION
URI: https://scholarbank.nus.edu.sg/handle/10635/230760
ISSN: 09502688
14694409
DOI: 10.1017/S0950268821000704
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