Development and validation of risk prediction models for COVID-19 positivity in a hospital setting

•Developed two simple-to-use nomograms to identify COVID-19-positive patients•Probabilities are provided to allow healthcare leaders to decide suitable cut-offs•Variables are age, white cell count, chest X-ray appearances, and contact history•Model variables are easily available in the general hospi...

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Published in:	International journal of infectious diseases Vol. 101; pp. 74 - 82
Main Authors:	Ng, Ming-Yen, Wan, Eric Yuk Fai, Wong, Ho Yuen Frank, Leung, Siu Ting, Lee, Jonan Chun Yin, Chin, Thomas Wing-Yan, Lo, Christine Shing Yen, Lui, Macy Mei-Sze, Chan, Edward Hung Tat, Fong, Ambrose Ho-Tung, Fung, Sau Yung, Ching, On Hang, Chiu, Keith Wan-Hang, Chung, Tom Wai Hin, Vardhanbhuti, Varut, Lam, Hiu Yin Sonia, To, Kelvin Kai Wang, Chiu, Jeffrey Long Fung, Lam, Tina Poy Wing, Khong, Pek Lan, Liu, Raymond Wai To, Chan, Johnny Wai Man, Wu, Alan Ka Lun, Lung, Kwok-Cheung, Hung, Ivan Fan Ngai, Lau, Chak Sing, Kuo, Michael D., Ip, Mary Sau-Man
Format:	Journal Article
Language:	English
Published:	Canada Elsevier Ltd 01-12-2020 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases Elsevier
Subjects:	Adult Aged Aged, 80 and over Area Under Curve Chest x-ray COVID-19 COVID-19 - diagnosis COVID-19 - etiology Female Hospitals Humans Logistic Models Male Middle Aged Nomogram Nomograms Prediction model Probability SARS-CoV-2 White cell count Chest x-ray OR H–L Nomogram Prediction model White cell count GGO AUC COVID-19 PEff CT SARS-CoV-2 NPV RT-PCR WCC PPV CXR
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Summary:	•Developed two simple-to-use nomograms to identify COVID-19-positive patients•Probabilities are provided to allow healthcare leaders to decide suitable cut-offs•Variables are age, white cell count, chest X-ray appearances, and contact history•Model variables are easily available in the general hospital setting To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer–Lemeshow (H–L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880−0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844−0.916]). Both were externally validated on the H–L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.
Bibliography:	Both authors contributed equally to the manuscript and are joint first authors.
ISSN:	1201-9712 1878-3511
DOI:	10.1016/j.ijid.2020.09.022