Author: 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; Yung, Fung Sau; 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; Man Chan, Johnny Wai; Ka Lun Alan, Wu; Lung, Kwok-Cheung; Hung, Ivan Fan Ngai; Lau, Chak Sing; Kuo, Michael D.; Ip, Mary Sau-Man
Title: Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting Cord-id: r1p7xn3a Document date: 2020_9_15
ID: r1p7xn3a
Snippet: OBJECTIVES: To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. Database was randomly split 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
Document: OBJECTIVES: To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. Database was randomly split 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, 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. 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]). Second model developed has same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on 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. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.
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