Skip to main content
Download PDF
- Main
Development and validation of risk prediction models for COVID-19 positivity in a hospital setting
- 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
- et al.
Published Web Location
https://doi.org/10.1016/j.ijid.2020.09.022Abstract
Objectives
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.Methods
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).Results
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.Conclusion
Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.
Main Content
For improved accessibility of PDF content, download the file to your device.
Enter the password to open this PDF file:
File name:
-
File size:
-
Title:
-
Author:
-
Subject:
-
Keywords:
-
Creation Date:
-
Modification Date:
-
Creator:
-
PDF Producer:
-
PDF Version:
-
Page Count:
-
Page Size:
-
Fast Web View:
-
Preparing document for printing…
0%