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

Abstract

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.