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

Int J Infect Dis. 2020 Dec:101:74-82. doi: 10.1016/j.ijid.2020.09.022. Epub 2020 Sep 15.

Abstract

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.

Keywords: COVID-19; Chest x-ray; Nomogram; Prediction model; White cell count.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Area Under Curve
  • COVID-19 / diagnosis*
  • COVID-19 / etiology
  • Female
  • Hospitals
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Nomograms
  • Probability
  • SARS-CoV-2*