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Development and validation of risk prediction models for COVID-19 positivity in a hospital setting
Ist Teil von
International journal of infectious diseases, 2020-12, Vol.101, p.74-82
Ort / Verlag
Canada: Elsevier Ltd
Erscheinungsjahr
2020
Link zum Volltext
Quelle
MEDLINE
Beschreibungen/Notizen
•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.