ABSTRACT
Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from Jan 17 to Feb 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920 (95% confidence interval : 0.902-0.938; AUROC=0.915, and its standard deviation of 0.028, as evaluated in 5-fold cross-validation). At a value of whether the predicted score >4.0, the model could detect NCP with a specificity of 98.3%; at a cut-off value of < -0.5, the model could rule out NCP with a sensitivity of 97.9%. The study demonstrated that the rapid screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.