Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

Abstract

Background The COVID-19 virus is an emerging virus associated with severe respiratory illness first detected in December, 2019, and rapidly spread worldwide. The aim of this study was to establish an effective diagnostic nomogram for suspected COVID-19 pneumonia patients. Methods We used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with COVID-19 pneumonia, and established the diagnostic nomogram for COVID-19 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram. Results The predictive factors including the epidemiological history, wedge-shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for COVID-19 pneumonia patients. Conclusion Our nomogram can be used to predict COVID-19 pneumonia accurately and favourably.

Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses

“…None of the studies of symptoms of COVID-19 have taken seasonality into account. [2][3][4][5][6][7][8][9][10][11][12] Published studies compare symptoms of COVID-19 to a nondescript control group composed of patients for whom COVID-19 diagnosis has been ruled out. In different seasons,…”