A Prediction Model of Mortality in COVID-19 Pneumonia Based on CT Score and Lymphocyte Count
Authors:   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, Y Xie , H Dong ,   Wuhan University, Wuhan Third Hospital, China #Contributed Equally, Y Liao ,   Wuhan University, Wuhan Third Hospital, China #Contributed Equally, J Zhang ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, H Lv ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, X Teng ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, T Wang ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, X Zhang ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, Y Xu ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, S Wu ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, Z He ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, R Wang ,   Shanghai Jiao Tong University School of Medicine, Shanghai General Hospital, China, L Yang ,   Wuhan University, Wuhan Third Hospital, China #Contributed Equally
Published: April 2021

Background: COVID-19 nucleic acid swab tests have a high false positive rate; therefore, diagnosing COVID-19 pneumonia and predicting prognosis by CT scan are very important. Methods: In this retrospective single-centre study, we included consecutive suspected critical COVID-19 pneumonia cases in the intensive care unit of Wuhan Third Hospital from January 31, 2020, to March 16, 2020. 204 cases were confirmed by real-time RT-PCR, and all patients were evaluated with CT, cut-off values were obtained according to the Youden index and were divided into a high CT score group and a low CT score group. Epidemiological, demographic, clinical, and laboratory data were collected. Finally, Through multi-factor logistic regression model, a prediction model based on multiple prediction indicators was formed, and new joint predictive factors were calculated. The prediction model of mortality in COVID-19 pneumonia based on CT score and lymphocyte count was constructed through data processing analysis. Results: The major imaging feature of COVID-19 pneumonia is Ground Glass Opacities (GGOs). Multivariate regression analysis found that the CT score and absolute lymphocyte count were independent risk factors for death and that the CT score predicted mortality (AUC-ROC =0.7, cut-off=1.45). When the absolute lymphocyte count was lower, the patient’s CT score was also lower. Based on this, a prediction model was established. The prediction model was: In [P/(1-P)]=0.667*gender+0.057*age-0.086CT score-0.831 lymphocyte count-3.91, the goodness of fit test of the model was P=0.041, and the area under the curve of the ROC curve of the model was 0.779. Conclusion: CT score and absolute lymphocyte count are independent risk factors for mortality, and patients with a high CT score may have a worse prognosis. A lower absolute lymphocyte count may indicate that the patient’s CT score is also reduced. The model established by combining CT scores and lymphocyte count showed a good degree of calibration and differentiation.