Establishment and Evaluation of a Prediction Model of BLR for Severity in Coronavirus Disease 2019

Abstract

Abstract Background: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and has spread worldwide. Clinical risk factors associated with the severity in COVID-19 patients have not yet been well delineated. The aim of this study was to explore the risk factors related with the progression of severe COVID-19 and establish a prediction model for severity in COVID-19 patients. Methods: We retrospectively recruited patients with confirmed COVID-19 admitted in Enze Hospital, Taizhou Enze Medical Center (Group) and Nanjing Drum Tower Hospital between January 24 and March 12, 2020. Take the Taizhou cohort as the training set and the Nanjing cohort as the validation set. Severe case was defined based on the World Health Organization Interim Guidance Report criteria for severe pneumonia. The patients were divided into severe and non-severe groups. Epidemiological, laboratory, clinical, and imaging data were recorded with data collection forms from the electronic medical record. The predictive model of severe COVID-19 was constructed, and the efficacy of the predictive model in predicting the risk of severe COVID-19 was analyzed by the receiver operating characteristic curve (ROC). Results: A total of 402 COVID-19 patients were included in the study, including 98 patients in the training set (Nanjing cohort) and 304 patients in the validation set (Nanjing cohort). There were 54 cases (13.43%) in severe group and 348 cases (86.57%) in non-severe group. Logistic regression analysis showed that body mass index (BMI) and lymphocyte count were independent risk factors for severe COVID-19 (all P < 0.05). Logistic regression equation based on risk factors was established as follows: Logit (BL)=–5.552–5.473 ×L + 0.418 × BMI. The area under the ROC curve (AUC) of the training set and the validation set were 0.928 and 0.848, respectively (all P < 0.001). The model was simplified to get a new model (BMI and lymphocyte count ratio, BLR) for predicting severe COVID-19 patients, and the AUC in the training set and validation set were 0.926 and 0.828, respectively (all P < 0.001). Conclusions: Higher BMI and lower lymphocyte count are critical factors associated with severity of COVID-19 patients. The simplified BLR model has a good predictive value for the severe COVID-19 patients. Metabolic factors involved in the development of COVID-19 need to be further investigated.