Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients

• Published in: Frontiers in medicine Vol. 10; p. 1137136
• Main Authors: Wu, Yinyan, Xiao, Benjie, Xiao, Jingjing, Han, Yudi, Liang, Huazheng, Yang, Zhangwei, Bi, Yong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 13.04.2023
• Subjects: coronavirus; COVID-19; deterioration model; Medicine; prediction; prognosis; COVID-19; prediction; coronavirus; deterioration model; prognosis
• ISSN: 2296-858X; 2296-858X
• DOI: 10.3389/fmed.2023.1137136

COVID-19 pandemic has imposed tremendous stress and burden on the economy and society worldwide. There is an urgent demand to find a new model to estimate the deterioration of patients inflicted by Omicron variants. This study aims to develop a model to predict the deterioration of elderly patients inflicted by Omicron Sub-variant BA.2. COVID-19 patients were randomly divided into the training and the validation cohorts. Both Lasso and Logistic regression analyses were performed to identify prediction factors, which were then selected to build a deterioration model in the training cohort. This model was validated in the validation cohort. The deterioration model of COVID-19 was constructed with five indices, including C-reactive protein, neutrophil count/lymphocyte count (NLR), albumin/globulin ratio (A/G), international normalized ratio (INR), and blood urea nitrogen (BUN). The area under the ROC curve (AUC) showed that this model displayed a high accuracy in predicting deterioration, which was 0.85 in the training cohort and 0.85 in the validation cohort. The nomogram provided an easy way to calculate the possibility of deterioration, and the decision curve analysis (DCA) and clinical impact curve analysis (CICA)showed good clinical net profit using this model. The model we constructed can identify and predict the risk of deterioration (requirement for ventilatory support or death) in elderly patients and it is clinically practical, which will facilitate medical decision making and allocating medical resources to those with critical conditions.