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

Frontiers in Medicine

Medicine

Yong Bi¹  Yinyan Wu²  Yudi Han²  Jingjing Xiao²  Benjie Xiao²  Zhangwei Yang³  Huazheng Liang⁴ 

[1] Affiliated Zhoupu Hospital, Shanghai University of Medicine and Health Siences, Shanghai, China;Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, China;Department of Neurology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, China;Medical Department, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China;Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China;Monash Suzhou Research Institute, Suzhou Industrial Park, Suzhou Jiangsu, China;

关键词: coronavirus;    COVID-19;    deterioration model;    prognosis;    prediction;   

DOI  :  10.3389/fmed.2023.1137136

 received in 2023-01-04, accepted in 2023-03-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

RationaleCOVID-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.ObjectiveThis study aims to develop a model to predict the deterioration of elderly patients inflicted by Omicron Sub-variant BA.2.MethodsCOVID-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.Measurements and main resultsThe 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.ConclusionThe 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.

【 授权许可】

Unknown   
Copyright © 2023 Wu, Xiao, Xiao, Han, Liang, Yang and Bi.