tetano
Editor, Senior Moderator
mSphere
. 2021 Oct 13;e0075221.
doi: 10.1128/mSphere.00752-21. Online ahead of print.
Early Prediction of Severe COVID-19 in Patients by a Novel Immune-Related Predictive Model
Caiyu Sun[SUP] 1 [/SUP], Mingshan Xue[SUP] 2 [/SUP], Min Yang[SUP] 1 [/SUP], Lihui Zhu[SUP] 3 [/SUP], Yunxue Zhao[SUP] 4 [/SUP], Xiaoting Lv[SUP] 1 [/SUP], Yueke Lin[SUP] 1 [/SUP], Dapeng Ma[SUP] 1 [/SUP], Xuecheng Shen[SUP] 1 [/SUP], Yeping Cheng[SUP] 1 [/SUP], Haocheng Xuan[SUP] 1 [/SUP], Xiaoqing Jia[SUP] 5 [/SUP], Tao Li[SUP] 6 [/SUP], Lihui Han[SUP] 1 [/SUP]
Affiliations
Abstract
During the progression of coronavirus disease 2019 (COVID-19), immune response and inflammation reactions are dynamic events that develop rapidly and are associated with the severity of disease. Here, we aimed to develop a predictive model based on the immune and inflammatory response to discriminate patients with severe COVID-19. COVID-19 patients were enrolled, and their demographic and immune inflammatory reaction indicators were collected and analyzed. Logistic regression analysis was performed to identify the independent predictors, which were further used to construct a predictive model. The predictive performance of the model was evaluated by receiver operating characteristic curve, and optimal diagnostic threshold was calculated; these were further validated by 5-fold cross-validation and external validation. We screened three key indicators, including neutrophils, eosinophils, and IgA, for predicting severe COVID-19 and obtained a combined neutrophil, eosinophil, and IgA ratio (NEAR) model (NEU [10[SUP]9[/SUP]/liter] - 150×EOS [10[SUP]9[/SUP]/liter] + 3×IgA [g/liter]). NEAR achieved an area under the curve (AUC) of 0.961, and when a threshold of 9 was applied, the sensitivity and specificity of the predicting model were 100% and 88.89%, respectively. Thus, NEAR is an effective index for predicting the severity of COVID-19 and can be used as a powerful tool for clinicians to make better clinical decisions. IMPORTANCE The immune inflammatory response changes rapidly with the progression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and is responsible for clearance of the virus and further recovery from the infection. However, the intensified immune and inflammatory response in the development of the disease may lead to more serious and fatal consequences, which indicates that immune indicators have the potential to predict serious cases. Here, we identified both eosinophils and serum IgA as prognostic markers of COVID-19, which sheds light on new research directions and is worthy of further research in the scientific research field as well as clinical application. In this study, the combination of NEU count, EOS count, and IgA level was included in a new predictive model of the severity of COVID-19, which can be used as a powerful tool for better clinical decision-making.
Keywords: COVID-19; IgA; SARS-CoV-2; eosinophils; neutrophils; predictive model.
. 2021 Oct 13;e0075221.
doi: 10.1128/mSphere.00752-21. Online ahead of print.
Early Prediction of Severe COVID-19 in Patients by a Novel Immune-Related Predictive Model
Caiyu Sun[SUP] 1 [/SUP], Mingshan Xue[SUP] 2 [/SUP], Min Yang[SUP] 1 [/SUP], Lihui Zhu[SUP] 3 [/SUP], Yunxue Zhao[SUP] 4 [/SUP], Xiaoting Lv[SUP] 1 [/SUP], Yueke Lin[SUP] 1 [/SUP], Dapeng Ma[SUP] 1 [/SUP], Xuecheng Shen[SUP] 1 [/SUP], Yeping Cheng[SUP] 1 [/SUP], Haocheng Xuan[SUP] 1 [/SUP], Xiaoqing Jia[SUP] 5 [/SUP], Tao Li[SUP] 6 [/SUP], Lihui Han[SUP] 1 [/SUP]
Affiliations
- PMID: 34643417
- DOI: 10.1128/mSphere.00752-21
Abstract
During the progression of coronavirus disease 2019 (COVID-19), immune response and inflammation reactions are dynamic events that develop rapidly and are associated with the severity of disease. Here, we aimed to develop a predictive model based on the immune and inflammatory response to discriminate patients with severe COVID-19. COVID-19 patients were enrolled, and their demographic and immune inflammatory reaction indicators were collected and analyzed. Logistic regression analysis was performed to identify the independent predictors, which were further used to construct a predictive model. The predictive performance of the model was evaluated by receiver operating characteristic curve, and optimal diagnostic threshold was calculated; these were further validated by 5-fold cross-validation and external validation. We screened three key indicators, including neutrophils, eosinophils, and IgA, for predicting severe COVID-19 and obtained a combined neutrophil, eosinophil, and IgA ratio (NEAR) model (NEU [10[SUP]9[/SUP]/liter] - 150×EOS [10[SUP]9[/SUP]/liter] + 3×IgA [g/liter]). NEAR achieved an area under the curve (AUC) of 0.961, and when a threshold of 9 was applied, the sensitivity and specificity of the predicting model were 100% and 88.89%, respectively. Thus, NEAR is an effective index for predicting the severity of COVID-19 and can be used as a powerful tool for clinicians to make better clinical decisions. IMPORTANCE The immune inflammatory response changes rapidly with the progression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and is responsible for clearance of the virus and further recovery from the infection. However, the intensified immune and inflammatory response in the development of the disease may lead to more serious and fatal consequences, which indicates that immune indicators have the potential to predict serious cases. Here, we identified both eosinophils and serum IgA as prognostic markers of COVID-19, which sheds light on new research directions and is worthy of further research in the scientific research field as well as clinical application. In this study, the combination of NEU count, EOS count, and IgA level was included in a new predictive model of the severity of COVID-19, which can be used as a powerful tool for better clinical decision-making.
Keywords: COVID-19; IgA; SARS-CoV-2; eosinophils; neutrophils; predictive model.