Construction and evaluation of a model for the prediction of risk factors associated with severe pneumonia in patients with positive hepatitis B core antibody and COVID-19 infection in China

authors:

avatar Sun Zhenmin 1 , avatar Yang Nan 1 , avatar Zhou Jiansuo 2 , avatar Wang Jun ORCID 1 , *

Department of Transfusion, Peking University Third Hospital, Beijing, 100000, China
Department of Laboratory, Peking University Third Hospital, Beijing, 100000, China
Jundishapur Journal of Microbiology: Vol.17, issue 8; e148377
published online: September 23, 2024
article type: Research Article
revised: August 05, 2024

how to cite: Zhenmin S, Nan Y, Jiansuo Z, Jun W. Construction and evaluation of a model for the prediction of risk factors associated with severe pneumonia in patients with positive hepatitis B core antibody and COVID-19 infection in China. Jundishapur J Microbiol. 2024;17(8):e148377. 

Abstract

Background: Hepatitis B virus (HBV) infection is a high-risk factor for severe COVID-19 cases, and clinicians will strengthen the monitoring of patients with concurrent HBV and COVID-19 infections. However, this is typically focused on patients with active HBV infection. For patients with HBV surface antigen (HBsAg) negative and HBV core antibody (Anti-HBc) positive, they are often considered to have had a past infection that has naturally cleared or to be in a low-replication state, and thus receive less clinical attention. However, in immunosuppressed states, such as during immunosuppressive treatment after COVID-19 infection, the virus in these patients may undergo reactivation. In such cases, it can increase the risk of COVID-19 progressing to a severe illness. So, HBsAg negative and Anti-HBc positive is a potential risk factor for severe COVID-19, the study for Anti-HBc positive combine with COVID-19 infection can help identify high-risk populations. This would allow for the implementation of targeted prevention and management measures, thereby reducing the occurrence of severe COVID-19 cases. 
Objectives: To establish and evaluate a model for the prediction of severe pneumonia in patients with positive Anti-HBc combined with COVID-19 infection. 
Methods: We retrospectively enrolled 380 patients testing positive for Anti-HBc , negative for HBsAg and HBV e-antigen (HBeAg) combined with COVID-19 infection in our hospital from December 2022 to May 2023. Based on the inclusion criteria, the study included 163 patients. We used the Lasso binary logistic regression model to optimize the feature selection, and eight non-zero coefficients were selected using a minimum of one standard error, Utilize the multiple logistic regression method with backward selection screened six factors out of the eight factors selected by the Lasso binary logistic regression model, six factors were used to construct the predictive model and a nomogram. The validity of our nomogram was determined using the area under the receiver operating characteristic curve (AUC), correction curve, Hosmer-Lemeshow test and decision-curve analysis (DCA).
Results: Hypertension, diabetes, decreased absolute lymphocyte count, prolonged prothrombin time, elevated aspartate aminotransferase, and decreased albumin are high-risk factors for severe pneumonia in patients with positive Anti-HBc combined with COVID-19 infection. The AUC of the predictive model constructed using these six factors is 0.785, with a 95% confidence interval of (0.709-0.862). The Hosmer-Lemeshow test was performed using the calibration curve with a p-value of 0.868. Application of this diagnostic curve will increase the net benefit when the threshold probability is between 5% and 75%. 
Conclusions: The constructed nomogram can be used to predict the risk of patients with positive Anti-HBc combined with COVID-19 infection progressing to severe pneumonia based on routine blood parameters, liver function, coagulation, lactate dehydrogenase, and the patient's underlying disease. The predictive model has good discrimination, calibration and clinical utility.