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: Zhenmin Sun , Nan Yang , Jiansuo Zhou , Jun Wang 
Published: October 2024

Background: Hepatitis B virus (HBV) infection is a high-risk factor for severe COVID-19 cases, leading clinicians to enhance the monitoring of patients with concurrent HBV and COVID-19 infections. However, this focus is typically on patients with active HBV infection. Patients who are HBV surface antigen (HBsAg) negative and HBV core antibody (anti-HBc) positive are often considered to have either 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 following COVID-19 infection, the virus in these patients may reactivate. In such cases, this reactivation can increase the risk of COVID-19 progressing to a severe illness. Therefore, being HBsAg negative and anti-HBc positive could be a potential risk factor for severe COVID-19. Studying the combination of anti-HBc positivity and COVID-19 infection can help identify high-risk populations, allowing 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 predicting severe pneumonia in patients with positive anti-HBc combined with COVID-19 infection. Methods: We retrospectively enrolled 380 patients who tested 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, 163 patients were included in the study. We applied the Lasso binary logistic regression model to optimize feature selection, identifying eight non-zero coefficients using a minimum of one standard error. Using the multiple logistic regression method with backward selection, we screened six factors from the eight selected by the Lasso binary logistic regression model. These six factors were used to construct the predictive model and a nomogram. The validity of our nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow (HL) 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 HL test, performed using the calibration curve, yielded a p-value of 0.868. The 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 levels, and the patient's underlying disease. The predictive model demonstrates good discrimination, calibration, and clinical utility.