A nomogram for predicting unfavorable outcomes of antituberculosis treatment among individuals with AIDS combined with pulmonary tuberculosis in China

Xiaoxu Han¹Jin Sun¹Yuan Gao¹Hongxia Yan¹Xiangchuan He²Yuanyuan Ma²Peng Xu¹Ning Ding¹XIN ZHANG¹Meixin Ren¹Taiyi Jiang¹Tong Zhang^1*Bin Su^1,3*

• ¹Beijing Key Laboratory for HIV/AIDS Research, Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China, Beijing, China
• ²Scientific and Technological Achievement Transformation Center, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China, Beijing, China
• ³Beijing Youan Hospital, Capital Medical University, Beijing, China

Background: Acquired Immune Deficiency Syndrome (AIDS) combined with tuberculosis (TB) is one of the key factors affecting global TB control, and timely and effective treatment is essential to improve the prognosis in this population. We explored the risk factors affecting the outcome of anti-TB treatment in patients with AIDS combined with TB and developing relevant predictive models will help clinicians rapidly identify patients at greater risk of treatment failure, which is highly valuable for clinical management.Methods: We conducted a retrospective cohort study including inpatients with AIDS combined with pulmonary tuberculosis (PTB) who were treated at Beijing Youan Hospital between January 2020 and January 2024.Results: A total of 203 inpatients with AIDS combined with PTB were enrolled in this study, including 141 (69.5%) with treatment success and 62 (30.5%) with unfavorable outcome. The results of the LASSO Cox regression model revealed that the CRP/albumin ratio (CAR), extrapulmonary disseminated tuberculosis, other pulmonary infectious diseases, and pulmonary cavitation were independent risk factors for unfavorable outcomes in patients with AIDS combined with PTB, whereas the CD4+ T-cell count was a protective factor affecting patient outcomes. The five variables in the final Cox regression model were further used to establish a predictive nomogram. The AUC (0.760 for the training set and 0.811 for the validation set) and C-index (0.765 for the training set and 0.768 for the validation set) showed that the model we constructed had good discrimination ability. The calibration curves indicated high consistency between the predictions and the actual observations in both the training set and the validation set. DCA plots for the training set and validation set revealed that the nomogram had clinical applicability.Conclusion: We identified prognostic factors for unfavorable anti-TB treatment outcomes and constructed a predictive nomogram to assess the risk of treatment failure in patients with AIDS combined with PTB. Our model performed satisfactorily and can be used for the clinical screening and management of high-risk patients.