AUTHOR=Han Xiaoxu , Sun Jin , Gao Yuan , Yan Hongxia , He Xiangchuan , Ma Yuanyuan , Xu Peng , Ding Ning , Zhang Xin , Ren Meixin , Jiang Taiyi , Zhang Tong , Su Bin 

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

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1594107

DOI=10.3389/fimmu.2025.1594107

ISSN=1664-3224

ABSTRACT=BackgroundAcquired 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. However, data from the WHO have shown that patients with AIDS combined with TB have a lower anti-TB treatment success rate than HIV-negative individuals do, which may lead to an increased incidence of treatment relapse and drug resistance. Therefore, exploring 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.MethodsWe 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. The baseline data and laboratory test data of all enrolled patients were collected from the electronic medical records system. We randomly divided the participants into a training set and a validation set at a ratio of 2:1 and established a LASSO Cox model on the basis of the training set to identify risk factors affecting the outcome of anti-TB treatment. The selected prognostic factors were then used to construct the final Cox model, which was visualized using a nomogram. The receiver operating characteristic (ROC) curves, concordance index (C-index), and calibration curves of the training set and validation set were used to evaluate the discrimination ability and consistency of the model, respectively. Decision curve analysis (DCA) was used to assess the clinical applicability of the prognostic models. Patients were subsequently risk stratified according to the optimal cutoff value selected by X-tile software for better clinical decision-making by clinicians.ResultsA 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 counts 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 for the training set and validation set revealed that the nomogram had clinical applicability. Patients were risk-stratified according to the total nomogram score, and the patients were divided into three groups: low risk (total points <358), medium risk (358 ≤ total points <373), and high risk (total points ≥373). Clinicians should focus on patients whose total score is more than 358 points.ConclusionWe 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.