Establishment and validation of a predictive model for immune reconstitution in people with HIV after antiretroviral therapy

Background Achieving complete immune reconstitution (CIR) in people with human immunodeficiency virus (PWH) following antiretroviral therapy (ART) is essential for preventing acquired immunodeficiency syndrome (AIDS) progression and improving survival. However, there is a paucity of robust prediction models for determining the likelihood of CIR in PWH after ART. We aimed to develop and validate a CIR prediction model utilizing baseline data. Methods Data including demographic information, immunological profiles, and routine laboratory test results, were collected from PWH in Yunnan, China. The participants were divided into training and validation sets (7:3 ratio). To construct the model and accompanying nomogram, univariate and multivariate Cox regression analyses were performed. The model was evaluated using the C-index, time-dependent receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves to assess discrimination, calibration, and clinical applicability. Results 5 408 PWH were included, with a CIR of 38.52%. Cox regression analysis revealed various independent factors associated with CIR, including infection route, marital status, baseline CD4 ⁺ T cell count, and baseline CD4/CD8 ratio. A nomogram was formulated to predict the probability of achieving CIR at years 4, 5, and 6. The model demonstrated good performance, as evidenced by an AUC of 0.8 for both sets. Calibration curve analysis demonstrated a high level of agreement, and decision curve analysis revealed a significant positive yield. Conclusions This study successfully developed a prediction model with robust performance. This model has considerable potential to aid clinicians in tailoring treatment strategies, which could enhance outcomes and quality of life for PWH.