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

BackgroundAchieving complete immune reconstitution (CIR) in people living with human immunodeficiency virus (PLWH) 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 PLWH after ART. We aimed to develop and validate a CIR prediction model utilizing baseline data.MethodsBaseline data including demographic information, immunological profiles, and routine laboratory test results, were collected from PLWH in Yunnan, China. Baseline referred to the first recorded results after HIV diagnosis but before initiating ART, and these initial measurements served as the baseline data for analysis. The participants were divided into training and validation sets (7:3 ratio). To construct the model and accompanying nomogram, univariable and multivariable 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.ResultsFive thousand four hundred eight PLWH were included, with a CIR of 38.52%. Cox regression analysis revealed various independent factors associated with CIR, including infection route, baseline CD4+T cell count, baseline CD4/CD8 ratio, interval from HIV diagnosis to ART initiation, and the level of PLT, Glu, Crea, HGB, ALT. 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.ConclusionsThis 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 PLWH.