Dynamic prediction of HIV-related incomplete immune reconstitution: A multicenter, large cohort study using advanced joint modeling.

Abstract

Incomplete immune reconstitution (IIR) is a serious complication affecting 10 to 40% of people living with HIV (PLWH) despite effective antiretroviral therapy, leading to increased morbidity and mortality. Current risk prediction models rely on single-time point measurements and lack dynamic assessment capabilities. We developed a dynamic joint prediction system for IIR risk (DJPSIIR) using Bayesian joint modeling to analyze longitudinal data from 21,862 PLWH across 31 Chinese provinces (2003-2024). The system integrates continuous CD4⁺ T cell counts and CD4/CD8 ratios with clinical parameters to generate real-time risk predictions. DJPSIIR demonstrated strong discriminatory performance with area under receiver operating characteristic curves of 0.890 to 0.912 for 5- to 7-year predictions, consistently outperforming expert assessments and 19 machine learning algorithms across multiple validation cohorts. Our dynamic prediction system enables precise identification of high-risk individuals and could transform clinical decision-making by facilitating timely interventions to prevent IIR progression in HIV care.