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

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 i…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-0664· cite & verify
Dynamic prediction of HIV-related incomplete immune reconstitution: A multicenter, large cohort study using advanced joint modeling
CCL3L1-CCR5 Genotype Improves the Assessment of AIDS Risk in HIV-1-Infected Individuals -Figure 2 by Hemant Kulkarni, Brian K. Agan, Vincent C. Marconi, Robert J. O'Connell, Jose F. Camargo, Weijing He, Judith Delmar, Kenneth R. Phelps, George Crawford, Robert A. Clark, Matthew J. Dolan, Sunil K. Ahuja. Public Domain · https://web.archive.org/web/20230926203737/https://creativecommons.org/licenses/publicdomain/

In brief

Researchers developed a dynamic joint prediction system for incomplete immune reconstitution risk in people living with HIV using Bayesian joint modeling of longitudinal CD4+ counts and CD4/CD8 ratios from 21,862 patients across 31 Chinese provinces between 2003 and 2024.

The system showed strong discriminatory performance for long-term risk and outperformed experts and other machine learning methods, suggesting potential to improve HIV care through earlier identification of high-risk patients, though real-world clinical impact and generalizability beyond the studied cohort remain to be demonstrated.

Main points

  1. Study analyzed longitudinal data from 21,862 people living with HIV across 31 Chinese provinces from 2003-2024.
  2. System named DJPSIIR integrates continuous CD4+ T cell counts and CD4/CD8 ratios with clinical parameters for real-time risk prediction.
  3. Model achieved AUCs of 0.890 to 0.912 for 5- to 7-year predictions and outperformed expert assessments and 19 machine learning algorithms across validation cohorts.

The gain

A Bayesian joint modeling system that integrates longitudinal CD4+ counts and CD4/CD8 ratios predicted 5- to 7-year risk of incomplete immune reconstitution in people living with HIV with high discrimination, enabling real-time identification of high-risk patients for timely intervention.

The rundown

Researchers built DJPSIIR using Bayesian joint modeling of longitudinal CD4+ counts and CD4/CD8 ratios plus clinical parameters from a multicenter cohort of 21,862 PLWH collected between 2003 and 2024 across 31 provinces.

Validation showed AUCs of 0.890 to 0.912 for 5- to 7-year IIR predictions, with performance consistently exceeding both expert assessments and 19 machine learning algorithms, addressing prior models that relied on single-time point measurements.

Sources

  1. Peer-reviewedScience Advances2026-08-05

The debate