Authors
Fang Liu, Hanxi Zhang, Xi Wang, Jinsong Huang, Jinchuan Shi, Zongxing Yang, Jianfeng Bao, Hongxin Zhao, Chunwen Pu, Shanshan Wang, Aifang Xu, Miaochan Wang, Dingyan Yan, Yunjiang Li, Rui Sun, Huiming Sheng, Jianhua Yu, Fujie Zhang, Lijun Sun
Published in
Science advances. Volume 12. Issue 32. Pages eaeb2781. Aug 07, 2026. Epub Aug 05, 2026.
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.
PMID:
42555735
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 1
- Comments 0