Dynamic prediction of JC polyomavirus reactivation after kidney transplantation
10.12464/j.issn.1674-7445.2026064
- VernacularTitle:肾移植后JC多瘤病毒再激活的动态预测
- Author:
Mengyao LI
1
;
Chengfeng ZHANG
2
;
Difei REN
1
;
Xin LIANG
1
;
Shuyu CHEN
2
;
Yushi PENG
1
;
Yanjie WANG
2
;
Meng ZHANG
1
;
Jian XU
1
;
Zheng CHEN
3
;
Yun MIAO
1
;
Yibin WANG
1
Author Information
1. Department of Organ Transplantation, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
2. null.
3. ;.
- Publication Type:OriginalArticle
- Keywords:
JC polyomavirus;
Reactivation;
Kidney transplantation;
Dynamic model;
Restricted mean survival time regression
- From:
Organ Transplantation
2026;17(4):635-643
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.
