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Research Paper|Volume 13, Issue 7|pp 9927—9947

Development and validation of routine clinical laboratory data derived marker-based nomograms for the prediction of 5-year graft survival in kidney transplant recipients

Yamei Li1, Lin Yan1, Yi Li1, Zhengli Wan1, Yangjuan Bai1, Xianding Wang2, Shumeng Hu3, Xiaojuan Wu1, Cuili Yang1, Jiwen Fan1, Huan Xu1, Lanlan Wang1, Yunying Shi3
  • 1Department of Laboratory Medicine/Research Centre of Clinical Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
  • 2Department of Urology/Organ Transplant Center, West China Hospital, Sichuan University, Chengdu, China
  • 3Department of Nephrology, West China Hospital, Sichuan University, Chengdu, China
* Equal contribution
Received: August 27, 2020Accepted: February 16, 2021Published: March 26, 2021
https://doi.org/10.18632/aging.202748

Copyright: © 2021 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: To develop and validate predictive nomograms for 5-year graft survival in kidney transplant recipients (KTRs) with easily-available laboratory data derived markers and clinical variables within the first year post-transplant.

Methods: The clinical and routine laboratory data from within the first year post-transplant of 1289 KTRs was collected to generate candidate predictors. Univariate and multivariate Cox analyses and LASSO were conducted to select final predictors. X-tile analysis was applied to identify optimal cutoff values to transform potential continuous factors into category variables and stratify patients. C-index, calibration curve, dynamic time-dependent AUC, decision curve analysis, and Kaplan-Meier curves were used to evaluate models’ predictive accuracy and clinical utility.

Results: Two predictive nomograms were constructed by using 0–6- and 0–12- month laboratory data, and showed good predictive performance with C-indexes of 0.78 and 0.85, respectively, in the training cohort. Calibration curves showed that the prediction probabilities of 5-year graft survival were in concordance with actual observations. Additionally, KTRs could be successfully stratified into three risk groups by nomograms.

Conclusions: These predictive nomograms combining demographic and 0–6- or 0–12- month markers derived from post-transplant laboratory data could serve as useful tools for early identification of 5-year graft survival probability in individual KTRs.