Development of a novel linear model for predicting recipient's post-transplant serum creatinine level after living donor kidney transplantation: A multicenter cross-validation study.

ABSTRACT:

Background

This study was designed to develop and cross-validate a statistical model for predicting post-transplant serum creatinine of living donor kidney transplantation.

Materials and methods

Adult recipients of living donor kidney transplantation from August 2012 to October 2017 at Samsung Medical Center (SMC) and Seoul National University Hospital (SNUH) with normal post-transplant protocol biopsy were included for modelling. Demographic data including recipient and donor's sex, age, body measurements and comorbidities, pre-transplant donor serum creatinine, graft weight, post-transplant recipient serum creatinine and the result of protocol biopsy were collected. Multivariate linear regression analysis was performed for developing the model based on SMC cohort. Internal validation was performed using leave-one-out cross-validation with the same cohort. External validation using leave-one-out cross-validation was performed based on the cohort of SNUH.

Results

A total of 238 and 191 recipients were included from SMC and SNUH, respectively. The prediction model included recipient's sex (? = 0.228, P<0.001), height (? = 0.007, P<0.001), and weight (? = 0.006, P<0.001), donor's age (? = 0.004, P<0.001), height (? = -0.007, P<0.001), pre-transplant serum Cr (? = 0.377, P<0.001) and graft weight (? = -0.002, P<0.001). The model showed R2 of 0.708, root mean square error of prediction (RMSEP) of 0.161 and intraclass correlation coefficient (ICC) of 0.83. The internal validation showed predicted ICC of 0.82, RMSEP of 0.161, and accuracy was calculated 0.895. The external validation showed predicted ICC of 0.78, RMSEP of 0.170, and accuracy was calculated 0.876.

Conclusions

The linear prediction model based on body measurement and donor serum creatinine and graft weight showed a high accuracy in cross-validation.

SUBMITTER: Rhu J 

PROVIDER: S-EPMC6472729 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature