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

• Published in: PloS one 2019-04, Vol.14 (4), p.e0214247-e0214247
• Main Authors: Rhu, Jinsoo, Kim, Sung Joo, Lee, Kyo Won, Park, Jae Berm, Kim, Kyunga, Yoo, Heejin, Mo, Hyejin, Choi, Chanjoong, Min, Sang-Il, Ha, Jongwon
• Format: Article
• Language: English
• Subjects: Antithymocyte globulin; Basiliximab; Biopsy; Body mass index; Body measurements; Comorbidity; Correlation coefficient; Correlation coefficients; Creatinine; Creatinine - blood; Demographics; Female; Gender; Grafting; Health aspects; Health care facilities; Humans; Kidney Transplantation; Kidney transplants; Kidneys; Linear Models; Linear prediction; Living Donors; Male; Mathematical models; Medical centers; Medical research; Medicine; Metabolism; Middle Aged; Multivariate Analysis; Organ transplant recipients; Organ transplantation; Patient outcomes; Prediction models; Regression analysis; Reproducibility of Results; Sex; Statistical analysis; Statistical models; Statistics; Surgery; Tissue donation; Tomography; Transplant Recipients; Transplantation; Transplants & implants; Weight
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0214247

This study was designed to develop and cross-validate a statistical model for predicting post-transplant serum creatinine of living donor kidney transplantation. 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. A total of 238 and 191 recipients were included from SMC and SNUH, respectively. The prediction model included recipient's sex (β = 0.228, P