NDLI: Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models

Content Provider	Springer Nature : BioMed Central
Author	Mulugeta, Getahun Zewotir, Temesgen Tegegne, Awoke Seyoum Muleta, Mahteme Bekele Juhar, Leja Hamza
Abstract	Introduction Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop prognostic models for predicting renal graft survival and compare the performance of statistical and machine learning models. Methodology The study utilized data from 278 renal transplant recipients at the Ethiopian National Kidney Transplantation Center between September 2015 and February 2022. To address the class imbalance of the data, SMOTE resampling was applied. Various models were evaluated, including Standard and penalized Cox models, Random Survival Forest, and Stochastic Gradient Boosting. Prognostic predictors were selected based on statistical significance and variable importance. Results The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The 3-month, 1-year, and 3-year graft survival rates were found to be 0.979, 0.953, and 0.911, respectively. The Stochastic Gradient Boosting (SGB) model demonstrated the best discrimination and calibration performance, with a C-index of 0.943 and a Brier score of 0.000351. The Ridge-based Cox model closely followed the SGB model’s prediction performance with better interpretability. The key prognostic predictors of graft survival included an episode of acute and chronic rejections, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, post-transplant regular exercise, and marital status. Conclusions The Stochastic Gradient Boosting model demonstrated the highest predictive performance, while the Ridge-Cox model offered better interpretability with a comparable performance. Clinicians should consider the trade-off between prediction accuracy and interpretability when selecting a model. Incorporating these findings into the clinical practice can improve risk stratification and personalized management strategies for kidney transplant recipients.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-025-02906-y.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-025-02906-y
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2025-02-03
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Renal transplant Graft survival SMOT oversampling Prognostic models Statistical models Machine learning models
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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