한빛사논문
- 조회251
Kim, Jin-Myung MDa; Jung, HyoJe BSb; Kwon, Hye Eun MDa; Ko, Youngmin MDa; Jung, Joo Hee MSNa; Kwon, Hyunwook MD, PhDa; Kim, Young Hoon MDa; Jun, Tae Joon PhDc,*; Hwang, Sang-Hyun MD, PhDd,*; Shin, Sung MD, PhDa,*
aDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
bDepartment of Information Medicine, Asan Medical Center, Seoul, Republic of Korea
cBig Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
dDepartment of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
Jin-Myung Kim, HyoJe Jung, These authors contributed equally to this study as co-first authors.
*Corresponding authors. Address: S. Shin, T. J. Jun, S. H Hwang
Abstract
Background: Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. Our study employs advanced machine learning (ML) algorithms to identify crucial prognostic indicators for kidney transplantation. By analyzing complex datasets with ML models, we aim to enhance prediction accuracy and provide valuable insights to support clinical decision-making.
Materials and methods: Analyzing data from 4077 KT patients (June 1990 - May 2015) at a single center, this research included 27 features encompassing recipient/donor traits and peri-transplant data. The dataset was divided into training (80%) and testing (20%) sets. Four ML models-eXtreme Gradient Boosting (XGBoost), Feedforward Neural Network, Logistic Regression, and Support Vector Machine-were trained on carefully selected features to predict the success of graft survival. Performance was assessed by precision, sensitivity, F1 score, Area Under the Receiver Operating Characteristic (AUROC), and Area Under the Precision-Recall Curve.
Results: XGBoost emerged as the best model, with an AUROC of 0.828, identifying key survival predictors like T-cell flow crossmatch positivity, creatinine levels two years post-transplant and human leukocyte antigen mismatch. The study also examined the prognostic importance of histological features identified by the Banff criteria for renal biopsy, emphasizing the significance of intimal arteritis, interstitial inflammation, and chronic glomerulopathy.
Conclusion: The study developed ML models that pinpoint clinical factors crucial for KT graft survival, aiding clinicians in making informed post-transplant care decisions. Incorporating these findings with the Banff classification could improve renal pathology diagnosis and treatment, offering a data-driven approach to prioritizing pathology scores.
논문정보
- Research article
- 2024년 08월 (BRIC 등록일 2024-08-09)
- 국내 연구진
- 바이오·의료융합 > 다학제 융합/복합
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