Machine Learning for 1-Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry.

IF 1.9 4区 医学 Q2 SURGERY
Dasom Noh, Sunyoung Kwon, Woo Hyun Cho, Jin Gu Lee, Song Yee Kim, Samina Park, Kyeongman Jeon, Hye Ju Yeo
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引用次数: 0

Abstract

Background: In regions with limited donor availability, optimizing efficiency in lung transplant decision-making is crucial. Preoperative prediction of 1-year graft failure can enhance candidate selection and clinical decision-making.

Methods: We utilized data from the Korean Organ Transplantation Registry to develop and validate a deep learning-based model for predicting 1-year graft failure after lung transplantation. A total of 240 cases were analyzed using 5-fold cross-validation. Among 25 preoperative factors associated with 1-year graft failure, we selected the top 9 variables with coefficients ≥ 0.25 for model development.

Results: Of the 240 lung transplant recipients, 55 (22.92%) developed graft failure within 1 year, while 185 survived. The final predictive model incorporated nine key pretransplant factors: age, bronchiolitis obliterans syndrome after hematopoietic cell transplantation, pretransplant bacteremia, bronchiectasis, creatinine, diabetes, positive human leukocyte antigen crossmatch, panel reactive antibody 1 peak mean fluorescence intensity, and pretransplant steroid use. The multilayer perceptron model demonstrated strong predictive performance, achieving an area under the curve of 0.780 and an accuracy of 0.733.

Conclusions: Our machine learning-based model effectively predicts 1-year graft failure in lung transplant recipients using a minimal set of pretransplant variables. Further validation is needed to confirm its clinical applicability.

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机器学习用于肺移植受者1年移植失败预测:韩国器官移植注册。
背景:在供体有限的地区,优化肺移植决策的效率至关重要。术前预测1年移植失败可提高患者的选择和临床决策。方法:我们利用韩国器官移植登记处的数据开发并验证了一个基于深度学习的模型,用于预测肺移植后1年的移植物衰竭。采用5倍交叉验证法对240例病例进行分析。在术前与1年移植物衰竭相关的25个因素中,我们选择系数≥0.25的前9个变量进行模型开发。结果:240例肺移植患者中,55例(22.92%)在1年内发生移植物衰竭,185例存活。最终的预测模型纳入了移植前9个关键因素:年龄、造血细胞移植后闭塞性细支气管炎综合征、移植前菌血症、支气管扩张、肌酐、糖尿病、人白细胞抗原交叉配型阳性、面板反应性抗体1峰值平均荧光强度和移植前类固醇使用情况。多层感知器模型表现出较强的预测性能,曲线下面积为0.780,精度为0.733。结论:我们基于机器学习的模型使用最小的移植前变量集有效地预测肺移植受者1年的移植失败。需要进一步验证以确认其临床适用性。
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来源期刊
Clinical Transplantation
Clinical Transplantation 医学-外科
CiteScore
3.70
自引率
4.80%
发文量
286
审稿时长
2 months
期刊介绍: Clinical Transplantation: The Journal of Clinical and Translational Research aims to serve as a channel of rapid communication for all those involved in the care of patients who require, or have had, organ or tissue transplants, including: kidney, intestine, liver, pancreas, islets, heart, heart valves, lung, bone marrow, cornea, skin, bone, and cartilage, viable or stored. Published monthly, Clinical Transplantation’s scope is focused on the complete spectrum of present transplant therapies, as well as also those that are experimental or may become possible in future. Topics include: Immunology and immunosuppression; Patient preparation; Social, ethical, and psychological issues; Complications, short- and long-term results; Artificial organs; Donation and preservation of organ and tissue; Translational studies; Advances in tissue typing; Updates on transplant pathology;. Clinical and translational studies are particularly welcome, as well as focused reviews. Full-length papers and short communications are invited. Clinical reviews are encouraged, as well as seminal papers in basic science which might lead to immediate clinical application. Prominence is regularly given to the results of cooperative surveys conducted by the organ and tissue transplant registries. Clinical Transplantation: The Journal of Clinical and Translational Research is essential reading for clinicians and researchers in the diverse field of transplantation: surgeons; clinical immunologists; cryobiologists; hematologists; gastroenterologists; hepatologists; pulmonologists; nephrologists; cardiologists; and endocrinologists. It will also be of interest to sociologists, psychologists, research workers, and to all health professionals whose combined efforts will improve the prognosis of transplant recipients.
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