First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.

Autor: Lukomski L; Department of General, Visceral, Cancer and Transplant Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany. leandra.lukomski@uk-koeln.de., Pisula J; Data Science of Bioimages Lab, Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Robert-Koch-Straße 21, 50937, Cologne, Germany., Wagner T; Department of General, Visceral, Cancer and Transplant Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Sabov A; Institute for Diagnostics and Interventional Radiology, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Große Hokamp N; Institute for Diagnostics and Interventional Radiology, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Bozek K; Data Science of Bioimages Lab, Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, University of Cologne, Robert-Koch-Straße 21, 50937, Cologne, Germany., Popp F; Department of General, Visceral, Cancer and Transplant Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Kann M; Department II of Internal Medicine and Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Kurschat C; Department II of Internal Medicine and Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Becker JU; Institute of Pathology, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Bruns C; Department of General, Visceral, Cancer and Transplant Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Thomas M; Department of General, Visceral, Cancer and Transplant Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany., Stippel D; Department of General, Visceral, Cancer and Transplant Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
Jazyk: angličtina
Zdroj: Journal of nephrology [J Nephrol] 2024 Jul; Vol. 37 (6), pp. 1631-1642. Date of Electronic Publication: 2024 Jun 05.
DOI: 10.1007/s40620-024-01967-y
Abstrakt: Background: Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR) slope as the target variable.
Methods: We included 238 living kidney donors who underwent donor nephrectomy. We divided the dataset based on the eGFR slope in the third follow-up year, resulting in 185 donors with an average eGFR slope and 53 donors with an accelerated declining eGFR-slope. We trained three Machine Learning-models (Random Forest [RF], Extreme Gradient Boosting [XG], Support Vector Machine [SVM]) and Logistic Regression (LR) for predictions. Predefined data subsets served for training to explore whether parameters of an ESKD risk score alone suffice or additional clinical and time-zero biopsy parameters enhance predictions. Machine learning-driven feature selection identified the best predictive parameters.
Results: None of the four models classified the eGFR slope with an AUC greater than 0.6 or an F1 score surpassing 0.41 despite training on different data subsets. Following machine learning-driven feature selection and subsequent retraining on these selected features, random forest and extreme gradient boosting outperformed other models, achieving an AUC of 0.66 and an F1 score of 0.44. After feature selection, two predictive donor attributes consistently appeared in all models: smoking-related features and glomerulitis of the Banff Lesion Score.
Conclusions: Training machine learning-models with distinct predefined data subsets yielded unsatisfactory results. However, the efficacy of random forest and extreme gradient boosting improved when trained exclusively with machine learning-driven selected features, suggesting that the quality, rather than the quantity, of features is crucial for machine learning-model performance. This study offers insights into the application of emerging machine learning-techniques for the screening of living kidney donors.
(© 2024. The Author(s).)
Databáze: MEDLINE
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