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

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Názov: First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation
Autori: Leandra Lukomski, Juan Pisula, Tristan Wagner, Andrii Sabov, Nils Große Hokamp, Katarzyna Bozek, Felix Popp, Martin Kann, Christine Kurschat, Jan Ulrich Becker, Christiane Bruns, Michael Thomas, Dirk Stippel
Zdroj: J Nephrol
Informácie o vydavateľovi: Springer Science and Business Media LLC, 2024.
Rok vydania: 2024
Predmety: Male, Adult, 0301 basic medicine, 0303 health sciences, Time Factors, Middle Aged, Kidney, Kidney Transplantation, Nephrectomy, Risk Assessment, Machine Learning, 03 medical and health sciences, Predictive Value of Tests, Risk Factors, Living Donors, Disease Progression, Humans, Kidney Failure, Chronic, Original Article, Female, Female [MeSH], Disease Progression [MeSH], Risk Assessment [MeSH], Adult [MeSH], Humans [MeSH], Kidney Failure, Chronic/physiopathology [MeSH], Predictive Value of Tests [MeSH], Machine learning in transplantation, Middle Aged [MeSH], Risk Factors [MeSH], Time Factors [MeSH], Glomerular Filtration Rate [MeSH], Kidney/physiopathology [MeSH], Male [MeSH], Living kidney donors, Kidney Transplantation [MeSH], Living Donors [MeSH], Machine Learning [MeSH], Nephrectomy [MeSH], eGFR slope, Living kidney transplantation, Glomerular Filtration Rate
Popis: 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. Graphical abstract
Druh dokumentu: Article
Other literature type
Jazyk: English
ISSN: 1724-6059
DOI: 10.1007/s40620-024-01967-y
Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38837004
https://repository.publisso.de/resource/frl:6518013
Rights: CC BY
Prístupové číslo: edsair.doi.dedup.....c07e1a737dc29f437cd29e8886085c7a
Databáza: OpenAIRE
Popis
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. Graphical abstract
ISSN:17246059
DOI:10.1007/s40620-024-01967-y