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Predicting the outcome of renal transplantation

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50407

Lasserre,  Julia
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

Arnold,  Steffen
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;
Department of Nephrology and Intensive Care Medicine, Charité Universitätsmedizin Berlin;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50613

Vingron,  Martin
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Lasserre.pdf
(Publisher version), 521KB

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Citation

Lasserre, J., Arnold, S., Vingron, M., Reinke, P., & Hinrichs, C. (2012). Predicting the outcome of renal transplantation. Journal of the American Medical Informatics Association, 19(2), 255-262. doi:10.1136/amiajnl-2010-000004.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-791E-6
Abstract
ObjectiveRenal transplantation has dramatically improved the survival rate of hemodialysis patients. However, with a growing proportion of marginal organs and improved immunosuppression, it is necessary to verify that the established allocation system, mostly based on human leukocyte antigen matching, still meets today's needs. The authors turn to machine-learning techniques to predict, from donor-recipient data, the estimated glomerular filtration rate (eGFR) of the recipient 1 year after transplantation.DesignThe patient's eGFR was predicted using donor-recipient characteristics available at the time of transplantation. Donors' data were obtained from Eurotransplant's database, while recipients' details were retrieved from Charite Campus Virchow-Klinikum's database. A total of 707 renal transplantations from cadaveric donors were included.MeasurementsTwo separate datasets were created, taking features with <10% missing values for one and <50% missing values for the other. Four established regressors were run on both datasets, with and without feature selection.ResultsThe authors obtained a Pearson correlation coefficient between predicted and real eGFR (COR) of 0.48. The best model for the dataset was a Gaussian support vector machine with recursive feature elimination on the more inclusive dataset. All results are available at http://transplant.molgen.mpg.de/.LimitationsFor now, missing values in the data must be predicted and filled in. The performance is not as high as hoped, but the dataset seems to be the main cause.ConclusionsPredicting the outcome is possible with the dataset at hand (COR=0.48). Valuable features include age and creatinine levels of the donor, as well as sex and weight of the recipient.