Dataset Information

Predicting the outcome of renal transplantation.

ABSTRACT:

Objective

Renal 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.

Design

The 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 Charité Campus Virchow-Klinikum's database. A total of 707 renal transplantations from cadaveric donors were included.

Measurements

Two 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.

Results

The 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/.

Limitations

For 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.

Conclusions

Predicting 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.

SUBMITTER: Lasserre J

PROVIDER: S-EPMC3277611 | biostudies-literature | 2012 Mar-Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting the outcome of renal transplantation.

Lasserre Julia J Arnold Steffen S Vingron Martin M Reinke Petra P Hinrichs Carl C

Journal of the American Medical Informatics Association : JAMIA 20110828 2

<h4>Objective</h4>Renal 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 af ...[more]

PMID: 21875867

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Project description:Background:The gaps in organ supply and demand necessitate the use of expanded criteria donor (ECD) kidneys.Objective:To identify which pre-transplant and post-transplant predictors are most informative regarding short- and long-term ECD transplant outcomes.Design:Retrospective cohort study.Setting:Single center, Quebec, Canada.Patients:The patients were 163 consecutive first-time ECD kidney only transplant recipients who underwent transplantation at McGill University Health Centre (MUHC) between January 1, 2008 and December 31, 2014 and had frozen section wedge procurement biopsies.Measurements:Short-term graft outcomes, including delayed graft function and 1-year estimated glomerular filtration rate (eGFR), as well as long-term outcomes including all-cause graft loss (defined as return to dialysis, retransplantation, and death with function).Methods:Pre-transplant donor, recipient, and transplant characteristics were assessed as predictors of transplant outcomes. The added value of post-transplant predictors, including longitudinal eGFR, was also assessed using time-varying Cox proportional hazards models.Results:In univariate analyses, among the pre-transplant donor characteristics, histopathologic variables did not show evidence of association with delayed graft function, 1-year post-transplant eGFR or all cause graft loss. Recipient age was associated with all-cause graft loss (hazard ratio: 1.038 [95% confidence interval: 1.002-1.075] and the model produced only modest discrimination (C-index: 0.590; standard error [SE]: 0.045). Inclusion of time-dependent post-transplant eGFR improved the model’s prediction accuracy (C-index: 0.711; SE = 0.047). Pre-transplant ECD characteristics were not associated with long-term survival, whereas post-transplant characteristics allowed better model discrimination.Limitations:Single-center study, small sample size, and potential incomplete capture of all covariate data.Conclusions:Incorporation of dynamic prediction models into electronic health records may enable timely mitigation of ECD graft failure risk and/or facilitate planning for renal replacement therapies. Histopathologic findings on preimplantation biopsies have a limited role in predicting long-term ECD outcomes.Trial registration:Not applicable.

Project description:Background Immune tolerance and persistent mixed chimerism can be achieved reproducibly after combined organ and hematopoietic cell transplantation in mice conditioned with total lymphoid irradiation plus anti-thymocyte globulin. We studied the safety and reproducibility of this approach in a cohort of kidney transplant patients, and tried to identify immune monitoring procedures that can predict tolerance and guide complete immunosuppressive drug withdrawal. Methods Ten patients conditioned with 10 doses of total lymphoid irradiation and 5 doses of anti-thymocyte globulin were given kidney transplants and an injection of CD34+ hematopoietic progenitor cells and T cells from HLA matched donors. Blood cell monitoring included changes in chimerism, balance of T cell subsets, gene expression, and responses to donor alloantigens. Results Nine of 10 patients developed multi-lineage chimerism without graft versus host disease (GVHD), and all had excellent graft function at the last observation point. Five of these with chimerism persisting for at least 12 months were completely withdrawn from immunosuppressive drugs for 6 to 35 months. Blood cells from patients off drugs showed development of specific unresponsiveness to donor alloantigens, M-bM-^@M-^\toleranceM-bM-^@M-^] profiles on gene microarrays, early high ratios of regulatory versus conventional naM-CM-/ve T cells, and early high levels of chimerism among NK cells. Conclusions Total lymphoid irradiation, and anti-thymocyte globulin promoted the development of persistent chimerism and tolerance in a cohort of patients given kidney transplants and donor cell injections. Assays were identified that can assist in the safe withdrawal of immunosuppressive drugs. 45 Agilent Microarray samples were conducted, including 16 tolerance patients, 10 chronic rejection patients, 5 healthy normal controls, and 7 paired pre and post-transplant induced tolerance patients. The aim is to see whether induced tolerance patients show operational tolerance gene expression signature and can withdraw or minimize the immunosuppression regimens.

Dataset Information

Predicting the outcome of renal transplantation.

Objective

Design

Measurements

Results

Limitations

Conclusions

Publications

Predicting the outcome of renal transplantation.

Similar Datasets

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