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Transplantation. 2017 Jun;101(6):1353-1364. doi: 10.1097/TP.0000000000001326.

Predictive Score for Posttransplantation Outcomes.

Author information

1
1 Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN. 2 Department of Medicine, University of California, Irvine School of Medicine, Orange, CA. 3 Institute for Clinical and Translational Science, University of California, Irvine, Irvine, CA. 4 Division of Nephrology, Irvine School of Medicine, University of California, Orange, CA. 5 DaVita, Inc., Denver, CO. 6 Nephrology Section, Memphis Veterans Affairs Medical Center, Memphis, TN. 7 Kidney Research Institute and Harborview Medical Center, Division of Nephrology, University of Washington, Seattle, WA.

Abstract

BACKGROUND:

Most current scoring tools to predict allograft and patient survival upon kidney transplantion are based on variables collected posttransplantation. We developed a novel score to predict posttransplant outcomes using pretransplant information including routine laboratory data available before or at the time of transplantation.

METHODS:

Linking the 5-year patient data of a large dialysis organization to the Scientific Registry of Transplant Recipients, we identified 15 125 hemodialysis patients who underwent first deceased transplantion. Prediction models were developed using Cox models for (a) mortality, (b) allograft loss (death censored), and (c) combined death or transplant failure. The cohort was randomly divided into a two thirds set (Nd = 10 083) for model development and a one third set (Nv = 5042) for validation. Model predictive discrimination was assessed using the index of concordance, or C statistic, which accounts for censoring in time-to-event models (a-c). We used the bootstrap method to assess model overfitting and calibration using the development dataset.

RESULTS:

Patients were 50 ± 13 years of age and included 39% women, 15% African Americans, and 36% persons with diabetes. For prediction of posttransplant mortality and graft loss, 10 predictors were used (recipients' age, cause and length of end-stage renal disease, hemoglobin, albumin, selected comorbidities, race and type of insurance as well as donor age, diabetes status, extended criterion donor kidney, and number of HLA mismatches). The new model (www.TransplantScore.com) showed the overall best discrimination (C-statistics, 0.70; 95% confidence interval [95% CI], 0.67-0.73 for mortality; 0.63; 95% CI, 0.60-0.66 for graft failure; 0.63; 95% CI, 0.61-0.66 for combined outcome).

CONCLUSIONS:

The new prediction tool, using data available before the time of transplantation, predicts relevant clinical outcomes and may perform better to predict patients' graft survival than currently used tools.

PMID:
27391198
PMCID:
PMC5219861
DOI:
10.1097/TP.0000000000001326
[Indexed for MEDLINE]
Free PMC Article

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