Predictive score for posttransplantation outcomes

Miklos Z. Molnar, Danh V. Nguyen, Yanjun Chen, Vanessa Ravel, Elani Streja, Mahesh Krishnan, Csaba P. Kovesdy, Rajnish Mehrotra, Kamyar Kalantar-Zadeh

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14 Citations (Scopus)

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) formodel 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 posttransplantmortality 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 newmodel (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.

Original languageEnglish
Pages (from-to)1353-1364
Number of pages12
JournalTransplantation
Volume101
Issue number6
DOIs
Publication statusPublished - Jan 1 2017

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ASJC Scopus subject areas

  • Transplantation

Cite this

Molnar, M. Z., Nguyen, D. V., Chen, Y., Ravel, V., Streja, E., Krishnan, M., Kovesdy, C. P., Mehrotra, R., & Kalantar-Zadeh, K. (2017). Predictive score for posttransplantation outcomes. Transplantation, 101(6), 1353-1364. https://doi.org/10.1097/TP.0000000000001326