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

Research output: Contribution to journalArticle

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) 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 (recipientsʼ 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 patientsʼ graft survival than currently used tools.

Original languageEnglish (US)
JournalTransplantation
DOIs
StateAccepted/In press - Jul 7 2016

Fingerprint

Confidence Intervals
Transplants
Allografts
Mortality
Transplantation
Tissue Donors
Kidney
Graft Survival
Insurance
Proportional Hazards Models
African Americans
Calibration
Chronic Kidney Failure
Registries
Renal Dialysis
Comorbidity
Dialysis
Albumins
Hemoglobins
Organizations

ASJC Scopus subject areas

  • Medicine(all)
  • Transplantation

Cite this

Molnar, M. Z., Nguyen, D. V., Chen, Y., Ravel, V., Streja, E., Krishnan, M., ... Kalantar-Zadeh, K. (Accepted/In press). Predictive Score for Posttransplantation Outcomes. Transplantation. https://doi.org/10.1097/TP.0000000000001326

Predictive Score for Posttransplantation Outcomes. / Molnar, Miklos Z.; Nguyen, Danh V.; Chen, Yanjun; Ravel, Vanessa; Streja, Elani; Krishnan, Mahesh; Kovesdy, Csaba P.; Mehrotra, Rajnish; Kalantar-Zadeh, Kamyar.

In: Transplantation, 07.07.2016.

Research output: Contribution to journalArticle

Molnar, MZ, Nguyen, DV, Chen, Y, Ravel, V, Streja, E, Krishnan, M, Kovesdy, CP, Mehrotra, R & Kalantar-Zadeh, K 2016, 'Predictive Score for Posttransplantation Outcomes', Transplantation. https://doi.org/10.1097/TP.0000000000001326
Molnar MZ, Nguyen DV, Chen Y, Ravel V, Streja E, Krishnan M et al. Predictive Score for Posttransplantation Outcomes. Transplantation. 2016 Jul 7. https://doi.org/10.1097/TP.0000000000001326
Molnar, Miklos Z. ; Nguyen, Danh V. ; Chen, Yanjun ; Ravel, Vanessa ; Streja, Elani ; Krishnan, Mahesh ; Kovesdy, Csaba P. ; Mehrotra, Rajnish ; Kalantar-Zadeh, Kamyar. / Predictive Score for Posttransplantation Outcomes. In: Transplantation. 2016.
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AU - Ravel, Vanessa

AU - Streja, Elani

AU - Krishnan, Mahesh

AU - Kovesdy, Csaba P.

AU - Mehrotra, Rajnish

AU - Kalantar-Zadeh, Kamyar

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