Background
Most current scoring tools to predict allograft and patient survival upon kidney transplantion(Tx) are based on variables collected posttransplantation. We developed a novel score to predict posttransplant outcomes using pretransplant information including routine laboratory data available prior to or at the time of transplantation.
Methods
Linking the 5-year patient data of a large dialysis organization to the SRTR, we identified 15,125 hemodialysis patients who underwent first deceased Tx. 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=5,042) 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 old and included 39% women, 15% African-Americans and 36% diabetics. For prediction of post-transplant mortality and graft loss, 10 predictors were used (recipients’ age, cause and length of ESRD, hemoglobin, albumin, selected comorbidities, race and type of insurance as well as donor age, diabetes status, extended criteria donor kidney(ECD), and number of HLA mismatches). The new model (www.TransplantScore.com) showed the overall best discrimination(C-statistics:0.70(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 prior to the time of transplantation, predicts relevant clinical outcomes and may perform better to predict patients’ graft survival than currently used tools.