Graft Survival

We followed the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) statement (supplementary methods) for reporting the development and validation of the multivariable prediction model.21 (link) We describe continuous variables by using means and standard deviations or medians and interquartile ranges. We compared means and proportions between groups by using Student’s t test, analysis of variance (Mann-Whitney test for mean fluorescence intensity), or the χ² test (or Fisher’s exact test if appropriate). We used the Kaplan-Meier method to estimate graft survival. The duration of follow-up was from the patient’s risk evaluation (starting point) to the date of kidney allograft loss or the end of the follow-up (31 March 2018). For patients who died with a functioning allograft, allograft survival was censored at the time of death as a surviving or functional allograft.22 (link)
In the derivation cohort, we used univariable Cox regression analyses to assess the associations between allograft failure and clinical, histological, functional, and immunological factors measured at the patient’s risk evaluation (see above). We used the log graphic method to test hazard proportional assumptions. The factors identified in these analyses were thereafter included in a final multivariable model.
We confirmed the internal validity of the final model by using a bootstrap procedure, which involved generating 1000 datasets derived from resampling the original dataset and permitting the calculation of optimism corrected performance estimates.23 (link) We tested the centre effect in stratified analyses. We investigated potential non-linear relations between continuous predictors and graft loss by using fractional polynomial methods (see supplementary methods).
We assessed the accuracy of the prediction model on the basis of its discrimination ability and calibration performance. We evaluated the discrimination ability (the ability to separate patients with different prognoses) of the final model by using Harrell’s concordance index (C index) (see supplementary methods).24 (link) We assessed calibration (the ability to provide unbiased survival predictions in groups of similar patients) on the basis of a visual examination of the calibration plots by using the rms package in R. We used the SurvIDINRI package in R to calculate net reclassification improvement for censored survival data.25 (link)
26 (link) We then evaluated the external validity of the final model in the external validation cohorts, including discrimination tests and model calibration as mentioned above.
We calculated a risk prediction score (integrative box risk prediction score—iBox) for each patient according to the β regression coefficients estimated from the final multivariable Cox model. Allograft survival probabilities are given at three, five, and seven years after iBox risk evaluation. The seven year post-transplant iBox risk assessment was guided by the median follow-up after iBox risk assessment of 7.65 (interquartile range 5.39-8.21) years.
We used R version 3.2.1 foe all analyses and considered P values below 0.05 to be significant; all tests were two tailed. Details of the interpretation of important statistical concepts are given in the supplementary methods.

The study outcomes were patient survival (PS) and death-censored graft survival (DCGS) following KT. DCGS was defined as time to re-transplantation or dialysis reinstatement, whichever came first. Recipient HCV status is reported but not necessarily confirmed or assessed at transplant. HCV+ status was defined as HCV Ab+ or HCV nucleic acid test (NAT) positive, while HCV- was defined as HCV Ab- without HCV NAT+.