Abstract
Background and Aims
With rising demand for kidney transplantation and the kidney donor pool lagging behind, the acceptance criteria for donor kidneys are expanding. Hence, reliable pre-transplant assessment of organ quality has become a top priority. Estimating the risk of adverse outcomes at the time of kidney allocation is challenging and particularly relevant for recipients of kidneys from older donors. The existing kidney donor risk index (KDRI) has been criticized for heavily depending on donor age. Therefore, the aim of the current study was to develop and validate a prediction model for adverse outcome after kidney transplantation from deceased donors aged 50 years or older and compare this model’s performance to the KDRI.
Method
We utilized the Dutch kidney transplant registry (NOTR) and identified patients who received a kidney from a deceased donor aged 50 years or older between 2006 and 2019. These recipients were included for model development and temporal validation. The prediction model was externally validated on the United States organ transplantation registry (OPTN), in which we selected patients that were transplanted between 2006 and 2017. Potential pre-transplant predictors were selected by an expert panel of nephrologists and surgeons. The predicted adverse outcome was defined as a composite of graft failure, recipient mortality or CKD stage 4/5 within 1 year of transplantation. A logistic regression model was developed, internally validated and shrunk for optimism through bootstrapping. Missing data were multiply imputed in 10-fold, non-linear continuous predictors were modelled with restricted cubic splines and clinically relevant interaction terms were included. The KDRI was validated on the same NOTR and OPTN cohorts for graft survival within 1 year. The developed model and the KDRI were recalibrated to the baseline risk of outcome in external validation. Model performance was assessed by discrimination and calibration.
Results
The model was developed on 2510 patients of whom 823 experienced an adverse outcome within the first year. The temporal validation cohort contained 837 patients of whom 230 had an adverse outcome and the US external validation cohort consisted of 31987 patients with 6758 adverse outcomes. Selected donor predictors were: age, gender, BMI, cause of death, CPR, inotropes use, serum creatinine, hypertension, hypotension, diabetes, smoking, left/right kidney, warm ischemic time, cold ischemic time and proteinuria. Recipient predictors were: age, gender, BMI, diabetes, cardiovascular comorbidity, primary kidney disease, dialysis duration, number of previous kidney transplantations, HLA mismatches and PRA. Discrimination of the adverse outcome model was moderate, yet considerably better than discrimination of the KDRI (see table). The adverse outcome model’s calibration and distribution of predicted risks were good in both the NOTR and OPTN (see figure).
Conclusion
A prediction model was developed and extensively validated for adverse outcome after kidney transplantation from older deceased donors. Despite the use of advanced and robust methodology, its discriminatory capacity was limited. However, the adverse outcome model showed good calibration and performed considerably better than the KDRI in this population of suboptimal donors. This model could potentially assist nephrologists in deciding whether to accept or decline a specific kidney from an older deceased donor.
Aggravation of fibrin deposition and microthrombus formation within the graft during kidney transplantation
