Abstract
With a rising demand for kidney transplantation, reliable pre-transplant assessment of organ quality becomes top priority. In clinical practice, physicians are regularly in doubt whether suboptimal kidney offers from older donors should be accepted. Here, we externally validate existing prediction models in a European population of older deceased donors, and subsequently developed and externally validated an adverse outcome prediction tool. Recipients of kidney grafts from deceased donors 50 years of age and older were included from the Netherlands Organ Transplant Registry (NOTR) and United States organ transplant registry from 2006-2018. The predicted adverse outcome was a composite of graft failure, death or chronic kidney disease stage 4 plus within one year after transplantation, modelled using logistic regression. Discrimination and calibration were assessed in internal, temporal and external validation. Seven existing models were validated with the same cohorts. The NOTR development cohort contained 2510 patients and 823 events. The temporal validation within NOTR had 837 patients and the external validation used 31987 patients in the United States organ transplant registry. Discrimination of our full adverse outcome model was moderate in external validation (C-statistic 0.63), though somewhat better than discrimination of the seven existing prediction models (average C-statistic 0.57). The model's calibration was highly accurate. Thus, since existing adverse outcome kidney graft survival models performed poorly in a population of older deceased donors, novel models were developed and externally validated, with maximum achievable performance in a population of older deceased kidney donors. These models could assist transplant clinicians in deciding whether to accept a kidney from an older donor.
Highlights
Kidney transplantation is the treatment of choice for patients with end-stage renal disease, in terms of survival and quality of life.[1,2] With rising demand for kidney transplantation and the kidney donor pool lagging behind, the acceptance criteria for donor kidneys continue to expand.[3,4] Grafts recovered from suboptimal donors, who are on average older with more comorbidities, come with higher rates of early graft dysfunction and recipient mortality.[5,6] The decision whether to accept or decline a kidney offer is largely subjective and depends on donor, organ preservation, and recipient-related characteristics
Our aim was to improve on these existing prediction models by developing and externally validating new prediction models of adverse outcome (AO) within 1 year after kidney transplantation from older deceased donors
Most existing models had a high risk of bias when assessed with the Prediction model Risk Of Bias ASsessment Tool (PROBAST; Supplementary Table S1).[23]
Summary
Kidney transplantation is the treatment of choice for patients with end-stage renal disease, in terms of survival and quality of life.[1,2] With rising demand for kidney transplantation and the kidney donor pool lagging behind, the acceptance criteria for donor kidneys continue to expand.[3,4] Grafts recovered from suboptimal donors, who are on average older with more comorbidities, come with higher rates of early graft dysfunction and recipient mortality.[5,6] The decision whether to accept or decline a kidney offer is largely subjective and depends on donor-, organ preservation–, and recipient-related characteristics. Various regression-based mathematical models have been developed that aim to predict outcomes after kidney transplantation.[10] As reliably predicting the risk of posttransplant graft failure prior to transplantation has proved to be challenging, several models have included predictors measured during transplant surgery or shortly after transplantation, such as the iBox risk score.[11] these models might be useful for monitoring patients, they cannot be used to guide physicians to accept or decline a kidney offer. We have externally validated existing prediction models that can be used prior to transplantation and predict graft survival, in a European and Northern American population of kidney transplant recipients who received organs from deceased donors aged $50 years. Our aim was to improve on these existing prediction models by developing and externally validating new prediction models of adverse outcome (AO) within 1 year after kidney transplantation from older deceased donors
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
