Transplantation 2021 Feb 23. Epub 2021 Feb 23.
University of Minnesota, Department of Surgery, Transplantation Division, Minneapolis, MN; University of Minnesota, School of Public Health, Biostatistics Division, Minneapolis, MN; University of Alabama, Department of Medicine, Birmingham, AL; Hennepin Healthcare, Division of Nephrology, Minneapolis, MN; University of Manitoba, Department of Internal Medicine, Winnipeg, Manitoba. University of Iowa, Department of Internal Medicine, Iowa City, IA; Mayo Clinic, Division of Nephrology & Hypertension, Department of Internal Medicine, Rochester, MN; Mayo Clinic, Division of Anatomic Pathology, Department of Laboratory Medicine and Pathology, Rochester, MN; University of California, UCLA Immunogenetics Center, Los Angeles, CA Univeristy of Nebraska Medical Center and Nebraska-Western Iowa Veterans Affairs Medical Center, Omaha, NE.
Background: Delayed graft function (DGF) of a kidney transplant results in increased cost and complexity of management. For clinical care or a DGF trial, it would be ideal to accurately predict individual DGF risk and provide preemptive treatment. A calculator developed by Irish et al has been useful for predicting population, but not individual risk.
Methods: We analyzed the Irish calculator (IC) in the DeKAF prospective cohort (incidence of DGF= 20.4%) and investigated potential improvements.
Results: We found that the predictive performance of the calculator in those meeting Irish inclusion criteria was comparable to that reported by Irish et. al. For cohorts excluded by Irish: a) in pump-perfused kidneys the IC over-estimated DGF risk; b) in simultaneous pancreas kidney (SPK) transplants, the DGF risk was exceptionally low. For all 3 cohorts, there was considerable overlap in IC scores between those with and those without DGF. Using a modified definition of DGF - excluding those with a single dialysis in the first 24 hours posttransplant - we found that the calculator had similar performance as with the traditional DGF definition. Studying whether DGF prediction could be improved, we found that recipient cardiovascular disease was strongly associated with DGF even after accounting for IC predicted risk.
Conclusions: The IC can be a useful population guide for predicting DGF in the population for which it was intended, but has limited scope in expanded populations (SPK, pump) and for individual risk prediction. DGF risk prediction can be improved by inclusion of recipient cardiovascular disease.