Donor kidney volume measured by computed tomography is a strong predictor of recipient eGFR in living donor kidney transplantation
- 14 Downloads
The effect of living donor kidney allograft size on recipient outcomes is not well understood. In this study, we sought to investigate the relationship between preoperatively measured donor kidney volume and recipient estimated glomerular filtration rate (eGFR) in living donor kidney transplantation (LDKT).
We studied computed tomography (CT) donor kidney volumes and recipient outcomes for 438 LDKTs at the Toronto General Hospital between 2007 and 2016. Estimated glomerular filtration rate (eGFR) was calculated at 1, 3, and 6 months and a multivariable linear regression model was fitted to study the effect of donor kidney volume on recipient eGFR.
The mean volume and weight of the donated kidneys were 157.3 (± 32.3) cc and 186.7 (± 48.7) g, respectively. Kidney volume was significantly associated with eGFR on multivariable analysis (P < 0.001). Specifically, for every 10 cc increase in kidney volume, there was a 1.68 mL/min, 1.25 mL/min and 0.97 mL/min rise in recipient eGFR at 1, 3, and 6 months, respectively.
Donor kidney volume is a strong independent predictor of recipient eGFR in LDKT, and therefore, may be a valuable addition to predictive models of eGFR after transplant. Further research may determine if the inclusion of donor kidney volume in matching algorithms can improve recipient outcomes.
KeywordsLiving donor kidney Kidney volume Computed tomography eGFR prediction Outcomes
Body mass index
Chronic kidney disease epidemiology collaboration formula
Estimated glomerular filtration rate
Living donor kidney transplantation
Mean squared error
Pannel reactive antibody
The authors thank the students of the multi-organ transplant student research training program for their dedication and diligence in collecting, entering, and auditing data for CoReTRIS at the Toronto General Hospital, University Health Network.
Al-Adra: Project development, Data collection and Management, Data analysis, Manuscript writing and editing. Lambadaris: Project development, Data collection and Management, Data analysis, Manuscript writing and editing. Barbas: Data collection and Management. Li: Data analysis. Selzner: Manuscript writing and editing. Singh: Manuscript writing and editing. Famure: Project development, Data analysis. Kim: Project development, Data analysis, Manuscript writing and editing. Ghanekar: Project development, Data analysis, Manuscript writing and editing.
Compliance with ethical standards
Conflict of interest
The authors of this manuscript have no conflicts of interest to disclose. Research was approved by University Health Network Institutional Review Board.