Prediction of measured GFR after living kidney donation from pre-donation parameters.

Autor:	van Londen M; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands., van der Weijden J; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands., Niznik RS; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA., Mullan AF; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA., Bakker SJL; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands., Berger SP; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands., Nolte IM; Department of Epidemiology, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands., Sanders JF; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands., Navis G; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands., Rule AD; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA., de Borst MH; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands.
Jazyk:	angličtina
Zdroj:	Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association [Nephrol Dial Transplant] 2023 Jan 23; Vol. 38 (1), pp. 212-221.
DOI:	10.1093/ndt/gfac202
Abstrakt:	Background: One of the challenges in living kidney donor screening is to estimate remaining kidney function after donation. Here we developed a new model to predict post-donation measured glomerular filtration rate (mGFR) from pre-donation serum creatinine, age and sex. Methods: In the prospective development cohort (TransplantLines, n = 511), several prediction models were constructed and tested for accuracy, precision and predictive capacity for short- and long-term post-donation 125I-iothalamate mGFR. The model with optimal performance was further tested in specific high-risk subgroups (pre-donation eGFR <90 mL/min/1.73 m2, a declining 5-year post-donation mGFR slope or age >65 years) and validated in internal (n = 509) and external (Mayo Clinic, n = 1087) cohorts. Results: In the development cohort, pre-donation estimated GFR (eGFR) was 86 ± 14 mL/min/1.73 m2 and post-donation mGFR was 64 ± 11 mL/min/1.73 m2. Donors with a pre-donation eGFR ≥90 mL/min/1.73 m2 (present in 43%) had a mean post-donation mGFR of 69 ± 10 mL/min/1.73 m2 and 5% of these donors reached an mGFR <55 mL/min/1.73 m2. A model using pre-donation serum creatinine, age and sex performed optimally, predicting mGFR with good accuracy (mean bias 2.56 mL/min/1.73 m2, R2 = 0.29, root mean square error = 11.61) and precision [bias interquartile range (IQR) 14 mL/min/1.73 m2] in the external validation cohort. This model also performed well in donors with pre-donation eGFR <90 mL/min/1.73 m2 [bias 0.35 mL/min/1.73 m2 (IQR 10)], in donors with a negative post-donation mGFR slope [bias 4.75 mL/min/1.73 m2 (IQR 13)] and in donors >65 years of age [bias 0.003 mL/min/1.73 m2 (IQR 9)]. Conclusions: We developed a novel post-donation mGFR prediction model based on pre-donation serum creatinine, age and sex. (© The Author(s) 2022. Published by Oxford University Press on behalf of the ERA.)
Databáze:	MEDLINE
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