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

Marco van Londen, Jessica van der Weijden, Robert S Niznik, Aidan F Mullan, Stephan J L Bakker, Stefan P Berger, Ilja M Nolte, Jan-Stephan F Sanders, Gerjan Navis, Andrew D Rule, Martin H de Borst*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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 GFR 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/1.73m2, 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 eGFR was 86 ± 14 mL/min/1.73m2, and post-donation mGFR 64 ± 11 mL/min/1.73m2. Donors with a pre-donation eGFR ≥ 90 mL/min/1.73m2 (present in 43%) had mean post-donation mGFR of 69 ± 10 mL/min/1.73m2, and 5% of these donors reached an mGFR < 55 mL/min/1.73m2. A model using pre-donation serum creatinine, age and sex performed optimally, predicting mGFR with good accuracy (mean bias 2.56 mL/min/1.73m2, R2 = 0.29, RMSE = 11.61) and precision (bias interquartile range (IQR) 14 mL/min/1.73m2) in the external validation cohort. This model also performed good in donors with pre-donation eGFR < 90 mL/min/1.73m2 (bias 0.35 mL/min/1.73m2 and IQR 10 mL/min/1.73m2), in donors with negative post-donation mGFR slope (bias 4.75 mL/min/1.73m2 and IQR 13 mL/min/1.73m2) and in donors > 65 years (bias 0.003 mL/min/1.73m2 and IQR 9 mL/min/1.73m2).

CONCLUSIONS: We developed a novel post-donation mGFR prediction model based on pre-donation serum creatinine, age, and sex.

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