Fig 4 - available via license: Creative Commons Attribution 4.0 International
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Distribution of age before and after 2009 per center. Differences between mean age were tested using the independent sample T-test, P-values are shown in the Fig. https://doi.org/10.1371/journal.pone.0270827.g004
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Background
Most transplant centers in the Netherlands use estimated glomerular filtration rate (eGFR) for evaluation of potential living kidney donors. Whereas eGFR often underestimates GFR, especially in healthy donors, measured GFR (mGFR) allows more precise kidney function assessment, and therefore holds potential to increase the living donor po...
Context in source publication
Context 1
... 2 , eGFR-cohort2: 93±12 mL/min/1.73m 2 ). When looking at age before and after 2009 (Fig 4), our data show that both eGFR-cohort1 and eGFR-cohort2 accepted older donors after 2009 compared to before 2009, although only significant in eGFR-cohort2 (mean±SD age eGFR-cohort1: 52±12 years before and 53±13 years after 2009 (P = 0.16), eGFR-cohort2: 51 ±10 before and 55±9 after 2009 (P = 0.01)), whereas in mGFR-cohort there does not seem to be a difference in age over time ( ...
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Unlabelled:
New estimated glomerular filtration rate (GFR) equations that do not include a race coefficient have been created to better estimate kidney function, reduce inequities in kidney disease care, and improve the historically limited access to transplantation in African Americans. The impact of these new equations on estimated GFR (eGFR) in...
Citations
Introduction
New estimated glomerular filtration rate (eGFR) equations using serum creatinine and/or cystatin C have been derived to eliminate adjustment by perceived Black ancestry. We sought to analyze the performance of newer eGFR equations among Black living kidney donor candidates.
Methods
Black candidates ( n = 64) who had measured iothalamate GFR between January 2015 and October 2021 were included, and eGFR was calculated using race adjusted (eGFRcr2009 and eGFRcr‐cys2012) and race unadjusted (eGFRcys2012, eGFRcr2021, and eGFRcr‐cys2021) CKD‐EPI equations. Bias and accuracy were calculated.
Results
The eGFRcr2021 equation had a negative bias of 9 mL/min/1.73 m ² , while other equations showed a modest positive bias. Accuracy within 10% and 30% was greatest using the eGFRcr‐cys2021 equation. With the eGFRcr2021 equation, 9.4% of donors with an mGFR > 80 mL/min/1.73 m ² were misclassified as having an eGFR < 80 mL/min/1.73 m ² . eGFR was also compared among 18 kidney donors at 6–24 months post‐donation. Post‐donation, the percentage of donors with an eGFR < 60 mL/min/1.73 m ² was 44% using the eGFRcr2021 equation compared to 11% using the eGFRcr‐cys2021 equation.
Conclusion
The CKD‐EPICr2021 equation appears to underestimate true GFR in Black living donor candidates. Alternatively, compared to CKD‐EPICr2021, the CKD‐EPICr‐CysC2021 equation appears to perform with less bias and improved accuracy.
Introduction
Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors’ high life expectancy and elderly donors’ comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning.
Methods
The study included 823 living kidney donors who underwent nephrectomy in 2009–2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m ² and ≥ 65% of the pre-donation values, respectively.
Results
The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762–0.930) and 0.626 (0.541–0.712), while the areas under the precision-recall curve were 0.965 (0.944–0.978) and 0.709 (0.647–0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed. ¹
Conclusion
The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.
