Living kidney donor evaluation and safety assessment
van Londen, Marco
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Living kidney donor evaluation
and safety assessment
ISBN: 978-94-632-3455-9
Illustratie: Anita van den Broek-Roijackers Layout by: Gildeprint
Living kidney donor evaluation
and safety assessment
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 16 januari 2019 om 14.30 uur
door
Marco van Londen
geboren op 17 Juli 1991 te Lienden
Prof. dr. S.P. Berger
Copromotor
Dr. M.H. de Borst
Beoordelingscommissie
Prof. dr. F.J. Bemelman Prof. dr. M.E.J. Reinders Prof. dr. R.J. Porte
Contents
Chapter 1 Introduction and Aims of Thesis. 9
Part one GFR in living kidney donation 21
Chapter 2 Estimated GFR Before Living Kidney Donation and Post-donation 23
Measured GFR.
In Preparation
Chapter 3 Estimated Glomerular Filtration rate for Longitudinal Follow-up of Living 41 Kidney Donors.
Nephrology Dialysis Transplantation 2018;33(6)
Chapter 4 Overweight Young Female Kidney Donors have low Renal Functional 65 Reserve Post-donation.
American Journal of Physiology Renal Physiology 2018;315(3)
Chapter 5 Renal Functional Reserve Capacity before and after Living Kidney 81
Donation.
American Journal of Physiology Renal Physiology - Accepted
Part two Living donation beyond the GFR 95
Chapter 6 Tubular Maximum Phosphate Reabsorption Capacity in Living Kidney 97 Donors is Independently Associated with One Year Recipient GFR.
American Journal of Physiology Renal Physiology 2017:0287
Chapter 7 Post-transplant Hypophosphatemia and the Risk of Death-censored 117 Graft Failure and Mortality after Kidney Transplantation.
Clinical Journal of the American Society of Nephrology 2017;12(8)
Chapter 8 Chronic Pain after Living Donor Nephrectomy 137
BMJ Open - Accepted
Chapter 10 Summary, discussion and future perspectives 181
Nederlandse samenvatting / Dutch summary 193
Dankwoord / Acknowledgements 199
About the author 204
CHAPTER 1
Introduction and
Aim of Theses
Living kidney donation
Living kidney donation is the best available treatment option for patient with end-stage kidney disease (ESRD), leading to better outcomes compared to dialysis or transplantation with a graft from a deceased donor1,2. Transplantation from a living kidney donor has become a
very important modality3 and in the Netherlands more than 50% of all kidney transplantations
are performed with a living donor (Doorenbosch et al, NTS Year report, 2016). Whereas the benefits of a successful kidney transplantation for the recipient are firmly established, living kidney donation could be considered in discordance with the medical ethical principle of non-maleficence, a principle that is well embedded in the Hippocratic Oath (‘Primum non
nocere”, Hippocratic Corpus – Of the Epidemics, Book I, section 11,5). However, from a
slightly different perspective, living kidney donation may provide benefit for both a donor and recipient4 and the principle of ‘Nil prodest quod non laedere possit idem’ (Ovid, Tristium
Liber Secundus, 2.265-6), meaning ‘nothing can benefit that cannot also harm’ may be more applicable5. Donor safety therefore is a major issue to be considered in any living donation
program, and bears consequences on both pre-donation screening and donor follow-up6,7.
In early studies, the absolute risk of end-stage renal disease and premature mortality after living kidney donation were generally lower than the general population in most studies6,8–10,
reflecting rigorous pre-donation screening and donor selection for above-average overall health status. However in recent years, the selection criteria for potential living donors have become more liberal, mainly driven by a persistent donor organ shortage6. The impact of the
changes in donor acceptance policy for donor outcome is still largely unclear. The increasing mean age of accepted donors, as well as small but significant absolute increases in adverse cardiovascular and renal outcomes compared with matched non-donors that were observed in recent reports, seem to challenge the old adagio that ‘Kidney donors live longer’8,11,12.
These developments underline the paramount importance of pre-donation risk assessment, in terms of both renal and cardiovascular risk factors as well as appropriate donor follow-up.
Changes in donor policy and potential implications
Over the past decades, the criteria for living kidney donation have gradually changed from very strict13, to more liberal 14,15. Compared with two decades ago, accepted donors are
now on average older, have a higher body mass index, and are and more likely to have hypertension6, all risk factors for cardiovascular and renal damage.
The (usefulness of an) upper limit for donor age is subject of debate: the current standard of practice is to accept donors >18 years of age, without a fixed upper limit13 Accordingly,
in recent years, the average age of accepted donors has increased in our center from 50 before 2010 to 54 after 2010 (University Medical Center Groningen; Figure 1). Older donors
Introduction and Aims of Thesis
1
are more likely to have co-morbidities such as hypertension or cardiovascular disease, and have a lower renal function than younger donors before as well as after donation11,16,17.
Shortly after donation, a compensatory increase in single kidney GFR occurs18. This GFR
increase is less prominent or even absent in older donors19. Older donors, particularly
donors over 60 years of age, have a higher risk of renal impairment (mGFR <60 ml/min) early after donation, independent of their pre-donation GFR (figure 2). On the other hand, while younger donors have a much lower 10-year ESRD risk, their life-time risk of ESRD is higher than that of older donors, due to their longer life expectancy 16 Thus, for an adequate
risk assessment life expectancy has to be taken into account as well, as illustrated in Figure 1.
Other risk factors for ESRD in living donors are obesity, high blood pressure and smoking. Obesity is linked to small increase in post-operative mortality risk20 and with ESRD in living
donors21. Smoking is a known risk factor for ESRD in many populations22 and has also
Figure 1. Donor Age per Donor Era and Life Expectancy of the general population in the Netherlands
This figure shows the distribution of donor age at nephrectomy in donors before and after 2010. Donors who donated after 2010 were significantly older and the proportion of donors > 65 years old has doubled after 2010, indicating that more high-risk donors are accepted (50±11 vs. 54±11, P<0.001). For reference, the life expectancy of the general population in the Netherlands of 2016 has been added (CBS Doodsoorzaken, 2016 zorggegevens.nl). On one hand, this graph shows that we accept donors with a higher mortality risk at the time of nephrectomy. These risks may interact with the risk a donor has from nephrectomy (donation-attributable risk) and should be assessed/predicted in individual donors before donation. It also may have consequences for their follow-up. On the other hand, while the risks are higher, the estimated life expectancy of older donor is lower. This means that risk prediction tools only need to model risks for 15-20 years in a 70 year old donor, while they need to model 40-45 years in a 40 year old donor.
been described as a risk factor in living donor candidates16. High blood pressure is a risk
factor for kidney damage in the general population and in renal patients, but in currently accepted hypertensive donors (that is: with blood pressure well-controlled with no more than two different classes of antihypertensive drugs, and a good renal function) short-term renal function risk not elevated as compared to normotensive donors23,24. More donors with
these risk factors are accepted for living kidney donation. To be able to ensure donor safety while accepting more donors for living donation, more insight in the effects of nephrectomy on the donor is needed.
Figure 2. Relation between donor Age and probability of having a GFR <60 mL/min after donation
In 992 donors, pre- and post-donation mGFR was measured. Donors were divided into quartiles based on their pre-donation mGFR. This figure shows the probability of having an mGFR 3 months post-donation below 60 mL/min per category of pre-post-donation mGFR and age.
Glomerular filtration rate and prediction in living kidney
donors
Clearly, reliable measurement of kidney function, usually expressed as the glomerular filtration rate (GFR, unit: mL/min, often standardized for body surface area: mL/min/1.73m2), is an
essential component of pre-donation evaluation. The gold-standard technique for measuring GFR (mGFR) is the calculation of the clearance of exogenous filtration markers25, such as
iohexol, inulin or iothalamate. These markers are completely filtrated by the glomerulus and (almost) not excreted, resorbed or metabolized by the tubular compartment, rendering them
Introduction and Aims of Thesis
1
highly suitable for precise GFR measurement. The GFR can also be estimated by using the serum concentration of creatinine, a breakdown product of creatine phosphate that is secreted by muscles at a reasonably constant rate26. Several formulas have been developed
to estimate GFR (eGFR) from serum creatinine in combination with other parameters including sex, age and race27. Limitations of these formulas are that the concentration of
creatinine is influenced by tubular secretion of creatinine28, body composition and muscle
mass26. Moreover, their performance is poor in populations without renal impairment, which
is particularly relevant for donor screening. Renal function can also be calculated as the renal clearance of creatinine using 24-hour urine collections26, although this method is
sensitive to collection errors29.
Given the limitations of GFR estimation, we perform measured GFR (mGFR) routinely in all living donors before and after donation in our center. While the mGFR is considered the gold standard30, it is a laborious and expensive procedure, and therefore not universally
feasible29. In the current KDIGO guidelines for living kidney donor evaluation, estimated
GFR (eGFR) is recommended as a first step for determining kidney function13. When the
eGFR is not ‘sufficient’ for a donor to be safely accepted for donation, an additional mGFR measurement is advised. However, in addition to the inherent limitations of the eGFR, it is currently unclear which eGFR values may be considered “sufficient”, in the absence of data linking pre-donation eGFR with post-donation mGFR values. As a result, donor selection varies considerably between centers, which may on the one hand put some accepted donors at risk, and on the other hand withhold potentially suitable donors from donation.
Assessment of Renal functional reserve in living kidney
donation
Besides the GFR, it also is important to predict the adaptive capacity of the remaining kidney to donation, especially in donors with a ‘marginal’ GFR. To predict adaptive capacity several approaches have been developed, such as measuring of the renal functional reserve (RFR, also called reserve capacity) as the ability of a donor to increase his/her GFR in response to a vasodilating agent (e.g. dopamine). The RFR is thought to be marker of the capacity of the kidneys to adapt after nephrectomy (figure 3), resulting in a GFR higher than 50% of the pre-donation GFR. Therefore, the pre-donation RFR was thought be a predictor of post-donation GFR31. A study in 2006, showed that the pre-donation RFR is indeed associated
with GFR 3 months after donation, but no data on the long-term predictive capacity exist32,
despite that fact that measurement of RFR was developed more than 30 years ago. The concept of measuring RFR for predictive purposes has also been applied in clinical conditions where renal hyperfiltration is assumed to be involved in the susceptibility to renal damage, such as obesity. Conditions associated with an elevated glomerular filtration rate,
such as obesity33, may “utilize” the RFR, leaving less reserve after kidney donation. This
may explain the increased ESRD risk in living donors with obesity21. During the first half of
pregnancy, vasodilatory hormones from the placenta also cause an increased filtration of the mother34. Lack of such renal vasodilation on the other hand, is a hallmark of preeclampsia.
In 2015, it was found that living kidney donors have an increased preeclampsia risk35. An
insufficient RFR because of obesity and living kidney donation may therefore play a role in the occurrence of adverse pregnancy outcomes in young female kidney donors.
Figure 3. Renal adaptation after living kidney donation.
At the time of nephrectomy, the donor single-kidney GFR is approximately 50% of its pre-donation value, corresponding to a reduction of renal mass by 50%. However, almost immediately after nephrectomy, GFR increased in an “early” hemodynamic response to 66% of its pre-donation value. This response is blunted after days-weeks, but the GFR continues to increase in a “late” phase that is hypothesized to be more structural in nature. This late renal adaptation varies on the individual donor and sometimes takes up to 15 years post-donation18.
Beyond GFR: Tubular compartment
While the aforementioned measurements encompass the glomerular filtration rate, and are accordingly used as markers of glomerular function, the tubuli are essential for, among others, electrolyte and fluid homeostasis36. Previously, tubular function was considered
to be secondary to the GFR, however this paradigm has been abandoned when studies demonstrated that the extent of tubulo-interstitial damage was an independent and major predictor of end-stage renal disease, even stronger than glomerular function37. Consequently,
markers related to electrolyte homeostasis, including renal phosphate homeostasis, have been proposed as indicators of tubular function. Renal phosphate homeostasis is regulated
Introduction and Aims of Thesis
1
primarily in proximal tubular epithelial cells by sodium phosphate co-transporters NaPi-IIa, NaPi-IIc and PiT-238–40. The renal tubular maximum reabsorption capacity of phosphate
per unit of glomerular filtration rate (TmP-GFR) represents the maximum renal capacity to reabsorb phosphate and may reflect tubular function, thereby being a functional marker to study “tubular quality”. As such, the TmP-GFR could be a useful tool to evaluate pre-donation tubular function. Similarly, post-transplant renal phosphate handling, reflected by the development of hypophosphatemia, could reflect tubular function and thus predict renal outcomes. Whether these markers could potentially have additional value for predicting graft outcomes in recipients remains unclear.
Beyond GFR: Towards predicting donation-attributable risk
The current KDIGO living donor guidelines advise to estimate an individual donor’s donation-attributable risk of developing end-stage renal disease after donation13 (figure4). However, it remains unclear how to implement this recommendation in daily clinical practice, in the absence of a universal risk calculator. The mGFR and donor age, the two
Figure 4. Demographic-related, aggregated and donation-attributable risk for living kidney donors
Examples of the different parts of donor risk according to the 2017 KDIGO living kidney donor guideline. The black dotted line represents the transplant program’s threshold for acceptable risk after donation.
most important determinants of post-donation renal function16, can predict only 68% of the
variation in mGFR at five years post-donation, underlining the need to identify additional factors that could facilitate personalized risk assessment, including both non-modifiable (age, disease history) and modifiable risk factors (overweight, blood pressure, lifestyle) These factors could include clinical parameters as well as lifestyle-related factors (e.g. diet, physical activity) and novel biomarkers, which should be related to long-term outcome parameters. This calls for detailed donor work-up, preferably supported by a large biobank of donor material (blood, urine, skin, biopsies), questionnaires and physical tests, together with structured long-term follow-up.
It is important to realize that renal function should not be the sole outcome of post-operative donor evaluation: evaluation of cardiovascular and overall risk, as well as donor well-being are highly important as well. Quality of life,after donation is importantly adversely influenced by the presence of pain, even despite a general increase in quality of life elicited by kidney donation in itself4,10,41. Structural assessment of quality of life, including the presence of both
short- and long-term post-operative pain in donors is an essential constituent of living donor follow-up.
Aim of the thesis
The aim of this thesis is to contribute to ongoing efforts to improve donor evaluation from a perspective of donor safety, with a primary focus on renal risk assessment. Part 1 is focused on available methods of renal function evaluation, including commonly used eGFR formulas, before and after donation. Part 2 addresses renal parameters other than GFR: the potential role of presumed markers of tubular function, as well as a potential approach to develop and validate novel biomarkers to predict donation-attributable risk for the individual donor.
Outline of the thesis
Part one: GFR in living kidney donation
Part one of this thesis focusses on the validity of established methods to estimate glomerular filtration rate. In chapter two we address whether the pre-donation eGFR can be used for prediction of post-donation mGFR and whether pre-donation eGFR cut-off values can be defined to yield a matching post-donation single kidney mGFR. In chapter three we aim to evaluate the predictive value eGFR for true kidney function (mGFR) after donation.
Chapter four focusses on the use of the RFR as an additional predictor of the mGFR. In this
chapter we focus on the changes of RFR after donation in overweight vs. non-overweight young women of child-bearing age, since this may have implications for pregnancy after
Introduction and Aims of Thesis
1
living kidney donation. In chapter five, we look at the predictive capacity of the pre-donation RFR in donors in more detail, and investigate if it can predict mGFR after donation and the mGFR increase after a reduction of renal mass by approximately 50%.
Part two: Living donation beyond the GFR
Part two of this thesis focusses on renal parameters other than GFR; the use of tubular compartment function in donors and the development of novel biomarkers to predict donation-attributable risk for the individual donor. In chapter six, we study whether pre-donation tubular phosphate handling in the donor can predict renal function in the recipient at one year post-transplantation. Chapter seven further addresses the prognostic value of phosphate handling, specifically the development of hypophosphatemia, in kidney transplant recipients.
Chapter eight addresses another important outcome after living kidney donation: chronic
pain. We investigated the prevalence of post-donation chronic pain in living kidney donors, and the accompanying long-term analgesic use.
Overall, it is apparent that more insights are needed in the effects of nephrectomy on the living donor. A proposed approach for the structured collection of biomaterials and functional and clinical data is described in chapter nine. At the UMCG, we recently established TransplantLines: a biobank study covering numerous aspects of solid organ transplantation, including but not restricted to, kidney donation. TransplantLines may serve as a basis for hypothesis-generating studies that yield new insights in, among many other things, donation-attributable risks. Ultimately, such information likely will contribute to improved living kidney donor screening and follow-up, and thereby to qualitatively and quantitatively improved outcomes after living kidney donation.
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4. Johnson EM, Anderson JK, Jacobs C, et al. Long-term follow-up of living kidney donors: quality of life after donation. Transplantation. 1999;67(5):717-21.
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7. Steiner RW. Moving closer to understanding the risks of living kidney donation. Clin Transplant. 2016;30(1):10-6.
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9. Fehrman-Ekholm I, Nordén G, Lennerling A, et al. Incidence of end-stage renal disease among live kidney donors. Transplantation. 2006;82(12):1646-8.
10. Ibrahim HN, Foley R, Tan L, et al. Long-term consequences of kidney donation. N Engl J Med. 2009;360(5):459-69.
11. Muzaale AD, Massie AB, Wang M-C, et al. Risk of end-stage renal disease following live kidney donation. JAMA. 2014;311(6):579-86.
12. Mjøen G, Hallan S, Hartmann A, et al. Long-term risks for kidney donors. Kidney Int. 2014;86(1):162-7.
13. Lentine KL, Kasiske BL, Levey AS, et al. KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors. Transplantation. 2017;101(8S Suppl 1):S1-S109.
14. Mandelbrot DA, Pavlakis M, Danovitch GM, et al. The medical evaluation of living kidney donors: A survey of US transplant centers. Am J Transplant. 2007;7(10):2333-2343.
15. Mandelbrot DA, Pavlakis M. Living donor practices in the United States. Adv Chronic Kidney Dis. 2012;19(4):212-9.
16. Grams ME, Sang Y, Levey AS, et al. Kidney-Failure Risk Projection for the Living Kidney-Donor Candidate. N Engl J Med. 2016;374(5):411-21.
17. Lenihan CR, Busque S, Derby G, Blouch K, Myers BD, Tan JC. Longitudinal study of living kidney donor glomerular dynamics after nephrectomy. J Clin Invest. 2015;125(3):1311-8.
18. Blantz RC, Steiner RW. Benign hyperfiltration after living kidney donation. J Clin Invest. 2015;125(3):972-4.
19. Rook M, Bosma RJ, van Son WJ, et al. Nephrectomy elicits impact of age and BMI on renal hemodynamics: lower postdonation reserve capacity in older or overweight kidney donors. Am J
Transplant. 2008;8(10):2077-85.
20. Heimbach JK, Taler SJ, Prieto M, et al. Obesity in living kidney donors: clinical characteristics and outcomes in the era of laparoscopic donor nephrectomy. Am J Transplant. 2005;5(5):1057-64. 21. Locke JE, Reed RD, Massie A, et al. Obesity increases the risk of end-stage renal disease among
living kidney donors. Kidney Int. 2017;91(3):699-703.
22. Orth SR, Hallan SI. Smoking: a risk factor for progression of chronic kidney disease and for cardiovascular morbidity and mortality in renal patients--absence of evidence or evidence of absence? Clin J Am Soc Nephrol. 2008;3(1):226-36.
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23. Tent H, Sanders J-SF, Rook M, et al. Effects of preexistent hypertension on blood pressure and residual renal function after donor nephrectomy. Transplantation. 2012;93(4):412-7.
24. Kazancioğlu R. Risk factors for chronic kidney disease: an update. Kidney Int Suppl. 2013;3(4):368-371.
25. Boele-Schutte E, Gansevoort RT. Measured GFR: not a gold, but a gold-plated standard. Nephrol
Dial Transplant. 2017;32(suppl_2):ii180-ii184.
26. Heymsfield SB, Arteaga C, McManus C, Smith J, Moffitt S. Measurement of muscle mass in humans: validity of the 24-hour urinary creatinine method. Am J Clin Nutr. 1983;37(3):478-94. 27. Huang N, Foster MC, Lentine KL, et al. Estimated GFR for Living Kidney Donor Evaluation. Am J
Transplant. 2016;16(1):171-80.
28. Sinkeler SJ, Visser FW, Krikken JA, Stegeman CA, Homan van der Heide JJ, Navis G. Higher body mass index is associated with higher fractional creatinine excretion in healthy subjects. Nephrol
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29. Levey AS, Inker LA. GFR Evaluation in Living Kidney Donor Candidates. J Am Soc Nephrol. 2017;28(4):1062-1071.
30. Apperloo AJ, de Zeeuw D, Donker AJ, de Jong PE. Precision of glomerular filtration rate determinations for long-term slope calculations is improved by simultaneous infusion of 125I-iothalamate and 131I-hippuran. J Am Soc Nephrol. 1996;7(4):567-72.
31. ter Wee PM, Tegzess AM, Donker AJ. Renal reserve filtration capacity before and after kidney donation. J Intern Med. 1990;228(4):393-9.
32. Rook M, Hofker HS, van Son WJ, Homan van der Heide JJ, Ploeg RJ, Navis GJ. Predictive capacity of pre-donation GFR and renal reserve capacity for donor renal function after living kidney donation. Am J Transplant. 2006;6(7):1653-1659.
33. Bosma RJ, Kwakernaak AJ, van der Heide JJH, de Jong PE, Navis GJ. Body mass index and glomerular hyperfiltration in renal transplant recipients: cross-sectional analysis and long-term impact. Am J Transplant. 2007;7(3):645-52.
34. Cheung KL, Lafayette RA. Renal physiology of pregnancy. Adv Chronic Kidney Dis. 2013;20(3):209-14.
35. Garg AX, Nevis IF, McArthur E, et al. Gestational hypertension and preeclampsia in living kidney donors. NEnglJ Med. 2015;372(1533-4406 (Electronic)):124-133.
36. Lowenstein J, Grantham JJ. The rebirth of interest in renal tubular function. Am J Physiol Renal
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37. Nath KA. Tubulointerstitial changes as a major determinant in the progression of renal damage. Am
J Kidney Dis. 1992;20(1):1-17.
38. Larsson T, Nisbeth U, Ljunggren O, Juppner H, Jonsson KB. Circulating concentration of FGF-23 increases as renal function declines in patients with chronic kidney disease, but does not change in response to variation in phosphate intake in healthy volunteers. Kidney Int. 2003;64(6):2272-2279. 39. Keusch I, Traebert M, Lotscher M, Kaissling B, Murer H, Biber J. Parathyroid hormone and dietary
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40. Villa-Bellosta R, Ravera S, Sorribas V, et al. The Na+-Pi cotransporter PiT-2 (SLC20A2) is expressed in the apical membrane of rat renal proximal tubules and regulated by dietary Pi. AJP
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GFR in Living
Kidney Donation
CHAPTER 2
Estimated GFR before Living
Kidney Donation and
Post-donation Measured GFR
Marco van Londen1*, MD; Jessica van der Weijden1*, Robert S. Niznik2,
MD; Stephan J.L. Bakker1, MD, PhD; Stefan P. Berger1, MD, PhD; Jan Stephan F.
Sanders1, MD, PhD; Gerjan Navis1, MD, PhD; Andrew D. Rule2, MD, MSc; and
Martin H. de Borst1, MD, PhD
In preparation
1 Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen and University of Groningen, Groningen, the Netherlands
2 Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Abstract
In living kidney donor screening, precise renal function measurements are vital to minimize donor ESRD risk. Although measured GFR (mGFR) is the gold standard renal function test, it is costly and laborious, and replacement by estimated GFR (eGFR) would be desirable. We therefore tested the capacity of pre-donation eGFR equations to predict post-donation mGFR and calculated eGFR cut-off for donor selection.
In 873 living donors who donated in our center between 1984 and 2017, we prospectively measured pre- and post-donation creatinine-based eGFR (CKD-EPI, Cockcroft-Gault, MDRD), 24 hour urinary creatinine clearance and mGFR (continuous iotholamate). We calculated bias and used linear regression to test the performance of the different eGFR formulas. We also calculated pre-donation eGFR cut-offs for post-donation mGFR with a 95% probability.
Mean donor age was 53±12 years, 48% of donors were male. Pre-donation mGFR was 102±16 mL/min/1.73m2 and short-term post-donation mGFR was 65±11 mL/min/1.73m2.
The pre-donation eGFRCKD-EPI threshold to predict a post-donation mGFR >50 mL/ min/1.73m2 with 95% probability was 94 mL/min/1.73m2, indicating that donors with a
pre-donation eGFRCKD-EPI of minimally 94 mL/min/1.73m2 (33% of donors) do not require mGFR
confirmation to achieve a post-donation single-kidney mGFR above 50 mL/min/1.73m2.
We show that eGFR can be used in a subgroup of donors with a high probability of good post-donation renal function without requiring mGFR measurement. The mGFR remains important in the majority of donors with an eGFR below these cut-offs values.
Estimated GFR Before Living Kidney Donation and Post-donation Measured GFR
2
Introduction
Living donor kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD)1,2. As a consequence of donor organ shortage, selection criteria for
potential living kidney donors have been liberalized3.
To minimize the life-time post-donation ESRD risk, all living kidney donor candidates undergo a screening procedure prior to donation, which includes an estimation of the glomerular filtration rate (GFR). The method of GFR estimation varies among centers, and may include the creatinine clearance (CrCl), creatinine-based estimated GFR (eGFRcr) using one of the available equations, or a true GFR measurement using exogenous filtration markers to obtain the measured GFR (mGFR). While the mGFR measurement is considered the gold standard, the technique is time-consuming and expensive. The 2017 KDIGO guideline for the evaluation and follow-up of living kidney donors suggests using a creatinine-based eGFR (eGFRcr) formula as initial assessment tool for living kidney donors4. This suggestions
is based on a study comparing eGFRCKD-EPI and mGFR in the CKD-EPI collaboration cohort5.
Although, this study indicated that the eGFRcr can be considered accurate, it also concluded that it is imprecise, particularly in the higher GFR range. This may cause under- and overestimation of GFR in healthy individuals, thereby reducing the utility of eGFRcr for living donor screening. The KDIGO guideline provides a general GFR cut-off, but does not specify if this should be estimated or measured GFR. Despite a useful calculator that can be used to calculate posttest probabilities for eGFRcr6,7, to date clinicians still have to extrapolate
eGFRcr values to individual decisions without clear guidance on if and when a confirmatory test is necessary.
The ultimate goal of GFR measurements in donor screening is to predict renal function for the remaing single kidney after donation. It is unknown whether pre-donation eGFR is also suitable for predicting post-donation donor renal function. We therefore tested the performance of pre-donation eGFR to predict post-donation mGFR. We also investigated which commonly used eGFR estimations best predicts post-donation mGFR and compared the results with the creatinine clearance.
Methods
Study design and population
In this prospective cohort study, we performed mGFR measurements in 873 living kidney donors who donated between 1982 and 2017 in the University Medical Center Groningen,
The Netherlands. The mGFR was measured as the urinary clearance of 125I-iothalamate 4
months before donation and at 7 weeks, 5 years and 10 years after living kidney donation, all as part of the screening program and post-donation evaluation8. The study was approved
by the institutional ethical review board (METc 2014/077). All procedures were conducted in accordance with the declaration of Helsinki and declaration of Istanbul.
As a result of our donor selection criteria all donors were normotensive or had an adequately regulated blood pressure while taking no more than two classes of antihypertensive drugs). Furthermore, individuals with a history of diabetes (or an abnormal glucose tolerance test), kidney disease or cardiovascular events were not allowed to donate. Any other condition that was considered a potential threat to long-term renal or cardiovascular outcome was considered a contra-indication for donation, at the discretion of the nephrologist involved in the selection procedure.
Laboratory measurements
Serum creatinine was measured routinely in our clinical chemistry laboratory by an isotope dilution mass spectrometry (IDMS) traceable enzymatic assay on the Roche Modular (Roche Ltd., Mannheim, Germany) from 1st March 2006 onwards. Before this date, samples were
measured by Jaffé alkaline picrate assay on the Merck Mega Analyzer (Merck, Darmstadt, Germany). Values obtained by the Jaffé method were converted to allow comparison with the Roche method by the formula (YRoche =(XJaffé -8)/1.07). Creatinine clearance was
calculated from the 24-hour urine collected the day before the measurements. Proteinuria was measured in 24-hour urine collections using routine laboratory techniques.
Renal function measurements
GFR measurements were performed using 125I-Iothalamate and 131I-hippurate infusion as
previously described9: Measurements were performed in a quiet room, with the participant
in semi-supine position. After drawing a blood sample, 125I-Iothalamate and 131I-hippurate
infusions was started (0.04 mL/kg containing 0.04 MBq and 0.03 MBq respectively). At 08.00 hour 0.6 MBq of 125I-Iothalamate was administered, followed by continuous infusion
of 12 mL/h. After a two-hour stabilization period, baseline measurements were performed in a steady state of plasma tracer levels. Clearances were calculated as (U*V)/P and (I*V)/P, where U*V represents the urinary excretion, I*V represents the infusion rate of the tracer and P represents the plasma tracer concentration per clearance period. We calculated the mGFR from clearance levels of these tracers. We corrected for voiding errors by multiplying the urinary 125I-Iothalamate clearances with the ratio of plasma and urinary 131I-hippurate
Estimated GFR Before Living Kidney Donation and Post-donation Measured GFR
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eGFR calculations
We used the abbreviated four-variable MDRD study equation, repressed for standardized serum creatinine samples11. The CKD-EPI equation (CKD-EPI collaboration) was calculated
gender specific and stratified by creatinine levels5. The Cockcroft-Gault formula was also
calculated12. The mGFR and the eGFR
CG were normalized for body surface area (BSA)
according to DuBois & DuBois13.
Figure 1. Comparison of pre-donation eGFR with pre- and post-donation mGFR
Deming regression plots of pre-donation eGFRCKD-EPI with measured GFR (A) pre-donation, at (B) 3 months, (C) 5 years and (D) 10 years post-donation.
Statistical analysis
Data are reported as mean (standard deviation) for normally distributed variables and median [interquartile range, IQR] for skewed data. Binary variables are shown as “number (%)”. GFR data are reported as absolute values (mL/min) and normalized for body surface area
(BSA; mL/min/1.73m2). We tested accuracy (defined as mean bias) and precision (defined as the IQR of the bias) per time-point. Ordinary least-squares and Deming regression analysis were used to assess the relationship between the different eGFR equations and post-donation mGFR. Wilcoxon matched pairs test was used to test bias differences. The performance of the different eGFR formulas to predict a post-donation mGFR <60 mL/ min/1.73m2 was measured by calculating positive predictive values (PPV) and negative
predictive values (NPV; Supplementary Table S1). We then calculated pre-donation eGFR cut-offs for prediction of fixed post-donation single-kidney mGFR thresholds of 40, 45, 50, 55 and 60 mL/min/1.73m2 using a 95% prediction interval (Figure 2). Similarly, we calculated
cut-offs for the 24h urine creatinine clearance. Statistical analyses were performed by SPSS version 22 for Windows (IBM, Armonk, NY), R version 3.0.1 (CRAN, Vienna, Austria), and Graphpad Prism 6 for Windows (Graphpad, San Diego, CA). P-values of <0.05 were considered statistically significant.
Figure 2. Pre-donation eGFR and post-donation mGFR
Scatter-dot plot of pre-donation (A) eGFRCKD-EPI, (B) eGFRMDRD, (C) eGFRCG/BSA, (D) creatinine clearance, and post-donation measured GFR.
Estimated GFR Before Living Kidney Donation and Post-donation Measured GFR
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Results
Patient characteristics
We included 873 living kidney donors. Mean age was 53±12 years, 48% were male. Mean pre-donation mGFR/BSA was 102±16 mL/min/1.73m2, and mean post-donation (2-3
months after UNx) mGFR/BSA was 65±12 mL/min/1.73m2. In 322 donors with available
five-year follow-up data, mean mGFR/BSA was 103±16 mL/min/1.73m2 pre-donation,
66±12 mL/min/1.73m2 2-3 months post-donation and 69±11 mL/min/1.73m2 at five years
post-donation. In 107 donors with 10-year follow-up, mean mGFR/BSA was 104±15 mL/ min/1.73m2 pre-donation, 69±13 mL/min/1.73m2 post-donation, 72±9 mL/min/1.73m2 at 5
years and 69±13 mL/min/1.73m2 at 10 years post-donation. On average, these donors had
a limited GFR loss between 5 and 10 years after transplantation (-0.7±1.8 mL/min/1.73m2/
year). Pre- and post-donation (short- and long-term) characteristics are shown in Table 1.
Table 1. Characteristics of living donors
Variable Pre-donation Post-donation
3 months 5 years 10 years
Number 873 873 322 107 Age, years 52.9 (11.8) 52.4 (11.7) 56.9 (10.0) 60.3 (9.1) Sex, N (% male) 48 48 48 45 Weight, kg 79.5 (13.7) 78.9 (13.0) 82.2 (14.4) 81.4 (15.4) Height, cm 174.8 (9.4) 174.5 (9.5) 173.8 (9.4) 172.4 (8.8) BMI, kg/m2 25.9 (3.6) 25.9 (3.4) 27.2 (3.9) 27.3 (4.2) BSA, m2 1.94 (0.20) 1.94 (0.19) 2.0 (0.2) 1.94 (0.21) mGFR, mL/min 114.2 (22.3) 73.5 (16.7) 78.4 (16.0) 77.2 (16.3) mGFR/BSA, mL/min/1.73m2 101.6 (16.3) 64.6 (11.8) 69.0 (11.4) 68.7 (12.4)
eGFRCKD-EPI, mL/min/1.73m2 87.6 (13.8) 57.7 (11.8) 64.4 (13.3) 64.1 (12.8)
eGFRMDRD, mL/min/1.73m2 85.8 (15.6) 56.2 (10.4) 63.1 (11.7) 63.1 (11.6)
eGFRCG/BSA, mL/min/1.73m2 92 (19) 63 (13) 70 (16) 68 (14)
Creatinine clearance, mL/min 132.5 (61.2) 80.3 (28.9) 76.8 (53.3) 85.9 (24.5) Systolic blood pressure,
mmHg
126.3 (13.1) 124.1 (13.4) 127.4 (13.7) 130.1 (15.7) Diastolic blood pressure, mmHg 75.9 (8.6) 76.4 (18.1) 76.7 (8.8) 77.5 (9.4) Proteinuria, g/24 hour 0.00 [0.00;0.20] 0.10 [0.00;0.20] 0.00 [0.00;0.20] 0.00 [0.00;0.20] Serum creatinine, µmol/L 78.1 (13.9) 112.4 (21.0) 100.1 (19.0) 94.8 (18.9) BSA, Body Surface Area; mGFR, measured Glomerular Filtration Rate; eGFR, estimated Glomerular Filtration Rate
Cross-sectional analyses
Both before and after donation all eGFR formulas showed an underestimation of mGFR (Table 2). The eGFRCKD-EPI had the highest accuracy and precision in prediction mGFR
Table 2. Crossectional analysis of eGFR and mGFR bias
eGFR bias in cross-sectional analysis
Variable Pre-donation mGFR /BSA (n=873) Post-donation mGFR /BSA (n=873) 5 year mGFR /BSA (n=322) 10 year mGFR /BSA (n=107) Mean (SD) IQR Mean (SD) IQR Mean (SD) IQR Mean (SD) IQR eGFR CKD-EPI -13.9 (15.3) [-23.1;4.2 ] -7.2 (10.3) [-13.0;-1.1] -5.2 (9.8) [-12.8;1.1] -5.3 (1 1.8) [-13.1;1.7] eGFR MDRD -28.5 (21.1) [-41.3;-15.1] -16.5 (13.3) [-24.0;-7.5] -15.8 (13) [-24.9;-6.8] -12.8 (15.4) [-23.0;-4.1] eGFR CG/BSA -22.2 (19.9) [-34.1;-10.7] -9.1 (12.0) [-16.2;-1.7] -9.5 (12.49) [-17.7;-1.27] -8.6 (12.0) [-17.0;1.1] CrCl 22.7 (39.5) [1.59;37.9] 16.0 (27.2) [4.0;26.0] 7.4 (31.9) [-2.8;26.8] 15.6 (23.9) [-0.99;25.7]
BSA, Body Surface
Area; mGFR, measured Glomerular Filtration Rate; eGFR, estimated Glomerular Filtration Rate; CKD-EPI, CKD-EPI formula; MDRD, MD
RD Study
Estimated GFR Before Living Kidney Donation and Post-donation Measured GFR
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(pre-donation bias -14±15, IQR [-23.1;-4.2], post-donation bias -7±10, IQR [-13.0;-1.1]).
eGFRCKD-EPI also had the highest association with pre-donation mGFR (St. β 0.57, p<0.001,
Table 2). The creatinine clearance, correctly measured in 627 donors, had a reasonable bias, but was very imprecise (pre-donation bias 14±15, IQR [4.2; 23.1], post-donation bias 16±27, IQR [4.0;26.0]).
Performance of pre-donation eGFR in predicting post-donation mGFR
All pre-donation eGFR formulas were associated with post-donation mGFR (Table 3). The pre-donation eGFRCG/BSA has the strongest association with post-donation mGFR (St. β 0.51, p<0.001), whereas the eGFRMDRD had the poorest association (St. β 0.41, p<0.001). The eGFRCG/BSA also had the strongest association with 5-year mGFR (St. β 0.45, p<0.001), while eGFRCKD-EPI had the strongest association with 10-year mGFR (St. β 0.41, p<0.001, Table 3). In Figure 1, deming regression plots are shown of the pre-donation eGFRCKD-EPI/ CrCl with post-donation mGFR. Deming regression plots for eGFRMDRD and eGFRCG/BSA are shown in supplementary figure S1.
Table 3. Univariate linear regression analyses of pre-donation eGFR on pre- and post-donation mGFR
Association of pre-donation eGFR with pre- and post-donation mGFR Pre-donation
Variables Pre-donation mGFR(n=873) /BSA Post-donation mGFRBSA (n=873) / 5 year mGFR(n=322) /BSA 10 year mGFR(n=107) /BSA
St. β P R2 St. β P R2 St. β P R2 St. β P R2
eGFRCKD-EPI 0.48 <0.001 0.23 0.48 <0.001 0.23 0.42 <0.001 0.17 0.41 <0.001 0.16
eGFRMDRD 0.41 <0.001 0.17 0.41 <0.001 0.17 0.33 <0.001 0.11 0.33 <0.001 0.10
eGFRCG/BSA 0.51 <0.001 0.26 0.51 <0.001 0.26 0.45 <0.001 0.20 0.39 <0.001 0.14
CrCl 0.39 <0.001 0.15 0.35 <0.001 0.12 0.33 <0.001 0.11 0.23 0.15 0.05 BSA, Body Surface Area; mGFR, measured Glomerular Filtration Rate; eGFR, estimated Glomerular Filtration Rate; CKD-EPI, CKD-EPI formula; MDRD, MDRD Study formula; CG, Cockcroft-Gault formula;CrCl, Creatinine Clearance
A pre-donation eGFRCKD-EPI of 94 mL/min/1.73m2 (present in 33% of donors) predicts a
post-donation mGFR >50 mL/min/1.73m2 with a 95% probability, meaning that an eGFR CKD-EPI
above 94 mL/min/1.73m2 is sufficient to achieve a post-donation mGFR of 50 mL/min/1.73m2
with 95% probability (Table 4). For pre-donation eGFRCG/BSA this threshold was 99 mL/ min/1.73m2 and for eGFR
MDRD 97 mL/min/1.73m2. For the creatinine clearance the threshold
was 158 mL/min. Stricter- and more liberal eGFR cut-offs are shown in table 4. The majority of donors do not meet the eGFR cut-offs. According to our model, a confirmatory test can be avoided in approximately 7-33% of the prospective donor population, depending on their
Figure 3. Flow-chart of living donor selection
An algorithm for selecting living kidney donors. Clinicians should first determine what post-donation single-kidney GFR is required for a prospective living kidney donor. This is an individual decision and should be based on donor risk factors (age, ethnicity, co-morbidities). Donors with an eGFR below cut-off should undergo confirmatory testing. Based on current literature, mGFR is advised as confirmatory test. Donors with an eGFR above the cut-off or with an mGFR above the individualized cut-off and no relevant contra-indications for donation should be accepted.
required post-donation single-kidney GFR, which is determined using a donors age and other risk factors (Table 4; Figure 3).
Candidate for
living dona�on
post-Determina�on of required dona�on GFR eGFR (CKD-EPI or CG) ( Confirmatory test mGFR) Below Cut-offMedical and pychological screening Above Cut-off -off Above Cut-off
Decline
Accept
-Contra indica�ons? No Yes Below Cut-off mGFR cutEstimated GFR Before Living Kidney Donation and Post-donation Measured GFR
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Table 4. Pre-donation eGFR cut-off values to archieve fixed post-donation mGFR thresholds Post-donation
threshold1 Pre-donation test cut-off
2 mGFR/BSA mGFR/ BSA eGFR CKD-EPI % donors eGFR MDRD % donors eGFR CG/BSA % donors CrCl % donors 40 80 69 91% 65 93% 67 95% N/A N/A 45 91 82 66% 81 59% 83 65% 102 71% 50 101 94 33% 97 21% 99 31% 158 16% 55 110 107 7% 113 6% 116 11% 215 3% 60 120 120 1% 128 1% 131 4% 271 1%
BSA, Body Surface Area; mGFR, measured Glomerular Filtration Rate; eGFR, estimated Glomerular Filtration Rate; CKD-EPI, CKD-EPI formula; MDRD, MDRD Study formula; CG, Cockcroft-Gault formula;CrCl, Creatinine Clearance 1 Post-donation single-kidney mGFR
2 to have post-donation mGFR with 95% predictid probability
Discussion
In this study, we provide pre-donation eGFR cut-off values to select donors with a high probability of having sufficient single-kidney renal function post-donation. Donors with a pre-donation eGFR above these cut-offs should be considered safe and do not require more precise measured GFR (mGFR or true GFR) measurement. Since only 7-33% of donors had eGFR values above these cut-offs, measured GFR is still warranted as a confirmatory test in the majority of donor candidates.
We show that eGFRCKD-EPI performs better in prediction of mGFR than eGFRMDRD, eGFRCG/
BSA and the creatinine clearance. Also we show that eGFRMDRD performs considerably poorer
in predicting post-donation renal function than the other formulas. This may be explained by the good pre-donation kidney function in this cohort of living donors, where the MDRD formula is known to have a poor performance in higher kidney function14.
Previous studies have determined possible cut-off values for eGFR to accept donors without confirmatory tests (e.g. mGFR)6,7, but these values were based on the predictive capacity
of eGFR on pre-donation mGFR, or even on eGFRcr-cys in the general population, while our study provides eGFR cut-offs for post-donation mGFR, in a single-kidney state.
While our study provides guidance for GFR cut-offs for donor selection, it should be noted that this strategy does not fully take long-term benign hyperfiltration and de-novo kidney disease occurrence into account. Hyperfiltration can cause GFR to increase up to 10 years post-donation and donor chronic kidney disease (CKD) develops later after donation and
cannot be predicted at young age15–18. Clinicians should take these factors into account
when determining what single-kidney GFR a donor should have post-donation. Also donor comorbidities, CKD and other factors relevant for the donation-attributable risk should be taken into account4,19–23. Based on our data, we propose an algorithm for selecting living
kidney donors that encompasses both mGFR and other risk factors (Figure 3). The first step in this algorithm is to determine what post-donation GFR is required for a prospective living kidney donor. This is an individual decision and should be based on donor risk factors (age, ethnicity, comorbidities)4,19–23. Donors with an eGFR above this cut-off that do not have other
contra-indications for donation, can be accepted without confirmatory GFR testing.
The algorithm suggests using a confirmatory test if eGFR is below the cut-off, in line with current living donor guidelines4. Based on the current state of literature and living donor
guidelines mGFR is best used as confirmatory test24, but it is expensive, laborious and
may vary dependent on the method used25. Creatinine clearance may be a reasonable
alternative: Although 24-hour urine collection is hampered by sampling errors leading to a poor precision, performing repeated collections may increase its precision and make it a viable confirmatory test26. eGFR
CysC has also been proposed as a confirmatory test, but is
considerably less accurate in most cases27,28. Further research is necessary to find the best
(combination of) tests for confirmation of renal function if eGFR is below the threshold. By implementing this algorithm in the selection of living kidney donors, the living donor pool can be extended in centers that do not have mGFR available24.
Strengths of this study include the extensive renal hemodynamic measurements with continuous infusion of 1.25iothalamate and the longitudinal design. Limitations of this study
are that our cohort consists almost exclusively of Caucasian. Also, long-term post-donation follow-up was only available in a subgroup of donors. Another limitation of this study is the low number of very young (< 30 years old) and very old donors (> 75 years old), reducing the usability of the calculated values in these groups. Also the selection of older donors in our center was very strict, which is reflected in a large proportion of donors having an adequate post-donation mGFR. Therefore, we are unable to make predictions on donors who should be rejected without the use of a confirmatory test.
In conclusion, we used a large cohort of living kidney donors with mGFR measurements before and at three time points after donation to provide cut-off values for pre-donation eGFR to select donors with adequate post-donation mGFR. These cut-offs may be used for donor guidelines to select donors that do not require mGFR confirmation of eGFR. We propose a selection algorithm incorporating these thresholds to further improve donor safety. Donors with an eGFR below the cut-off should undergo additional renal function evaluation, preferably by mGFR, while donors with eGFR above the cut-off can be safely accepted without additional renal function measurement.
Estimated GFR Before Living Kidney Donation and Post-donation Measured GFR
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Acknowledgments
Dr. De Borst is supported by a Veni grant from the Dutch Organization for Scientific Research (grant no 016.146.014). The authors would like to thank all living donors that participated in this study.
References
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2. Macías LB, Poblet MS, Pérez NN et al. Assessment of the Renal Function in Potential Donors of Living Kidney Transplants: Expanded Study. Transplant. Proc. 2015; 47: 2603–7.
3. Reese PP, Boudville N, Garg AX. Living kidney donation: Outcomes, ethics, and uncertainty. Lancet 2015; 385: 2003–2013.
4. Lentine KL, Kasiske BL, Levey AS et al. KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors. Transplantation 2017; 101: S1–S109.
5. Levey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate. Ann.
Intern. Med. 2009; 150: 604–612.
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Transplant 2016; 16: 171–80.
7. Gaillard F, Flamant M, Lemoine S et al. Estimated or Measured GFR in Living Kidney Donors Work-up? Am. J. Transplant 2016; 16: 3024–3032.
8. Rook M, Bosma RJ, van Son WJ et al. Nephrectomy elicits impact of age and BMI on renal hemodynamics: lower postdonation reserve capacity in older or overweight kidney donors. Am. J.
Transplant 2008; 8: 2077–85.
9. Tent H, Rook M, Stevens LA et al. Renal function equations before and after living kidney donation: a within-individual comparison of performance at different levels of renal function. Clin. J. Am. Soc.
Nephrol. 2010; 5: 1960–8.
10. Apperloo AJ, de Zeeuw D, Donker AJ et al. Precision of glomerular filtration rate determinations for long-term slope calculations is improved by simultaneous infusion of 125I-iothalamate and 131I-hippuran. J. Am. Soc. Nephrol. 1996; 7: 567–572.
11. Levey AS, Coresh J, Greene T et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann. Intern. Med. 2006; 145: 247–54.
12. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;
16: 31–41.
13. Du Bois D, Du Bois EF. A formula to estimate the approximate surface area if height and weight be known. 1916. Nutrition 5: 303-11–3.
14. Stevens LA, Coresh J, Feldman HI et al. Evaluation of the modification of diet in renal disease study equation in a large diverse population. J. Am. Soc. Nephrol. 2007; 18: 2749–57.
15. Rook M, Hofker HS, van Son WJ et al. Predictive capacity of pre-donation GFR and renal reserve capacity for donor renal function after living kidney donation. Am. J. Transplant 2006; 6: 1653–1659. 16. Mjøen G, Hallan S, Hartmann A et al. Long-term risks for kidney donors. Kidney Int. 2014; 86:
162–7.
17. Steiner RW. Moving closer to understanding the risks of living kidney donation. Clin. Transplant. 2016; 30: 10–6.
18. Fehrman-Ekholm I, Kvarnström N, Söfteland JM et al. Post-nephrectomy development of renal function in living kidney donors: a cross-sectional retrospective study. Nephrol. Dial. Transplant 2011; 26: 2377–81.
19. Ibrahim HN, Berglund DM, Jackson S et al. Renal Consequences of Diabetes After Kidney Donation. Am. J. Transplant 2017.
20. Grams ME, Sang Y, Levey AS et al. Kidney-Failure Risk Projection for the Living Kidney-Donor Candidate. N. Engl. J. Med. 2016; 374: 411–21.
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21. Garg AX, Nevis IF, McArthur E et al. Gestational hypertension and preeclampsia in living kidney donors. N.Engl.J Med 2015; 372: 124–133.
22. Locke JE, Reed RD, Massie A et al. Obesity increases the risk of end-stage renal disease among living kidney donors. Kidney Int. 2017; 91: 699–703.
23. Steiner RW. The Risks of Living Kidney Donation. N. Engl. J. Med. 2016; 374: 479–80.
24. Levey AS, Inker LA. GFR Evaluation in Living Kidney Donor Candidates. J. Am. Soc. Nephrol. 2017;
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Supplementary data
Table S1. Positive and negative predictive values of eGFR equations and the creatinine clearance to detect a post-donation mGFR < 60 mL/min/1.73m2
Predictive Values for post-donation mGFR
Predonation GFR cut-off 3 months (N=873) 5 years (N=322) 10 years (N=108)
Method Value PPV NPV PPV NPV PPV NPV CKD-EPI 99 42.9 92.1 30.5 98.2 22.3 84.6 MDRD 102 39.7 89.4 27.0 88.9 22.0 85.7 CG/BSA 108 41.9 90.7 29.3 94.3 21.5 78.6 CrCl 164 40.6 85.6 30.5 89.8 22.0 73.3 mGFR 106 53.2 95.2 39.6 97.5 30.5 89.6
BSA, Body Surface Area; mGFR, measured Glomerular Filtration Rate; eGFR, estimated Glomerular Filtration Rate; CKD-EPI, CKD-EPI formula; MDRD, MDRD Study formula; CG, Cockcroft-Gault formula;CrCl, Creatinine Clearance; PPV,positive predictive value; NPV, negative predictive value
Figure S1. Comparison of pre-donation eGFR/creatinine clearance with pre- and post-donation mGFR
Deming regression analysis of pre-donation eGFRMDRD (A), eGFRCG/BSA (B) and the creatinine clearance (C) with mGFR at different time-points. (1) shows the association with pre-donation mGFR, (2) with mGFR 3 months post-donation, (3) with mGFR five years post-donation and (4) with mGFR 10 years post-donation.
CHAPTER 3
Estimated Glomerular Filtration
Rate for Longitudinal Follow-up
of Living Didney Donors
Marco van Londen, MD1, Anthony B. Wijninga, BSc1, Jannieta de Vries, BSc1,
Jan-Stephan Sanders, MD, PhD1, Margriet F.C. de Jong, MD, PhD1,
Robert. A. Pol, MD, PhD2, Stefan P. Berger, MD, PhD1, Gerjan Navis, MD, PhD1,
Martin H. de Borst, MD, PhD1
Nephrology Dialysis Transplantation 2018 Jun 1;33(6):1054-1064.
1 Department of Internal Medicine, Division of Nephrology, University Medical Center Groningen and University of Groningen, Groningen, the Netherlands
2 Department of Surgery, University Medical Center Groningen and University of Groningen, Groningen, the Netherlands
Abstract
Background: Living kidney donor safety requires reliable long-term follow-up of renal
function after donation. The current study aimed to define the precision and accuracy of post-donation estimated GFR (eGFR) slopes compared with measured GFR (mGFR) slopes.
Methods: In 349 donors (age 51±10, 54% female), we analyzed eGFR (CKD-EPI, MDRD,
Cockcroft-Gault (CG/BSA), creatinine clearance (CrCl)) and mGFR (125I-iothalamate)
changes from 3 months until 5 years post-donation.
Results: Donors had a pre-donation mGFR of 116±23 mL/min; at 3 months post-donation
mGFR was 73±14 mL/min, and at 5 years 79±16 mL/min. Between 3 months and 5 years post-donation, 28% of donors had a declining mGFR (-0.82±0.79 mL/min/year), 47% had a stable and 25% an increasing mGFR. Overall, eGFR equations showed good slope estimates (bias eGFRCKD-EPI 0.13±2.16 mL/min/year, eGFRMDRD 0.19±2.10 mL/min/year,
eGFRCG/BSA -0.08±2.06 mL/min/year, CrCl -0.12±4.75 mL/min/year), but in donors with a
decreasing mGFR the slope was underestimated (bias eGFRCKD-EPI 1.41±2.03 mL/min/year,
eGFRMDRD 1.51±1.96 mL/min/year, eGFRCG/BSA 1.20±1.87 mL/min/year). The CrCl had a high
imprecision (bias interquartile range [-1.51;3.41] mL/min/year).
Conclusions: All eGFR equations underestimated GFR slopes in donors with a declining
GFR between 3 months and 5 years post-donation. This study underlines the value of mGFR in the follow-up of donors with risk of progressive GFR loss.
Estimated glomerular filtration rate for longitudinal follow-up of living kidney donors
3
Introduction
Due to a persistent donor organ shortage, selection criteria for potential living kidney donors have been liberalized, resulting in a higher proportion of, marginal donors with more co-morbidities1. This might have impact on donor outcomes including accelerated renal
function loss. Although the absolute risk for end-stage renal disease (ESRD) after donation is low (0.31-0.47%), the relative risk is high compared with matched controls (11.42-18.99 times)2,3.
Accurate follow-up and assessment of kidney function is essential to identify donors at risk for ESRD in a timely manner. Measured Glomerular Filtration Rate (mGFR) using an exogenous marker is considered the optimal method for measuring kidney function4.
However, its complexity and costs limit the availability of this technique in most centers worldwide. Alternatively, estimated GFR (eGFR) equations, including the Cockroft-Gault (CG), MDRD and CKD-EPI formulas, are considered reasonable alternatives5. However,
eGFR equations which have been designed for and validated in populations with chronic kidney disease, generally provide an underestimation of mGFR in the higher range6–11. The
creatinine clearance may also be of use, but is generally shows a relatively large between-measurement variation12.
Furthermore, to identify donors at risk for accelerated renal function loss, considering the course of renal function is preferable over a single point-estimate13. Few studies have
evaluated longitudinal performance of eGFR equations5,14–17. Therefore, the main aim of
this study was to evaluate the performance of the most commonly used eGFR equations to detect changes in mGFR, with a particular focus on donors displaying a progressive decline in post-donation GFR.
Materials and Methods
Study design
In this prospective cohort study, we performed repeated mGFR and eGFR measurements in 349 non-black living kidney donors, who donated between 1994 and 2012 in the University Medical Center Groningen (Figure S1). To comply with our donor selection criteria, all donors were normotensive or had an adequately regulated blood pressure with a maximum of two antihypertensive drugs. Furthermore, individuals with a history of diabetes (or an abnormal glucose tolerance test), kidney disease or cardiovascular events were excluded from kidney donation. At approximately 4 months before donation, and at 3 months, 5 years and 10
years (in a subgroup) after living kidney donation, mGFR was measured as the urinary clearance of 125I-iothalamate18. The study was approved by the institutional ethical review
board (METc 2014/077). All procedures were conducted in accordance with the declarations of Helsinki and Istanbul.
Clinical and biochemical measurements
At all data collection visits, height, weight and blood pressure were measured. Serum creatinine was measured routinely by enzymatic assay on the Roche Modular (Roche Ltd., Mannheim, Germany) from 1st March 2006. Before this date, samples were measured by
Jaffé alkaline picrate assay on the Merck Mega Analyzer (Merck, Darmstadt, Germany). Values obtained by the Jaffé method were converted to allow comparison with the Roche method by the formula (YRoche =(XJaffé -8)/1.07). Urinary creatinine was measured from a
24-hour urine specimen, and creatinine clearance (CrCl) was calculated as:
[Urinary creatinine concentration (mg/dL) × volume of 24-hour urine (mL)/[urine collection time (min)]/plasma concentration (mg/dL).
Proteinuria was measured (g/24h) using routine laboratory measurements from 24-hour urine collection.
Renal function measurements
The mGFR was determined using 125I-iothalamate and 131I-hippurate infusion as previously
described19. Briefly, measurements were with the participant in semi-supine position.
After drawing a blood sample, 125I-iothalamate and 131I-hippurate infusions were started
(0.04 mL/kg containing 0.04 MBq and 0.03 MBq respectively). At 08:00 a.m., 0.6 MBq of
125I-iothalamate was administered, followed by continuous infusion of 12 mL/h. After a
two-hour stabilization period, baseline measurements were performed in a steady state of plasma tracer levels. Clearances were calculated as (U*V)/P and (I*V)/P, where U*V represents the urinary excretion, I*V represents the infusion rate of the tracer and P represents the plasma tracer concentration per clearance period. To reduce the intertest coefficient of variation , we corrected for incomplete bladder emptying and dead space was achieved by multiplying the urinary 125I-iothalamate clearances with plasma and urinary 131I-hippurate clearance, as has
been described previously20. Day-to-day variability of mGFR is 2.5% 20.
eGFR calculations
We used the abbreviated four-variable MDRD study equation, repressed for standardized serum creatinine samples21. The CKD-EPI equation (CKD-EPI collaboration) was calculated
gender-specific and stratified by creatinine levels22. The Cockcroft-Gault formula was
calculated as23: eGFR
CG = (140 − age) * body weight/(72 * SCr) (*0.85 if female). mGFR
and the eGFRCG were normalized for body surface area (BSA) according to DuBois and Dubois24.
