An observational cohort study examined the change point of kidney function stabilization in the initial period after transplantation
Authors: Evert Cleenders, Maarten Coemans, Soufian Meziyerh, Jasper Callemeyn, Marie-Paule Emonds, Wilfried Gwinner, Jesper Kers, Dirk Kuypers, Irina Scheffner, Aleksandar Senev, Elisabet Van Loon, Karolien Wellekens, Aiko P.J. de Vries, Geert Verbeke, Maarten Naesens
Journal: Kidney International (2024)

Baseline kidney function following kidney transplantation is often used in research and clinical decision-making yet is not well defined. Here, a method to determine baseline function was proposed and validated on three single-center retrospective cohorts consisting of 922 patients from Belgium (main cohort) and two validation cohorts of 987 patients from the Netherlands and 519 patients from Germany. For each transplant, a segmented regression model was fitted on the estimated glomerular filtration rate (eGFR) evolution during the first-year post-transplantation. This yielded estimates for change point timing, rate of eGFR change before and after change point and eGFR value at change point, now considered the "baseline function". Associations of eGFR evolution with recipient/donor characteristics and the graft failure rate were assessed with linear regression and Cox regression respectively. The change point occurred on average at an eGFR value of 43.7±14.6 mL/min/1.73m2, at a median time of 6.5 days post-transplantation. Despite significant associations with several baseline donor-recipient characteristics (particularly, donor type; living vs deceased), the predictive value of these characteristics for eGFR value and timing of the change point was limited. This followed from a large heterogeneity within eGFR trajectories, which in turn indicated that favorable levels of kidney function could be reached despite a suboptimal initial evolution. Segmented regression consistently provided a good fit to early eGFR evolution, and its estimate of the change point can be a useful reference value in future analyses. Thus, our study shows that baseline kidney function after transplantation is heterogeneous and partly related to pretransplant donor characteristics. Baseline kidney function following kidney transplantation is often used in research and clinical decision-making yet is not well defined. Here, a method to determine baseline function was proposed and validated on three single-center retrospective cohorts consisting of 922 patients from Belgium (main cohort) and two validation cohorts of 987 patients from the Netherlands and 519 patients from Germany. For each transplant, a segmented regression model was fitted on the estimated glomerular filtration rate (eGFR) evolution during the first-year post-transplantation. This yielded estimates for change point timing, rate of eGFR change before and after change point and eGFR value at change point, now considered the "baseline function". Associations of eGFR evolution with recipient/donor characteristics and the graft failure rate were assessed with linear regression and Cox regression respectively. The change point occurred on average at an eGFR value of 43.7±14.6 mL/min/1.73m2, at a median time of 6.5 days post-transplantation. Despite significant associations with several baseline donor-recipient characteristics (particularly, donor type; living vs deceased), the predictive value of these characteristics for eGFR value and timing of the change point was limited. This followed from a large heterogeneity within eGFR trajectories, which in turn indicated that favorable levels of kidney function could be reached despite a suboptimal initial evolution. Segmented regression consistently provided a good fit to early eGFR evolution, and its estimate of the change point can be a useful reference value in future analyses. Thus, our study shows that baseline kidney function after transplantation is heterogeneous and partly related to pretransplant donor characteristics. Lay SummaryDecline of kidney function (quantified via the estimated glomerular filtration rate [eGFR]) is increasingly used as an alternative end point to graft failure, as it is observed earlier after transplantation and in more recipients. However, as "normal" kidney function differs widely between patients, this decline is defined as a relative decrease compared with the patient-specific "baseline kidney function." Currently, established methods to determine this baseline function are lacking. We propose to use segmented regression, which models the change point at which the initial, rapid eGFR increase following transplantation reaches a more stable eGFR level. This model showed an excellent fit to the eGFR evolutions of 922 recipients in the main cohort, and 1506 recipients in 2 validation cohorts. We conclude that the eGFR value at the change point is useful as a reference eGFR value, expressing the baseline kidney function, in future studies that use eGFR decline as an end point. Decline of kidney function (quantified via the estimated glomerular filtration rate [eGFR]) is increasingly used as an alternative end point to graft failure, as it is observed earlier after transplantation and in more recipients. However, as "normal" kidney function differs widely between patients, this decline is defined as a relative decrease compared with the patient-specific "baseline kidney function." Currently, established methods to determine this baseline function are lacking. We propose to use segmented regression, which models the change point at which the initial, rapid eGFR increase following transplantation reaches a more stable eGFR level. This model showed an excellent fit to the eGFR evolutions of 922 recipients in the main cohort, and 1506 recipients in 2 validation cohorts. We conclude that the eGFR value at the change point is useful as a reference eGFR value, expressing the baseline kidney function, in future studies that use eGFR decline as an end point. The early evolution of kidney function following kidney transplantation is characterized by a steep increase, with the exception of cases with delayed graft function. After a couple of days to weeks, kidney function stabilizes, reaching a change point that can be considered the "baseline kidney function." There is a need for an objective and robust method to define this baseline function, as kidney function decline (compared with such a reference value) is commonly part of the composite end point in clinical studies of kidney disease.1Hilbrands L. Budde K. Bellini M.I. et al.Allograft function as endpoint for clinical trials in kidney transplantation.Transpl Int. 2022; 3510139Crossref Scopus (12) Google Scholar This trend is elicited by the decline in graft failure events, which can lead to issues with underpowered analyses when graft survival is the primary end point. More generally, the clinical relevance of the early evolution of kidney transplant function is not clear. In research where the prognostic value of kidney function evolution following transplantation is evaluated, the early evolution is often disregarded because of the large variability in early estimated glomerular filtration rate (eGFR) values, rapid fluid shifts, and frequent adjustments of the immunosuppression.2Hall I.E. Schröppel B. Doshi M.D. et al.Associations of deceased donor kidney injury with kidney discard and function after transplantation.Am J Transplant. 2015; 15: 1623-1631Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar Time series models,3Chang S.S. Hung C.J. Lin Y.J. et al.Association between preoperative allograft function (effective renal plasma flow) and the change in glomerular filtration rate among living-donor kidney transplant recipients.Transplant Proc. 2012; 44: 248-253Crossref PubMed Scopus (6) Google Scholar latent class mixed models,4Raynaud M. Aubert O. Reese P.P. et al.Trajectories of glomerular filtration rate and progression to end stage kidney disease after kidney transplantation.Kidney Int. 2021; 99: 186-197Abstract Full Text Full Text PDF PubMed Scopus (39) Google Scholar and deep learning models5Van Loon E. Zhang W. Coemans M. et al.Forecasting of patient-specific kidney transplant function with a sequence-to-sequence deep learning model.JAMA Netw Open. 2021; 4e2141617Crossref Scopus (5) Google Scholar have been used to analyze post-transplant kidney functional trajectories, but no attempts were made to model baseline kidney function. Furthermore, none of the studies on early kidney transplant function has assessed the importance of the change point between the first phase (steep increase) and the second phase (stabilization) of the evolution for post-transplant prognosis. Because of the erratic evolution of eGFR in the early post-transplant period, it is challenging to define the timing and eGFR value of this change point consistently.2Hall I.E. Schröppel B. Doshi M.D. et al.Associations of deceased donor kidney injury with kidney discard and function after transplantation.Am J Transplant. 2015; 15: 1623-1631Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar However, appropriate modeling could offer an objective way of defining this change point, compared with a determination purely based on clinical estimation. The transition between different stages of a disease or natural evolution has been studied extensively in other medical areas, such as disease progression in patients with HIV,6Kiuchi A.S. Hartigan J.A. Holford T.R. et al.Change points in the series of T4 counts prior to AIDS.Biometrics. 1995; 51: 236-248Crossref PubMed Scopus (57) Google Scholar onset of cognitive decline in elderly individuals,7Dominicus A. Ripatti S. Pedersen N.L. et al.A random change point model for assessing variability in repeated measures of cognitive function.Stat Med. 2008; 27: 5786-5798Crossref PubMed Scopus (38) Google Scholar,8van den Hout A. Muniz-Terrera G. Matthews F.E. Smooth random change point models.Stat Med. 2011; 30: 599-610Crossref PubMed Scopus (21) Google Scholar and lung function measurements in children with cystic fibrosis.9Moss A. Juarez-Colunga E. Nathoo F. et al.A comparison of change point models with application to longitudinal lung function measurements in children with cystic fibrosis.Stat Med. 2016; 35: 2058-2073Crossref PubMed Scopus (18) Google Scholar In these articles, the evolution was modeled with segmented regression models. Once the appropriate method to determine the change point has been established, the prognostic value of this point and related factors can be assessed. Earlier studies regarding kidney function after transplantation have demonstrated associations between the rate of graft failure beyond the first year and eGFR values at certain time points within the first year after transplantation.10Hariharan S. McBride M.A. Cherikh W.S. et al.Post-transplant renal function in the first year predicts long-term kidney transplant survival.Kidney Int. 2002; 62: 311-318Abstract Full Text Full Text PDF PubMed Scopus (638) Google Scholar, 11Nicol D. MacDonald A.S. Lawen J. et al.Early prediction of renal allograft loss beyond one year.Transpl Int. 1993; 6: 153-157Crossref PubMed Google Scholar, 12Pascual J. Marcén R. Ortuño J. Renal function: defining long-term success.Nephrol Dial Transplant. 2004; 19: vi3-vi7Crossref PubMed Scopus (15) Google Scholar, 13Kasiske B.L. Israni A.K. Snyder J.J. et al.The relationship between kidney function and long-term graft survival after kidney transplant.Am J Kidney Dis. 2011; 57: 466-475Abstract Full Text Full Text PDF PubMed Scopus (124) Google Scholar, 14Schnitzler M.A. Lentine K.L. Gheorghian A. et al.Renal function following living, standard criteria deceased and expanded criteria deceased donor kidney transplantation: impact on graft failure and death.Transpl Int. 2012; 25: 179-191Crossref PubMed Scopus (24) Google Scholar More recent studies have suggested that variables that describe the dynamics of the kidney function (such as slopes over particular time intervals) might be more informative than values at single time points.10Hariharan S. McBride M.A. Cherikh W.S. et al.Post-transplant renal function in the first year predicts long-term kidney transplant survival.Kidney Int. 2002; 62: 311-318Abstract Full Text Full Text PDF PubMed Scopus (638) Google Scholar,15Srinivas T.R. Taber D.J. Su Z. et al.Big data, predictive analytics, and quality improvement in kidney transplantation: a proof of concept.Am J Transplant. 2017; 17: 671-681Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar However, neither the clinical determinants of the change point nor the prognostic importance of the change point has been established in kidney transplantation. The goal of this study is to objectively define a change point in the early eGFR evolution to obtain a robust parameter of the baseline kidney function following kidney transplantation. Furthermore, we aim to relate the early evolution of kidney function following transplantation, described in terms of the change point of the kidney function evolution and related slopes, to baseline clinical characteristics and to graft failure after 1-year post-transplantation. Adults who received a kidney transplant at the University Hospitals Leuven between March 2004 and February 2013 were eligible for this observational cohort study. Patients had to be under follow-up at 1-year post-transplantation. Recipients of combined transplantation or kidney transplantation after another solid organ transplantation were excluded. Other reasons for exclusion were primary nonfunction (i.e., permanent dialysis treatment since transplantation), graft loss or death within the first transplant year, and too few eGFR measurements (i.e., <5 measurements during the first month post-transplantation). All transplants were crossmatch negative with the complement-dependent cytotoxicity assay. Clinical data were collected prospectively during routine follow-up and stored in electronic medical records that were linked to an SAS database (SAS Institute). The pre-transplant donor-specific antibody (DSA) status was determined as described in the study by Senev et al.16Senev A. Coemans M. Lerut E. et al.Eplet mismatch load and de novo occurrence of donor-specific anti-HLA antibodies, rejection, and graft failure after kidney transplantation: an observational cohort study.J Am Soc Nephrol. 2020; 31: 2193-2204Crossref PubMed Scopus (104) Google Scholar This study was approved by the ethical committee of the University Hospitals Leuven (S64006) and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement (Supplementary Table S1). There were 2 validation cohorts. The first consisted of 987 transplantations, performed between February 2006 and July 2019 at Leiden University Medical Center in the Netherlands. The second consisted of 519 transplantations, performed between April 2003 and January 2008 at Hannover Medical School in Germany. Data collection and analysis were performed with informed consent of the patients and with approval of the institutional ethics board (Leiden: W2020.031; Hannover: number 2765). Exclusion criteria for the validation cohorts were identical to those for the main cohort. The level of kidney allograft function was quantified using the estimated glomerular filtration rate (eGFR; estimated using the Modification of Diet in Renal Disease Study equation17Levey A.S. Bosch J.P. Lewis J.B. et al.Modification of Diet in Renal Disease Study Group. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation.Ann Intern Med. 1999; 130: 461-470Crossref PubMed Scopus (13627) Google Scholar). These eGFR values were monitored frequently at irregular intervals, during either hospitalization or outpatient clinic visits. Only eGFR values collected during the first year after transplantation were included in this study. Delayed graft function (DGF) was defined as the need for dialysis within the first week after transplantation. Only eGFR values without dialysis reflect the actual kidney function following transplantation. Therefore, the timing and duration of this dialysis treatment were taken into account while assessing associations of the first-year eGFR evolution with recipient-donor characteristics and outcome. The demographics of recipients/donors were summarized by the mean value (with SD), median value and interquartile range (IQR) in case of continuous variables, and frequencies and proportions in case of categorical variables. The occurrence of graft failure and recipient death (with functioning graft) was described using Aalen-Johansen estimates, to account for competing risks.18Coemans M. Verbeke G. Döhler B. et al.Bias by censoring for competing events in survival analysis.BMJ. 2022; 378e071349PubMed Google Scholar To describe the eGFR trajectory in the first year after transplantation, we applied segmented regression for each patient individually.19Muggeo V.M.R. Estimating regression models with unknown break-points.Stat Med. 2003; 22: 3055-3071Crossref PubMed Scopus (1519) Google Scholar The early, nonlinear evolution of kidney function was modeled by considering 2 distinct phases: an initial period of rapid eGFR increase (starting at eGFR = 0 ml/min per 1.73 m2) and a stabilizing period (with gradual positive or negative trend). Our model included parameters for the steepness of the initial increase, the timing of the transition to the stabilizing phase (i.e., the change point), and the steepness of the further evolution. The eGFR value at the change point (i.e., the baseline kidney function) was derived from the initial slope and the timing of the change point. In addition, the evolution was modeled using a model with 2 change points, to assess possible benefits of using multiple change points. Model fit was assessed on the basis of a histogram of the subject-specific R2 values (note that R software uses an alternative formula for R2 for models where the intercept is fixed at 0). Furthermore, segmented regression was applied to simulated eGFR data with a similar trend, spread, and identical timings to the real observations. The estimates for the real and simulated data were compared to assess the sensitivity of the model fit to eGFR fluctuations. More details on this simulation study and on the segmented regression model and its application in R software are available in the Supplementary Methods. For subjects without post-transplant dialysis (or only a single treatment on the first day), all estimates from the segmented regression model were included in further analyses. In case of >1 post-transplant dialysis treatment, the estimate for initial slope did not represent the functioning of the graft itself and was excluded from further analyses. Similarly, the estimates for timing and eGFR value at change point and slope after change point were only included in further analyses if the estimated change point occurred after the last dialysis. Multiple linear regression was used to describe the association between the early evolution of eGFR (described by natural log of the initial slope, natural log of the timing of change point, eGFR value at change point, and slope after change point) and clinical characteristics: recipient sex and age, donor sex and age, donor type (living donation, donation after brain death [DBD], and donation after circulatory death [DCD]), repeat transplantation, pre-transplant DSA status, number of human leukocyte antigen mismatches on locus A, B, and DR, and cold ischemia time. The most influential variables were identified with a standardized multiple linear regression analysis. The predictive accuracy of these clinical characteristics for the early evolution was assessed using R2 and mean absolute error values, after the estimates for (loge-transformed) initial slope and timing of change point were converted to the original scale using the Smearing retransformation method.20Duan N. Smearing estimate: a nonparametric retransformation method.J Am Stat Assoc. 1983; 78: 605-610Crossref Scopus (1648) Google Scholar Associations of clinical characteristics with the cause-specific graft failure rate were examined using a Cox proportional hazard model (censoring at loss to follow-up or death). Although death with a functioning graft is a competing event for graft failure, competing event models are not appropriate, as this study is not intended to establish a prediction model but the interest is with etiology.18Coemans M. Verbeke G. Döhler B. et al.Bias by censoring for competing events in survival analysis.BMJ. 2022; 378e071349PubMed Google Scholar,21Geskus R.B. Cause-specific cumulative incidence estimation and the Fine and Gray model under both left truncation and right censoring.Biometrics. 2011; 67: 39-49Crossref PubMed Scopus (181) Google Scholar,22Wolkewitz M. Cooper B.S. Bonten M.J.M. et al.Interpreting and comparing risks in the presence of competing events.BMJ. 2014; 349g5060Crossref PubMed Scopus (149) Google Scholar Similarly, we analyzed the association of the initial slope, timing of change point, eGFR value at change point, and slope after change point with the cause-specific rate of graft failure following 1-year post-transplantation, corrected for the occurrence of T cell–mediated rejection (excluding borderline) or antibody-mediated rejection and rejection treatment (i.e., corticosteroids, thymoglobulin, plasmapheresis, and rituximab) during the first year after transplantation. The eGFR value at 1-year post-transplantation was added as a confounder in additional analyses, to examine the prognostic value of the early evolution parameters. All analyses were performed using R software version 4.3.1. We used 2-sided hypotheses tests and considered P < 0.05 as significant. The main cohort for this study included 922 transplantations, with a median of 8.2 person years of follow-up (IQR, 5.8–10.7 person years). Full details about the cohort characteristics are presented in Figure 1 and Table 1. There was no missingness in the demographic data for the main cohort. Most allografts were DBD (715 of 922, 77.5%), whereas DCD (151 of 922, 16.4%) or donation by a living donor (56 of 922, 6.1%) occurred less frequently. There were 563 male (61.1%) and 359 female (38.9%) recipients, with an average age of 53.3 ± 13.2 years.Table 1Cohort characteristics: demographic and clinical characteristics of the main cohort from Leuven and the validation cohorts from Leiden and HannoverCharacteristicsLeuven cohort (N = 922)Leiden cohort (N = 987)Hannover cohort (N = 519)Recipients Sex, n (%)Male563 (61.1)627 (63.5)299 (57.6)Female359 (38.9)360 (36.5)220 (42.4) Age, yr, mean ± SD53.3 ± 13.253.3 ± 13.549.9 ± 12.9 Repeat transplantation, n (%)137 (14.9)120 (12.2)64 (12.3) Body mass index, kg/m2, mean ± SD25.4 ± 4.526.6 ± 4.724.4 ± 3.6 Cold ischemia time, h, mean ± SD14.2 ± 5.78.3 ± 6.013.6 ± 7.2 A-B-DR mismatches, mean ± SD2.7 ± 1.33.1 ± 1.52.4 ± 1.8 Delayed graft function, n (%)239 (25.9)267 (27.1)143 (27.6) Dialysis pre-transplant, n (%)895 (97.1)718 (72.7)499 (96.1) Dialysis post-transplant, n (%)Yes, started during first week239 (25.9)–143 (27.6)Yes, started after first week2 (0.2)–0 (0)No681 (73.9)–376 (72.4) Pretransplant DSA, n (%)Positive96 (10.4)47 (4.8)–Negative826 (89.6)940 (95.2)– EthnicityCaucasian906 (98.3)––African12 (1.3)––Asian3 (0.3)–– Hispanic1 (0.1)––Donors Sex, n (%)Male493 (53.5)488 (49.4)278 (53.6)Female429 (46.5)499 (50.6)241 (46.4) Age, yr, mean ± SD47.5 ± 14.753.4 ± 13.249.1 ± 14.8 Type, n (%)Living donor56 (6.1)479 (48.5)86 (16.6)Donation after brain death715 (77.5)232 (23.5)433 (83.4)Donation after circulatory death151 (16.4)276 (28.0)0 (0) Graft failure, %aAalen-Johansen estimate of the proportion of recipients who experienced the event of interest, up to selected time points.At 2 yr1.21.03.5At 3 yr2.72.25.4At 6 yr7.35.89.0 Death with functioning graft, %aAt 2 yr1.61.51.9At 3 yr3.72.84.5At 6 yr11.88.26.5 Follow-up after transplantationNo. of eGFR measurements within the first-year post-transplantTotal49,25835,85721,447Minimum251610Median493236p25–p7541–6127–4129–48Maximum202177147xxx; DSA, donor-specific antibody; eGFR, estimated glomerular filtration rate; p25, xxx; p75, xxx.a Aalen-Johansen estimate of the proportion of recipients who experienced the event of interest, up to selected time points. Open table in a new tab xxx; DSA, donor-specific antibody; eGFR, estimated glomerular filtration rate; p25, xxx; p75, xxx. The R2 of the individual fits of the segmented regression model were consistently high, ranging from 0.84 to 1.00 (median, 0.98; IQR, 0.97–0.99; Figure 2a). The distribution of R2 values was similar in cases with and without DGF (Supplementary Figure S1). Several examples of observed eGFR evolutions and the corresponding fits from the segmented regression model are presented in Figure 2b through d. Fitting the segmented regression model on simulated data with the same underlying trend and spread as the observed eGFR values resulted in highly similar estimates for the early evolution parameters (Supplementary Figure S2). With the model with 2 change points, the R2 values were even higher (median, 0.99; IQR, 0.98–0.99), as expected when using a more flexible model. However, the improvement in model fit was minimal in most cases (median, 0.004; IQR, 0.002–0.009; Supplementary Figure S3). The timing of the second change point proved to be highly heterogeneous, compared with the first change point. In most cases (76.4%), the first change point represented baseline kidney function, and only in few cases (4.9%), it was expressed by the second change point. In 8.4% of cases, both the first and second change point occurred within 7 days of the change point estimated by the single change point model, such that each could represent baseline kidney function. In the remaining 10.3% of cases, neither the first nor the second change point occurred within this time frame. The ambiguous link between these change points and the stabilization of kidney function, and the minimal improvement in model fit, motivated our decision to proceed using the single change point model in further analyses. DGF occurred after 239 of 922 (26.0%) transplants. The median duration of dialysis following transplantation in these recipients was 4 days, and it ranged from a single dialysis session to 7.9 months of treatment (IQR, 2–13 days). Among 227 recipients who received >1 day of dialysis, treatment was ceased before the estimated change point in 195 cases, and after the change point in the remaining 32 cases. The baseline characteristics for the recipients who experienced DGF are described in more detail in Supplementary Table 2. The median time until change point was 6.5 days (IQR, 3.5–15.8 days; N = 890), and the mean eGFR value at that point was 43.7 ± 14.6 ml/min per 1.73 m2 (N = 890; Figure 3a–d). Before the change point, the eGFR increased at a median rate of 41.8 ml/min per 1.73 m2 per week (IQR, 16.8–84.7 ml/min per 1.73 m2; N = 695). After the change point, the mean change in eGFR was 0.1 ± 0.4 ml/min per 1.73 m2 per week (N = 890) until 1-year post-transplantation, which indicates stabilization of the kidney function. The relatively broad IQRs and high SDs demonstrate the heterogeneity of eGFR evolutions during the first-year post-transplantation. Next, we evaluated the clinical determinants of the parameters of the change point (Table 2, Table 3; Supplementary Figures S4 and S5). The change point occurred later in male recipients (male: 14.4 ± 18.7 days; female: 11.8 ± 16.5 days; P = 0.001; Table 2), whereas in female donors, it occurred at a lower eGFR value (male: 45.8 ± 15.1 ml/min per 1.73 m2; female: 41.7 ± 13.6 ml/min per 1.73 m2; P < 0.001). Increasing donor and recipient age had a negative effect on the initial increase of eGFR, and on the eGFR value at the time of the change point (donor age: –3.4 ml/min per 1.73 m2 per 10 years; 95% confidence interval [CI], –4.0 to –2.8 ml/min per 1.73 m2 per 10 years; P < 0.001; recipient age: –1.8 ml/min per 1.73 m2 per 10 years; 95% CI, –2.6 to –1.1 ml/min per 1.73 m2 per 10 years; P < 0.001). After the change point, older recipients tended to have a positive effect on the eGFR change (0.04 ml/min per 1.73 m2 per week, per 10 years; 95% CI, 0.02–0.06 ml/min per 1.73 m2 per week, per 10 years; P < 0.001), whereas older donors tended to have a negative effect on the eGFR change (–0.02 ml/min per 1.73 m2 per week, per 10 years; 95% CI, 0.04 to –0.01 ml/min per 1.73 m2 per week, per 10 years; P = 0.012).Table 2Clinical determinants of the first-year eGFR evolution after kidney transplantation (univariate)ParameterTiming change point, mo since transplantation (N = 890)aLoge transformed.eGFR at change point, ml/min per 1.73 m2 (N = 890)eGFR change before change point, ml/min per 1.73 m2 per wk (N = 695)aLoge transformed.eGFR change after change point, ml/min per 1.73 m2 per wk (N = 890)Estimate β (95% CI)PEstimate β (95% CI)PEstimate β (95% CI)PEstimate β (95% CI)PDemographics Sex recipient (male = 1)0.21 (0.08 to 0.34)0.0010.92 (–1.05 to 2.89)0.361–0.10 (–0.24 to 0.04)0.1550.02 (–0.04 to 0.08)0.483 Sex donor (male = 1)0.05 (–0.08 to 0.17)0.4784.07 (2.17 to 5.98)<0.0010.11 (–0.03 to 0.24)0.1110.05 (–0.00 to 0.11)0.053 Age recipient (decades)0.047 (–0.00 to 0.10)0.053–1.85 (–2.56 to –1.13)<0.001–0.08 (–0.13 to –0.03)0.0020.04 (0.02 to 0.06)<0.001 Age donor (decades)0.03 (–0.02 to 0.07)0.247–3.39 (–4.01 to –2.78)<0.001–0.10 (–0.15 to –0.06)<0.001–0.02 (–0.04 to –0.01)0.012 Compared with donation after brain death:Donation after circulatory death0.26 (0.09 to 0.42)0.002–0.79 (–3.34 to 1.76)0.544–0.11 (–0.28 to 0.07)0.2460.02 (–0.05 to 0.09)0.606 Living donor–0.91 (–1.17 to –0.65)<0.00110.99 (6.98 to 15.00)<0.0011.00 (0.75 to 1.25)<0.001–0.32 (–0.43 to –0.21)<0.001 Cold ischemia time (per 5 h)0.16 (0.09 to 0.23)<0.001–1.16 (–2.20 to –0.12)0.030–0.13 (–0.20 to –0.06)<0.0010.03 (–0.00 to 0.06)0.073 Repeated transplantation0.10 (–0.08 to 0.28)0.2793.07 (0.36 to 5.78)0.0260.14 (–0.07 to 0.34)0.191–0.09 (–0.16 to –0.01)0.031 Pre-transplant DSA status0.37 (0.16 to 0.57)0.001–0.73 (–3.89 to 2.44)0.651–0.20 (–0