ABSTRACT
Background
Early graft function after pediatric kidney transplantation (KT) may be influenced by perioperative hemodynamic and fluid management variables. However, previous studies have yielded conflicting results. This study aimed to evaluate the impact of perioperative factors on early graft function in pediatric KT recipients.
Methods
This retrospective multicenter study included pediatric KT recipients from four academic centers in Turkiye and France. Early graft function was assessed using estimated glomerular filtration rate (eGFR) at postoperative day 7 and day 30. Delayed graft function (DGF) was defined as the need for dialysis within the first 7 days post‐transplant. Mean arterial pressure (MAP) was calculated where reperfusion blood pressure data were available, and a sensitivity analysis accounting for transplant center was performed. Associations between perioperative variables and outcomes were evaluated using univariate and multivariate regression analyses.
Results
Ninety‐seven patients were included (median age 154 months; 45.4% female). DGF occurred in 11.3% of recipients. In multivariate analysis, no perioperative variable was independently associated with DGF. Longer cold ischemia time was independently associated with lower 7‐day eGFR (B = −0.05; 95% CI −0.10 to −0.01; p = 0.014). At 30 days, preemptive transplantation (B = −15.99; 95% CI −27.02 to −4.96; p = 0.005) and DGF (B = −36.94; 95% CI −58.39 to −15.50; p = 0.001) were independently associated with lower eGFR; the preemptive association was attenuated after accounting for transplant center.
Conclusion
Intraoperative hemodynamic parameters were not independently associated with DGF or early graft function. At day 7, early graft function was primarily associated with cold ischemia time, whereas at day 30 it was mainly associated with preemptive transplantation status and DGF.
Keywords: cold ischemia time, delayed graft function, graft function, pediatric kidney transplantation, perioperative management
In this four‐center study of 97 pediatric kidney transplant recipients across Turkiye and France, perioperative surgical factors did not predict early graft function.
1. Introduction
End‐stage renal disease (ESRD) in children is an uncommon yet severe condition that contributes to impaired growth and development and is closely associated with metabolic disturbances as well as cardiovascular complications [1]. Unlike adults, in whom acquired diseases are more common etiologies, pediatric ESRD primarily results from congenital anomalies of the kidney and urinary tract (CAKUT). Kidney transplantation (KT) is widely regarded as the optimal therapeutic option for children with established or impending ESRD [2, 3]. Over the years, significant advancements have led to marked improvements in both graft function and graft survival in the pediatric transplant population [4].
Since pediatric renal transplantation confers unique benefits on neurocognitive, psychosocial, sexual, and overall development, beyond its effects on the cardiovascular system, optimizing graft survival is of paramount importance in children [5]. On the other hand, early graft function is a critical determinant of both short‐ and long‐term outcomes in pediatric renal transplantation, as delayed graft function (DGF) is associated with increased acute rejection rates and inferior graft survival [6]. Previous studies have examined the relationship between various perioperative factors and early graft function; however, their findings have been reported as conflicting.
In the study by Zhang et al., neither average central venous pressure (CVP) after revascularization nor mean systolic blood pressure (BP) within the first 24 h post‐transplant was associated with early graft function [7]. In contrast, Michelet et al. demonstrated that the mean BP‐to‐weight ratio was correlated with creatinine clearance on postoperative day 1 [8]. There is also considerable variability in perioperative fluid management, and no consensus exists regarding the optimal fluid strategy for pediatric KT [9]. In a study from the UK, higher perioperative fluid administration was reported to be negatively associated with estimated glomerular filtration rate (eGFR) on postoperative day 7 [10]. Conversely, another group found that fluid overload was associated with better immediate post‐transplant renal function [11]. Taken together, additional research is warranted to clarify the effect of hemodynamic variables on early graft function.
Accordingly, the objective of this study was to evaluate how perioperative factors influence early graft function in pediatric KT. We hypothesized that perioperative factors including blood pressure at the time of reperfusion, fluid administration, and the use of vasoactive agents may influence early graft function after KT.
2. Materials and Methods
2.1. Study Population
This multicenter study was carried out across four referral centers in Turkiye (Istanbul University, n = 20; Istinye University, n = 16; and Necmettin Erbakan University (Meram Faculty of Medicine), n = 10) and France (Nantes, n = 51), under the coordination of the Young Academic Urologists Kidney Transplantation Working Group of the European Association of Urology. Patients who underwent KT between January 2006 and December 2024 were included, during which surgical and immunosuppression protocols did not change substantially. The analysis included retrospective data from 97 pediatric patients who underwent KT at these institutions. The study protocol was approved by the Necmettin Erbakan University Non‐Pharmaceutical and Non‐Medical Device Clinical Research Ethics Committee (Decision No: 2026/6317), with institutional approval obtained at each participating center, and the study was conducted in accordance with the Declaration of Helsinki.
Each participating center followed its own local transplant protocol to guide perioperative management, including BP control, CVP and urine output targets, and fluid replacement regimens.
2.2. Variables
For the purposes of this study, the following variables were collected: Baseline characteristics/demographics: age, gender, body mass index (BMI), body surface area (BSA), primary kidney disease, type and duration of renal replacement treatment, history of prior KT, urological surgical history, donor type, donor gender, donor age, donor preoperative creatinine levels, donor kidney laterality, and immunological mismatch.
Intraoperative and graft‐related variables: BP at the time of reperfusion, mean intraoperative CVP, mean intraoperative blood pH, procedure time, warm ischemia time, cold ischemia time, intraoperative complications, additional procedures performed during KT, intravenous fluid administration and use of inotropes, implantation site, preservation fluid type, number of anastomoses, type of ureteral anastomosis and induction immunosuppressive regimen. BP at reperfusion corresponded to the first value documented immediately after graft reperfusion following vascular unclamping. Mean arterial pressure (MAP) was calculated as (SBP + 2 × DBP)/3 for patients with available reperfusion blood pressure. Mean intraoperative CVP was available in only 16 of 97 patients (16.5%) and was therefore not included in the analyses; estimated blood loss was not recorded consistently across centers and was likewise not analyzed.
Postoperative variables: maintenance immunosuppressive regimen, occurrence of DGF, length of hospital stay (LOS), postoperative complications, eGFR at 7 days and 30 days post‐transplant and the findings of any postoperative graft biopsy.
Complications occurring within 30 days after surgery were assessed using the Clavien–Dindo classification [12]. The revised Schwartz equation was used to calculate eGFR at 7 days and 30 days after KT [13]. DGF was defined as the need for hemodialysis within the first 7 days after KT.
2.3. Statistical Analysis
All statistical analyses were performed using SPSS version 24.0 (IBM Corp., Chicago, IL, USA) for Windows. A p‐value < 0.05 was considered statistically significant. Data distribution was evaluated using the Kolmogorov–Smirnov test. Categorical variables were reported as percentages, nonparametric variables as medians with interquartile ranges, and parametric variables as means ± standard deviation. Associations between perioperative variables and eGFR at 7 days and 30 days post‐transplant were first explored using univariate linear regression analyses. Factors found to be significant in univariate analysis were entered into multivariate linear regression models. Univariate analyses were conducted to identify factors associated with DGF. For DGF, univariate associations were assessed using Firth penalized logistic regression to reduce small‐sample bias; because only 11 DGF events occurred, the multivariable Firth penalized logistic regression model was considered exploratory, and univariate odds ratios with 95% confidence intervals are reported. Group comparisons used the Mann–Whitney U or independent‐samples t‐test for continuous variables and the chi‐square or Fisher exact test for categorical variables, as appropriate. Missing data were handled by complete‐case analysis, and the proportion of missing values for each variable is reported. A sensitivity analysis was performed by adding transplant center as a covariate and by repeating the analyses after excluding the largest contributing center. Given the exploratory, retrospective design, no a priori power calculation was performed; all eligible recipients during the study period were included.
3. Results
3.1. Demographics and Baseline Characteristics
A total of 44 patients (45.4%) were female. The median donor age at transplantation was 17 (IQR 30) years, and most of grafts were obtained from deceased donors (61, 62.9%). The median recipient age at transplantation was 154 (IQR 88.5) months. The most common cause of kidney failure was CAKUT (44, 45.4%), followed by glomerular diseases (n = 36, 37.1%). Regarding pretransplant renal replacement therapy, 34 patients (35%) underwent preemptive transplantation, 32 (33%) were treated with continuous ambulatory peritoneal dialysis and 31 (32%) with hemodialysis. HLA mismatch was identified in 90 patients (97.8%), while ABO incompatibility was observed in only one patient (1.1%). Baseline clinical characteristics of donors and recipients are summarized in Table 1.
TABLE 1.
Baseline characteristics of the donors and recipients.
| Variable | Category | Value |
|---|---|---|
| Gender, N (%) | Female | 44 (45.4) |
| Male | 53 (54.6) | |
| Age, month, median (IQR) | 154 (88.5) | |
| Body mass index, kg/m2, mean (SD), (n = 88) | 17 (3) | |
| Body surface area, m2, mean (SD), (n = 88) | 1.1 (0.4) | |
| Primary kidney disease, N (%) | CAKUT | 44 (45.4) |
| GN | 36 (37.1) | |
| Other | 17 (17.5) | |
| Pretransplant dialysis modality, N (%) | Preemptive | 34 (35) |
| HD | 31 (32) | |
| CAPD | 32 (33) | |
| Pretransplant dialysis, month, median (IQR) | 12 (23) | |
| History of urological surgery, N (%) | UUT | 23 (23.7) |
| LUT | 10 (10.3) | |
| No | 64 (66) | |
| History of prior Tx, N (%), (n = 96) | 5 (5.2) | |
| Donor type, N (%) | Living | 36 (37.1) |
| Deceased | 61 (62.9) | |
| Donor gender, N (%) | Female | 42 (43.3) |
| Male | 55 (56.7) | |
| Donor age, year, median (IQR) | 17 (30) | |
| Donor pre‐op creatinine, mg/dL, median (IQR), (n = 96) | 0.65 (0.27) | |
| Donor kidney side, N (%), (n = 96) | Left | 42 (43.3) |
| Right | 54 (55.7) |
Note: Categorical variables are given as N (%); continuous variables as mean (SD) or median (IQR) according to distribution.
Abbreviations: CAKUT, congenital anomalies of the kidney and urinary tract; CAPD, continuous ambulatory peritoneal dialysis; GN, glomerulonephritis; HD, hemodialysis; IQR, interquartile range; LUT, lower urinary tract; SD, standard deviation; Tx, transplantation; UUT, upper urinary tract.
3.2. Intraoperative Outcomes
Most donor kidneys were implanted extraperitoneally (75, 78.1%). The median operative time was 143 (IQR 68.3) minutes. The mean systolic BP and median diastolic BP at the time of revascularization were 107.6 (SD 20.9) mmHg and 60 (IQR 34) mmHg, respectively. Multiple arterial anastomoses and venous anastomoses were performed in 15 (16.0%) and 3 (3.7%) patients, respectively. Four patients required the use of inotropic agents. Intraoperative variables and surgical characteristics are presented in Table 2.
TABLE 2.
Intraoperative and graft‐related characteristics of the recipients.
| Variable | Category | Value |
|---|---|---|
| Perfusion fluid, N (%), (n = 46) | UW | 25 (54.3) |
| Ringer lactate | 2 (4.4) | |
| Custodiol HTK | 19 (41.3) | |
| Systolic BP at reperfusion, mmHg, mean (SD), (n = 71) | 107.6 (20.9) | |
| Diastolic BP at reperfusion, mmHg, median (IQR), (n = 71) | 60 (34) | |
| Mean arterial pressure at reperfusion, mmHg, median (IQR), (n = 71) | 72.7 (27.3) | |
| pH, median (IQR), (n = 86) | 7.29 (0.08) | |
| Cold ischemia time, minutes, median (IQR), (n = 87) | 619 (791) | |
| Operation time, minutes, median (IQR), (n = 96) | 143 (68.3) | |
| Site of implantation, N (%), (n = 96) | Extraperitoneal | 75 (78.1) |
| Intraperitoneal | 21 (21.9) | |
| Multiple arterial anastomosis, N (%), (n = 94) | 15 (16) | |
| Multiple venous anastomosis, N (%), (n = 81) | 3 (3.7) | |
| Ureteral anastomosis, N (%) | UNC | 74 (76.3) |
| PU | 2 (2.1) | |
| Other | 21 (21.6) | |
| Bolus fluid administration, N (%), (n = 86) | 18 (20.9) | |
| Colloid fluid therapy, N (%), (n = 89) | 16 (18) | |
| Volume of fluid administered per BSA, mL/m2, median (IQR), (n = 41) | 1077.1 (1390.1) | |
| Postoperative complications within 30 days, N (%) | 24 (24.7) | |
| Induction immunosuppression, N (%) | Basiliximab | 48 (49.5) |
| ATG | 35 (36.1) | |
| Combined | 6 (6.2) |
Note: Mean intraoperative central venous pressure (available in 16.5%) and estimated blood loss (recorded inconsistently) were not analyzed.
Abbreviations: ATG, antithymocyte globulin; BP, blood pressure; BSA, body surface area; HTK, histidine–tryptophan–ketoglutarate; IQR, interquartile range; MAP calculated as (systolic + 2 × diastolic)/3; PU, pyeloureterostomy; SD, standard deviation; UNC, ureteroneocystostomy; UW, University of Wisconsin solution.
3.3. Postoperative Outcomes
The mean eGFR on postoperative day 7 and day 30 was 74.9 mL/min/1.73m2 (SD 37.8) and 75.2 mL/min/1.73m2 (SD 27.6), respectively. Dialysis was required in 11 patients (11.3%) during the first 7 days following KT. The median postoperative LOS was 12 (IQR 7) days. Nineteen patients (20%) required a blood transfusion. Postoperative complications within 30 days occurred in 24 patients (24.7%). Induction immunosuppression was administered to 94 patients (basiliximab, n = 48; antithymocyte globulin, n = 35; combined, n = 6, unspecified regimen, n = 5). A protocol graft biopsy was not routinely performed, and documented biopsy findings were available in only a few patients.
3.4. Perioperative Variables and Graft Function
Age, pretransplant dialysis modality, pretransplant dialysis duration, and systolic BP at reperfusion were significantly associated with DGF in univariate analysis (p < 0.001, p = 0.008, p = 0.029, and p = 0.020, respectively). In multivariate analysis, none of the evaluated variables remained independently associated with DGF (Table 3).
TABLE 3.
Univariate and multivariate analysis of factors associated with delayed graft function (DGF).
| Variable | DGF (n = 11) | Immediate (n = 86) | Univariate OR (95% CI) | p | Multivariate OR (95% CI) | p |
|---|---|---|---|---|---|---|
| Gender, male | 5 (45.5) | 39 (45.3) | 0.98 (0.29, 3.34) | 0.978 | — | — |
| Age, month | 191 (31) | 140 (95) | 1.03 (1.01, 1.05) | < 0.001 | 1.02 (1.00, 1.04) | 0.070 |
| Body mass index, kg/m2 | 17.23 (2.33) | 16.92 (3.05) | 1.05 (0.82, 1.33) | 0.719 | — | — |
| Body surface area, m2 | 1.30 (0.23) | 1.03 (0.36) | 7.86 (0.73, 84.75) | 0.070 | — | — |
| Primary disease: GN vs. CAKUT | 6 (54.5) | 30 (34.9) | 1.92 (0.52, 7.08) | 0.318 | — | — |
| Primary disease: Other vs. CAKUT | 1 (9.1) | 16 (18.6) | 0.82 (0.11, 5.94) | 0.837 | — | — |
| Dialysis: HD vs. preemptive | 8 (72.7) | 23 (26.7) | 8.08 (1.28, 50.92) | 0.008 | 17.09 (0.94, 309.43) | 0.055 |
| Dialysis: CAPD vs. preemptive | 2 (18.2) | 30 (34.9) | 1.83 (0.22, 15.16) | 0.563 | 7.45 (0.38, 148.06) | 0.142 |
| Pretransplant dialysis, month | 35 (81) | 12 (22) | 1.02 (1.00, 1.04) | 0.029 | — | — |
| Prior Urol. Surgery: UUT vs. No | 1 (9.1) | 22 (25.6) | 0.39 (0.06, 2.42) | 0.258 | — | — |
| Prior Urol. Surgery: LUT vs. No | 1 (9.1) | 9 (10.5) | 0.92 (0.13, 6.38) | 0.932 | — | — |
| Donor type, living | 4 (36.4) | 32 (37.2) | 1.01 (0.28, 3.55) | 0.992 | — | — |
| Donor gender, male | 5 (45.5) | 50 (58.1) | 0.61 (0.18, 2.08) | 0.426 | — | — |
| Donor age, year | 38 (44) | 17 (29) | 1.01 (0.98, 1.05) | 0.498 | — | — |
| Donor pre‐op creatinine, mg/dL | 0.68 (0.35) | 0.65 (0.26) | 1.41 (0.43, 4.66) | 0.554 | — | — |
| Perfusion: Ringer vs. UW | 0 (0) | 2 (5.3) | 0.49 (0.01, 22.46) | 0.640 | — | — |
| Perfusion: HTK vs. UW | 1 (12.5) | 18 (47.4) | 0.20 (0.03, 1.36) | 0.058 | — | — |
| Systolic BP at reperfusion | 124 (23) | 105 (19) | 1.04 (1.00, 1.08) | 0.020 | 1.01 (0.97, 1.05) | 0.724 |
| Diastolic BP at reperfusion | 75 (39) | 57 (29) | 1.03 (0.99, 1.07) | 0.137 | — | — |
| Mean arterial pressure at reperfusion | — | — | 1.04 (1.00, 1.08) | 0.066 | — | — |
| pH | 7.23 (0.16) | 7.29 (0.06) | Unstable* | — | — | — |
| Cold ischemia time, minutes | 765 (581) | 601.5 (834) | 1.00 (1.00, 1.00) | 0.258 | — | — |
| Operation time, minutes | 180 (101) | 143 (55) | 1.01 (1.00, 1.02) | 0.140 | — | — |
| Multiple arterial anastomosis | 2 (18.2) | 13 (15.7) | 1.37 (0.29, 6.48) | 0.687 | — | — |
| Multiple venous anastomosis | 0 (0) | 3 (4.2) | 0.93 (0.03, 30.36) | 0.963 | — | — |
| Ureteral: PU vs. UNC | 0 (0) | 2 (2.3) | 1.23 (0.03, 53.64) | 0.899 | — | — |
| Ureteral: Other vs. UNC | 1 (9.1) | 20 (23.3) | 0.45 (0.07, 2.77) | 0.340 | — | — |
| Bolus fluid administration | 2 (22.2) | 16 (20.8) | 1.24 (0.26, 5.95) | 0.784 | — | — |
| Colloid fluid therapy | 1 (9.1) | 15 (17.4) | 0.86 (0.13, 5.67) | 0.869 | 2.06 (0.21, 20.09) | 0.538 |
Note: Univariate and multivariate associations assessed by Firth penalized logistic regression. Descriptive values: N (%) for categorical and mean (SD) or median (IQR) for continuous variables.
pH estimate was unstable owing to its narrow range and the small number of events. Because only 11 DGF events occurred (events per variable ≈2), the multivariate model is exploratory; variables selected a priori or considered clinically relevant were entered into the exploratory multivariable model. Pretransplant dialysis duration was not included because of its overlap with dialysis modality. CAKUT, CAPD, GN, HD, HTK, PU, UNC, UW as defined previously.
For 7‐day post‐transplant eGFR, univariate analysis demonstrated significant associations with recipient age (p < 0.001), BSA (p < 0.001), pretransplant dialysis duration (p = 0.008), systolic BP at the time of reperfusion (p = 0.019), MAP (p = 0.037), cold ischemia time (p = 0.047), and the presence of DGF (p < 0.001). In multivariate analysis, only cold ischemia time remained independently associated with 7‐day eGFR [B = −0.055, 95% CI: (−0.098, −0.012); p = 0.014] (Table 4).
TABLE 4.
Univariate and multivariate linear regression analyses of factors associated with 7‐day eGFR.
| Variable | Univariate B (95% CI) | p | Multivariate B (95% CI) | p |
|---|---|---|---|---|
| Gender, male | 9.63 (−6.16, 25.42) | 0.229 | — | — |
| Age, month | −0.25 (−0.37, −0.13) | < 0.001 | −0.130 (−0.998, 0.739) | 0.761 |
| Body mass index, kg/m2 | −2.27 (−4.95, 0.40) | 0.095 | — | — |
| Body surface area, m2 | −35.96 (−56.75, −15.16) | < 0.001 | −27.922 (−157.306, 101.461) | 0.661 |
| Primary disease: GN vs. CAKUT | −0.07 (−17.45, 17.31) | 0.994 | — | — |
| Primary disease: Other vs. CAKUT | 20.16 (−1.28, 41.60) | 0.065 | 17.568 (−20.982, 56.117) | 0.357 |
| Preemptive transplantation | −9.07 (−25.33, 7.20) | 0.271 | — | — |
| Dialysis: HD vs. preemptive | 6.93 (−12.31, 26.18) | 0.476 | — | — |
| Dialysis: CAPD vs. preemptive | 11.13 (−7.93, 30.19) | 0.249 | — | — |
| Pretransplant dialysis, month | −0.50 (−0.86, −0.14) | 0.008 | −0.194 (−0.792, 0.404) | 0.510 |
| Prior Urol. Surgery: UUT vs. No | 2.57 (−16.63, 21.78) | 0.791 | — | — |
| Prior Urol. Surgery: LUT vs. No | −8.66 (−34.54, 17.21) | 0.508 | — | — |
| Donor type, living | 12.58 (−3.40, 28.56) | 0.121 | — | — |
| Donor gender, male | −0.10 (−16.02, 15.82) | 0.990 | — | — |
| Donor age, year | 0.02 (−0.44, 0.48) | 0.928 | — | — |
| Donor pre‐op creatinine | 0.75 (−27.68, 29.19) | 0.958 | — | — |
| Perfusion: Ringer vs. UW | 2.74 (−55.63, 61.11) | 0.925 | — | — |
| Perfusion: HTK vs. UW | 15.45 (−10.03, 40.92) | 0.227 | — | — |
| Systolic BP at reperfusion | −0.61 (−1.12, −0.10) | 0.019 | −0.349 (−1.270, 0.572) | 0.442 |
| Diastolic BP at reperfusion | −0.54 (−1.11, 0.03) | 0.063 | — | — |
| Mean arterial pressure at reperfusion | −0.60 (−1.16, −0.04) | 0.037 | — | — |
| pH | 71.21 (−67.86, 210.28) | 0.311 | — | — |
| Cold ischemia time, minutes | −0.02 (−0.04, −0.00) | 0.047 | −0.055 (−0.098, −0.012) | 0.014 |
| Operation time, minutes | 0.03 (−0.16, 0.21) | 0.770 | — | — |
| Multiple arterial anastomosis | −10.30 (−33.18, 12.58) | 0.373 | — | — |
| Multiple venous anastomosis | −0.61 (−59.08, 57.87) | 0.984 | — | — |
| Ureteral: PU vs. UNC | 45.16 (−8.52, 98.83) | 0.098 | — | — |
| Ureteral: Other vs. UNC | −0.30 (−18.98, 18.38) | 0.974 | — | — |
| Bolus fluid administration | 17.51 (−2.98, 38.01) | 0.093 | — | — |
| Colloid fluid therapy | 20.40 (−0.59, 41.40) | 0.057 | — | — |
| DGF | −44.55 (−69.37, −19.73) | < 0.001 | −18.332 (−62.978, 26.314) | 0.406 |
Note: Univariate B (95% CI) reported for all variables; multivariate model includes variables significant in univariate analysis. MAP was not included in the final model to avoid redundancy, as systolic and diastolic blood pressure were already entered as separate variables (univariate p = 0.037). The multivariate model was fitted on complete cases (n = 33); the corresponding model for 30‐day eGFR (Table 5) included 87 patients
Abbreviations: B, unstandardized coefficient; eGFR, estimated glomerular filtration rate.
Regarding 30‐day post‐transplant eGFR, univariate analysis identified recipient age (p = 0.027), BMI (p = 0.021), BSA (p = 0.039), preemptive transplantation status (p = 0.041), donor gender (p = 0.044), and DGF (p < 0.001) as significant factors. In multivariate analysis, preemptive transplantation [B = −15.990, 95% CI: (−27.017, −4.963) p = 0.005] and DGF [B = −36.943, 95% CI: (−58.390, −15.495); p = 0.001] remained independently associated with lower 30‐day eGFR (Table 5). MAP was not associated with 30‐day eGFR (p = 0.281). In a sensitivity analysis, the association between preemptive transplantation and 30‐day eGFR was no longer significant after excluding the largest center (B = −0.87; p = 0.94), and a significant center effect was observed, whereas the associations of cold ischemia time (7‐day) and DGF (30‐day) were preserved (Table S1).
TABLE 5.
Univariate and multivariate linear regression analyses of factors associated with 30‐day eGFR.
| Variable | Univariate B (95% CI) | p | Multivariate B (95% CI) | p |
|---|---|---|---|---|
| Gender, male | 1.35 (−10.33, 13.04) | 0.819 | — | — |
| Age, month | −0.11 (−0.20, −0.01) | 0.027 | −0.035 (−0.287, 0.218) | 0.786 |
| Body mass index, kg/m2 | −2.26 (−4.16, −0.35) | 0.021 | −2.093 (−4.356, 0.170) | 0.069 |
| Body surface area, m2 | −16.45 (−32.01, −0.88) | 0.039 | 7.430 (−36.980, 51.840) | 0.740 |
| Primary disease: GN vs. CAKUT | −1.38 (−14.31, 11.55) | 0.832 | — | — |
| Primary disease: Other vs. CAKUT | 9.75 (−6.06, 25.56) | 0.224 | — | — |
| Preemptive transplantation | −12.21 (−23.91, −0.51) | 0.041 | −15.99 (−27.02, −4.96) | 0.005 |
| Dialysis: HD vs. preemptive | 8.53 (−5.35, 22.40) | 0.225 | — | — |
| Dialysis: CAPD vs. preemptive | 15.64 (2.04, 29.25) | 0.025 | — | — |
| Pretransplant dialysis, month | −0.17 (−0.45, 0.11) | 0.220 | — | — |
| Prior Urol. Surgery: UUT vs. No | 2.99 (−11.07, 17.05) | 0.674 | — | — |
| Prior Urol. Surgery: LUT vs. No | −2.89 (−21.81, 16.03) | 0.762 | — | — |
| Donor type, living | −2.31 (−14.16, 9.53) | 0.699 | — | — |
| Donor gender, male | 11.75 (0.33, 23.17) | 0.044 | 7.386 (−3.169, 17.941) | 0.168 |
| Donor age, year | −0.15 (−0.48, 0.19) | 0.388 | — | — |
| Donor pre‐op creatinine | 4.71 (−15.79, 25.20) | 0.649 | — | — |
| Perfusion: Ringer vs. UW | −7.68 (−52.97, 37.60) | 0.733 | — | — |
| Perfusion: HTK vs. UW | −4.25 (−24.28, 15.79) | 0.670 | — | — |
| Systolic BP at reperfusion | −0.28 (−0.64, 0.08) | 0.120 | — | — |
| Diastolic BP at reperfusion | −0.16 (−0.55, 0.24) | 0.438 | — | — |
| Mean arterial pressure at reperfusion | −0.21 (−0.61, 0.18) | 0.281 | — | — |
| pH | 55.18 (−41.23, 151.59) | 0.258 | — | — |
| Cold ischemia time, minutes | 0.00 (−0.01, 0.02) | 0.782 | — | — |
| Operation time, minutes | 0.06 (−0.08, 0.20) | 0.382 | — | — |
| Multiple arterial anastomosis | −9.74 (−26.44, 6.97) | 0.250 | — | — |
| Multiple venous anastomosis | 4.98 (−35.64, 45.61) | 0.808 | — | — |
| Ureteral: PU vs. UNC | 16.66 (−22.91, 56.23) | 0.405 | — | — |
| Ureteral: Other vs. UNC | 3.66 (−10.13, 17.45) | 0.599 | — | — |
| Bolus fluid administration | 8.73 (−5.43, 22.90) | 0.223 | — | — |
| Colloid fluid therapy | 10.78 (−4.31, 25.87) | 0.159 | — | — |
| DGF | −34.58 (−53.63, −15.53) | < 0.001 | −36.943 (−58.390, −15.495) | 0.001 |
Note: The multivariate association of preemptive transplantation was no longer significant after excluding the largest center (B = −0.87; p = 0.94; see sensitivity analysis), indicating a probable center effect. MAP was not associated with 30‐day eGFR (univariate p = 0.281).
Abbreviations: B, unstandardized coefficient; eGFR, estimated glomerular filtration rate.
4. Discussion
Pediatric KT has its own distinct challenges when compared to adults. Moreover, compared with adults, pediatric transplantation recipients present distinct anesthetic and intraoperative challenges due to limited physiological reserve, small‐caliber vessels, confined working space, age‐dependent pharmacokinetics, and a narrow margin for hemodynamic and fluid imbalance, all of which may critically influence early graft function [14]. Still, outcomes after pediatric KT have improved substantially over time, with marked gains observed in both graft function and graft survival across all recipient populations [4].
Among the many factors influencing KT outcomes in children, the role of intraoperative factors remains relatively understudied, with existing studies reporting conflicting results [7, 8, 10, 11]. Moreover, intraoperative management practices for pediatric KT recipients vary widely among centers of expertise and within centers [9]. Therefore, in this multicenter study, we aimed to investigate the effect of perioperative factors on DGF and early graft function assessed at 7 and 30 days post‐transplant. Based on our findings, intraoperative fluid management parameters, including BP, colloid fluid therapy, bolus fluid administration, and intraoperative pH, were not associated with graft function. Early graft function at 7 days was mainly determined by cold ischemia time, while preemptive transplantation status and the occurrence of DGF were the main determinants at 30 days post‐transplant. This accords with Zhang et al., who found that neither intraoperative nor postoperative fluid nor hemodynamic characteristics predicted early graft function [7].
Reported rates of DGF after pediatric KT range from 4.2% to 22% [15, 16, 17]. In the present study, the incidence of DGF was 11.3%, which is consistent with previously published data. In their study, Boussetta et al. reported that DGF is a negative determinant of early graft function [15]. In line with these findings, our study demonstrated a significant association between DGF and 30‐day eGFR, indicating that the development of DGF has an adverse impact on subsequent graft function. Previous studies have identified factors such as multiple renal arteries, prolonged cold ischemia time, and small recipient size as being associated with the development of DGF in pediatric KT. However, in the present study, although several variables were significant in univariate analyses, none reached statistical significance in multivariate analysis. This finding is likely attributable to the limited number of patients who developed DGF (n = 11) in our cohort.
Regarding allograft outcomes, early function at day 7 was independently determined by cold ischemia time, pointing to ischemia–reperfusion injury as the dominant influence in the immediate postoperative period. This argues for minimizing the interval to transplantation wherever possible, particularly in deceased‐donor recipients. At day 30, the independent associations shifted to preemptive transplantation status and DGF.
The lower 30‐day eGFR observed with preemptive transplantation runs counter to the usual expectation of an early advantage, and several observations suggest it does not represent a true biological effect. The association was confined to a single center: it was significant in the Nantes cohort (B = −20.8; 95% CI −34.5 to −7.1; p = 0.004) but absent, and numerically reversed, in the Turkish centers (B = +4.1; 95% CI −18.7 to 27.0; p = 0.72), with a borderline center interaction (p = 0.052) and full attenuation once the largest center was excluded (Figure S1). Preemptive recipients neither received a more conservative fluid regimen (median intravenous fluid per body surface area 1404 vs. 826 mL/m2; p = 0.99) nor differed hemodynamically from dialysis recipients. Finally, DGF was almost absent among preemptive recipients (1/34, 2.9% vs. 10/63, 15.9%), so adjustment for DGF may have introduced a suppressor effect that exaggerated the negative preemptive coefficient. Given the small preemptive subgroup (n = 34) and a literature that largely reports comparable or better outcomes after preemptive transplantation, including evidence that post‐transplant function is largely independent of pretransplant kidney function [18], we interpret this finding as most likely reflecting between‐center differences rather than an intrinsic disadvantage of preemptive transplantation. DGF, by contrast, was associated with markedly lower 30‐day eGFR, in keeping with its definition as early graft dysfunction.
Taken together, these findings indicate that while perioperative and hemodynamic management remain clinically important, early and short‐term graft function in pediatric KT appears to be predominantly influenced by non‐intraoperative factors rather than intraoperative management parameters.
4.1. Limitations of the Study
The primary limitations of this study were its small sample size and retrospective design, which rendered it susceptible to selection bias. Nevertheless, the limited number of pediatric KT performed worldwide represents a common constraint across studies in this field. Additional limitations include missing data for several key parameters in a subset of patients; in particular, CVP was available in only 16.5% of patients and total intravenous fluid per BSA in 42.3%. The absence of associations for hemodynamic and fluid variables should therefore be interpreted as absence of evidence rather than evidence of absence. Although the effect of intraoperative CVP on graft outcomes was intended to be assessed, this analysis could not be performed because of missing data in 83.5% of patients (Table S2). Another limitation of the study is that BP during reperfusion was represented by a single measurement or a median value, which may not adequately capture the dynamic hemodynamic fluctuations occurring throughout the reperfusion period. The inclusion of both deceased and living donor KT in a single analysis may be considered another limitation of this study. However, a decline in living donor KT in the pediatric population has been observed in recent years [19]. Therefore, both donor types were evaluated together to provide a more representative and generalizable assessment of contemporary pediatric KT. The limited number of patients in the DGF group was considered another limitation of our study. Finally, because most patients were treated at a single center, between‐center differences may have influenced the results, as indicated by our sensitivity analysis.
Despite these limitations, the inclusion of patients from four centers in Turkiye and France strengthens the study and enhances the generalizability of its findings. Another key strength of our study is the combined evaluation of both DGF and eGFR.
5. Conclusion
In this study conducted in pediatric KT recipients, DGF was observed in a limited proportion of recipients and was not independently associated with donor, recipient, or intraoperative variables. Early graft function at 7 days was primarily associated with cold ischemia time, whereas function at 30 days was associated with preemptive transplantation status and DGF. Notably, no independent association was observed between intraoperative parameters, including BP, colloid fluid therapy, bolus fluid administration, or intraoperative pH, and either the development of DGF or early postoperative graft function. Further prospective, randomized, multicenter trials examining these findings are warranted to confirm the results of this study.
Author Contributions
M.I.D.: Protocol development, data collection, data analysis, article writing. H.B.H.: Protocol development, data collection, data analysis, article writing. M.T.: Protocol development, data collection, article editing. A.V.: Protocol development, data collection, article editing. H.B.: Data collection, article editing. A.D.: Data collection, article editing. M.E.P.: Data analysis, article editing. A.L.A.: Protocol development, article editing. A.P.: Data analysis, article editing. B.M.: Data collection, article editing. J.B.: Data collection, article editing. T.L.: Data collection, article editing. B.B.M.: Data analysis, article editing. T.P.: Protocol development, data collection, article editing. A.P.: Data analysis, article editing. A.C.S.: Data analysis, article editing. A.T.: Data analysis, article editing.
Funding
The authors have nothing to report.
Ethics Statement
This multicenter study was approved by the Ethics Committee of Necmettin Erbakan University, Faculty of Medicine, Non‐Pharmaceutical and Non‐Medical Device Clinical Research Ethics Committee (Meeting No: 235; Decision No: 2026/6317; Date: February 13, 2026; Application ID: 29249). The study was conducted in accordance with the principles of the Declaration of Helsinki. All necessary institutional permissions were obtained, and legal responsibilities rest with the investigators.
Consent
All authors gave informed consent for the publication of this study.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1: 30‐day eGFR by preemptive status, stratified by transplant center.
Table S2: Missing data for key variables (N = 97).
Figure S1: Forest plot of the adjusted difference in 30‐day eGFR for preemptive versus dialysis recipients, overall and by center group.
Acknowledgments
The authors have nothing to report.
EAU YAU Kidney Transplantation Working Group: The European Association of Urology (EAU) Young Academic Urologists (YAU) Kidney Transplantation Working Group is a collaborative research network operating under the EAU Young Academic Urologists (YAU) initiative. It brings together early‐career academic urologists from EAU‐affiliated centers to design and conduct multicenter clinical research in kidney transplantation, with the aim of generating evidence to improve donor and recipient outcomes. The members of the working group who contributed to this manuscript are: Muhammet Irfan Donmez, Hakan Bahadir Haberal, Muhammed Emin Polat, Alicia López‐Abad, Alessio Pecoraro, Beatriz Bañuelos Marco, Thomas Prudhomme, Alberto Piana, Alberto Costa Silva, and Angelo Territo.
Donmez M. I., Haberal H. B., Tasdemir M., et al., “Do Perioperative Surgical Factors Affect Early Graft Function in Pediatric Kidney Transplantation?,” Pediatric Transplantation 30, no. 8 (2026): e70414, 10.1111/petr.70414.
For a complete list of the EAU YAU Kidney Transplantation Working Group see Appendix.
Contributor Information
Muhammet Irfan Donmez, Email: muhammet.donmez@istanbul.edu.tr.
the European Association of Urology (EAU) Young Academic Urologists (YAU) Kidney Transplantation Working Group:
Muhammet Irfan Donmez, Hakan Bahadir Haberal, Muhammed Emin Polat, Alicia López‐Abad, Alessio Pecoraro, Beatriz Bañuelos Marco, Thomas Prudhomme, Alberto Piana, Alberto Costa Silva, and Angelo Territo
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
References
- 1. VanSickle J. S. and Warady B. A., “Chronic Kidney Disease in Children,” Pediatric Clinics of North America 69, no. 6 (2022): 1239–1254. [DOI] [PubMed] [Google Scholar]
- 2. Roach J. P., Bock M. E., and Goebel J., “Pediatric Kidney Transplantation,” Seminars in Pediatric Surgery 26, no. 4 (2017): 233–240. [DOI] [PubMed] [Google Scholar]
- 3. Hebert S. A., Swinford R. D., Hall D. R., Au J. K., and Bynon J. S., “Special Considerations in Pediatric Kidney Transplantation,” Advances in Chronic Kidney Disease 24, no. 6 (2017): 398–404. [DOI] [PubMed] [Google Scholar]
- 4. Van Arendonk K. J., Boyarsky B. J., Orandi B. J., et al., “National Trends Over 25 Years in Pediatric Kidney Transplant Outcomes,” Pediatrics 133, no. 4 (2014): 594–601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Harambat J., van Stralen K. J., Kim J. J., and Tizard E. J., “Epidemiology of Chronic Kidney Disease in Children,” Pediatric Nephrology 27, no. 3 (2012): 363–373. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Yarlagadda S. G., Coca S. G., R. N. Formica, Jr. , Poggio E. D., and Parikh C. R., “Association Between Delayed Graft Function and Allograft and Patient Survival: A Systematic Review and Meta‐Analysis,” Nephrology, Dialysis, Transplantation 24, no. 3 (2009): 1039–1047. [DOI] [PubMed] [Google Scholar]
- 7. Zhang J. Q. J., Cavazzoni E., Durkan A. M., et al., “Effect of Perioperative Management on Early Graft Function in Living Donor Paediatric Kidney Transplantation,” Pediatric Nephrology 40, no. 1 (2025): 231–242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Michelet D., Brasher C., Marsac L., et al., “Intraoperative Hemodynamic Factors Predicting Early Postoperative Renal Function in Pediatric Kidney Transplantation,” Paediatric Anaesthesia 27, no. 9 (2017): 927–934. [DOI] [PubMed] [Google Scholar]
- 9. Baboolal H. A., Lane J., and Westreich K. D., “Intraoperative Management of Pediatric Renal Transplant Recipients: An Opportunity for Improvement,” Pediatric Transplantation 27, no. 6 (2023): e14545. [DOI] [PubMed] [Google Scholar]
- 10. Wyatt N., Norman K., Ryan K., et al., “Perioperative Fluid Management and Associated Complications in Children Receiving Kidney Transplants in the UK,” Pediatric Nephrology 38, no. 4 (2023): 1299–1307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Porn‐Feldman H., Davidovits M., Nahum E., Weissbach A., Kaplan E., and Kadmon G., “Fluid Overload and Renal Function in Children After Living‐Donor Renal Transplantation: A Single‐Center Retrospective Analysis,” Pediatric Research 90, no. 3 (2021): 625–631. [DOI] [PubMed] [Google Scholar]
- 12. Clavien P. A., Barkun J., de Oliveira M. L., et al., “The Clavien‐Dindo Classification of Surgical Complications: Five‐Year Experience,” Annals of Surgery 250, no. 2 (2009): 187–196. [DOI] [PubMed] [Google Scholar]
- 13. Schwartz G. J., Munoz A., Schneider M. F., et al., “New Equations to Estimate GFR in Children With CKD,” Journal of the American Society of Nephrology 20, no. 3 (2009): 629–637. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Gulati A. and Sarwal M. M., “Pediatric Renal Transplantation: An Overview and Update,” Current Opinion in Pediatrics 22, no. 2 (2010): 189–196. [DOI] [PubMed] [Google Scholar]
- 15. Boussetta A., Abida N., Jellouli M., Ziadi J., and Gargah T., “Delayed Graft Function in Pediatric Kidney Transplant: Risk Factors and Outcomes,” Experimental and Clinical Transplantation 22, no. Suppl 1 (2024): 110–117. [DOI] [PubMed] [Google Scholar]
- 16. Hwang C. S., Kadakia Y., Sanchez‐Vivaldi J. A., et al., “Delayed Graft Function in Pediatric Living Donor Kidney Transplantation,” Pediatric Transplantation 27, no. 2 (2023): e14432. [DOI] [PubMed] [Google Scholar]
- 17. Lim W. H., McDonald S. P., Kennedy S. E., Larkins N., and Wong G., “Association Between Slow and Delayed Graft Function With Graft Outcomes in Pediatric and Adolescent Deceased Donor Kidney Transplant Recipients,” Transplantation 101, no. 8 (2017): 1906–1912. [DOI] [PubMed] [Google Scholar]
- 18. Ishani A., Ibrahim H. N., Gilbertson D., and Collins A. J., “The Impact of Residual Renal Function on Graft and Patient Survival Rates in Recipients of Preemptive Renal Transplants,” American Journal of Kidney Diseases 42, no. 6 (2003): 1275–1282. [DOI] [PubMed] [Google Scholar]
- 19. Smith J. M. and Brewer E. D., “Decreasing Living Donor Rates in Pediatric Kidney Transplantation: A Time for Action,” Pediatric Transplantation 21, no. 2 (2017): 1–2. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: 30‐day eGFR by preemptive status, stratified by transplant center.
Table S2: Missing data for key variables (N = 97).
Figure S1: Forest plot of the adjusted difference in 30‐day eGFR for preemptive versus dialysis recipients, overall and by center group.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.