Objectives: The Prognostic Nutritional Index, which combines serum albumin and lymphocyte count, captures protein reserves and immune competence in a single, easily obtained metric. Whether this index can predict delayed graft function among pediatric kidney transplant recipients has not been thoroughly evaluated. Here, we examined whether use of this index pretransplant can predict delayed graft function in this population.
Materials and Methods: We reviewed children (aged 1-18 years) who received kidney transplants at Başkent University Hospital from January 2013 to December 2024. Patients were stratified by delayed graft function status. We collected recipient and donor demographics, clinical characteristics, and routine preoperative laboratory values. The Prognostic Nutritional Index and other inflammatory indices (neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic inflammatory index derived from the same preoperative blood counts) were calculated.
Results: Twenty-eight of 162 children (17.3%) experienced delayed graft function. In regression analysis, each 1-point increase in the Prognostic Nutritional Index, which reflects better nutritional and immune status, was linked to an 8% lower chance of developing delayed graft function (odds ratio 0.92; 95% CI, 0.85-0.99; P = .⁰⁴²). Higher neutrophil-to-lymphocyte ratio indicated significantly greater risk of delayed graft function (odds ratio 1.30; 95% CI, 1.05-1.61; P = .⁰¹⁶), and a 1-unit rise in the platelet-to-lymphocyte ratio provided a small but meaningful protective effect (odds ratio 0.99; 95% CI, 0.993-0.999; P = .⁰⁴¹). Higher systemic inflammatory index indicated a slightly lower risk of delayed graft function (odds ratio 0.9997/unit increase; P = .⁰⁴⁹). Recipient characteristics did not differ between those with and without delayed graft function.
Conclusions: Among the study patients, the Prognostic Nutritional Index emerged as a strong predictor of delayed graft function. Integrating this index with simple inflammatory indices into preoperative assessments may enable early identification of high-risk children, inform donor selection, and guide perioperative management to improve graft outcomes.

Key words : Graft outcomes, Inflammatory index, Nutritional status, Renal transplant

Introduction
Kidney transplantation is the preferred renal replacement therapy for children with end-stage kidney disease, offering clear advantages in survival, quality of life, and growth when compared with long-term dialysis.^1,2 Despite these benefits, the early posttransplant period remains a critical phase, during which complications can influence both short-term and long-term graft outcomes. One such complication is delayed graft function (DGF), commonly defined as the need for dialysis within the first week after transplant.^3,4 Delayed graft function is clinically relevant because it is often associated with prolonged hospitalization, more complex perioperative management, and uncertainty with early graft recovery.⁴ In adult kidney transplant, DGF has also been consistently linked to poorer graft outcomes, highlighting the importance of identifying patients at risk before transplant.^5,6 Historically, the risk of DGF has been largely attributed to donor-related and perioperative factors, such as donor age, ischemia-reperfusion injury, and technical aspects of the transplant procedure.⁷ However, increasing attention has been directed toward the role of recipient-related factors in shaping early graft recovery. Baseline nutritional and inflammatory status may influence how well a transplanted kidney tolerates perioperative stress and recovers after reperfusion.^6,8 Children approaching kidney transplant frequently experience complex nutritional disturbances because of chronic kidney disease (CKD).^9,10 Although these factors are well known to impair growth, they may also affect tissue repair mechanisms, immune competence, and inflammatory responses, all of which are relevant during the immediate posttransplant period.^6,8 Traditional approaches, including detailed dietary assessments and anthropometric measurements, provide valuable information but are time-consuming and may not fully capture the interaction between nutrition and inflammation.^6,8-10 As a result, interest has grown in composite indices derived from routine laboratory parameters that can reflect both nutritional reserve and immune-inflammatory status.^6,8 The Prognostic Nutritional Index (PNI), calculated by using serum albumin level and absolute lymphocyte count, is one such index.⁸ The PNI integrates markers of protein reserves, systemic inflammation, and immune function, offering a simple and widely available measure of biological vulnerability.⁸ In patients with CKD, PNI has been used to stratify nutritional risk and has been associated with adverse renal function trajectories.⁸ Similar concepts have been explored in other transplant settings. In liver transplant, PNI-based models have demonstrated predictive value for early postoperative complications, supporting the broader principle that baseline nutritional and immune status may influence early organ recovery after major surgery.¹¹ In addition to PNI, inflammatory indices derived from complete blood counts, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), have gained attention as accessible markers of immune and inflammatory activity.⁶ These indices are attractive in clinical practice because they require no additional testing and may provide complementary information to PNI. Against this background, in this study, we aimed to evaluate whether preoperative PNI is associated with DGF in pediatric kidney transplant recipients. We also sought to examine the relationship between DGF and other routinely available blood-based inflammatory indices, including NLR, PLR, and SII.

Materials and Methods
We conducted a retrospective cohort study at Başkent University Hospital, Ankara, Türkiye, reviewing pediatric kidney transplants performed between January 2013 and December 2024. Inclusion criteria were as follows: age 1 to 18 years at transplant, received a kidney transplant within the study period, and had available preoperative laboratory data to calculate inflammatory indexes and PNI. Exclusion criteria were as follows: missing key laboratory components required to compute indices (albumin or full blood count), where imputation was not appropriate, and history of retransplant. Delayed graft function was defined as the need for at least 1 dialysis session within the first 7 days after kidney transplant, consistent with widely used clinical definitions.^4,12 We extracted data from electronic medical records and transplant databases, including recipient demographics (age, sex), anthropometric measurements (weight, height), dialysis modality before transplant, and preoperative laboratory parameters (serum albumin, complete blood count, hemoglobin, and other routinely collected values). Postoperative outcomes included the occurrence of DGF and postoperative complications, such as infections, rejection episodes, and surgical complications, recorded according to standard clinical definitions and documentation. We also collected age, sex, weight, and height of donors. The PNI was calculated using the established formula¹³ (Figure 1): PNI = 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (/mm³). This approach has been used in pediatric CKD to capture nutritional and immune-related vulnerability.¹³ From the same preoperative complete blood count,^6,14 NLR (neutrophil count/lymphocyte count), PLR (platelet count/lymphocyte count), and SII ([platelet count × neutrophil count]/lymphocyte count) were calculated. All indices were computed from the closest available preoperative blood test prior to transplant. We presented normally distributed continuous variables as mean ± SD and nonnormally distributed variables as median with interquartile range. We summarized categorical variables as frequencies and percentages. For group comparisons, we analyzed continuous variables with the independent-samples t test for normally distributed data or the Mann Whitney U test for nonnormally distributed data. We compared categorical variables with the χ2 test or the Fisher exact test, as appropriate, based on expected cell counts. We conducted univariable logistic regression analysis to assess associations between candidate predictors (PNI, NLR, PLR, SII, and selected clinical covariates) and DGF. We conducted multivariable logistic regression analysis to estimate adjusted associations, with results reported as odds ratios (ORs) and 95% CIs. We used SPSS version 2025 to conduct all statistical analyses, applying 2-sided tests with a significance level of P < .⁰⁵.

Results

Baseline recipient and donor characteristics
Among 162 pediatric kidney transplant recipients included in our study, 28 (17.3%) developed DGF. Baseline recipient and donor characteristics stratified by DGF status are summarized in Table 1. Median recipient age was similar between the non-DGF and DGF groups (12.0 vs 11.5; P > .⁰⁵) years. The distribution of recipient sex did not differ between groups, with males comprising 52.2% in the non-DGF group and 53.6% in the DGF group (P > 0.05). Median recipient weight (40.5 vs 36.0 kg; P = .⁷⁹⁴) and height (146.0 vs 146.0 cm; P = .⁷¹⁹) were comparable between groups. Median dialysis duration pretransplant was 0.45 years in the non-DGF group and 0.60 years in the DGF group (P = 0.368). The distribution of dialysis modality did not differ significantly between groups (P = 0.206), with 39.6% of recipients on hemodialysis, 15.7% on peritoneal dialysis, and 44.8% having preemptive transplant in the non-DGF group compared with 57.1%, 14.3%, and 28.6%, respectively, in the DGF group. With respect to donor characteristics, median donor age was 39.0 years (range, 34.0-43.8 y) in the non-DGF group and 33.0 years (range, 19.0-42.3 y) in the DGF group. Median donor weight (72.0 vs 67.5 kg; P = .⁰⁶⁸) and donor height (168.0 vs 161.0 cm; P = 0098) were similar between groups. Donor sex distribution did not differ significantly.

Preoperative laboratory and inflammatory indices
Preoperative laboratory parameters were comparable between recipients with and without DGF (Table 2). Median serum albumin levels did not differ significantly between the non-DGF and DGF groups (3.95 vs 3.70 g/dL; P = .³⁶³). Similarly, preoperative hemoglobin concentrations were comparable between groups (9.05 vs 8.88 g/dL; P = .⁶⁵⁰). There were no statistically significant differences in individual preoperative inflammatory cell counts. Median neutrophil counts were 5.30 ×109/L in the non-DGF group and 4.38 ×109/L in the DGF group (P = .⁸⁴⁰), whereas lymphocyte counts were 1.19 ×109/L and 1.66 ×109/L, respectively (P = .¹⁶⁹). Platelet counts were also similar between groups (233.5 vs 204.0 ×109/L; P = .²⁶⁸). Composite inflammatory and nutritional indices derived from these parameters showed no significant unadjusted differences.

Regression analysis for predictors of delayed graft function
Table 3 lists results of univariable and multivariable logistic regression analyses for DGF. In univariable analysis, PNI was associated with an OR of 0.94 (95% CI, 0.88-1.00; P = .⁰⁶¹), NLR demonstrated an OR of 1.18 (95% CI, 1.01-1.38; P = .⁰³⁴), PLR was associated with an OR of 0.995 (95% CI, 0.990-1.000; P = .⁰⁶⁷), and SII demonstrated an OR of 0.9998 (95% CI, 0.9996-1.0000; P = .⁰⁸¹). In univariable analysis, higher NLR was associated with an increased risk of DGF, and PNI, PLR, and SII demonstrated trends toward association with DGF but did not reach statistical significance at the univariable level. In the multivariable logistic regression model, PNI emerged as an independent protective factor for DGF. Each 1-point increase in PNI was associated with an 8% reduction in the odds of DGF (OR 0.92; 95% CI, 0.85-0.99; P = .⁰⁴²). In contrast, higher NLR independently increased the risk of DGF, with a 30% increase in odds per unit rise (OR 1.30; 95% CI, 1.05-1.61; P = .⁰¹⁶). Both PLR and SII remained significant in the adjusted model, demonstrating modest protective associations with DGF (PLR showed OR 0.99; 95% CI, 0.993-0.999; P = .⁰⁴¹; and SII showed OR 0.9997; 95% CI, 0.9994-1.0000; P = .⁰⁴⁹).

Discussion
In this retrospective cohort of 162 pediatric kidney transplant recipients, we found that preoperative PNI was independently associated with DGF, with higher PNI conferring a protective effect. In other words, a child’s pretransplant nutritional immune profile, captured by 2 routinely available laboratory values, carried measurable information about the likelihood of early graft recovery. The PNI is not simply a “nutrition score.” Albumin, while influenced by protein intake, also behaves as a negative acute phase reactant and often reflects inflammation, fluid status, hepatic synthesis, and systemic illness.^13,14 Lymphocyte count, likewise, is shaped by immune activation, stress responses, uremia-related immune dysregulation, and comorbid infections.^13-15 When combined, the PNI can be viewed as an accessible parameter for physiological reserve in a child approaching a major surgical event. This conceptual framing is supported by pediatric CKD data. In children before dialysis, lower PNI indicated patients with poorer nutritional markers and was associated with faster decline in kidney function.¹⁶ This study, however, did not address transplant directly; however, the biological plausibility that PNI reflects clinically important vulnerability in pediatric kidney disease populations was strengthened. Transplant medicine more broadly has increasingly explored nutrition-inflammation composites to anticipate early postoperative organ dysfunction. In living donor liver transplant, a PNI-based modified model predicted acute kidney injury within 1 week with strong discrimination (area under the curve of 0.82), supporting the general hypothesis that baseline nutritional status and immune status influence immediate postoperative physiology.¹¹ Although the mechanisms of acute kidney injury after liver transplant differ from DGF after kidney transplant, both outcomes share ischemia-reperfusion stress, hemodynamic perturbations, inflammation, and the need for rapid tissue recovery. Adult kidney transplant studies have emphasized that risk of DGF is shaped by donor quality, ischemia times, preservation, and recipient phenotype.^7,17,18 Notably, recipient body composition and metabolic context appear relevant. In a large registry cohort, Molnar and colleagues showed that higher body mass index (BMI) independently increased DGF risk, with a graded rise across overweight and obesity categories.¹⁹ A recent meta-analysis similarly demonstrated that DGF cases had higher BMI (recipient and donor), alongside older donor and recipient age, reinforcing how baseline phenotype contributes to early graft recovery.²⁰ Our study differed in 2 clinically important ways. First, we focused on children, where BMI patterns, CKD-related growth failure, and nutritional disturbances are qualitatively different from adults. Second, rather than anthropometry alone, we evaluated a nutrition-immune composite index that may capture both malnutrition and inflammatory stress, 2 phenomena that are highly prevalent in children with advanced kidney disease and that may not be reflected by BMI. If validated externally, PNI could complement established DGF predictors by offering a practical preoperative marker that is available even in resource-limited settings. We observed that NLR was positively associated with DGF risk, consistent with the idea that heightened inflammatory tone before transplant may predispose to poorer early graft recovery.^6,21 The associations observed for PLR and SII in our dataset were statistically significant but modest in magnitude. From a practical standpoint, our findings suggest that incorporating the PNI into the pretransplant assessment may add clinically meaningful context to routine decision-making. A low PNI could help support realistic counselling for families and facilitate early discussion within the multidisciplinary team, including dietetics and metabolic medicine, regarding the anticipated early posttransplant course and the need for closer monitoring before transplant. Where time and clinical stability allow, PNI may also help identify children who could benefit from targeted prehabilitation strategies, such as nutritional optimization and systematic evaluation for occult inflammation or infection. Importantly, PNI is not intended to replace clinical judgement; rather, this index should be viewed as a simple, low-cost risk signal that complements existing clinical assessment and enriches multidisciplinary transplant decision-making. Our study should be interpreted in light of several limitations inherent to a retrospective, single-center analysis. Findings may not be fully generalizable, as local transplant protocols and perioperative practices can influence DGF. Some degree of residual confounding was also likely, particularly from donor, preservation, and intraoperative factors that were not completely captured. In this pediatric kidney transplant cohort, preoperative PNI was the strongest routine blood-based predictor of DGF, with higher PNI associated with lower odds of DGF. In addition, NLR showed a meaningful association with increased DGF risk. Integrating PNI and simple inflammatory indices into preoperative assessment may help identify children at higher risk for early graft dysfunction and allow proactive perioperative management. These findings warrant validation in external pediatric transplant populations.