Complete Blood Count in Delayed Graft Function: A Narrative Review

Abstract

Delayed graft function (DGF) is a common and critical complication following kidney transplantation, marked by acute kidney injury necessitating dialysis within the first postoperative week. Early detection of patients at risk is crucial for optimizing perioperative management and enhancing graft outcomes. While novel biomarkers have been suggested, their clinical application remains limited. This review explores the potential of complete blood count (CBC) parameters and derived indices as cost-effective, accessible alternatives. This narrative review synthesizes findings from studies examining the relationship between CBC parameters – such as hemoglobin, white blood cell count, and platelet count, and hematological indices like neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) – and DGF risk in kidney transplantation. A comprehensive literature search was conducted across major biomedical databases using targeted keywords. Selected studies were analyzed to assess the predictive value, underlying mechanisms, and clinical utility of these parameters. This review highlights a significant association between specific CBC parameters and DGF risk. Notably, low pre- or perioperative hemoglobin levels, significant perioperative hemoglobin drops, elevated pre- or post-transplant NLR, and altered PLR are correlated with increased DGF risk. These associations are thought to reflect underlying pathophysiological mechanisms, including inflammatory responses, ischemia–reperfusion injury, and immune cell activation. However, variability in study design, sampling time points, donor types, and DGF definitions complicates interpretation, underscoring the need for prospective multicenter validation. CBC-derived parameters demonstrate promising associations with DGF risk, particularly low pre-transplant hemoglobin levels and perioperative declines >1.3 g/dL. Preoperative NLR >3.5 and postoperative leukocyte ratios may aid in early graft dysfunction detection. Despite inconsistent evidence for platelet-related indices, further prospective multicenter studies are essential to confirm clinical utility and establish standardized cutoff values.

Introduction

Kidney transplantation is the treatment of choice for patients with stage 5 chronic kidney disease (CKD5), as it offers a better quality of life and increased survival compared to other replacement therapies such as hemodialysis or peritoneal dialysis. Despite major biomedical advances, complications that predispose to poor graft survival still exist, such as delayed graft function (DGF) [1].

DGF is a form of acute kidney injury that results in oliguria after transplantation and is associated with the need for dialysis during the first postoperative week. The prevalence of DGF depends on the definitions, case-mix (high-risk donor enrichment), and methodological differences transplant context. For living-donor kidney transplantations, DGF has been reported to be as low as 3.2% [2], while for deceased donors, DGF is around 20% to 30% [3]. The last annual data report of the Organ Procurement and Transplantation Network (OPTN) stated that although the incidence of DGF has increased over the last decade, it now appears to have plateaued, reaching 26.1% in adult kidney transplants in 2023 [4].

There are several factors that can promote the development of DGF, classified as donor-related, procurement-related, and recipient-related factors, as summarized in Table 1.

Table 1.

Factors that promote the development of DGF.

Donor-related	Procurement-related	Recipient-related
Donor type (living, deceased, marginal) Donor inotropic support Age	Cold ischemia time Warm ischemia time	Dialysis time and type body mass index Number of previous transplants Urethral obstruction

Beyond the clinical impact of DGF, this complication is associated with longer hospitalization (up to 6 additional days), which leads to a cost increase of approximately 10% [3]. Therefore, the early prediction or diagnosis of DGF is useful for initiating or modifying therapeutic measures. Although several new biomarkers have been investigated in recent years in the search for an ideal marker, their application in clinical practice has been limited due to the lack of standardization across populations and the additional costs involved [5].

Definition and Biomarkers

The definition of DGF has evolved over time and is often related to serum creatinine accumulation, the need for dialysis after transplantation, or a combination of both, resulting in heterogeneous reported incidences across populations and making study comparisons challenging. Currently, the most widely accepted definition is the need for dialysis during the first week after transplantation, which is not indicated due to vascular or urinary tract complications, or hyperkalemia [6].

Although alternative diagnostic markers such as neutrophil gelatinase-associated lipocalin (NGAL), cystatin C, liver-type fatty acid-binding proteins (L-FABP), interleukin-18 (IL-18), and microRNAs, these biomarkers have not yet been incorporated into routine clinical practice, partly because of the lack of standardization between populations and associated additional costs. In this context, the assessment of complete blood count (CBC) parameters becomes particularly relevant, as it is a routine test in transplant recipients and is widely accessible due to its low cost.

Association Between Blood Cells and DGF in Kidney Transplantation

DGF is a multifactorial clinical entity characterized by numerous pathophysiological processes, primarily renal ischemia–reperfusion injury. In recent years, studies on the role of blood cells in DGF have provided insights that help better understand these mechanisms, as summarized in Figure 1.

Figure 1.

Pathophysiological mechanisms involved in delayed graft function. Schematic representation of the involvement of blood cells in DGF in kidney transplantation. Created in https://BioRender.com

Donor

In kidney transplantation, organs can come from living donors, donors after cardiac death, or brain-dead donors. Regardless of donor type, an inflammatory process begins even before organ procurement and is triggered by surgical stress or the so-called “cytokine storm” observed in deceased donors. Notably, this inflammatory response is particularly intense in brain-dead donors as a result of medical interventions and electrocardiographic, hemodynamic, and hormonal changes occurring during this process [7]. Elevated cytokines reported in brain-dead donors include G-CSF, IL-6, IL-9, IL-16, and MCP-1, whereas in living and cardiac arrest donors, IL-6 and MCP-1, respectively, predominate [8]. The main characteristics are summarized in Table 2.

Table 2.

Characteristics by donor type: living, cardiac arrest, and brain death.

	Living donor	Cardiac arrest donor	Brain-dead donor
Main inflammatory cause	Surgical stress	Ischemia	Immune activation and cytokine storm
Predominant cytokines	Moderate increase IL-6, IL-10, TNF-α	IL-6, TNF-α, IL-8, MCP-1	G-CSF, IL-6, IL-9, IL-16 and MCP-1
Duration of the inflammation	Transient	Triggered after reperfusion	Persistent before transplantation
Impact on graft	Minimal	Increased endothelial damage, and oxidative stress	Increased macrophage and lymphocyte infiltration

In addition, cytokines induce endothelial activation in organs, increasing leukocyte infiltration into renal tissue [8]. The injured endothelium expresses ICAM-1, P-, and E-selectins, promoting neutrophil rolling and adhesion, potentially generating microvascular obstructions [9]. Neutrophils in renal tissue release enzymes such as proteases, myeloperoxidase, reactive oxygen species (ROS), and pro-inflammatory cytokines, amplifying tubular damage. Additionally, they form extracellular traps (NETs), exacerbating injury and inflammation [10]. Meanwhile, macrophages become activated, polarizing toward an M1 phenotype characterized by pro-inflammatory cytokine production (TNF-α, IL-1β, and IL-6), contributing to further leukocyte recruitment and intensifying the local inflammatory response [11]. Brain death is also a source of potent platelet-activating and procoagulant molecules that often promote disseminated intravascular coagulation [12]. Platelets activated by endothelial damage expose P-selectin, which interacts with leukocytes (mainly neutrophils and monocytes), favoring their recruitment and infiltration into the tissue [13], and enhances NET formation [14]. Although platelets were once considered potentially beneficial for tissue repair owing to their high growth factor content, experiments using platelet-rich plasma therapy and platelet depletion have ruled out this hypothesis [15].

All of these processes contribute to the donor’s systemic inflammatory environment, predisposing grafts to greater alloreactivity after transplantation. Some studies suggest that optimizing donor conditions before procurement is more beneficial than merely accelerating organ retrieval [16].

Recipient

Patients awaiting kidney transplantation have already developed CKD5, placing them in a chronic inflammatory state that affects the cellular immune system and elevates acute-phase inflammatory markers. Additionally, many have undergone dialysis, which induces leukocyte activation and subsequent cytokine production [17,18].

Once the kidney is reperfused during transplantation, an acute inflammatory response known as ischemia–reperfusion injury (IRI) is triggered. In this context, all toxic metabolites and radicals generated during ischemia enter systemic circulation, stimulating the inflammatory response and promoting leukocyte accumulation and activation within the kidney, thus increasing the threat to the graft [8]. Neutrophils continue to release reactive oxygen species (ROS), proteases, and NETs. Macrophages play a dual role: M1 phenotype cells amplify inflammation, whereas M2 phenotype cells attempt to initiate resolution and tissue repair. Platelets continue to release pro-inflammatory and procoagulant mediators, favoring microthrombus formation and cooperating with leukocytes to amplify the innate immune response.

The previously described pathophysiological mechanisms suggest that changes in peripheral blood cell counts can reflect the systemic immune activation and ischemia–reperfusion injury underlying DGF. Consequently, there is increasing interest in evaluating hematological parameters derived from routine complete blood counts (CBC) as early, accessible, and cost-effective indicators of DGF risk. The main findings for each element of the CBC are summarized in Table 3.

Table 3.

Complete blood count parameters and outcomes.

Study	Design	Donor subtype	n sutdied recipients	Predictive statistics	Main finding
Red blood cells
Molnar MZ et al	Retrospective	Deceased donor and living donor	11,836	OR 1.25 (95% CI 1.01–1.55; <0.05) in brain-death donor; OR 1.15 (95% CI 0.98–1.34; <0.05) in living donor	U-shaped association between pre-transplant hemoglobin and DGF; both low and high Hb linked to higher DGF risk
Moreira-Silva	Retrospective	Deceased donor	206	OR 0.252 (95% CI 0.159–0.041; p<0.001)	Low pre-transplant Hb independently associated with DGF
Chutipongtanate A et al	Retrospective	Deceased donor and living donors	269	OR 3.62 (95% CI 2.13–6.14; p<0.001)	Perioperative Hb drop >1.35 g/dL predicted poor early graft function and higher DGF risk
Song T et al	Retrospective	Living donor	702	HR 1.186 (95% CI 0.53–2.65; p>0.05)	Pre-transplant Hb <10 g/dL not associated with DGF in living donor transplants
Lukaszewski M et al	Retrospective	Deceased donor	86	p>0.05	Hemodilution-related Hb changes showed no impact on DGF occurrence
White blood cells (absolute counts and ratios)
Hogendorf P et al	Retrospective	Deceased donor	135	OR 0.331, (95% CI: 0.151–0.728; p=0.006) OR 2.653, (95% CI: 1.158–6.078; p=0.021)	Higher pre-transplant lymphocyte counts predicts early graft dysfunction Low preoperative lnNLR predicts early graft dysfunction
Pilichowska et al	Retrospectiva	Brain death donors	365	Pre – AUC=0.566, p=0.034; cut-off=0.71 G/L Post 24 h – AUC=0.623, p<0.001; cut-off=0.21 G/L Pre (p=0.431); Post 24 h day 1 (p=0.912)	DGF had higher monocyte counts in pre- and 24-hour post-transplant blood samples Recipient NLR not predictive of DGF development
Baral et al	Retrospective	Deceased donors	289	OR 13.48 (95% CI: 4.79–37.99; p<0.001)	Post-transplant NLR >3.5 increased DGF risk.
Halazun KJ et al	Retrospective	Deceased donor and living donor	398	H=10.673, (CI=6.151–18.518, p<.0001)	Early post-transplant NLR >3.5 predicts high DGF risk, especially in living-donor grafts
Cankaya E et al	Retrospective	Not specified	137	p=0.01 (lower NLR in preemptive vs non-preemptive at 1 year)	Preemptive kidney recipients showed lower NLR at 1 year
Zhang et al	Retrospective	Deceased donor	199	OR 2.8 (95% CI: 1.2–6.6; p=0.018)	Dynamic donor NLR change associated with higher DGF occurrence in recipients
Platelets and ratios
Pilichowska et al	Retrospectiva	Deceased donor	365	P>0.5	No predictive value for platelet count or platelet ratios
Baral et al	Retrospective	Deceased donors	289	HR 3.163 (95% CI 1.256–7.963; p=0.015). AUC 0.655; sensitivity 72.7%, specificity 58.2%	Higher pre-transplant PLR independently predicted DGF
Siddiqui et al	Retrospective	Deceased donor and living donors	51	AUC 0.69; sensitivity 68.8%, specificity 31.4%	Lower PLR associated with DGF in pediatric recipients

Complete Blood Count Parameters and DGF

Red Blood Cells

Decreased hemoglobin levels are a common complication in CKD5 patients awaiting transplantation, and are related to reduced erythropoietin production or nutrient deficiencies (folate and vitamin B12) necessary for red blood cell synthesis. Additionally, perioperative factors, such as blood loss or hemodilution from intravenous fluid administration, can further reduce hemoglobin levels, and early post-transplant inflammatory processes can contribute to anemia development [19].

Few studies have analyzed pre-transplant hemoglobin levels in kidney transplant recipients, and it is interesting to note the contrast between the 2 studies with the largest population. Molnar et al reported a U-shaped association between pre-transplant hemoglobin and DGF occurrence, and low hemoglobin values (10.00–10.99 g/dL; OR 1.25, 95% CI: 1.01–1.55) and high levels (>13 g/dL; OR 1.15, 95% CI: 0.98–1.34) were linked to DGF. Additionally, the use of high-dose erythropoiesis-stimulating agents prior to transplant increased DGF risk (OR 1.05, 95% CI: 1.02–1.09) [20]. In contrast, Song reported that pre-transplant hemoglobin <10 g/dL in a cohort of 702 living-donor kidney recipients was not associated with DGF development (HR 1.186, 95% CI: 0.53–2.654, P>0.05) [21]. This contradiction underscores the critical importance of the clinical context, specifically the type of donor from which the graft originates. In deceased-donor transplants, where the baseline risk of ischemia–reperfusion injury is higher, suboptimal hemoglobin levels are a significant risk factor, whereas in living-donor transplantation, the predictive value of anemia is considerably diminished.

Other studies with limited cohorts have reported an association between hemoglobin levels and DGF, particularly in the context of death donors. Silva et al found an association between low pre-transplant hemoglobin and DGF development (OR 0.252, 95% CI: 0.159–0.041; p<0.001) [22]. Additionally, Chutipongtanate et al found that a perioperative hemoglobin drop >1.35 g/dL was a risk marker for poor early graft function (OR 3.62, 95% CI: 2.13–6.14, P<0.001) [23]. In contrast, Lukaszewski et al found no association between hemoglobin decrease and DGF occurrence due to hemodilution in kidney transplant recipients [24]. Although this finding is relevant for its focus on hemodilution, it has limited statistical power to counterbalance the results derived from larger cohorts.

Taken together, while the predictive value of pre-transplant hemoglobin depends on donor type, the consistency observed across studies of moderate to high robustness suggests that both pre-existing anemia and acute perioperative hemoglobin decline are systemic stressors that exacerbate the risk of DGF. This may be because lower hemoglobin concentrations are associated with hypoxia and acidosis in the kidney at a critical moment, thereby exacerbating IRI [25].

White Blood Cells

The role of leukocytes in DGF pathophysiology has been explored in studies analyzing absolute peripheral leukocyte counts. Hogendorf et al described an increased absolute lymphocyte count in recipients of cardiac arrest donor kidneys associated with DGF development (OR 0.331, 95% CI: 0.151–0.728; P=0.006) [18]. This may reflect a pre-existing activation of adaptive immune pathways, as lymphocytes participate in ischemia–reperfusion-induced inflammation by producing cytokines that amplify endothelial injury and delay functional recovery.

Pilichowska et al reported that recipients who developed DGF had higher monocyte counts in pre- and 24-hour post-transplant blood samples than non-DGF recipients. The perioperative decline in the absolute monocyte count was interpreted as evidence of recruitment into the graft, where they differentiated into macrophages. In the same study, the absolute neutrophil counts were not significantly different between the groups [26].

These findings suggest that elevated monocyte or lymphocyte counts are systemic inflammatory priming that predisposes patients to maladaptive graft responses. Importantly, these peripheral changes align with the broader immune cascades previously described: cytokine surges in brain-dead donors promote endothelial activation and leukocyte recruitment, while in recipients, pre-existing uremic inflammation and dialysis-induced leukocyte activation enhance the pro-inflammatory milieu into which the graft is placed. Thus, absolute leukocyte counts may not act in isolation but rather integrate signals from donor-derived inflammation, endothelial activation, and recipient immune priming, serving as accessible surrogates for the complex immune crosstalk that underlies DGF. Nevertheless, as current evidence is mainly observational, these correlations should be interpreted cautiously until mechanistic studies confirm their causal contributions.

Leukocyte-Associated Hematological Indices

The ratios between different leukocyte populations can serve as dynamic markers of rapid response to injury, potentially reflecting clinical improvement or deterioration. The neutrophil-to-lymphocyte ratio (NLR) is the most widely used index for DGF. It is considered an excellent indicator of inflammation and stress because it captures the role of these cells in immunoinflammatory reactions and neuroendocrine stress [27].

Studies by Baral et al and Halazun et al reported similar findings: preoperative NLR values above 3.5 were significantly correlated with a higher risk of developing DGF, particularly in recipients of living-donor grafts [28,29]. These studies employed multivariate analyses adjusted for ischemic time and donor source. Although the inclusion criteria differed slightly, their results consistently indicated that elevated systemic inflammation before reperfusion increased the probability of early graft dysfunction. Extending these observations to long-term outcomes, Çankaya et al examined preemptive transplant recipients and found that NLR levels remained low 1 year after transplantation, in contrast to non-preemptive recipients. This persistence of a lower NLR suggests an improved inflammatory and immunologic profile in patients who avoided prolonged dialysis exposure, supporting the notion that the pre-transplant inflammatory burden strongly influences post-transplant recovery [17]. In contrast, Pilichowska et al did not find NLR to be a significant predictor of DGF in a large cohort of 365 brain-dead donor transplants. Their analysis identified the neutrophil-to-monocyte (NMR) and lymphocyte-to-monocyte (LMR) ratios on postoperative day 1 as stronger discriminators. This discrepancy may be explained by the study’s focus on early postoperative measurements, when the leukocyte landscape is markedly altered by immunosuppressive induction and surgical stress [26]. Therefore, rather than contradicting previous studies, these findings highlight the importance of timing and dynamic context when interpreting hematologic ratios.

More recently, Zhang et al shifted the analytical focus from recipients to donors, introducing the concept of dynamic donor NLR (ΔNLR), which is the change in NLR between pre-and post-brain death evaluations. Their study demonstrated that an increasing donor NLR was independently associated with DGF in recipients, although the association weakened after adjusting for acute kidney injury in the donor. This approach is noteworthy because most studies assessed recipient-derived markers, while donor inflammatory activation, often initiated during brain death, remains an underexplored determinant of graft outcomes. Therefore, evaluating NLR dynamics in donors could provide valuable insight into the pre-transplant inflammatory “priming” that predisposes grafts to ischemia–reperfusion injury [30].

Collectively, these findings suggest that while the recipient NLR reflects the host’s inflammatory readiness, donor NLR dynamics mirror the extent of tissue injury already underway before procurement. Integrating both perspectives aligns with the current understanding of DGF pathophysiology: a bidirectional inflammatory interplay beginning in the donor and amplified upon reperfusion in the recipient. Hence, the clinical value of NLR-based indices may ultimately depend on their capacity to capture the continuum of inflammatory events in both the donor and recipient.

Platelets

Few studies have evaluated the association between absolute platelet count or its ratios and delayed graft function (DGF). Platelets are primarily involved in reperfusion and are activated by endothelial injury, which leads to adhesion and promotes leukocyte infiltration and aggregation [13]. Furthermore, their consumption in the formation of microthrombi detrimentally affects renal microcirculation. Clinical studies have also correlated a decrease in DGF incidence with treatment of the underlying complement-mediated processes involved in microthrombi formation [31].However, Pilichowska et al reported no correlation between platelet count and DGF development [26].

Beyond their established role in promoting adhesion and infiltration, studies suggest that platelets influence the free radical balance. Dołegowska et al evaluated platelet antioxidant activity in the platelet-rich plasma fraction of kidney recipients before anastomosis and at 1 and 5 minutes after reperfusion. They found that patients who developed DGF had lower catalase, glucose-6-phosphate dehydrogenase (G6PD), and glutathione-transferase (GST) levels than those with immediate graft function [32].

Although these findings are promising, their interpretation requires caution, as quantification is not routine and may be subject to pre-analytical variability. Furthermore, it remains unclear whether decreased activity is a cause or a consequence of ischemia–reperfusion injury.

Platelet-Associated Hematological Indices

The role of platelets in DGF is further suggested by hematological indices that reflect chronic activation and inflammation. Indices that combine platelet and leukocyte counts have been linked to inflammatory changes, tissue repair, and ischemia–reperfusion.

With respect to the platelet-to-lymphocyte ratio (PLR), the available data are inconsistent, and their robustness differs considerably. In an adult cohort of 289 deceased brain-dead donor kidney recipients, Baral et al reported that a PLR>120 significantly predicted DGF (HR 3.163, 95% CI: 1.256–7.963; P=0.015) after multivariable adjustment [28]. In contrast, Siddiqui et al, in a much smaller single-center pediatric series, reported that a preoperative PLR <175 was associated with DGF development, based solely on unadjusted comparisons and ROC analysis (AUC 0.69) and with very low specificity (31%) [33].

Strengths and Limitations

Complete blood count parameters may be useful tools for estimating the risk of DGF development. The absolute counts and their ratios were also explored. The availability, low cost, and potential use of these markers are significant advantages. Despite these promising findings, current evidence is almost exclusively derived from retrospective and single-center cohorts. Additionally, methodological heterogeneity across studies regarding population type, sampling timing, and laboratory parameters evaluated makes it difficult to integrate results into uniform clinical recommendations. Furthermore, better analyses should consider various “confounding factors,” such as the presence of acute kidney injury and the use of different immunosuppressive regimens. Finally, all but one of the studies were conducted in recipients, and the evaluation of these parameters in different donor types is lacking.

Conclusions

DGF remains a common and costly complication of kidney transplantation, underscoring the critical need for early and accessible risk-stratification tools.

Complete blood count and its associated ratios are promising, low-cost tools for the early identification of patients at risk of DGF. Consistent retrospective evidence links low hemoglobin levels to an increased risk of DGF, particularly in deceased-donor transplant recipients. Elevated NLR has been associated with early graft dysfunction, reflecting systemic immune and inflammatory activation in donors and recipients. Alterations in PLR, although supported by less consistent data, suggest a role for platelet activation and chronic inflammation in the pathogenesis of DGF.

These associations are pathophysiologically plausible, given the response that characterizes DGF, which includes donor-induced inflammatory activation (especially in deceased donors) and IRI amplified by the chronic inflammatory state of CKD5 recipients. Changes in the peripheral blood cell counts appear to mirror this complex interaction.

While this narrative review provides a structured summary of the literature, its design inherently lacks the methodological rigor of a systematic review or meta-analysis, precluding a formal assessment of publication bias or a quantitative evaluation of heterogeneity.