Early Allograft Dysfunction: Causes, Recognition, and Management

As we try to offer grafts to as many candidates as possible on our liver transplant waiting list, we are called to expand our organ donor pool. One problem is the variability in early graft function. Although a mild postoperative rise in aminotransferases during the first 48 to 72 hours is a relatively benign event, primary graft nonfunction requires emergency retransplantation. Marginal function of the newly implanted graft also represents an adverse event. Although the nomenclature for defining such poorly functioning grafts in the early posttransplant period is not well standardized, it is accepted that they are an important cause of morbidity and mortality for recipients. Various indices of hepatocellular damage and synthetic impairment during the first week after transplantation have been used to describe these grafts with initial poor graft function.1-5 Depending on the criteria used, the reported incidence has varied in different series and ranged from 2% to 23%. With the liver transplant database of the National Institute of Diabetes and Digestive and Kidney Diseases, a definition of early allograft dysfunction (EAD) was developed to describe the quintile of transplant recipients with the worst graft function.1 The definition was based on markers of hepatic function during the first week after transplantation: the serum bilirubin level, the prothrombin time, and the presence of hepatic encephalopathy. Recipients with EAD had longer intensive care unit and hospital stays than recipients without EAD. EAD was associated with worse graft and recipient survival rates. Recently, Olthoff et al.5 validated a modified version of the original EAD definition in the Model for End-Stage Liver Disease (MELD) era of organ sharing: a bilirubin level > 10 mg/dL on day 7, an international normalized ratio > 1.6 on day 7, and an alanine aminotransferase or aspartate aminotransferase level > 2000 IU/L within the first 7 days after transplantation. Among the 23.2% of recipients with EAD, 18.8% died, whereas 1.8% of recipients without EAD died. The rate of graft loss was 26.1% for patients with EAD and 3.5% for patients without EAD. EAD represents an extreme case of ischemia/reperfusion injury to the liver graft. It is associated with damage from reactive oxygen species, and sinusoidal endothelial cell damage is central to the pathophysiology of the graft injury.6 Histological findings of EAD, including acute inflammatory infiltration of the graft, hepatocellular damage presenting as coagulative necrosis, and ballooning degeneration or cyto-aggregation of hepatocytes, indeed can already be observed immediately after reperfusion and complete revascularization of the graft by closure biopsy.7 The interplay of the various host and donor factors affecting cold and warm ischemia injury, however, is still poorly understood. A number of donor and recipient risk factors contributing to the development of EAD have been identified.1-5 Recipient risk factors include age, bilirubin, international normalized ratio, urgency of transplantation, creatinine, presence of acidosis, and intensive care unit hospitalization. Indeed, directly or indirectly, most of these factors are captured in the MELD score. Identified donor risk factors include age, cold ischemia time, hepatic steatosis, and length of the intensive care unit stay. A number of potential strategies have been studied to reduce ischemia/reperfusion injury in liver resection.8 Intuitively, the pharmacological treatment of the graft with glucose, antioxidants, microcirculatory vasoactive mediators, and anti-inflammatory and antiapoptotic drugs appears to be a mechanistically possible intervention for preventing EAD. In one randomized study, the preoperative administration of 500 mg of methylprednisolone was effective in reducing serum aminotransferases, bilirubin, and inflammatory cytokines.9 Although some of the early published evidence is promising, there is, however, still little evidence for recommending these interventions in clinical practice for liver transplantation at this point. In our balancing act of preventing EAD and needing to offer grafts to our sickest patients, reducing the cold ischemia time often remains the only variable that is amenable to manipulation. A number of prognostic models allowing a quantitative risk assessment of possible graft failure have been published in recent years. They are helpful in assisting our selection process for donor grafts in the MELD era. Using US national data, Feng et al.10 developed a quantitative donor risk index based on 7 characteristics: age, donation after cardiac death, split graft, race, height, cerebrovascular accident, and other etiologies of brain death. Ioannou11 used United Network for Organ Sharing data to derive a predictive model based on both donor and recipient factors. Four donor characteristics were included in that model: age, cold ischemia time, sex, and race. A multivariate analysis of 21,673 transplant recipients from the United Network for Organ Sharing database allowed Rana et al.12 to develop the survival outcomes following liver transplantation score, which is based on recipient, donor, and operative factors. The donor and operative factors considered in the model were age, cause of death, creatinine, procurement distance, and cold ischemia time. Halldorson et al.13 found that the product of the donor age and the candidate's MELD score [the Donor Model for End-Stage Liver Disease (D-MELD) score] was simple to use and predictive of transplantation outcomes. A D-MELD cutoff of 1600 identified donor-recipient matches with the worst outcomes. It is indeed difficult at the present time to advocate the refusal of potential grafts on the basis of such models until the costs and benefits of such an intervention have been evaluated. The various predictive models are also limited by the characteristics of the included patient populations and the collected data. However, they have proved useful in furthering our understanding and have become (consciously or unconsciously) part of the decision-making process of accepting a donor for a given recipient. Recently, some exploratory work has looked at identifying serum biomarkers associated with EAD.14 Patients with EAD had lower preoperative interleukin-6 levels and higher interleukin-2 receptor serum levels. Higher levels of monocyte chemoattractant protein 1 [chemokine (C-C motif) ligand 2], interleukin-8 [chemokine (C-X-C motif) ligand 8], regulated upon activation, normal T cell expressed, and secreted [chemokine (C-C motif) ligand 5], monokine induced by gamma interferon [chemokine (C-X-C motif) ligand 9], interferon-inducible protein 10 [chemokine (C-X-C motif) ligand 10], and interleukin-2 receptor suggest nuclear factor kappa B pathway up-regulation and T cell activation postoperatively. These findings must be validated by other groups. Rather than causing EAD, the identified chemokines and cytokines may be the result of the processes leading to EAD. Nevertheless, such research represents a new era in our understanding of the pathways associated with EAD. It is hoped that the highlighted associations of cytokines with EAD will lead to future investigations to improve our ability to define, predict, prevent, and eventually treat EAD. EAD defines grafts with marginal function early after liver transplantation. Although the nomenclature is still evolving, it is clear that EAD is an important cause of morbidity and mortality for liver transplant recipients. A number of donor and recipient risk factors for EAD have been identified. Transplant physicians attempt to match donor and recipient factors for a given transplant. Practically, the MELD score does capture most of the involved recipient risk factors. A number of prognostic models that help to give a quantitative assessment of donor risk factors have been elaborated in the past decade. In all models, donor age is a significant independent predictive variable. Recent work in identifying serum biomarkers has highlighted associations with cytokines. Such studies should improve our understanding of EAD and allow therapeutic interventions based on its pathophysiology in the future.

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