Molecular profiling of postreperfusion milieu determines acute kidney injury after liver transplantation: A prospective study

Real-world implementation of normothermic machine perfusion: A detailed analysis of intraoperative and early postoperative impact

BACKGROUND

Outcome data for the great majority of liver normothermic machine perfusion (NMP) cases derive from the strict confines of clinical trials. Detailed specifics regarding the intraoperative and early postoperative impact of NMP on reperfusion injury and its sequelae during real-world use of this emerging technology remain largely unavailable.

METHODS

We analyzed transplants performed in a 3-month pilot period during which surgeons invoked commercial NMP at their discretion. Living donor, multi-organ, and hypothermic machine perfusion transplants were excluded.

RESULTS

Intraoperatively, NMP (n = 24) compared to static cold storage (n = 25) recipients required less peri-reperfusion bolus epinephrine (0 vs. 60 μg; p < .001) and post-reperfusion fresh frozen plasma (2.5 vs. 7.0 units; p = .0069), platelets (.0 vs. 2.0 units; p = .042), and hemostatic agents (0% vs. 24%; p = .010). Time from incision to venous reperfusion did not differ (3.6 vs. 3.1; p = .095) but time from venous reperfusion to surgery end was shorter for NMP recipients (2.3 vs. 2.8 h; p = .0045). Postoperatively, NMP recipients required fewer red blood cell (1.0 vs. 4.0 units; p = .0083) and fresh frozen plasma (4.0 vs. 7.0 units; p = .046) transfusions, had shorter intensive care unit stays (33.5 vs. 58.4 h; p = .012), and experienced less early allograft dysfunction according to both the Model for Early Allograft Function Score (3.4 vs. 5.0; p = .0047) and peak AST within 10 days of transplant (619 vs. 1,181 U/L; p = .036). Liver acceptance for the corresponding recipient was conditional on NMP use for 63% (15/24) of cases.

CONCLUSION

Real-world NMP use was associated with significantly lower intensity of reperfusion injury and intraoperative and postoperative care that may translate into patient benefit.

Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

BACKGROUND

Acute kidney injury (AKI) is common after liver transplantation (LT). We developed a nomogram model to predict post-LT AKI.

METHODS

A total of 120 patients were eligible for inclusion in the study. Clinical information was extracted from the institutional electronic medical record system. Blood samples were collected prior to surgery and immediately after surgery. Univariable and multivariate logistic regression were used to identify independent risk factors. Finally, a nomogram was developed based on the final multivariable logistic regression model.

RESULTS

In total, 58 (48.3%) patients developed AKI. Multivariable logistic regression revealed four independent risk factors for post-LT AKI: operation duration [odds ratio (OR) = 1.728, 95% confidence interval (CI) = 1.121-2.663, p = 0.013], intraoperative hypotension (OR = 3.235, 95% CI = 1.316-7.952, p = 0.011), postoperative cystatin C level (OR = 1.002, 95% CI = 1.001-1.004, p = 0.005) and shock (OR = 4.002, 95% CI = 0.893-17.945, p = 0.070). Receiver operating characteristic curve analysis was used to evaluate model discrimination. The area under the curve value was 0.815 (95% CI = 0.737-0.894).

CONCLUSION

The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance for post-LT AKI than the model based on clinical parameters or postoperative cystatin C level alone. Additionally, we developed an easy-to-use nomogram based on the final model, which could aid in the early detection of AKI and improve the prognosis of patients after LT.

A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study

Acute kidney injury (AKI) after liver transplantation (LT) is a common complication, and its development is thought to be multifactorial. We aimed to investigate potential risk factors and build a model to identify high-risk patients. A total of 199 LT patients were enrolled and each patient data was collected from the electronic medical records. Our primary outcome was postoperative AKI as diagnosed and classified by the KDIGO criteria. A least absolute shrinkage and selection operating algorithm and multivariate logistic regression were utilized to select factors and construct the model. Discrimination and calibration were used to estimate the model performance. Decision curve analysis (DCA) was applied to assess the clinical application value. Five variables were identified as independent predictors for post-LT AKI, including whole blood serum lymphocyte count, RBC count, serum sodium, insulin dosage and anhepatic phase urine volume. The nomogram model showed excellent discrimination with an AUC of 0.817 (95% CI: 0.758-0.876) in the training set. The DCA showed that at a threshold probability between 1% and 70%, using this model clinically may add more benefit. In conclusion, we developed an easy-to-use tool to calculate the risk of post-LT AKI. This model may help clinicians identify high-risk patients.

Involvement of Inflammasome Components in Kidney Disease

Inflammasomes are multiprotein complexes with an important role in the innate immune response. Canonical activation of inflammasomes results in caspase-1 activation and maturation of cytokines interleukin-1β and -18. These cytokines can elicit their effects through receptor activation, both locally within a certain tissue and systemically. Animal models of kidney diseases have shown inflammasome involvement in inflammation, pyroptosis and fibrosis. In particular, the inflammasome component nucleotide-binding domain-like receptor family pyrin domain containing 3 (NLRP3) and related canonical mechanisms have been investigated. However, it has become increasingly clear that other inflammasome components are also of importance in kidney disease. Moreover, it is becoming obvious that the range of molecular interaction partners of inflammasome components in kidney diseases is wide. This review provides insights into these current areas of research, with special emphasis on the interaction of inflammasome components and redox signalling, endoplasmic reticulum stress, and mitochondrial function. We present our findings separately for acute kidney injury and chronic kidney disease. As we strictly divided the results into preclinical and clinical data, this review enables comparison of results from those complementary research specialities. However, it also reveals that knowledge gaps exist, especially in clinical acute kidney injury inflammasome research. Furthermore, patient comorbidities and treatments seem important drivers of inflammasome component alterations in human kidney disease.

Literature review of the mechanisms of acute kidney injury secondary to acute liver injury

People exposed to liver ischaemia reperfusion (IR) injury often develop acute kidney injury and the combination is associated with significant morbidity and mortality. Molecular mediators released by the liver in response to IR injury are the likely cause of acute kidney injury (AKI) in this setting, but the mediators have not yet been identified. Identifying the mechanism of injury will allow the identification of therapeutic targets which may modulate both liver IR injury and AKI following liver IR injury.

Biomarkers for detecting and improving AKI after liver transplantation: From diagnosis to treatment

Orthotopic liver transplantation (OLT) is a well-established treatment for patients with liver failure. The shortage of donor organs and postoperative complications remain major obstacles for improving patient survival. Among these complications, acute kidney injury (AKI) is one of the most frequent types, contributing to graft loss. The timely detection and reversal of AKI can reduce its adverse influences on graft and patient outcomes. Traditional markers for detecting AKI are often limited with regard to their accuracy and specificity, and the discovery of better AKI markers and therapeutic targets assumes great importance. During past decades, studies directed toward early detection and treatment of AKI in OLT have been available. This review summarizes the evidence of these biomarkers for the prediction, diagnosis, treatment and prognosis stratification of AKI associated with OLT.

Update on C1 Esterase Inhibitor in Human Solid Organ Transplantation

Complement plays important roles in both ischemia-reperfusion injury (IRI) and antibody-mediated rejection (AMR) of solid organ allografts. One approach to possibly improve outcomes after transplantation is the use of C1 inhibitor (C1-INH), which blocks the first step in both the classical and lectin pathways of complement activation and also inhibits the contact, coagulation, and kinin systems. C1-INH can also directly block leukocyte-endothelial cell adhesion. C1-INH contrasts with eculizumab and other distal inhibitors, which do not affect C4b or C3b deposition or noncomplement pathways. Authors of reports on trials in kidney transplant recipients have suggested that C1-INH treatment may reduce IRI and delayed graft function, based on decreased requirements for dialysis in the first month after transplantation. This effect was particularly marked with grafts with Kidney Disease Profile Index ≥ 85. Other clinical studies and models suggest that C1-INH may decrease sensitization and donor-specific antibody production and might improve outcomes in AMR, including in patients who are refractory to other modalities. However, the studies have been small and often only single-center. This article reviews clinical data and ongoing trials with C1-INH in transplant recipients, compares the results with those of other complement inhibitors, and summarizes potentially productive directions for future research.

The AKI Prediction Score: a new prediction model for acute kidney injury after liver transplantation

BACKGROUND

Acute kidney injury (AKI) is a frequent complication after liver transplantation. Although numerous risk factors for AKI have been identified, their cumulative impact remains unclear. Our aim was therefore to design a new model to predict post-transplant AKI.

METHODS

Risk analysis was performed in patients undergoing liver transplantation in two centres (n = 1230). A model to predict severe AKI was calculated, based on weight of donor and recipient risk factors in a multivariable regression analysis according to the Framingham risk-scheme.

RESULTS

Overall, 34% developed severe AKI, including 18% requiring postoperative renal replacement therapy (RRT). Five factors were identified as strongest predictors: donor and recipient BMI, DCD grafts, FFP requirements, and recipient warm ischemia time, leading to a range of 0-25 score points with an AUC of 0.70. Three risk classes were identified: low, intermediate and high-risk. Severe AKI was less frequently observed if recipients with an intermediate or high-risk were treated with a renal-sparing immunosuppression regimen (29 vs. 45%; p = 0.007).

CONCLUSION

The AKI Prediction Score is a new instrument to identify recipients at risk for severe post-transplant AKI. This score is readily available at end of the transplant procedure, as a tool to timely decide on the use of kidney-sparing immunosuppression and early RRT.

Abdominal Organ Transplantation: Noteworthy Literature in 2018

A PubMed search revealed 1382 articles on pancreatic transplantation, 781 on intestinal transplantation, more than 7200 on kidney transplantation, and more than 5500 on liver transplantation published between January 1, 2018, and December 31, 2018. After narrowing the list down to human studies, 436 pancreatic, 302 intestinal, 1920 liver, and more than 2000 kidney transplantation studies were screened for inclusion in this review.