Acute kidney injury following liver transplantation: a systematic review of published predictive models

Prostaglandin E1 administration post liver transplantation and renal outcomes: A retrospective single center experience

BACKGROUND

Prostaglandin E1 (PGE1), or alprostadil, is a potent vasodilator that improves hepatic blood flow and reduces ischemia-reperfusion injury post-liver transplantation (LT). However, the benefits of PGE1 on renal function after LT have not yet been well described.

AIM

To assess the impact of PGE1 administration on renal function in patients who underwent liver or liver-kidney transplant.

METHODS

This retrospective study included all patients who underwent liver or liver-kidney transplant at our institution from January, 2011 to December, 2021. Patients were classified based on whether they received PGE1. PGE1 was administered post-LT to those with transaminases > 1000 U/L in the immediate postoperative period. Demographics, post-LT treatments and/or complications, renal function, and survival were analyzed. Multivariable logistic regression analysis was performed, and a two-tailed P value < 0.05 was considered statistically significant.

RESULTS

A total of 145 patients underwent LT, with 44 (30%) receiving PGE1. Baseline patient characteristics were comparable, except the PGE1 group had significantly higher aspartate aminotransferase (AST) (1961.9 U/L ± 1862.3 U/L vs 878 U/L ± 741.4 U/L, P = 0.000), alanine aminotransferase (1070.6 U/L ± 895 U/L vs 547.7 U/L ± 410 U/L, P = 0.000), international normalized ratio on post-LT day 1 (2 ± 0.74 vs 1.8 ± 0.4, P = 0.03), a longer intensive care unit stay (8.1 days ± 11.8 days vs 3.8 days ± 4.6 days, P = 0.003), more vasopressor use (55.53 hours ± 111 hours vs 16.33 hours ± 26.3 hours, P = 0.002), and higher immediate postoperative complications (18.6% vs 4.9%, P = 0.04). The PGE1 group also had a significantly higher 90-day readmission rate (29.6% vs 13.1%, P = 0.02) and lower 1-year liver graft survival (87.5% vs 98.9%, P = 0.005). However, 30-day readmission (31.6% vs 27.4%, P = 0.64), LT complications (hepatic artery thrombosis, biliary complications, rejection of liver graft, cardiomyopathy), 1-year patient survival (96.9% vs 97.8%, P = 0.77), overall liver graft survival, and overall patient survival were similar between the two groups (95.4% vs 93.9%, P = 0.74 and 88.4% vs 86.9%, P = 0.81 respectively). Although the PGE1 group had a significantly lower glomerular filtration rate (eGFR) on post-LT day 7 (46.3 mL/minute ± 26.7 mL/minute vs 62.5 mL/minute ± 34 mL/minute, P = 0.009), the eventual need for renal replacement therapy (13.6% vs 5.9%, P = 0.09), the number of dialysis sessions (0.91 vs 0.27, P = 0.13), and eGFR at 1-month (37.2 mL/minute ± 35.9 mL/minute vs 42 mL/minute ± 36.9 mL/minute, P = 0.49), 6-months (54.8 mL/minute ± 21.6 mL/minute vs 62 mL/minute ± 21.4 mL/minute, P = 0.09), and 12-months (63.7 mL/minute ± 20.7 mL/minute vs 62.8 mL/minute ± 20.3 mL/minute, P = 0.85) post-LT were similar to those in the non-PGE1 group.

CONCLUSION

In patients who received PGE1 for ischemia-reperfusion injury, despite immediate acute renal injury post-LT, the renal function at 1-month, 6-months, and 12-months post-LT was similar compared to those without ischemia-reperfusion injury. Prospective clinical trials are needed to further elucidate the benefits of PGE1 use in renal function.

Intraoperative hypotension during liver transplantation and postoperative outcomes: Retrospective cohort study

STUDY OBJECTIVES

Evaluation of the association between intraoperative hypotension (IOH) and important postoperative outcomes after liver transplant such as incidence and severity of acute kidney injury (AKI), MACE and early allograft dysfunction (EAD).

DESIGN

Retrospective, single institution study.

SETTINGS

Operating room.

PATIENTS

1576 patients who underwent liver transplant in our institution between January 2005 and February 2022.

MEASUREMENTS

IOH was measured as the time, area under the threshold (AUT), or time-weighted average (TWA) of mean arterial pressure (MAP) less than certain thresholds (55,60 and 65 mmHg). Associations between IOH exposures and AKI severity were assessed via proportional odds models. The odds ratio from the proportional odds model estimated the relative odds of having higher stage of AKI for higher exposure to IOH. Associations between exposures and MACE and EAD were assessed through logistic regression models. Potential confounding variables including patient baseline and surgical characteristics were adjusted for all models.

MAIN RESULTS

The primary analysis included 1576 surgeries that met the inclusion and exclusion criteria. Of those, 1160 patients (74%) experienced AKI after liver transplant surgery, with 780 (49%), 248(16%), and 132 (8.4%) experiencing mild, moderate, and severe injury, respectively. No significant association between hypotension exposure and postoperative AKI (yes or no) nor severity of AKI was observed. The odds ratios (95% CI) of having more severe AKI were 1.02 (0.997, 1.04) for a 50-mmHg·min increase in AUT of MAP <55 mmHg (P = 0.092); 1.03 (0.98, 1.07) for a 15-min increase in time spent under MAP <55 mmHg (P = 0.27); and 1.24 (0.98, 1.57) for a 1 mmHg increase in TWA of MAP <55 mmHg (P = 0.068). The associations between IOH and the incidence of MACE or EAD were not significant.

CONCLUSION

Our results did not show the association between IOH and investigated outcomes.

Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisal

OBJECTIVE

This study aims to systematically review and critical evaluation of the risk of bias and the applicability of existing prediction models for acute kidney injury post liver transplantation.

DATA SOURCE

A comprehensive literature search up until February 7, 2024, was conducted across nine databases: PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, CNKI, Wanfang, CBM, and VIP.

STUDY DESIGN

Systematic review of observational studies.

EXTRACTION METHODS

Literature screening and data extraction were independently conducted by two researchers using a standardized checklist designed for the critical appraisal of prediction modelling studies in systematic reviews. The prediction model risk of bias assessment tool was utilized to assess both the risk of bias and the models' applicability.

PRINCIPAL FINDINGS

Thirty studies were included, identifying 34 prediction models. External validation was conducted in seven studies, while internal validation exclusively took place in eight studies. Three models were subjected to both internal and external validation, the area under the curve ranging from 0.610 to 0.921. A meta-analysis of high-frequency predictors identified several statistically significant factors, including recipient body mass index, Model for End-stage Liver Disease score, preoperative albumin levels, international normalized ratio, and surgical-related factors such as cold ischemia time. All studies were demonstrated a high risk of bias, mainly due to the use of unsuitable data sources and inadequate detail in the analysis reporting.

CONCLUSIONS

The evaluation with prediction model risk of bias assessment tool indicated a considerable bias risk in current predictive models for acute kidney injury post liver transplantation.

IMPLICATIONS FOR CLINICAL PRACTICE

The recognition of high bias in existing models calls for future research to employ rigorous methodologies and robust data sources, aiming to develop and validate more accurate and clinically applicable predictive models for acute kidney injury post liver transplantation.

Post Liver Transplant Renal Dysfunction-Evaluation, Management and Immunosuppressive Practice

Liver transplantation (LT) is an effective and lifesaving treatment for patients with end-stage liver disease and hepatocellular carcinoma. Significant improvement in intermediate and long-term survival has been possible due to advancements in immunosuppressive therapy, perioperative care, and surgical techniques. Despite these advances, metabolic complications, including diabetes mellitus, cardiovascular diseases, malignancies, and renal dysfunction, are challenging issues after LT. Acute kidney injury (AKI) and chronic kidney disease (CKD) after LT are common and result in significant morbidity and mortality. Early diagnosis of kidney injury after LT is challenging, and no technique has yet proven effective in prediction of renal dysfunction. The methods for assessing renal function range from formulas that predict glomerular filtration rate to non-invasive biomarkers. The universal adoption of the model for end-stage liver disease has a direct impact on the incidence of peri-transplant AKI and development of CKD in the long-term. Post-LT renal dysfunction is multifactorial and is usually a result of pre-transplantation comorbidities, occurrence of renal dysfunction on the waiting list, perioperative events, and post-transplant nephrotoxic immunosuppressive medication use. Early identification of patients at risk for renal dysfunction and adoption of preventive measures are crucial in the pre-transplant period. No data are currently available to suggest a surgical technique that reliably demonstrates renal protection. Nephroprotective strategies during LT follow accepted surgical practice guidelines, such as maintenance of intravascular volume and mean arterial pressure. The management of kidney disease following LT is challenging, as by the time the serum creatinine is significantly elevated, few interventions impact the course of progression. Early nephroprotective measures are strongly advised and they mostly center on delaying the administration of calcineurin inhibitors (CNIs) during the initial postoperative period, lowering CNI dosage and combining CNI with mycophenolate mofetil and everolimus. The reasons for renal failure following LT, the techniques used to diagnose it, and the therapies designed to preserve renal function both immediately and late after LT are all critically evaluated in this review.

Intraoperative hypotension and the risk of acute kidney injury following liver transplantation

BACKGROUND

Acute kidney injury (AKI) is a frequent adverse outcome following liver transplantation (LT) with a multifactorial etiology. It is critical to identify modifiable risk factors to mitigate the risk. One key area of interest is the role of intraoperative hypotension, which remains relatively unexplored in liver transplant cohorts.

METHODS

This was a retrospective observational cohort study of 1292 adult patients who underwent LT (between 2009 and 2019). Multivariable logistic regression analysis was used to explore the association between intraoperative hypotension, quantified by time duration (in min) under various mean arterial pressure (MAP) thresholds, and the primary outcome of early postoperative AKI according to the KDIGO criteria.

RESULTS

AKI occurred in 40% of patients and was independently associated with greater than 20 min spent below MAP thresholds of 55 mm Hg (adjusted OR = 1.866; 95% CI = 1.037-3.44; P = 0.041) and 50 mm Hg (adjusted OR = 1.801; 95% CI = 1.087-2.992; P = 0.023). Further sensitivity analyses demonstrated that the association between intraoperative hypotension and postoperative AKI was accentuated after restricting the analysis to patients with a normal preoperative renal function.

CONCLUSIONS

Prolonged (>20 min) intraoperative hypotension (below a MAP of 55 mm Hg) was independently associated with AKI following LT, after adjusting for several known confounders.

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.

Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal

BACKGROUND

The number of studies applying machine learning (ML) to predict acute kidney injury (AKI) has grown steadily over the past decade. We assess and critically appraise the state of the art in ML models for AKI prediction, considering performance, methodological soundness, and applicability.

METHODS

We searched PubMed and ArXiv, extracted data, and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines.

RESULTS

Forty-six studies from 3166 titles were included. Thirty-eight studies developed a model, five developed and externally validated one, and three studies externally validated one. Flexible ML methods were used more often than deep learning, although the latter was common with temporal variables and text as predictors. Predictive performance showed an area under receiver operating curves ranging from 0.49 to 0.99. Our critical appraisal identified a high risk of bias in 39 studies. Some studies lacked internal validation, whereas external validation and interpretability of results were rarely considered. Fifteen studies focused on AKI prediction in the intensive care setting, and the US-derived Medical Information Mart for Intensive Care (MIMIC) data set was commonly used. Reproducibility was limited as data and code were usually unavailable.

CONCLUSIONS

Flexible ML methods are popular for the prediction of AKI, although more complex models based on deep learning are emerging. Our critical appraisal identified a high risk of bias in most models: Studies should use calibration measures and external validation more often, improve model interpretability, and share data and code to improve reproducibility.

Renal function after liver transplantation: Real-world experience with basiliximab induction and delayed reduced-dose tacrolimus

BACKGROUND

Routine use of delayed reduced-dose calcineurin-inhibitor treatment with induction immunosuppression in liver transplantation to minimize post-operative kidney injury is still scarce.

AIM

To evaluate real-world experience of basiliximab induction with delayed reduced-dose tacrolimus.

METHODS

In a retrospective cohort study, kidney function was evaluated pre- and postoperatively by measured glomerular filtration rate (mGFR). Adult patients undergoing liver transplantation between 2000 and 2017 were divided into a conventional treatment group (immediate-introduction of tacrolimus, target trough levels 10-15 ng/mL, and corticosteroids, n = 203) and a revised treatment group (basiliximab induction, reduced-dose tacrolimus, target through levels 5-8 ng/mL, delayed until day three, and mycophenolate mofetil 2000 mg/day, n = 343).

RESULTS

Mean mGFR was similar between groups at wait-listing (85.3 vs 84.1 ml/min/1.73m², p = 0.60), but higher in the revised treatment group at 3 (56.8 vs 63.4 ml/min/1.73m², p = 0.004) and 12 months post-transplant (60.9 vs 69.7 ml/min/1.73m², p<0.001); this difference remained after correcting for multiple confounders and was independent of pre-transplant mGFR. In the revised treatment group, biopsy proven acute rejection rate was lower (38% vs. 21%, p<0.001), and graft-survival better (p = 0.01).

CONCLUSION

Basiliximab induction with delayed reduced-dose tacrolimus is associated with less kidney injury when compared to standard-dose tacrolimus, without increased risk of rejection, graft loss or death.

Risk factors for acute kidney injury after liver transplantation in intensive care unit: a retrospective cohort study

BACKGROUND

Acute kidney injury (AKI) is a frequent complication during the postoperative period following liver transplantation. Occurrence of AKI in intensive care unit (ICU) patients is associated with increased mortality and higher costs.

OBJECTIVE

To evaluate occurrences of moderate or severe AKI among patients admitted to the ICU after liver transplantation and investigate characteristics associated with this complication.

DESIGN AND SETTING

Single-center retrospective cohort study in a public hospital, Belo Horizonte, Brazil.

METHODS

Forty-nine patients admitted to the ICU between January 2015 and April 2017 were included. AKI was defined from a modified Kidney Disease Improving Global Outcomes (KDIGO) score (i.e. based exclusively on serum creatinine levels).

RESULTS

Eighteen patients (36.7%) developed AKI KDIGO 2 or 3; mostly KDIGO 3 (16 out of the 18 patients). Lactate level within the first six hours after ICU admission (odds ratio, OR: 1.3; 95% confidence interval, CI: 1.021-1.717; P = 0.034) and blood transfusion requirement within the first week following transplantation (OR: 8.4; 95% CI: 1.687-41.824; P = 0.009) were independently associated with development of AKI. Patients with AKI KDIGO 2 or 3 underwent more renal replacement therapy (72.2% versus 3.2%; P < 0.01), had longer hospital stay (20 days versus 15 days; P = 0.001), higher in-hospital mortality (44.4% versus 6.5%; P < 0.01) and higher mortality rate after one year (44.4% versus 9.7%; P = 0.01).

CONCLUSION

Need for blood transfusion during ICU stay and hyperlactatemia within the first six postoperative hours after liver transplantation are independently associated with moderate or severe AKI. Developing AKI is apparently associated with poor outcomes.

Hepatorenal Syndrome and Other Post-Liver Transplantation Complications: Case Studies and Literature Review

Hepatorenal syndrome (HRS) was originally defined as a renal dysfunction caused by a decreased renal perfusion due to hemodynamic disturbances in the arterial circulation and an excessive activity of endogenous vasoactive systems in the course of cirrhosis. Considering the latest research, this syndrome may have a more complex pathomechanism. Equally often as in cirrhosis, HRS develops after orthotopic liver transplantation (OLTx) and worsens the prognosis significantly increasing mortality rates in this patient population. The prevalence of renal complications after OLTx and their negative prognostic impact on the survival of both the graft and the recipient prompted the authors of this work to analyze in detail 2 cases of HRS after OLTx to indicate the multiplicity of factors contributing to the pathophysiology of this syndrome. Attention was paid to risk factors for HRS found in the anamnesis before OLTx, especially a pre-existing renal dysfunction. In both cases early post-OLTx complications associated with the transplantation procedure were described: destabilization of the circulatory system, transfusions of blood products, prolonged stay at an intensive care unit, and necessity of introducing continuous renal replacement therapy. In the later period after the OLTx, infections (bacterial, fungal, viral) and drug nephrotoxicity, including the activity of immunosuppressants (tacrolimus), contributed primarily to the renal function impairment.

Risk factors and prediction of acute kidney injury after liver transplantation: Logistic regression and artificial neural network approaches

BACKGROUND

Acute kidney injury (AKI) has serious consequences on the prognosis of patients undergoing liver transplantation. Recently, artificial neural network (ANN) was reported to have better predictive ability than the classical logistic regression (LR) for this postoperative outcome.

AIM

To identify the risk factors of AKI after deceased-donor liver transplantation (DDLT) and compare the prediction performance of ANN with that of LR for this complication.

METHODS

Adult patients with no evidence of end-stage kidney dysfunction (KD) who underwent the first DDLT according to model for end-stage liver disease (MELD) score allocation system was evaluated. AKI was defined according to the International Club of Ascites criteria, and potential predictors of postoperative AKI were identified by LR. The prediction performance of both ANN and LR was tested.

RESULTS

The incidence of AKI was 60.6% (n = 88/145) and the following predictors were identified by LR: MELD score > 25 (odds ratio [OR] = 1.999), preoperative kidney dysfunction (OR = 1.279), extended criteria donors (OR = 1.191), intraoperative arterial hypotension (OR = 1.935), intraoperative massive blood transfusion (MBT) (OR = 1.830), and postoperative serum lactate (SL) (OR = 2.001). The area under the receiver-operating characteristic curve was best for ANN (0.81, 95% confidence interval [CI]: 0.75-0.83) than for LR (0.71, 95%CI: 0.67-0.76). The root-mean-square error and mean absolute error in the ANN model were 0.47 and 0.38, respectively.

CONCLUSION

The severity of liver disease, pre-existing kidney dysfunction, marginal grafts, hemodynamic instability, MBT, and SL are predictors of postoperative AKI, and ANN has better prediction performance than LR in this scenario.

Current aspects of renal dysfunction after liver transplantation

The development of chronic kidney disease (CKD) after liver transplantation (LT) exerts a severe effect on the survival of patients. The widespread adoption of the model for end-stage liver disease score strongly impacted CKD incidence after the procedure, as several patients are transplanted with previously deteriorated renal function. Due to its multifactorial nature, encompassing pre-transplantation conditions, perioperative events, and nephrotoxic immunosuppressor therapies, the accurate identification of patients under risk of renal disease, and the implementation of preventive approaches, are extremely important. Methods for the evaluation of renal function in this setting range from formulas that estimate the glomerular filtration rate, to non-invasive markers, although no option has yet proved efficient in early detection of kidney injury. Considering the nephrotoxicity of calcineurin inhibitors (CNI) as a factor of utmost importance after LT, early nephroprotective strategies are highly recommended. They are based mainly on delaying the application of CNI during the immediate postoperative-period, reducing their dosage, and associating them with other less nephrotoxic drugs, such as mycophenolate mofetil and everolimus. This review provides a critical assessment of the causes of renal dysfunction after LT, the methods of its evaluation, and the interventions aimed at preserving renal function early and belatedly after LT.

Prophylactic Perioperative Terlipressin Therapy for Preventing Acute Kidney Injury in Living Donor Liver Transplant Recipients: A Systematic Review and Meta-Analysis

Background

Acute kidney injury (AKI) is common in the perioperative transplant period and is associated with poor outcomes. Few studies reported a reduction in AKI incidence with terlipressin therapy by counteracting the hemodynamic alterations occurring during liver transplantation. However, the effect of terlipressin on posttransplant outcomes has not been systematically reviewed.

Methods

A comprehensive search of electronic databases was performed. Studies reporting the use of terlipressin in the perioperative period of living donor liver transplantation were included. We expressed the dichotomous outcomes as risk ratio (RR, 95% confidence interval [CI]) using the random effects model. The primary aim was to assess the posttransplant risk of AKI. The secondary aims were to assess the need for renal replacement therapy (RRT), vasopressors, effect on hemodynamics, blood loss during surgery, hospital and intensive care unit (ICU) stay, and in-hospital mortality.

Results

A total of nine studies reporting 711 patients (309 patients in the terlipressin group and 402 in the control group) were included for analysis. Terlipressin was administered for a mean duration of 53.44 ± 28.61 h postsurgery. The risk of AKI was lower with terlipressin (0.6 [95% CI, 0.44-0.8]; P = 0.001). However, on sensitivity analysis including only four randomized controlled trials (I² = 0; P = 0.54), the risk of AKI was similar in both the groups (0.7 [0.43-1.09]; P = 0.11). The need for RRT was similar in both the groups (0.75 [0.35-1.56]; P = 0.44). Terlipressin therapy reduced the need for another vasopressor (0.34 [0.25-0.47]; P < 0.001) with a concomitant rise in mean arterial pressure and systemic vascular resistance by 3.2 mm Hg (1.64-4.7; P < 0.001) and 77.64 dyne cm^-1.sec^-5 (21.27-134; P = 0.007), respectively. Blood loss, duration of hospital/ICU stay, and mortality were similar in both groups.

Conclusions

Perioperative terlipressin therapy has no clinically relevant benefit.

Development and validation of a nomogram for predicting acute kidney injury after orthotopic liver transplantation

BACKGROUND

We aim to develop and validate a nomogram model for predicting severe acute kidney injury (AKI) after orthotopic liver transplantation (OLT).

METHODS

A total of 576 patients who received OLT in our center were enrolled. They were assigned to the development and validation cohort according to the time of inclusion. Univariable and multivariable logistic regression using the forward variable selection routine were applied to find risk factors for post-OLT severe AKI. Based on the results of multivariable analysis, a nomogram was developed and validated. Patients were followed up to assess the long-term mortality and development of chronic kidney disease (CKD).

RESULTS

Overall, 35.9% of patients were diagnosed with severe AKI. Multivariable logistic regression analysis revealed that recipients' BMI (OR 1.10, 95% CI 1.04-1.17, p = 0.012), hypertension (OR 2.32, 95% CI 1.22-4.45, p = 0.010), preoperative serum creatine (sCr) (OR 0.96, 95% CI 0.95-0.97, p < 0.001), and intraoperative fresh frozen plasm (FFP) transfusion (OR for each 1000 ml increase 1.34, 95% CI 1.03-1.75, p = 0.031) were independent risk factors for post-OLT severe AKI. They were all incorporated into the nomogram. The area under the ROC curve (AUC) was 0.73 (p < 0.05) and 0.81 (p < 0.05) in the development and validation cohort. The calibration curve demonstrated the predicted probabilities of severe AKI agreed with the observed probabilities (p > 0.05). Kaplan-Meier survival analysis showed that patients in the high-risk group stratified by the nomogram suffered significantly poorer long-term survival than the low-risk group (HR 1.92, p < 0.01). The cumulative risk of CKD was higher in the severe AKI group than no severe AKI group after competitive risk analysis (HR 1.48, p < 0.05).

CONCLUSIONS

With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for severe AKI and poor long-term prognosis after OLT.

Acute Kidney Injury After Liver Transplant: Incidence, Risk Factors, and Impact on Patient Outcomes

OBJECTIVES

Acute kidney injury is a frequent complication of liver transplant. Here, we assessed the rate and contributing factors of acute kidney injury and need for renal replacement therapy in patients undergoing liver transplant at a transplant center in Tehran, Iran.

MATERIAL AND METHODS

We identified all patients who underwent liver transplant at the Imam Khomeini Hospital Complex from March 2018 to March 2019 and who were followed for 3 months after transplant. Acute kidney injury was defined based on the Acute Kidney Injury Network criteria. We collected demographic and pretransplant, intraoperative, and posttransplant data. Univariable and multivariable models were applied to explore independent risk factors for acute kidney injury incidence and need for renal replacement therapy.

RESULTS

Our study included 173 deceased donor liver transplant recipients. Rates of incidence of acute kidney injury and need for renal replacement therapy were 68.2% and 14.5%, respectively. The 3-month mortality rate among those with severe and mild or moderate acute kidney injury was 44.0% (14/25) and 9.7% (9/ 93), respectively (P < .001). Multivariable analyses indicated that serum albumin (relative risk of 0.55; 95% confidence interval, 0.34-0.87; P = .021), baseline serum creatinine (relative risk of 2.11; 95% confidence interval, 1.56-2.90; P = .037), and intraoperative mean arterial pressure (relative risk of 0.76; 95% confidence interval, 0.63-0.82; P = .008) were independent factors for predicting posttransplant acute kidney injury. Independent risk factors for requiring renal replacement therapy were pretransplant serum creatinine (relative risk of 1.99; 95% confidence interval, 1.89-4.47; P = .044) and intraoperative vasopressor infusion (relative risk of 1.41; 95% confidence interval, 1.38-2.00; P = .021).

CONCLUSIONS

We found a high incidence of acute kidney injury among liver transplant recipients in our center. There was a significant association between severity of acute kidney injury and 3-month and in-hospital mortality.

Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation

BACKGROUND

Acute kidney injury (AKI) is a common complication after liver transplantation (LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach.

METHODS

A total of 493 patients with donation after cardiac death LT (DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes (KDIGO). The clinical data of patients with AKI (AKI group) and without AKI (non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The incidence of AKI was 35.7% (176/493) during the follow-up period. Compared with the non-AKI group, the AKI group showed a remarkably lower survival rate (P < 0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval (CI): 0.794-0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models (P < 0.001).

CONCLUSIONS

The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

The importance of the urinary output criterion for the detection and prognostic meaning of AKI

Most reports on AKI claim to use KDIGO guidelines but fail to include the urinary output (UO) criterion in their definition of AKI. We postulated that ignoring UO alters the incidence of AKI, may delay diagnosis of AKI, and leads to underestimation of the association between AKI and ICU mortality. Using routinely collected data of adult patients admitted to an intensive care unit (ICU), we retrospectively classified patients according to whether and when they would be diagnosed with KDIGO AKI stage ≥ 2 based on baseline serum creatinine (Screa) and/or urinary output (UO) criterion. As outcomes, we assessed incidence of AKI and association with ICU mortality. In 13,403 ICU admissions (62.2% male, 60.8 ± 16.8 years, SOFA 7.0 ± 4.1), incidence of KDIGO AKI stage ≥ 2 was 13.2% when based only the SCrea criterion, 34.3% when based only the UO criterion, and 38.7% when based on both criteria. By ignoring the UO criterion, 66% of AKI cases were missed and 13% had a delayed diagnosis. The cause-specific hazard ratios of ICU mortality associated with KDIGO AKI stage ≥ 2 diagnosis based on only the SCrea criterion, only the UO criterion and based on both criteria were 2.11 (95% CI 1.85–2.42), 3.21 (2.79–3.69) and 2.85 (95% CI 2.43–3.34), respectively. Ignoring UO in the diagnosis of KDIGO AKI stage ≥ 2 decreases sensitivity, may lead to delayed diagnosis and results in underestimation of KDIGO AKI stage ≥ 2 associated mortality.

Risk prediction models for acute kidney injury in adults: An overview of systematic reviews

Background

The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. One way to reduce the burden associated with AKI is to prevent the event altogether. An important step in prevention lies in AKI risk prediction. Due to the increasing number of available risk prediction models (RPMs) clinicians need to be able to rely on systematic reviews (SRs) to provide an objective assessment on which RPM can be used in a specific setting. Our aim was to assess the quality of SRs of RPMs in AKI.

Methods

The protocol for this overview was registered in PROSPERO. MEDLINE and Embase were searched for SRs of RPMs of AKI in any setting from 2003 till August 2020. We used the ROBIS tool to assess the methodological quality of the retrieved SRs.

Results

Eight SRs were retrieved. All studies were assessed as being at high risk for bias using the ROBIS tool. Eight reviews had a high risk of bias in study eligibility criteria (domain 1), five for study identification and selection (domain 2), seven for data collection and appraisal (domain 3) and seven for synthesis and findings (domain 4). Five reviews were scored at high risk of bias across all four domains. Risk of bias assessment with a formal risk of bias tool was only performed in five reviews. Primary studies were heterogeneous and used a wide range of AKI definitions. Only 19 unique RPM were externally validated, of which 11 had only 1 external validation report.

Conclusion

The methodological quality of SRs of RPMs of AKI is inconsistent. Most SRs lack a formal risk of bias assessment. SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient.

Trial registration

PROSPERO registration number is CRD 42020204236, available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=204236.

Prediction Models for AKI in ICU: A Comparative Study

PURPOSE

To assess the performance of models for early prediction of acute kidney injury (AKI) in the Intensive Care Unit (ICU) setting.

PATIENTS AND METHODS

Data were collected from the Medical Information Mart for Intensive Care (MIMIC)-III database for all patients aged ≥18 years who had their serum creatinine (SCr) level measured for 72 h following ICU admission. Those with existing conditions of kidney disease upon ICU admission were excluded from our analyses. Seventeen predictor variables comprising patient demographics and physiological indicators were selected on the basis of the Kidney Disease Improving Global Outcomes (KDIGO) and medical literature. Six models from three types of methods were tested: Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Decision Machine (LightGBM), and Convolutional Neural Network (CNN). The area under receiver operating characteristic curve (AUC), accuracy, precision, recall and F-measure (F1) were calculated for each model to evaluate performance.

RESULTS

We extracted the ICU records of 17,205 patients from MIMIC-III dataset. LightGBM had the best performance, with all evaluation indicators achieving the highest value (average AUC = 0.905, F1 = 0.897, recall = 0.836). XGBoost had the second best performance and LR, RF, SVM performed similarly (P = 0.082, 0.158 and 0.710, respectively) on AUC. The CNN model achieved the lowest score for accuracy, precision, F1 and AUC. SVM and LR had relatively low recall compared with that of the other models. The SCr level had the most significant effect on the early prediction of AKI onset in LR, RF, SVM and LightGBM.

CONCLUSION

LightGBM demonstrated the best capability for predicting AKI in the first 72 h of ICU admission. LightGBM and XGBoost showed great potential for clinical application owing to their high recall value. This study can provide references for artificial intelligence-powered clinical decision support systems for AKI early prediction in the ICU setting.

Acute Kidney Injury Following Pediatric Liver Transplant

OBJECTIVE

To determine the incidence, severity, and risk factors of postoperative acute kidney injury in pediatric liver transplant patients with and without inborn errors of metabolism.

DESIGN

Retrospective cohort study.

SETTING

Single-center PICU.

PATIENTS

All children less than or equal to 18 years old who received a liver transplant between January 2009 and July 2019.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Following exclusion criteria there were 92 transplant encounters. After excluding patients who received combined kidney-liver transplantation, acute kidney injury occurred in 57% of patients (N = 49), with 25.6% (N = 22) stage 1, 15.1% (N = 13) stage 2, and 16.3% (N = 14) stage 3. In an adjusted analysis, metabolic indication for transplant was not significantly associated with presence of acute kidney injury (p = 0.45). For the subset of patients without inborn errors of metabolism, the odds of having acute kidney injury was 1.50 (95% CI: 1.00-2.26) for each 1-unit increase in preoperative INR after adjusting for the covariates of age, preoperative albumin, CMV status of donor, and preoperative creatinine. In the full cohort, as well as the sample of children without inborn errors of metabolism, presence of acute kidney injury was associated with longer total hospital stay as well as number of ICU days.

CONCLUSIONS

Acute kidney injury in the early postoperative period is common in pediatric liver transplant patients (57%), 31.4% of whom had severe disease. In patients without inborn errors of metabolism, each unit increase in preoperative INR suggests a higher risk of acute kidney injury after adjusting for covariates including preoperative creatinine. This finding suggests an association between the severity of preoperative synthetic liver function and the risk of developing postoperative acute kidney injury which requires further investigation.

Clinical prediction models for acute kidney injury

Objective

To report on the currently available prediction models for the development of acute kidney injury in heterogeneous adult intensive care units.

Methods

A systematic review of clinical prediction models for acute kidney injury in adult intensive care unit populations was carried out. PubMed^® was searched for publications reporting on the development of a novel prediction model, validation of an established model, or impact of an existing prediction model for early acute kidney injury diagnosis in intensive care units.

Results

We screened 583 potentially relevant articles. Among the 32 remaining articles in the first selection, only 5 met the inclusion criteria. The nonstandardized adaptations that were made to define baseline serum creatinine when the preadmission value was missing led to heterogeneous definitions of the outcome. Commonly included predictors were sepsis, age, and serum creatinine level. The final models included between 5 and 19 risk factors. The areas under the Receiver Operating Characteristic curves to predict acute kidney injury development in the internal validation cohorts ranged from 0.78 to 0.88. Only two studies were externally validated.

Conclusion

Clinical prediction models for acute kidney injury can help in applying more timely preventive interventions to the right patients. However, in intensive care unit populations, few models have been externally validated. In addition, heterogeneous definitions for acute kidney injury and evaluation criteria and the lack of impact analysis hamper a thorough comparison of existing models. Future research is needed to validate the established models and to analyze their clinical impact before they can be applied in clinical practice.

Outcomes of Liver Transplantation in Patients on Renal Replacement Therapy: Considerations for Simultaneous Liver Kidney Transplantation Versus Safety Net

As the liver transplant (LT) waiting list continues to outpace organ availability, many patients require renal replacement therapy (RRT) before LT. It is unclear which patients will benefit from simultaneous liver kidney (SLK) transplant as opposed to awaiting a Safety Net kidney transplant (KT) post-LT.

Methods

In this study, a retrospective analysis of the United Network for Organ Sharing dataset was performed to identify risk factors associated with poor outcome for patients on RRT before LT who were listed for SLK and received either SLK vs LT alone (LTA).

Results

Between January 2003 and December 2016, 8971 adult LT recipients were on RRT at the time of LT. 5359 were listed for and received LTA (Group 1). Of 3612 patients listed for SLK, 3414 (38.1%) received SLK (Group 2) and 198 (2.2%) received LTA (Group 3). Overall, Group 3 had lower graft and patient survival post-LT when compared with Groups 1 and 2 (P < 0.001). Serum creatinine at 1 year post-LT and cumulative incidence for KT at 3 years post-LT were higher for Group 3 (P < 0.001). On multivariate analysis, pre-LT diabetes (P = 0.002), Model of End-Stage Liver Disease score (P = 0.01), and donor kidney donor profile index (P = 0.025) were significant in Group 2. Recipient age >60 (P < 0.001) and RRT pre-LT (>90 days; P = 0.001) were associated with lower patient survival in Group 3.

Conclusions

Among LT recipients on RRT before LT who were listed for SLK, RRT >90 days, and age >60 were associated with poor outcome following LTA. This suggests that programs should carefully weigh the decision to proceed with LTA vs waiting for SLK in this patient population. Future access to Safety Net KT will be an important consideration for these patients moving forward.

Incidence and Impact of Acute Kidney Injury after Liver Transplantation: A Meta-Analysis

Background: The study’s aim was to summarize the incidence and impacts of post-liver transplant (LTx) acute kidney injury (AKI) on outcomes after LTx. Methods: A literature search was performed using the MEDLINE, EMBASE and Cochrane Databases from inception until December 2018 to identify studies assessing the incidence of AKI (using a standard AKI definition) in adult patients undergoing LTx. Effect estimates from the individual studies were derived and consolidated utilizing random-effect, the generic inverse variance approach of DerSimonian and Laird. The protocol for this systematic review is registered with PROSPERO (no. CRD42018100664). Results: Thirty-eight cohort studies, with a total of 13,422 LTx patients, were enrolled. Overall, the pooled estimated incidence rates of post-LTx AKI and severe AKI requiring renal replacement therapy (RRT) were 40.7% (95% CI: 35.4%–46.2%) and 7.7% (95% CI: 5.1%–11.4%), respectively. Meta-regression showed that the year of study did not significantly affect the incidence of post-LTx AKI (p = 0.81). The pooled estimated in-hospital or 30-day mortality, and 1-year mortality rates of patients with post-LTx AKI were 16.5% (95% CI: 10.8%–24.3%) and 31.1% (95% CI: 22.4%–41.5%), respectively. Post-LTx AKI and severe AKI requiring RRT were associated with significantly higher mortality with pooled ORs of 2.96 (95% CI: 2.32–3.77) and 8.15 (95%CI: 4.52–14.69), respectively. Compared to those without post-LTx AKI, recipients with post-LTx AKI had significantly increased risk of liver graft failure and chronic kidney disease with pooled ORs of 3.76 (95% CI: 1.56–9.03) and 2.35 (95% CI: 1.53–3.61), respectively. Conclusion: The overall estimated incidence rates of post-LTx AKI and severe AKI requiring RRT are 40.8% and 7.0%, respectively. There are significant associations of post-LTx AKI with increased mortality and graft failure after transplantation. Furthermore, the incidence of post-LTx AKI has remained stable over the ten years of the study.

Risk prediction for acute kidney injury in acute medical admissions in the UK

BACKGROUND

Acute Kidney Injury (AKI) is associated with adverse outcomes; therefore identifying patients who are at risk of developing AKI in hospital may lead to targeted prevention.

AIM

We undertook a UK-wide study in acute medical units (AMUs) to define those who develop hospital-acquired AKI (hAKI); to determine risk factors associated with hAKI and to assess the feasibility of developing a risk prediction score.

DESIGN

Prospective multi-centre cohort study across 72 AMUs in the UK.

METHODS

Data collected from all patients who presented over a 24-h period. Chronic dialysis, community-acquired AKI (cAKI) and those with fewer than two creatinine measurements were excluded. Primary outcome was the development of h-AKI.

RESULTS

Two thousand four hundred and fourty-six individuals were admitted to the seventy-two participating centres. Three hundred and eighty-four patients (16%) sustained AKI of whom two hundred and eighty-seven (75%) were cAKI and ninety-seven (25%) were hAKI. After exclusions, chronic kidney disease [Odds Ratio (OR) 3.08, 95% Confidence Interval (CI) 1.96-4.83], diuretic prescription (OR 2.33, 95% CI 1.5-3.65), a lower haemoglobin concentration and elevated serum bilirubin were independently associated with development of hAKI. Multi-variable model discrimination was only moderate (c-statistic 0.75).

CONCLUSIONS

AKI in AMUs is common and associated with worse outcomes, with the majority of cases community acquired. Only a small proportion of patients develop hAKI. Prognostic risk factor modelling demonstrated only moderate discrimination implying that widespread adoption of such an AKI clinical risk score across all AMU admissions is not currently justified. More targeted risk assessment or automated methods of calculating individual risk may be more appropriate alternatives.

Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations

OBJECTIVE

Critically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations.

DESIGN

Systematic review.

DATA SOURCES

Medline, Embase and Web of Science until November 2016.

ELIGIBILITY

Studies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal.

RESULTS

14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71-0.80 in derivation (reported in 8/11 studies), 0.66-0.80 for internal validation studies (n=7) and 0.65-0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found.

CONCLUSIONS

AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI.

Temporal trends of dialysis requiring acute kidney injury after orthotopic cardiac and liver transplant hospitalizations

Background

The epidemiology and outcomes of acute kidney injury (AKI) in prevalent non-renal solid organ transplant recipients is unknown.

Methods

We assessed the epidemiology of trends in acute kidney injury (AKI) in orthotopic cardiac and liver transplant recipients in the United States. We used the Nationwide Inpatient Sample to evaluate the yearly incidence trends (2002 to 2013) of the primary outcome, defined as AKI requiring dialysis (AKI-D) in hospitalizations after cardiac and liver transplantation. We also evaluated the trend and impact of AKI-D on hospital mortality and adverse discharge using adjusted odds ratios (aOR).

Results

The proportion of hospitalizations with AKI (9.7 to 32.7% in cardiac and 8.5 to 28.1% in liver transplant hospitalizations; p_trend<0.01) and AKI-D (1.63 to 2.33% in cardiac and 1.32 to 2.65% in liver transplant hospitalizations; p_trend<0.01) increased from 2002-2013. This increase in AKI-D was explained by changes in race and increase in age and comorbidity burden of transplant hospitalizations. AKI-D was associated with increased odds of in hospital mortality (aOR 2.85; 95% CI 2.11-3.80 in cardiac and aOR 2.00; 95% CI 1.55-2.59 in liver transplant hospitalizations) and adverse discharge [discharge other than home] (aOR 1.97; 95% CI 1.53-2.55 in cardiac and 1.91; 95% CI 1.57-2.30 in liver transplant hospitalizations).

Conclusions

This study highlights the growing burden of AKI-D in non-renal solid organ transplant recipients and its devastating impact, and emphasizes the need to develop strategies to reduce the risk of AKI to improve health outcomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12882-017-0657-8) contains supplementary material, which is available to authorized users.

Incidence and outcomes of acute kidney injury in patients with hepatocellular carcinoma after liver transplantation

PURPOSE

To describe the incidence and outcomes linked with acute kidney injury (AKI) after liver transplantation (LT) in hepatocellular carcinoma (HCC) patients.

METHODS

From January 2003 to February 2011, HCC patients undergoing LT were retrospectively enrolled. Patient with a glomerular filtration rate (GFR) <60 mL/min/1.73 m² was excluded. AKI was defined and classified according to the AKIN criteria.

RESULTS

Of the 566 eligible patients, AKI was found in 109 (19.26%) patients (stage I, 66 cases; stage II, 15 cases; and stage III, 28 cases). Risk factors for AKI were the long anhepatic time (OR = 3.59, P = 0.009) and prolonged duration of systolic blood pressure (SBP) < 90 mmHg (OR = 1.07, P < 0.0001). Post-LT AKI was an independent risk factor associated with 30-day mortality (HR = 4.05, P = 0.047). Complete recovery occurred in 84 (77.06%) of all AKI episodes within 1 month after operation, while 25 patients (22.94%) suffered from prolonged AKI. Patients with prolonged AKI had a poorer 1-year survival than those with transient AKI (40 vs 86.90%; P < 0.0001). Patients with severe AKI more often developed prolonged AKI. 13 patients (52%) of the prolonged AKI progressed to chronic kidney disease (CKD) defined as eGFR <60 mL/min/1.73 m² after 1 year post-operation.

CONCLUSIONS

Post-LT AKI is not an uncommon complication. Intra-operative hemodynamic instability is crucial in the development of post-LT AKI and deserves more attention. Most post-LT AKI is transient and reversible, while the prolonged form may predict a decrease survival.

Incidence and Patient Outcomes in Renal Replacement Therapy After Orthotopic Liver Transplant

OBJECTIVES

Our objective was to evaluate the incidence of renal replacement therapy after orthotopic liver transplant and to evaluate and analyze patient outcomes.

MATERIALS AND METHODS

We performed a retrospective analysis of 177 consecutive patients at a tertiary care unit who underwent orthotopic liver transplant between January 2010 and June 2016. Patients who were admitted to the intensive care unit after orthotopic liver transplant and who required renal replacement therapy were included.

RESULTS

A total of 177 (79 adult, 98 pediatric) orthotopic liver transplants were performed during the study period. Of these, 35 patients (19%) required renal replacement therapy during the early posttransplantation period. After excluding 5 patients with previous chronic renal failure, 30 patients (17%; 20 adult [25% ], 10 pediatric [10% ]) with acute kidney injury required renal replacement therapy. The mean patient age was 31.1 ± 20.0 years, with a mean Model for End-stage Liver Disease score of 16.7 ± 12.3. Of the patients with acute kidney injury who underwent renal replacement therapy, in-hospital mortality was 23.3% (7 of 30 patients), and 40% remained on dialysis. No significant difference was seen in mortality between early versus delayed initiation of renal replacement therapy in patients with stage 3 acute kidney injury (P = .17).

CONCLUSIONS

Of liver transplant recipients who present with acute kidney injury, 19% require renal replacement therapy, and in-hospital mortality is 20% in the early postoperative period.