Clinical Risk Scoring Models for Prediction of Acute Kidney Injury after Living Donor Liver Transplantation: A Retrospective Observational Study

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.

Pulmonary artery catheter monitoring versus arterial waveform-based monitoring during liver transplantation: a retrospective cohort study

Although pulmonary artery catheter (PAC) has been used during liver transplantation surgery, the usefulness of PAC has rarely been investigated. We evaluated whether the use of PAC is associated with better clinical outcomes compared to arterial waveform-based monitoring after liver transplantation. A total of 1565 cases undergoing liver transplantation were reviewed. We determined whether patients received PAC or not and divided our cohort into the PAC with hemodynamic monitoring using PAC and the non-PAC with arterial waveform-based monitoring using FloTrac-Vigileo. Propensity score matching was performed. Acute kidney injury (AKI), early allograft dysfunction (EAD) and 1-year all-cause mortality or graft failure were compared in the matched cohorts. Logistic regression analysis was performed in the inverse probability of treatment-weighted (IPTW) cohort for postoperative EAD and AKI, respectively. Five-year overall survival was compared between the two groups. In the matched cohort, there was no significant difference in the incidence of AKI, EAD, length of hospital or ICU stay, and 1-year all-cause mortality between the groups. In the IPTW cohort, the use of PAC was not a significant predictor for AKI or EAD (AKI: odds ratio (95% confidence interval) of 1.20 (0.47-1.56), p = 0.229; EAD: 0.99 (0.38-1.14), p = 0.323). There was no significant difference in the survival between groups after propensity score matching (Log-rank test p = 0.578). In conclusion, posttransplant clinical outcomes were not significantly different between the groups with and without PAC. Anesthetic management without the use of PAC may be possible in low-risk patients during liver transplantation. The risk should be carefully assessed by considering MELD scores, ischemic time, surgical history, previous treatment of underlying liver disease, and degree of portal and pulmonary hypertension.Registration: https://clinicaltrials.gov/ct2/show/NCT05457114 (registration date: July 15, 2022).

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.

Effects of 20% albumin infusion therapy during liver transplantation on plasma neutrophil gelatinase-associated lipocalin level: A randomized controlled trial

The risk of acute kidney injury (AKI) after liver transplantation was lower in patients with serum albumin levels ≥3.0 mg/dL during surgery. We tested whether intraoperative infusion of 20% albumin affects neutrophil gelatinase-associated lipocalin (NGAL) level, a reliable indicator of AKI. We randomly assigned 134 patients undergoing liver transplantation into albumin group (n=70, 20% albumin 200 mL) and the control group (n=66, crystalloid solution 200 mL). The 2 study fluids were infused at 100 mL/h from the start of the anhepatic phase. The primary outcome was plasma NGAL level at 1 hour after graft reperfusion. Albumin level at the start of graft reperfusion was significantly greater in albumin group than in the control group [2.9 (2.4-3.3) g/dL vs. 2.3 (2.0-2.7) g/dL, p <0.001]. The NGAL level at 1 hour after graft reperfusion was not significantly different between the 2 groups [100.2 (66.7-138.8) ng/mL vs. 92.9 (70.8-120.6) ng/mL, p =0.46], and the AKI risk was not either (63.9% vs. 67.8%, adjusted p =0.73). There were no significant differences between the 2 groups regarding hospital readmission within 30 days/90 days after transplantation (32.6% vs. 41.5%, adjusted p =0.19 and 55.0% vs. 55.7%, adjusted p =0.87). Graft survival probability at 30 days/90 days/1 year after transplantation was 90.0%/84.3%/78.6% in albumin group and 97.0%/90.9%/89.4% in the control group [HR=1.6 (0.6-4.0), adjusted p =0.31]. In conclusion, intraoperative infusion of 20% albumin 200 mL increased the albumin level but failed to maintain serum albumin ≥3.0 mg/dL during surgery. The hypertonic albumin therapy did not significantly affect plasma NGAL level and clinical outcomes including AKI.

Serum neutrophil gelatinase-associated lipocalin and lactate level during surgery predict acute kidney injury and early allograft dysfunction after liver transplantation

Early allograft dysfunction (EAD) and acute kidney injury (AKI) are common and clinically important complications after liver transplantation. Serum lactate level at the end of surgery could predict EAD and neutrophil gelatinase-associated lipocalin (NGAL) is known as a biomarker for AKI after liver transplantation. The authors investigated whether the combination of these two laboratory tests could be used as an early predictor of these two complications of EAD and AKI. We reviewed cases undergoing living donor liver transplantation (n = 353). Lactate-adjusted NGAL level, a combination of these two predictors, was calculated as the sum of each value multiplied by the odds ratio for EAD or AKI. We evaluated whether this combined predictor at the end of surgery is significantly associated with both postoperative AKI or EAD. We compared the area under the receiver operating characteristic curve (AUC) between our multivariable regression models with and without NGAL, lactate, or lactate-adjusted NGAL. NGAL, lactate and lactate-adjusted NGAL are significant predictors for EAD and AKI. The regression model for EAD or AKI including lactate-adjusted NGAL showed a greater AUC (for EAD: odds ratio [OR] 0.88, 95% confidence interval [CI] 0.84-0.91; for AKI: OR 0.89, 95% CI 0.85-0.92) compared to the AUC of the models including lactate (for EAD: OR 0.84, 95% CI 0.81-0.88; for AKI: OR 0.79, 95% CI 0.74-0.83) or NGAL alone (for EAD: OR 0.82, 95% CI 0.77-0.86; for AKI: OR 0.84, 95% CI 0.80-0.88) or the model without lactate or NGAL (for EAD: OR 0.64, 95% CI 0.58-0.69, for AKI: OR 0.75, 95% CI 0.70-0.79). In conclusion, lactate-adjusted NGAL level at the end of surgery could be a reliable combined laboratory predictor for postoperative EAD or AKI after liver transplantation with a greater discriminative ability than lactate or NGAL alone.

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.

Comparing the effect of administering gelatin-low dose albumin versus albumin on renal function in liver transplantation: A randomized clinical trial

BACKGROUND

Acute kidney injury (AKI) is a common complication after liver transplantation (LT) that is independently associated with an increased risk of morbidity and mortality. This study aimed to evaluate the effects of administering gelatin-low dose albumin versus albumin on renal function and other early outcomes in LT.

METHODS

This randomized controlled clinical trial was conducted on 140 patients undergoing LT from brain death donors. Patients were randomly assigned to two groups: albumin or modified gelatin with albumin. Blood samples were collected before (T0) and on the first (T1), second (T2), third (T3), fifth (T4), and last day of hospitalization (T5) after LT for the detection of laboratory parameters, including renal and liver function tests.

RESULTS

The incidence of AKT on the basis of RIFLE criteria was 31.42% in the gelatin group (R: 59.10%, I: 36.40%, and F: 4.50%) and 25.71% in the albumin group (R: 66.70%, I: 27.80%, and F: 5.50%) (p = .845). Two patients in the gelatin and one in the albumin groups required renal replacement therapy (RRT). There was no significant difference between groups when the trends of changes in renal and liver function parameters were assessed during the study period (T0-T5). Furthermore, the incidence of complications was similar across groups.

CONCLUSION

This study showed that modified gelatin could be used without inappropriate outcomes on renal function in patients with normal preoperative kidney function tests undergoing LT.

Modelling kidney outcomes based on MELD eras - impact of MELD score in renal endpoints after liver transplantation

Background

Acute kidney injury is a common complication in solid organ transplants, notably liver transplantation. The MELD is a score validated to predict mortality of cirrhotic patients, which is also used for organ allocation, however the influence of this allocation criteria on AKI incidence and mortality after liver transplantation is still uncertain.

Methods

This is a retrospective single center study of a cohort of patients submitted to liver transplant in a tertiary Brazilian hospital: Jan/2002 to Dec/2013, divided in two groups, before and after MELD implementation (pre-MELD and post MELD). We evaluate the differences in AKI based on KDIGO stages and mortality rates between the two groups.

Results

Eight hundred seventy-four patients were included, 408 in pre-MELD and 466 in the post MELD era. The proportion of patients that developed AKI was lower in the post MELD era (p 0.04), although renal replacement therapy requirement was more frequent in this group (p < 0.01). Overall mortality rate at 28, 90 and 365 days was respectively 7%, 11% and 15%. The 1-year mortality rate was lower in the post MELD era (20% vs. 11%, p < 0.01). AKI incidence was 50% lower in the post MELD era even when adjusted for clinically relevant covariates (p < 0.01).

Conclusion

Liver transplants performed in the post MELD era had a lower incidence of AKI, although there were more cases requiring dialysis. 1-year mortality was lower in the post MELD era, suggesting that patient care was improved during this period.

Improvement of compliance to the Portland intensive insulin therapy during liver transplantation after introducing an application software: a retrospective single center cohort study

Background

The Portland intensive insulin therapy effectively controls acute hyperglycemic change after graft reperfusion during liver transplantation. However, the time-consuming sophistication acts as a barrier leading to misinterpretation and decreasing compliance to the protocol; thus, we newly introduced an application software “Insulin protocol calculator” which automatically calculates therapeutic bolus/continuous insulin doses based on the Portland protocol.

Methods

Of 144 patients who underwent liver transplantation, 74 patients were treated before the introduction of "Insulin protocol calculator" by using a paper manual, and 70 patients were treated by using the application. Compliance was defined as the proportion of patients treated with exact bolus/continuous insulin dose according to the Portland protocol.

Results

Compliance was significantly greater in app group than in paper group regarding bolus dose (94.5% and 86.9%, P < 0.001), continuous dose (88.9% and 77.3%, P = 0.001), and both doses (86.6% and 73.8%, P < 0.001). Blood glucose concentration was significantly lower in app group at 3 h (125 ± 17 mg/dl vs. 136 ± 19 mg/dl, P = 0.014) and 4 h (135 ± 22 mg/dl vs. 115 ± 15 mg/dl, P = 0.029) after graft reperfusion. Acute hyperglycemic change during 30 min was more prominent in app group while hyperglycemia incidence was 71.4% vs. 54.1% (P = 0.031). However, hyperglycemia risk was comparable at 2 h (31.4% vs. 31.1%, P = 0.964), and even insignificantly lower in app group at 3 h (7.1% vs. 19.5%, P = 0.184).

Conclusions

Compliance to the Portland protocol was significantly improved after introducing the application software; post-reperfusion hyperglycemia was better controlled. “Insulin protocol calculator” is cost-effective and time-saving with potential clinical benefits.

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records

IMPORTANCE

Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among hospitalized patients. Personalized risk estimation and risk factor identification may allow effective intervention and improved outcomes.

OBJECTIVE

To develop and validate personalized AKI risk estimation models using electronic health records (EHRs), examine whether personalized models were beneficial in comparison with global and subgroup models, and assess the heterogeneity of risk factors and their outcomes in different subpopulations.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study analyzed EHR data from 1 tertiary care hospital and used machine learning and logistic regression to develop and validate global, subgroup, and personalized risk estimation models. Transfer learning was implemented to enhance the personalized model. Predictor outcomes across subpopulations were analyzed, and metaregression was used to explore predictor interactions. Adults who were hospitalized for 2 or more days from November 1, 2007, to December 31, 2016, were included in the analysis. Patients with moderate or severe kidney dysfunction at admission were excluded. Data were analyzed between August 28, 2019, and May 8, 2022.

EXPOSURES

Clinical and laboratory variables in the EHR.

MAIN OUTCOMES AND MEASURES

The main outcome was AKI of any severity, and AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine criteria. Performance of the models was measured with area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and calibration.

RESULTS

The study cohort comprised 76 957 inpatient encounters. Patients had a mean (SD) age of 55.5 (17.4) years and included 42 159 men (54.8%). The personalized model with transfer learning outperformed the global model for AKI estimation in terms of AUROC among general inpatients (0.78 [95% CI, 0.77-0.79] vs 0.76 [95% CI, 0.75-0.76]; P < .001) and across the high-risk subgroups (0.79 [95% CI, 0.78-0.80] vs 0.75 [95% CI, 0.74-0.77]; P < .001) and low-risk subgroups (0.74 [95% CI, 0.73-0.75] vs 0.71 [95% CI, 0.70-0.72]; P < .001). The AUROC improvement reached 0.13 for the high-risk subgroups, such as those undergoing liver transplant and cardiac surgery. Moreover, the personalized model with transfer learning performed better than or comparably with the best published models in well-studied AKI subgroups. Predictor outcomes varied significantly between patients, and interaction analysis uncovered modifiers of the predictor outcomes.

CONCLUSIONS AND RELEVANCE

Results of this study demonstrated that a personalized modeling with transfer learning is an improved AKI risk estimation approach that can be used across diverse patient subgroups. Risk factor heterogeneity and interactions at the individual level highlighted the need for agile, personalized care.

Perioperative management of adult living donor liver transplantation: Part 1 - recipients

Living donor liver transplantation was first developed to mitigate the limited access to deceased donor organs in Asia in the 1990s. This alternative liver transplantation option has become an established and widely practiced transplantation method for adult patients suffering from end-stage liver disease. It has successfully addressed the shortage of deceased donors. The Society for the Advancement of Transplant Anesthesia and the Korean Society of Transplant Anesthesia jointly reviewed published studies on the perioperative management of live donor liver transplant recipients. The review aims to offer transplant anesthesiologists and critical care physicians a comprehensive overview of the perioperative management of adult live liver transplantation recipients. We feature the status, outcomes, surgical procedure, portal venous decompression, anesthetic management, prevention of acute kidney injury, avoidance of blood transfusion, monitoring and therapeutic strategies of hemodynamic derangements, and Enhanced Recovery After Surgery protocols for liver transplant recipients. This article is protected by copyright. All rights reserved.

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.

Machine learning approach to predict acute kidney injury after liver surgery

BACKGROUND

Acute kidney injury (AKI) after surgery appears to increase the risk of death in patients with liver cancer. In recent years, machine learning algorithms have been shown to offer higher discriminative efficiency than classical statistical analysis.

AIM

To develop prediction models for AKI after liver cancer resection using machine learning techniques.

METHODS

We screened a total of 2450 patients who had undergone primary hepatocellular carcinoma resection at Changzheng Hospital, Shanghai City, China, from January 1, 2015 to August 31, 2020. The AKI definition used was consistent with the Kidney Disease: Improving Global Outcomes. We included in our analysis preoperative data such as demographic characteristics, laboratory findings, comorbidities, and medication, as well as perioperative data such as duration of surgery. Computerized algorithms used for model development included logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and decision tree (DT). Feature importance was also ranked according to its contribution to model development.

RESULTS

AKI events occurred in 296 patients (12.1%) within 7 d after surgery. Among the original models based on machine learning techniques, the RF algorithm had optimal discrimination with an area under the curve value of 0.92, compared to 0.87 for XGBoost, 0.90 for DT, 0.90 for SVM, and 0.85 for LR. The RF algorithm also had the highest concordance-index (0.86) and the lowest Brier score (0.076). The variable that contributed the most in the RF algorithm was age, followed by cholesterol, and surgery time.

CONCLUSION

Machine learning algorithms are highly effective in discriminating patients at high risk of developing AKI. The successful application of machine learning models may help guide clinical decisions and help improve the long-term prognosis of patients.

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.

Post-Liver Transplant Acute Kidney Injury

Acute kidney injury (AKI) is a common condition following liver transplantation (LT). It negatively impacts patient outcomes by increasing the chances of developing chronic kidney disease and reducing graft and patient survival rates. Multiple definitions of AKI have been proposed and used throughout the years, with the International Club of Ascites definition being the most widely now used for patients with cirrhosis. Multiple factors are associated with the development of post-LT AKI and can be categorized into pre-LT comorbidities, donor and recipient characteristics, operative factors, and post-LT factors. Many of these factors can be optimized in an attempt to minimize the risk of AKI occurring and to improve renal function if AKI is already present. A special consideration during the post-LT phase is needed for immunosuppression as certain immunosuppressive medications can be nephrotoxic. The calcineurin inhibitor tacrolimus (TAC) is the mainstay of immunosuppression but can result in AKI. Several strategies including use of the monoclonoal antibody basilixamab to allow for delayed initiation of tacrolimus therapy and minimization through combination and minimization or elimination of TAC through combination with mycophenolate mofetil or mammalian target of rapamycin inhibitors have been implemented to reverse and avoid AKI in the post-LT setting. Renal replacement therapy may ultimately be required to support patients until recovery of AKI after LT. Overall, by improving renal function in post-LT patients with AKI, outcomes can be improved.

Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma

Acute kidney injury (AKI) has serious consequences on the prognosis of patients undergoing liver transplantation (LT) for liver cancer and cirrhosis. Artificial neural network (ANN) has recently been proposed as a useful tool in many fields in the setting of solid organ transplantation and surgical oncology, where patient prognosis depends on a multidimensional and nonlinear relationship between variables pertaining to the surgical procedure, the donor (graft characteristics), and the recipient comorbidities. In the specific case of LT, ANN models have been developed mainly to predict survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a specific opinion review on the role of ANN in the prediction of AKI after LT for liver cancer and cirrhosis, highlighting potential strengths of the method to forecast this serious postoperative complication.

Low Preoperative Antithrombin III Level Is Associated with Postoperative Acute Kidney Injury after Liver Transplantation

We aimed to determine the association between the preoperative antithrombin III (ATIII) level and postoperative acute kidney injury (AKI) after LT (post-LT AKI). We retrospectively evaluated 2395 LT recipients between 2010 and 2018 whose data of perioperative ATIII levels were available. Patients were divided into two groups based on the preoperative level of ATIII (ATIII < 50% vs. ATIII ≥ 50%). Multivariable regression analysis was performed to assess the risk factors for post-LT AKI. The mean preoperative ATIII levels were 30.2 ± 11.8% in the ATIII < 50% group and 67.2 ± 13.2% in the ATIII ≥ 50% group. The incidence of post-LT AKI was significantly lower in the ATIII ≥ 50% group compared to that in the ATIII < 50% group (54.7% vs. 75.5%, p < 0.001); odds ratio (OR, per 10% increase in ATIII level) 0.86, 95% confidence interval (CI) 0.81–0.92; p < 0.001. After a backward stepwise regression model, female sex, high body mass index, low albumin, deceased donor LT, longer duration of surgery, and high red blood cell transfusion remained significantly associated with post-LT AKI. A low preoperative ATIII level is associated with post-LT AKI, suggesting that preoperative ATIII might be a prognostic factor for predicting post-LT AKI.

Early prediction of acute kidney injury after liver transplantation by scoring system and decision tree

BACKGROUND AND AIMS

Early detection of acute kidney injury (AKI) is crucial for the prognosis of patients after liver transplantation (LT). This passage aims to analyze the perioperative clinical markers of AKI after LT and establish predictive models based on clinical variables for early detection of AKI after LT.

METHODS

We prospectively collected 109 patients with LT, and compared the differences of perioperative clinical markers between the AKI group and non-AKI group. The scoring system and decision tree model were established through the risk factors. Another 163 patients who underwent LT in the same center from 2017 to 2018 were retrospectively collected to verify the models.

RESULTS

In multiple comparisons of risk factors of post-LT AKI, pre-operative factors were excluded automatically, intraoperative and post-operative factors including operating time, intraoperative hypotension time, post-operative infection, the peak of post-operative AST, and post-operative shock were the independent risk factors for post-LT AKI. The scoring system established with the risk factors has good predictive power (AUC = 0.755) in the validation cohort. The decision tree also shows that post-operative shock was the most important marker, followed by post-operative infection.

CONCLUSION

Five intraoperative and post-operative factors are independently associated with post-LT AKI rather than pre-operative factors, which indicates that operation technique and post-operative management may more important for the prevention of post-LT AKI. The scoring system and decision tree model could complement each other, and provide quantitative and intuitive prediction tools for clinical practice of early detection of post-LT AKI.

Perioperative ABO Blood Group Isoagglutinin Titer and the Risk of Acute Kidney Injury after ABO-Incompatible Living Donor Liver Transplantation

For ABO-incompatible liver transplantation (ABO-i LT), therapeutic plasma exchange (TPE) is performed preoperatively to reduce the isoagglutinin titer of anti-ABO blood type antibodies. We evaluated whether perioperative high isoagglutinin titer is associated with postoperative risk of acute kidney injury (AKI). In 130 cases of ABO-i LT, we collected immunoglobulin (Ig) G and Ig M isoagglutinin titers of baseline, pre-LT, and postoperative peak values. These values were compared between the patients with and without postoperative AKI. Multivariable logistic regression analysis was used to evaluate the association between perioperative isoagglutinin titers and postoperative AKI. Clinical and graft-related outcomes were compared between high and low baseline and postoperative peak isoagglutinin groups. The incidence of AKI was 42.3%. Preoperative baseline and postoperative peak isoagglutinin titers of both Ig M and Ig G were significantly higher in the patients with AKI than those without AKI. Multivariable logistic regression analysis showed that preoperative baseline and postoperative peak Ig M isoagglutinin titers were significantly associated with the risk of AKI (baseline: odds ratio 1.06, 95% confidence interval 1.02 to 1.09; postoperative peak: odds ratio 1.08, 95% confidence interval 1.04 to 1.13). Cubic spline function curves show a positive relationship between the baseline and postoperative peak isoagglutinin titers and the risk of AKI. Clinical outcomes other than AKI were not significantly different according to the baseline and postoperative peak isoagglutinin titers. Preoperative high initial and postoperative peak Ig M isoagglutinin titers were significantly associated with the development of AKI. As the causal relationship between high isoagglutinin titers and risk of AKI is unclear, the high baseline and postoperative isoagglutinin titers could be used simply as a warning sign for the risk of AKI after liver transplantation.

Different Severity of Clinical Outcomes Between the 2 Subgroups of Stage 1 Acute Kidney Injury After Liver Transplantation

BACKGROUND

Although the Kidney Disease: Improving Global Outcomes (KDIGO) criteria is used to define acute kidney injury (AKI) after liver transplantation, the criteria was criticized for including 2 heterogeneous groups of different serum creatinine (sCr) criteria together in AKI stage 1. We investigated whether there are significant differences in clinical outcomes between 2 subgroups of patients within AKI stage 1.

METHODS

A total of 1440 cases were reviewed. The AKI stage 1 (n = 443) were divided into 2 subgroups based on changes in sCr level (stage 1a: ≥0.3 mg/dL of absolute sCr increase, n = 251; stage 1b: ≥50% relative sCr increase, n = 192). Propensity score analysis was performed between stage 1a and 1b groups, yielding 157 matched pairs. We compared the length of hospital stay, early allograft dysfunction, and 5-year all-cause mortality between these subgroups after matching. Kaplan-Meier analyses were performed to compare the graft or overall survival between the subgroups after matching. Sensitivity analysis for Acute Kidney Injury Network (AKIN) criteria was performed.

RESULTS

Length of hospital stay and 5-year all-cause mortality was significantly worse in patients with stage 1b compared to stage 1a after matching. Five-year graft or patient survival was significantly worse in patients with stage 1b compared to stage 1a after matching (Log-rank test P = 0.022 and P = 0.027, respectively). These results were the same regarding AKIN criteria.

CONCLUSIONS

The KDIGO criteria for AKI stage 1 could be further divided into 2 substages with different severity of clinical outcomes. This modified criteria could give additional prognostic information in patients undergoing liver transplantation.

Intraoperative hypotension during liver transplant surgery is associated with postoperative acute kidney injury: a historical cohort study

Background

Acute kidney injury (AKI) occurs frequently after liver transplant surgery and is associated with significant morbidity and mortality. While the impact of intraoperative hypotension (IOH) on postoperative AKI has been well demonstrated in patients undergoing a wide variety of non-cardiac surgeries, it remains poorly studied in liver transplant surgery. We tested the hypothesis that IOH is associated with AKI following liver transplant surgery.

Methods

This historical cohort study included all patients who underwent liver transplant surgery between 2014 and 2019 except those with a preoperative creatinine > 1.5 mg/dl and/or who had combined transplantation surgery. IOH was defined as any mean arterial pressure (MAP) < 65 mmHg and was classified according to the percentage of case time during which the MAP was < 65 mmHg into three groups, based on the interquartile range of the study cohort: “short” (Quartile 1, < 8.6% of case time), “intermediate” (Quartiles 2–3, 8.6–39.5%) and “long” (Quartile 4, > 39.5%) duration. AKI stages were classified according to a “modified” “Kidney Disease: Improving Global Outcomes” (KDIGO) criteria. Logistic regression modelling was conducted to assess the association between IOH and postoperative AKI. The model was run both as a univariate and with multiple perioperative covariates to test for robustness to confounders.

Results

Of the 205 patients who met our inclusion criteria, 117 (57.1%) developed AKI. Fifty-two (25%), 102 (50%) and 51 (25%) patients had short, intermediate and long duration of IOH respectively. In multivariate analysis, IOH was independently associated with an increased risk of AKI (adjusted odds ratio [OR] 1.05; 95%CI 1.02–1.09; P < 0.001). Compared to “short duration” of IOH, “intermediate duration” was associated with a 10-fold increased risk of developing AKI (OR 9.7; 95%CI 4.1–22.7; P < 0.001). “Long duration” was associated with an even greater risk of AKI compared to “short duration” (OR 34.6; 95%CI 11.5-108.6; P < 0.001).

Conclusions

Intraoperative hypotension is independently associated with the development of AKI after liver transplant surgery. The longer the MAP is < 65 mmHg, the higher the risk the patient will develop AKI in the immediate postoperative period, and the greater the likely severity. Anesthesiologists and surgeons must therefore make every effort to avoid IOH during surgery.

Nomogram to predict the risk of septic acute kidney injury in the first 24 h of admission: an analysis of intensive care unit data

Background: Acute kidney injury (AKI) is a significant cause of morbidity and mortality, especially in sepsis patients. Early prediction of AKI can help physicians determine the appropriate intervention, and thus, improve the outcome. This study aimed to develop a nomogram to predict the risk of AKI in sepsis patients (S-AKI) in the initial 24 h following admission.Methods: Sepsis patients with AKI who met the Sepsis 3.0 criteria and Kidney Disease: Improving Global Outcomes criteria in the Massachusetts Institute of Technology critical care database, Medical Information Mart for Intensive Care (MIMIC-III), were identified for analysis. Data were analyzed using multiple logistic regression, and the performance of the proposed nomogram was evaluated based on Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve.Results: We included 2917 patients in the analysis; 1167 of 2042 patients (57.14%) and 469 of 875 patients (53.6%) had AKI in the training and validation cohorts, respectively. The predictive factors identified by multivariate logistic regression were blood urea nitrogen level, infusion volume, lactate level, weight, blood chloride level, body temperature, and age. With the incorporation of these factors, our model had well-fitted calibration curves and achieved good C-indexes of 0.80 [95% confidence interval (CI): 0.78-0.82] and 0.79 (95% CI: 0.76-0.82) in predicting S-AKI in the training and validation cohorts, respectively.Conclusion: The proposed nomogram effectively predicted AKI risk in sepsis patients admitted to the intensive care unit in the first 24 h.

Association of overweight with postoperative acute kidney injury among patients receiving orthotopic liver transplantation: an observational cohort study

BACKGROUND

Acute kidney injury (AKI) is a common postoperative complication of orthotopic liver transplantation (OLT). So far, little attention has been paid on the association between overweight and AKI after OLT, and animal models or clinical studies have drawn conflicting conclusions. The objective of our study was to determine whether overweight (BMI [Body Mass Index] ≥ 25 kg/m2) is associated with an increased risk of AKI after OLT.

METHODS

This retrospective cohort study included 244 patients receiving OLT in the Affiliated Hospital of Qingdao University between January 1, 2017, and August 29, 2019. Preoperative, intraoperative, and postoperative data were collected retrospectively. The primary outcome was the development of AKI as defined by Kidney Disease, Improving Global Outcome (KIDGO) staging system. Logistic regression analysis was used to determine the relationship between overweight and the occurrence of postoperative AKI. Data analysis was conducted from September to October 2019, revision in April 2020.

RESULTS

Among 244 patients receiving OLT (mean [standard deviation] age, 54.1 [9.6] years; 84.0% male) identified, 163 patients (66.8%) developed postoperative AKI. Overweight (BMI ≥ 25 kg/m2) was associated with a higher rate of postoperative severe AKI (stage 2/3) compared with normal weight (18.5 ≤ BMI < 25 kg/m2) (41 [47.7%] vs 39 [28.7%]; adjusted odds ratio [OR], 2.539; 95% confidence interval [CI], 1.389-4.642; P = 0.002). Furthermore, patients with obese were at even higher risk of postoperative severe AKI after controlling for confounding factors (adjusted OR: 3.705; 95% CI: 1.108-12.388; P = 0.033).

CONCLUSIONS

Overweight is independently associated with an increased risk of postoperative severe AKI among patients receiving OLT. The association of BMI with severe AKI after OLT is J-shaped.

Intraoperative Hemodynamic Parameters and Acute Kidney Injury After Living Donor Liver Transplantation

BACKGROUND

Acute kidney injury (AKI) after living donor liver transplantation (LDLT) is associated with increased mortality. We sought to identify associations between intraoperative hemodynamic variables and postoperative AKI.

METHODS

We retrospectively reviewed 734 cases of LDLT. Intraoperative hemodynamic variables of systemic and pulmonary arterial pressure, central venous pressure (CVP), and pulmonary artery catheter-derived parameters including mixed venous oxygen saturation (SvO2), right ventricular end-diastolic volume (RVEDV), stroke volume, systemic vascular resistance, right ventricular ejection fraction, and stroke work index were collected. Propensity score matching analysis was performed between patients with (n = 265) and without (n = 265) postoperative AKI. Hemodynamic variables were compared between patients with AKI, defined by Kidney Disease Improving Global Outcomes criteria, and those without AKI in the matched sample.

RESULTS

The incidence of AKI was 36.1% (265/734). Baseline CVP, baseline RVEDV, and SvO2 at 5 minutes before reperfusion were significantly different between patients with and without AKI in the matched sample of 265 pairs. Multivariable logistic regression analysis revealed that baseline CVP, baseline RVEDV, and SvO2 at 5 minutes before reperfusion were independent predictors of AKI (CVP per 5 cm H2O increase: odds ratio [OR], 1.20; 95% confidence interval [CI], 1.09-1.32; SvO2: OR, 1.45; 95% CI, 1.27-1.71; RVEDV: OR, 1.48; 95% CI, 1.24-1.78).

CONCLUSIONS

The elevated baseline CVP, elevated baseline RVEDV after anesthesia induction, and decreased SvO2 during anhepatic phase were associated with postoperative AKI. Prospective trials are required to evaluate whether the optimization of these variables may decrease the risk of AKI after LDLT.

Liver Transplant Patients with High Levels of Preoperative Serum Ammonia Are at Increased Risk for Postoperative Acute Kidney Injury: A Retrospective Study

Acute kidney injury (AKI) is one of the most frequent postoperative complications after liver transplantation (LT). Increased serum ammonia levels due to the liver disease itself may affect postoperative renal function. This study aimed to compare the incidence of postoperative AKI according to preoperative serum ammonia levels in patients after LT. Medical records from 436 patients who underwent LT from January 2010 to February 2020 in a single university hospital were retrospectively reviewed. The patients were then categorized according to changes in plasma creatinine concentrations within 48 h of LT using the Acute Kidney Injury Network criteria. A preoperative serum ammonia level above 45 mg/dL was associated with postoperative AKI (p < 0.0001). Even in patients with a normal preoperative creatinine level, when the ammonia level was greater than 45 μg/dL, the incidence of postoperative AKI was significantly higher (p < 0.0001); the AKI stage was also higher in this group than in the group with preoperative ammonia levels less than or equal to 45 μg/dL (p < 0.0001). Based on the results of our research, an elevation in preoperative serum ammonia levels above 45 μg/dL is related to postoperative AKI after LT.

Development of a risk scoring system for predicting acute kidney injury after minimally invasive partial and radical nephrectomy: a retrospective study

BACKGROUND

Acute kidney injury after partial or radical nephrectomy remains an unsolved problem even when using minimally invasive techniques. We aimed to identify risk factors for acute kidney injury (AKI) after minimally invasive nephrectomy and to develop a clinical risk scoring system.

METHODS

Medical records of 1762 patients who underwent minimally invasive laparoscopic or robot-assisted laparoscopic partial (n = 1009) or radical (n = 753) nephrectomy from December 2005 to November 2018 were reviewed. Candidate risk factors were screened using univariate analysis and ranked using linear discriminant analysis; top ranking factors were incorporated into a multivariate logistic regression model. Then, the final clinical scoring system was created based on the estimated odds ratios.

RESULTS

The incidence of acute kidney injury after partial or radical nephrectomy was 20.3 and 61.6%, respectively. Risk factors incorporated into the scoring system included: size of the parenchymal mass removed (3 < parenchymal mass ≤ 4 cm, 1 point; 4 < parenchymal mass ≤ 6 cm, 3 points; parenchymal mass > 6 cm, 5 points), male sex (2 points), diabetes mellitus (1 point), warm ischemia time ≥ 25 min (1 point), and immediate postoperative neutrophil count ≥ 12,000 µl^-1 (1 point) in patients with partial nephrectomy, and sex (male, 10 points; female, 7 points) in patients with radical nephrectomy. For risk scores of 0-4, 5-6, 7, 8-9, and 10 points, the probabilities of acute kidney injury were approximately 10, 20, 40, 60, and 80%, respectively. The predictive accuracy of the scoring system was 0.827 (95% CI 0.789-0.865).

CONCLUSION

Our risk scoring system could help clinicians identify those at risk of acute kidney injury after minimally invasive partial or radical nephrectomy, thereby optimizing postoperative management.

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury

BACKGROUND

More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients' data to extract the temporal features using their structural temporal patterns, that is, trends.

OBJECTIVE

This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI).

METHODS

Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation.

RESULTS

Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001).

CONCLUSIONS

Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

Intraoperative Oxygen Delivery and Acute Kidney Injury after Liver Transplantation

Although intraoperative hemodynamic variables were reported to be associated with acute kidney injury (AKI) after liver transplantation, the time-dependent association between intraoperative oxygen delivery and AKI has not yet been evaluated. We reviewed 676 cases of liver transplantation. Oxygen delivery index (DO2I) was calculated at least ten times during surgery. AKI was defined according to the Kidney Disease Improving Global Outcomes criteria. The area under the curve (AUC) was calculated as below a DO2I of 300 (AUC < 300), 400 and 500 mL/min/m2 threshold. Also, the cumulative time below a DO2I of 300 (Time < 300), 400, and 500 mL/min/m2 were calculated. Multivariable logistic regression analysis was performed to evaluate whether AUC < 300 or time < 300 was independently associated with the risk of AKI. As a sensitivity analysis, propensity score matching analysis was performed between the two intraoperative mean DO2I groups using a cutoff of 500 ml/min/m2, and the incidence of AKI was compared between the groups. Multivariable analysis showed that AUC < 300 or time < 300 was an independent predictor of AKI (AUC < 300: odds ratio [OR] = 1.10, 95% confidence interval [CI] 1.06-1.13, time < 300: OR = 1.10, 95% CI 1.08-1.14). Propensity score matching yielded 192 pairs of low and high mean DO2I groups. The incidence of overall and stage 2 or 3 AKI was significantly higher in the lower DO2I group compared to the higher group (overall AKI: lower group, n = 64 (33.3%) vs. higher group, n = 106 (55.2%), P < 0.001). In conclusion, there was a significant time-dependent association between the intraoperative poor oxygen delivery <300 mL/min/m2 and the risk of AKI after liver transplantation. The intraoperative optimization of oxygen delivery may mitigate the risk of AKI.

Validation of acute kidney injury prediction scores in critically ill patients

Prediction of acute kidney injury (AKI) in critically ill patients allows prompt intervention that improves outcome. We aimed for external validation of two AKI prediction scores that can be bedside calculated. A prospective observational study included patients admitted to medical and surgical critical care units. Performance of two AKI prediction scores, Malhotra score and acute kidney injury prediction score (APS), was assessed for their ability to predict AKI. The best cutoff point for each score was determined by Youden index. Area under the receiving operation characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were used to assess performance of each score. Univariate and multivariate regression analyses were done to detect the predictability of AKI. Goodness-of-fit and kappa Cohen agreement tests were done to show whether the expected score results fit well and agree with the observed results. AKI prevalence was 37.6%. The best cutoff values were 5 and 4 for Malhotra score and APS, respectively. Area under the curve for Malhotra 5 was 0.712 and for APS 4 was 0.652 with nearly similar sensitivity and specificity. Regression analysis demonstrated that Malhotra 5 was the independent predictor of AKI. Goodness-of-fit test showed significant results denoting lack of fit between the scores and the actual results. Kappa test showed moderate agreement for Malhotra 5 and fair agreement for APS 4. Both scores showed moderate performance for AKI prediction. Malhotra 5 showed better performance compared to APS 4. Multicenter international study is warranted to develop a universal model that can predict AKI in critically ill patients.

Influence of Transfusion on the Risk of Acute Kidney Injury: ABO-Compatible versus ABO-Incompatible Liver Transplantation

ABO-incompatible liver transplantation (ABO-i LT) is associated with a higher risk of acute kidney injury (AKI) compared to ABO-compatible liver transplantation (ABO-c LT). We compared the risk of AKI associated with transfusion between ABO-c and ABO-i living donor liver transplantation (LDLT). In 885 cases of LDLT, we used a propensity score analysis to match patients who underwent ABO-c (n = 766) and ABO-i (n = 119) LDLT. Baseline medical status, laboratory findings, and surgical- and anesthesia-related parameters were used as contributors for propensity score matching. AKI was defined according to the "Kidney Disease Improving Global Outcomes" criteria. After 1:2 propensity score matching, a conditional logistic regression analysis was performed to evaluate the relationship between the intraoperative transfusion of packed red blood cells (pRBCs) and fresh frozen plasma (FFP) on the risk of AKI. The incidence of AKI was higher in ABO-i LT than in ABO-c LT before and after matching (after matching, 65.8% in ABO-i vs 39.7% in ABO-c, p < 0.001). The incidence of AKI increased in direct proportion to the amount of transfusion, and this increase was more pronounced in ABO-i LT. The risk of pRBC transfusion for AKI was greater in ABO-i LT (multivariable adjusted odds ratio (OR) 1.32 per unit) than in ABO-c LT (OR 1.11 per unit). The risk of FFP transfusion was even greater in ABO-i LT (OR 1.44 per unit) than in ABO-c LT (OR 1.07 per unit). In conclusion, the association between transfusion and risk of AKI was stronger in patients with ABO-i LT than with ABO-c LT. Interventions to reduce perioperative transfusions may attenuate the risk of AKI in patients with ABO-i LT.

Early detection of acute kidney injury in the perioperative period of liver transplant with neutrophil gelatinase-associated lipocalin

Background

Acute kidney injury (AKI) is a common complication in patients undergoing liver transplant (LT) and is associated with high morbidity and mortality. We aim to evaluate the pattern of urine and plasma neutrophil gelatinase-associated lipocalin (NGAL) elevation during the perioperative period of LT and to assess it as a prognostic marker for AKI progression, need for dialysis and mortality.

Methods

We assessed NGAL levels before induction of anesthesia, after portal reperfusion and at 6, 18, 24, and 48 h after surgery. Patients were monitored daily during the first week after LT.

Results

Of 100 enrolled patients undergoing liver transplant, 59 developed severe AKI based on the KDIGO serum creatinine (sCr) criterion; 34 were dialysed, and 21 died within 60 days after LT. Applying a cut-off value of 136 ng/ml, UNGAL values 6 h after surgery was a good predictor of AKI development within 7 days after surgery, having a positive predictive value (PPV) of 80% with an AUC of 0.76 (95% CI 0.67–0.86). PNGAL at 18 h after LT was also a good predictor of AKI in the first week, having a PPV of 81% and AUC of 0.74 (95% CI 0.60–0.88). Based on PNGAL and UNGAL cut-off criteria levels, time to AKI diagnosis was 28 and 23 h earlier than by sCr, respectively. The best times to assess the need for dialysis were 18 h after LT by PNGAL and 06 h after LT by UNGAL.

Conclusion

In conclusion, the plasma and urine NGAL elevation pattern in the perioperative period of the liver transplant can predict AKI diagnosis earlier. UNGAL was an early independent predictor of AKI development and need for dialysis. Further studies are needed to assess whether the clinical use of biomarkers can improve patient outcomes.

Trial registration

Registered at Clinical Trials (clinicaltrials.gov) in March 24th, 2014 by title “Acute Kidney Injury Biomarkers: Diagnosis and Application in Pre-operative Period of Liver Transplantation (AKIB)” and identifier NCT02095431, retrospectively registered.

Acute Kidney Injury After Pediatric Liver Transplantation

BACKGROUND

The aim of the present study is to assess acute kidney injury (AKI) incidence according to the pRIFLE and AKIN criteria and to evaluate the risk factors for early developing AKI in postoperative intensive care unit after pediatric liver transplantation (LT).

MATERIALS

After exclusion of retransplantations, 7 cadaveric and 44 living donors, totaling 51 pediatric LT patients that were performed between 2005 and 2017, were reviewed retrospectively. AKI was defined according to both pediatric RIFLE (Risk for renal dysfunction, Injury to the kidney, Failure of kidney function, Loss of kidney function, and End-stage renal disease) and Acute Kidney Injury Network (AKIN) criteria. Documented data were compared between AKI and non-AKI patients.

RESULTS

AKI incidences were 17.6% by AKIN and 37.8% by pRIFLE criteria. AKIN-defined AKI group had statistically lower serum albumin level, higher serum sodium level, higher furosemide dose, and higher rate of red blood cell (RBC) transfusion than the non-AKI group (P = .02, P = .02, P = .01 and P = .04, respectively). AKI patients had significantly prolonged mechanical ventilation (P = .01) and hospital LOS (P = .02). The pRIFLE-defined AKI group had significantly lower serum albumin level, higher blood urea nitrogen (BUN) level, and higher ascites drained and also showed higher requirement for RBC and 20% human albumin transfusions than the non-AKI group (P = .02, P = .04, P: =.007, P = .02 and P = .05, respectively).

CONCLUSION

We evaluated that hypoalbuminemia, high requirement for RBC and 20% human albumin transfusions, high serum sodium, high furosemide use, and high flow of ascites are risk factors for AKI and high BUN levels can be predictive for AKI in pediatric LT patients. The effect of AKI on outcome variables were prolonged mechanical ventilation and hospital LOS.

Intraoperative Hepatic Blood Inflow Can Predict Early Acute Kidney Injury following DCD Liver Transplantation: A Retrospective Observational Study

Purpose

Acute kidney injury (AKI) is a major and severe complication following donation-after-circulatory-death (DCD) liver transplantation (LT) and is associated with increased postoperative morbidity and mortality. However, the risk factors and the prognosis factors of AKI still need to be further explored, and the relativity of intraoperative hepatic blood inflow (HBI) and AKI following LT has not been discussed yet. The purpose of this study was to investigate the correlation between HBI and AKI and to construct a prediction model of early acute kidney injury (EAKI) following DCD LT with the combination of HBI and other clinical parameters.

Methods

Clinical data of 132 patients who underwent DCD liver transplantation at the first hospital of China Medical University from April 2005 to March 2017 were analyzed. Data of 105 patients (the first ten years of patients) were used to develop the prediction model. Then we assessed the clinical usefulness of the prediction models in the validation cohort (27 patients). EAKI according to Kidney Disease Improving Global Outcomes (KDIGO) criteria based on serum creatinine increase during 7-day of postoperative follow-up.

Results

After Least Absolute Shrinkage and Selection Operator (LASSO) regression and simplification, a simplified prediction model consisting of the Child-Turcotte-Pugh (CTP) score (p=0.033), anhepatic phase (p=0.014), packed red blood cell (pRBC) transfusion (p=0.027), and the HBI indexed by height (HBI/h) (p=0.002) was established. The C-indexes of the model in the development and validation cohort were 0.823 [95% CI, 0.738-0.908] and 0.921 [95% CI, 0.816-1.000], respectively.

Conclusions

In this study, we demonstrated the utility of HBI/h as a predictor for EAKI following DCD LT, as well as the clinical usefulness of the prediction model through the combination of the CTP score, anhepatic phase, pRBC transfusion and HBI/h.

Automated Continuous Acute Kidney Injury Prediction and Surveillance: A Random Forest Model

OBJECTIVE

To develop and validate a prediction model of acute kidney injury (AKI) of any severity that could be used for AKI surveillance and management to improve clinical outcomes.

PATIENTS AND METHODS

This retrospective cohort study was conducted in medical, surgical, and mixed intensive care units (ICUs) at Mayo Clinic in Rochester, Minnesota, including adult (≥18 years of age) ICU-unique patients admitted between October 1, 2004, and April 30, 2011. Our primary objective was prediction of AKI using extant clinical data following ICU admission. We used random forest classification to provide continuous AKI risk score.

RESULTS

We included 4572 and 1958 patients in the training and validation mutually exclusive cohorts, respectively. Acute kidney injury occurred in 1355 patients (30%) in the training cohort and 580 (30%) in the validation cohort. We incorporated known AKI risk factors and routinely measured vital characteristics and laboratory results. The model was run throughout ICU admission every 15 minutes and achieved an area under the receiver operating characteristic curve of 0.88 on validation. It was 92% sensitive and 68% specific and detected 30% of AKI cases at least 6 hours before the criterion standard time (AKI stages 1-3). For discrimination of AKI stages 2 to 3, the model had 91% sensitivity, 71% specificity, and 53% detection of AKI cases at least 6 hours before AKI onset.

CONCLUSION

We developed and validated an AKI prediction model using random forest for continuous monitoring of ICU patients. This model could be used to identify high-risk patients for preventive measures or identifying patients of prospective interventional trials.

Crosstalk Between Connexin32 and Mitochondrial Apoptotic Signaling Pathway Plays a Pivotal Role in Renal Ischemia Reperfusion-Induced Acute Kidney Injury

Aims: Perioperative acute kidney injury (AKI) resulting from renal ischemia reperfusion (IR) is not conducive to the postoperative surgical recovery. Our previous study demonstrated that reactive oxygen species (ROS) transmitted by gap junction (GJ) composed of connexin32 (Cx32) contributed to AKI. However, the precise underlying pathophysiologic mechanisms were largely unknown. This study focuses on the underlying mechanisms related to ROS transmitted by Cx32 responsible for AKI aggravation. Results: In a set of in vivo studies, renal IR was found to cause severe impairment in renal tissues with massive ROS generation, which occurred contemporaneously with activation of NF-κB/p53/p53 upregulated modulator of apoptosis (PUMA)-mediated mitochondrial apoptosis pathways. Cx32 deficiency alleviated renal IR-induced AKI, and simultaneously attenuated ROS generation and distribution in renal tissues, which further inhibited NF-κB/p53/PUMA-mediated mitochondrial apoptotic pathways. Correspondingly, in a set of in vitro studies, hypoxia reoxygenation (HR)-induced cellular injury, and cell apoptosis in both human kidney tubular epithelial cells (HK-2s) and rat kidney tubular epithelial cells (NRK52Es) were significantly attenuated by Cx32 inhibitors or Cx32 gene knockdown. More importantly, Cx32 inhibition not only decreased ROS generation and distribution in human or rat kidney tubular epithelial cells but also inhibited its downstream NF-κB/p53/PUMA-mediated mitochondrial apoptotic pathway activation. Innovation and Conclusion: This is the first identification of the underlying mechanisms of IR-induced renal injury integrally which demonstrates the critical role played by Cx32 in IR-induced AKI. Moreover, GJ composed of Cx32 manipulates ROS generation and distribution between neighboring cells, and alters activation of NF-κB/p53/PUMA-mediated mitochondrial apoptotic pathways. Both inhibiting Cx32 function and scavenging ROS effectively reduce mitochondrial apoptosis and subsequently attenuate AKI, providing effective strategies for kidney protection.

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.

Postoperative respiratory failure in liver transplantation: Risk factors and effect on prognosis

Background

Postoperative respiratory failure (PRF, namely mechanical ventilation >48 hours) significantly affects morbidity and mortality in liver transplantation (LTx). Previous studies analyzed only one or two categories of PRF risk factors (preoperative, intraoperative or postoperative ones).

The aims of this study were to identify PRF predictors, to assess the length of stay (LoS) in ICU and the 90-day survival according to the PRF in LTx patients.

Methods

Two classification approaches were used: systematic classification (recipient-related preoperative factors; intraoperative factors; logistic factors; donor factors; postoperative ICU factors; postoperative surgical factors) and patient/organ classification (patient-related general factors; native-liver factors; new-liver factors; kidney factors; heart factors; brain factors; lung factors). Two hundred adult non-acute patients were included. Missing analysis was performed. The competitive role of each factor was assessed.

Results

PRF occurred in 36.0% of cases. Among 28 significant PRF predictors at univariate analysis, 6 were excluded because of collinearity, 22 were investigated by ROC curves and by logistic regression analysis. Recipient age (OR = 1.05; p = 0.010), female sex (OR = 2.75; p = 0.018), Model for End-Stage Liver Disease (MELD, OR = 1.09; p<0.001), restrictive lung pattern (OR = 2.49; p = 0.027), intraoperative veno-venous bypass (VVBP, OR = 3.03; p = 0.008), pre-extubation PaCO₂ (OR = 1.11; p = 0.003) and Model for Early Allograft Function (MEAF, OR = 1.37; p<0.001) resulted independent PRF risk factors. As compared to patients without PRF, the PRF-group had longer LoS (10 days IQR 7–18 versus 5 days IQR 4–7, respectively; p<0.001) and lower day-90 survival (86.0% versus 97.6% respectively, p<0.001).

Conclusion

In conclusion, MELD, restrictive lung pattern, surgical complexity as captured by VVBP, pre-extubation PaCO₂ and MEAF are the main predictors of PRF in non-acute LTx patients.

Intraoperative Oliguria with Decreased SvO₂ Predicts Acute Kidney Injury after Living Donor Liver Transplantation

Acute kidney injury (AKI) is a frequent complication after living donor liver transplantation (LDLT), and is associated with increased mortality. However, the association between intraoperative oliguria and the risk of AKI remains uncertain for LDLT. We sought to determine the association between intraoperative oliguria alone and oliguria coupled with hemodynamic derangement and the risk of AKI after LDLT. We evaluated the hemodynamic variables, including mean arterial pressure, cardiac index, and mixed venous oxygen saturation (SvO₂). We reviewed 583 adult patients without baseline renal dysfunction and who did not receive hydroxyethyl starch during surgery. AKI was defined using the Kidney Disease Improving Global Outcomes criteria according to the serum creatinine criteria. Multivariable logistic regression analysis was performed with and without oliguria and oliguria coupled with a decrease in SvO₂. The performance was compared with respect to the area under the receiver operating characteristic curve (AUC). Intraoperative oliguria <0.5 and <0.3 mL/kg/h were significantly associated with the risk of AKI; however, their performance in predicting AKI was poor. The AUC of single predictors increased significantly when oliguria was combined with decreased SvO₂ (AUC 0.72; 95% confidence interval (CI) 0.68–0.75 vs. AUC of oliguria alone 0.61; 95% CI 0.56–0.61; p < 0.0001; vs. AUC of SvO₂ alone 0.66; 95% CI 0.61–0.70; p < 0.0001). Addition of oliguria coupled with SvO₂ reduction also increased the AUC of multivariable prediction (AUC 0.87; 95% CI 0.84–0.90 vs. AUC with oliguria 0.73; 95% CI 0.69–0.77; p < 0.0001; vs. AUC with neither oliguria nor SvO₂ reduction 0.68; 95% CI 0.64–0.72; p < 0.0001). Intraoperative oliguria coupled with a decrease in SvO₂ may suggest the risk of AKI after LDLT more reliably than oliguria alone or decrease in SvO₂ alone. Intraoperative oliguria should be interpreted in conjunction with SvO₂ to predict AKI in patients with normal preoperative renal function and who did not receive hydroxyethyl starch during surgery.

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

Calibration drift in regression and machine learning models for acute kidney injury

Objective

Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population.

Materials and Methods

Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years.

Results

Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods.

Conclusions

Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.

Reliability of Point-of-Care Hematocrit Measurement During Liver Transplantation

BACKGROUND

Although point-of-care (POC) analyzers are commonly used during liver transplantation (LT), the accuracy of hematocrit measurement using a POC analyzer has not been evaluated. In this retrospective observational study, we aimed to evaluate the accuracy of hematocrit measurement using a POC analyzer and identify potential contributors to the measurement error and their influence on mistransfusion during LT.

METHODS

We retrospectively collected 6461 pairs of simultaneous intraoperative hematocrit measurements using POC analyzers and laboratory devices during LTs in 901 patients. The agreement of hematocrit measurements was assessed using Bland-Altman analysis for repeated measurements, while the incidence and magnitude of hematocrit measurement error were compared among 16 different laboratory abnormality categories. A generalized estimating equation analysis was performed to identify potential contributors to falsely low-measured POC hematocrit. Additionally, we defined potential "overtransfusion" in the case when POC hematocrit was <20% and laboratory hematocrit was ≥20% and investigated its association with intraoperative transfusion.

RESULTS

The POC hematocrit measurements were falsely lower than the laboratory hematocrit measurements in 70.3% (4541/6461) of pairs. The median (interquartile range) of hematocrit measurement error was -1.20 (-2.60 to 0.20). Bland-Altman analysis showed that 24.5% (1583/6461) of the errors were outside our a priori defined clinically acceptable limits of ±3%. The incidence of falsely low-measured hematocrit was significantly higher with the presence of concomitant hypoalbuminemia and hypoproteinemia. Hypoalbuminemia combined with hyperglycemia showed significantly larger hematocrit measurement error. Hypoalbuminemia, hypoproteinemia, and hyperglycemia were predictors of falsely low-measured hematocrit. Furthermore, the overtransfusion group showed larger amount of transfusion than the adequately transfused group, with a median difference of 2 units (95% confidence interval [0-4], P = .039), despite similar amount of blood loss.

CONCLUSIONS

Hematocrit measured using the POC device tends to be lower than the laboratory hematocrit measured during LT. Commonly encountered laboratory abnormalities during LT include hypoalbuminemia, hypoproteinemia, and hyperglycemia, which may contribute to falsely low-measured POC hematocrit. Careful consideration of these confounders may help reduce overtransfusion that occurs due to falsely low-measured POC hematocrit.