Subramanian, Ram

OBJECTIVES: To develop evidence-based recommendations for clinicians caring for adults with acute liver failure (ALF) or acute on chronic liver failure (ACLF) in the ICU. DESIGN: The guideline panel comprised 27 members with expertise in aspects of care of the critically ill patient with liver failure or methodology. We adhered to the Society of Critical Care Medicine standard operating procedures manual and conflict-of-interest policy. Teleconferences and electronic-based discussion among the panel, as well as within subgroups, served as an integral part of the guideline development. INTERVENTIONS: In part 2 of this guideline, the panel was divided into four subgroups: neurology, peri-transplant, infectious diseases, and gastrointestinal groups. We developed and selected Population, Intervention, Comparison, and Outcomes (PICO) questions according to importance to patients and practicing clinicians. For each PICO question, we conducted a systematic review and meta-analysis where applicable. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. We used the evidence to decision framework to facilitate recommendations formulation as strong or conditional. We followed strict criteria to formulate best practice statements. MEASUREMENTS AND MAIN RESULTS: We report 28 recommendations (from 31 PICO questions) on the management ALF and ACLF in the ICU. Overall, five were strong recommendations, 21 were conditional recommendations, two were best-practice statements, and we were unable to issue a recommendation for five questions due to insufficient evidence. CONCLUSIONS: Multidisciplinary, international experts formulated evidence-based recommendations for the management ALF and ACLF patients in the ICU, acknowledging that most recommendations were based on low quality and indirect evidence.

Abstract Cirrhosis is complicated by a high rate of nosocomial infections (NIs), which result in poor outcomes and are challenging to predict using clinical variables alone. Our aim was to determine predictors of NI using admission serum metabolomics and gut microbiota in inpatients with cirrhosis. In this multicenter inpatient cirrhosis study, serum was collected on admission for liquid chromatography–mass spectrometry metabolomics, and a subset provided stool for 16SrRNA analysis. Hospital course, including NI development and death, were analyzed. Metabolomic analysis using analysis of covariance (ANCOVA) (demographics, Model for End‐Stage Liver Disease [MELD] admission score, white blood count [WBC], rifaximin, and infection status adjusted) and random forest analyses for NI development were performed. Additional values of serum metabolites over clinical variables toward NI were evaluated using logistic regression. Stool microbiota and metabolomic correlations were compared in patients with and without NI development. A total of 602 patients (231 infection admissions) were included; 101 (17%) developed NIs, which resulted in worse inpatient outcomes, including intensive care unit transfer, organ failure, and death. A total of 127 patients also gave stool samples, and 20 of these patients developed NIs. The most common NIs were spontaneous bacterial peritonitis followed by urinary tract infection, Clostridioides difficile, and pneumonia. A total of 247 metabolites were significantly altered on ANCOVA. Higher MELD scores (odds ratio, 1.05; p < 0.0001), admission infection (odds ratio, 3.54; p < 0.0001), and admission WBC (odds ratio, 1.05; p = 0.04) predicted NI (area under the curve, 0.74), which increased to 0.77 (p = 0.05) with lower 1‐linolenoyl‐glycerolphosphocholine (GPC) and 1‐stearoyl‐GPC and higher N‐acetyltryptophan and N‐acetyl isoputreanine. Commensal microbiota were lower and pathobionts were higher in those who developed NIs. Microbial–metabolite correlation networks were complex and dense in patients with NIs, especially sub‐networks centered on Ruminococcaceae and Pseudomonadaceae. NIs are common and associated with poor outcomes in cirrhosis. Admission gut microbiota in patients with NIs showed higher pathobionts and lower commensal microbiota. Microbial–metabolomic correlations were more complex, dense, and homogeneous among those who developed NIs, indicating greater linkage strength. Serum metabolites and gut microbiota on admission are associated with NI development in cirrhosis.

AbstractBackgroundThe prognosis of acute kidney disease (AKD), defined as a glomerular filtration rate of <60 ml/min/1.73 m2 or a rise in serum creatinine (sCr) of <50% for <3 months, is not clearly known.AimTo study the prevalence, predictive factors and clinical outcomes in hospitalized cirrhotic patients with AKD.MethodsThe North American Consortium for the Study of End‐Stage Liver Disease prospectively enrolled hospitalized decompensated cirrhotic patients. Patients were separated into those with normal renal function (controls or C), AKD or stage 1 AKI as their worst renal dysfunction per International Club of Ascites definition and compared. Parameters assessed included demographics, laboratory data, haemodynamics, renal and patient outcomes.Results1244 patients with cirrhosis and ascites (C: 704 or 57%; AKD: 176 or 14%; stage 1 AKI: 364 or 29%) with similar demographics were enrolled. AKD patients had similar baseline sCr but higher hospital admission in the previous 6 months, and higher peak sCr, compared to controls, with their peak sCr being lower than that in stage 1 AKI patients (all P < .0001). The in‐hospital and 30‐day survival for AKD patients were intermediary between that for controls and stage 1 AKI patients (96% vs 91% vs 86%, P < .0001). The strongest predictors for AKD development while in hospital were the presence of a second infection (OR: 2.44) and diabetes (OR: 1.53).ConclusionsPatients with AKD had intermediate outcomes between stage 1 AKI and controls. AKD patients, especially those with diabetes and a second infection, need careful monitoring and prompt treatment for AKD to prevent negative outcomes.

Background and Aims Acute kidney injury (AKI) has a poor prognosis in cirrhosis. Given the variability of creatinine, the prediction of AKI and dialysis by other markers is needed. The aim of this study is to determine the role of serum and urine metabolomics in the prediction of AKI and dialysis in an inpatient cirrhosis cohort. Approach and Results Inpatients with cirrhosis from 11 North American Consortium of End‐stage Liver Disease centers who provided admission serum/urine when they were AKI and dialysis‐free were included. Analysis of covariance adjusted for demographics, infection, and cirrhosis severity was performed to identify metabolites that differed among patients (1) who developed AKI or not; (2) required dialysis or not; and/pr (3) within AKI subgroups who needed dialysis or not. We performed random forest and AUC analyses to identify specific metabolite(s) associated with outcomes. Logistic regression with clinical variables with/without metabolites was performed. A total of 602 patients gave serum (218 developed AKI, 80 needed dialysis) and 435 gave urine (164 developed AKI, 61 needed dialysis). For AKI prediction, clinical factor–adjusted AUC was 0.91 for serum and 0.88 for urine. Major metabolites such as uremic toxins (2,3‐dihydroxy‐5‐methylthio‐4‐pentenoic acid [DMTPA], N2N2dimethylguanosine, uridine/pseudouridine) and tryptophan/tyrosine metabolites (kynunerate, 8‐methoxykyunerate, quinolinate) were higher in patients who developed AKI. For dialysis prediction, clinical factor–adjusted AUC was 0.93 for serum and 0.91 for urine. Similar metabolites as AKI were altered here. For dialysis prediction in those with AKI, the AUC was 0.81 and 0.79 for serum/urine. Lower branched‐chain amino‐acid (BCAA) metabolites but higher cysteine, tryptophan, glutamate, and DMTPA were seen in patients with AKI needing dialysis. Serum/urine metabolites were additive to clinical variables for all outcomes. Conclusions Specific admission urinary and serum metabolites were significantly additive to clinical variables to predict AKI development and dialysis initiation in inpatients with cirrhosis. These observations can potentially facilitate earlier initiation of renoprotective measures.

INTRODUCTION: Readmission and death in cirrhosis are common, expensive, and difficult to predict. Our aim was to evaluate the abilities of multiple artificial intelligence (AI) techniques to predict clinical outcomes based on variables collected at admission, during hospitalization, and at discharge. METHODS: We used the multicenter North American Consortium for the Study of End-Stage Liver Disease (NACSELD) cohort of cirrhotic inpatients who are followed up through 90-days postdischarge for readmission and death. We used statistical methods to select variables that are significant for readmission and death and trained 3 AI models, including logistic regression (LR), kernel support vector machine (SVM), and random forest classifiers (RFC), to predict readmission and death. We used the area under the receiver operating characteristic curve (AUC) from 10-fold crossvalidation for evaluation to compare sexes. Data were compared with model for end-stage liver disease (MELD) at discharge. RESULTS: We included 2,170 patients (57 ± 11 years, MELD 18 ± 7, 61% men, 79% White, and 8% Hispanic). The 30-day and 90-day readmission rates were 28% and 47%, respectively, and 13% died at 90 days. Prediction for 30-day readmission resulted in 0.60 AUC for all patients with RFC, 0.57 AUC with LR for women-only subpopulation, and 0.61 AUC with LR for men-only subpopulation. For 90-day readmission, the highest AUC was achieved with kernel SVM and RFC (AUC = 0.62). We observed higher predictive value when training models with only women (AUC = 0.68 LR) vs men (AUC = 0.62 kernel SVM). Prediction for death resulted in 0.67 AUC for all patients, 0.72 for women-only subpopulation, and 0.69 for men-only subpopulation, all with LR. MELD-Na model AUC was similar to those from the AI models. DISCUSSION: Despite using multiple AI techniques, it is difficult to predict 30- and 90-day readmissions and death in cirrhosis. AI model accuracies were equivalent to models generated using only MELD-Na scores. Additional biomarkers are needed to improve our predictive capability (See also the visual abstract at http://links.lww.com/AJG/B710).

Background. Critically ill cirrhotic patients are increasingly transplanted, but there is no consensus about futile liver transplantation (LT). Therefore, the decision to delay or deny LT is often extensively debated. These debates arise from different opinions of futility among transplant team members. This study aims to achieve a multinational and multidisciplinary consensus on the definition of futility in LT and to develop well-articulated criteria for not proceeding with LT due to futility. Methods. Thirty-five international experts from anesthesiology/intensive care, hepatology, and transplant surgery were surveyed using the Delphi method. More than 70% of similar answers to a question were necessary to define agreement. Results. The panel recommended patient and graft survival at 1 year after LT to define futility. Severe frailty and persistent fever or <72 hours of appropriate antimicrobial therapy in case of ongoing sepsis were considered reasons to delay LT. A simple assessment of the number of organs failing was considered the most appropriate way to decide whether LT should be delayed or denied, with respiratory, circulatory and metabolic failures having the most influence in this decision. The thresholds of severity of organ failures contraindicating LT for which a consensus was achieved were a Pa o 2 /FiO2 ratio<150 mm Hg, a norepinephrine dose >1 μg/kg per minute and a serum lactate level >9 mmol/L. Conclusions. Our expert panel provides a consensus on the definition of futile LT and on specific criteria for postponing or denying LT. A framework that may facilitate the decision if a patient is too sick for transplant is presented.