Bajaj, Jasmohan S.

Nosocomial infections (NIs) in critically ill patients with cirrhosis result in higher death and transplant delisting. NIs are promoted by staff, visitors, and the environment, all of which were altered to reduce pathogen transmission after COVID-19. Two cohorts of intensive care unit patients with cirrhosis from March 2019 to February 2020 (pre-COVID, n = 234) and March 2020 to March 2021 (COVID era, n = 296) were included. We found that despite a higher admission MELD-Na, qSOFA, and WBC count and requiring a longer intensive care unit stay, COVID-era patients developed lower NIs (3% vs 10%, P < 0.001) and had higher liver transplant rates vs pre-COVID patients. COVID-era restrictions could reduce NIs in critically ill patients with 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.

In patients with cirrhosis and chronic liver disease, acute-on-chronic liver failure is emerging as a major cause of mortality. These guidelines indicate the preferred approach to the management of patients with acute-on-chronic liver failure and represent the official practice recommendations of the American College of Gastroenterology. The scientific evidence for these guidelines was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation process. In instances where the evidence was not appropriate for Grading of Recommendations, Assessment, Development, and Evaluation, but there was consensus of significant clinical merit, key concept statements were developed using expert consensus. These guidelines are meant to be broadly applicable and should be viewed as the preferred, but not only, approach to clinical scenarios.

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).