García–Tsao, Guadalupe

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.

Objective Alcohol-related liver disease (ALD) ranges from never-decompensated ALD (ndALD) to the life-threatening decompensated phenotype, known as alcohol-related hepatitis (AH). A multidimensional study of the clinical, histological and molecular features of these subtypes is lacking. Design Two large cohorts of patients were recruited in an international, observational multicentre study: a retrospective cohort of patients with ndALD (n=110) and a prospective cohort of patients with AH (n=225). Clinical, analytical, immunohistochemistry and hepatic RNA microarray analysis of both disease phenotypes were performed. Results Age and mean alcohol intake were similar in both groups. AH patients had greater aspartate amino transferase/alanine amino transferase ratio and lower gamma-glutamyl transferase levels than in ndALD patients. Patients with AH demonstrated profound liver failure and increased mortality. One-year mortality was 10% in ndALD and 50% in AH. Histologically, steatosis grade, ballooning and pericellular fibrosis were similar in both groups, while advanced fibrosis, Mallory-Denk bodies, bilirubinostasis, severe neutrophil infiltration and ductular reaction were more frequent among AH patients. Transcriptome analysis revealed a profound gene dysregulation within both phenotypes when compare to controls. While ndALD was characterised by deregulated expression of genes involved in matrisome and immune response, the development of AH resulted in a marked deregulation of genes involved in hepatocyte reprogramming and bile acid metabolism. Conclusions Despite comparable alcohol intake, AH patients presented with worse liver function compared with ndALD patients. Bilirubinostasis, severe fibrosis and ductular reaction were prominent features of AH. AH patients exhibited a more profound deregulation of gene expression compared with ndALD patients.

INTRODUCTION: Several scoring systems predict mortality in alcohol-associated hepatitis (AH), including the Maddrey discriminant function (mDF) and model for end-stage liver disease (MELD) score developed in the United States, Glasgow alcoholic hepatitis score in the United Kingdom, and age, bilirubin, international normalized ratio, and creatinine score in Spain. To date, no global studies have examined the utility of these scores, nor has the MELD-sodium been evaluated for outcome prediction in AH. In this study, we assessed the accuracy of different scores to predict short-term mortality in AH and investigated additional factors to improve mortality prediction. METHODS: Patients admitted to hospital with a definite or probable AH were recruited by 85 tertiary centers in 11 countries and across 3 continents. Baseline demographic and laboratory variables were obtained. The primary outcome was all-cause mortality at 28 and 90 days. RESULTS: In total, 3,101 patients were eligible for inclusion. After exclusions (n = 520), 2,581 patients were enrolled (74.4% male, median age 48 years, interquartile range 40.9–55.0 years). The median MELD score was 23.5 (interquartile range 20.5–27.8). Mortality at 28 and 90 days was 20% and 30.9%, respectively. The area under the receiver operating characteristic curve for 28-day mortality ranged from 0.776 for MELD-sodium to 0.701 for mDF, and for 90-day mortality, it ranged from 0.773 for MELD to 0.709 for mDF. The area under the receiver operating characteristic curve for mDF to predict death was significantly lower than all other scores. Age added to MELD obtained only a small improvement of AUC. DISCUSSION: These results suggest that the mDF score should no longer be used to assess AH's prognosis. The MELD score has the best performance in predicting short-term mortality.

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: Urinary neutrophil gelatinase-associated lipocalin (NGAL) has shown promise in differentiating acute tubular necrosis (ATN) from other types of acute kidney injuries (AKIs) in cirrhosis, particularly hepatorenal syndrome (HRS). However, NGAL is not currently available in clinical practice in North America. METHODS: Urinary NGAL was measured in a prospective cohort of 213 US hospitalized patients with decompensated cirrhosis (161 with AKI and 52 reference patients without AKI). NGAL was assessed for its ability to discriminate ATN from non-ATN AKI and to predict 90-day outcomes. RESULTS: Among patients with AKI, 57 (35%) had prerenal AKI, 55 (34%) had HRS, and 49 (30%) had ATN, with a median serum creatinine of 2.0 (interquartile range 1.5, 3.0) mg/dL at enrollment. At an optimal cutpoint of 244 μg/g creatinine, NGAL distinguished ATN (344 [132, 1,429] μg/g creatinine) from prerenal AKI (45 [0, 154] μg/g) or HRS (110 [50, 393] μg/g; P < 0.001), with a C statistic of 0.762 (95% confidence interval 0.682, 0.842). By 90 days, 71 of 213 patients (33%) died. Higher median NGAL was associated with death (159 [50, 865] vs 58 [0, 191] μg/g; P < 0.001). In adjusted and unadjusted analysis, NGAL significantly predicted 90-day transplant-free survival ( P < 0.05 for all Cox models) and outperformed Model for End-Stage Liver Disease score by C statistic (0.697 vs 0.686; P = 0.04), net reclassification index (37%; P = 0.008), and integrated discrimination increment (2.7%; P = 0.02). DISCUSSION: NGAL differentiates the type of AKI in cirrhosis and may improve prediction of mortality; therefore, it holds potential to affect management of AKI in cirrhosis.

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).