Thacker, Leroy R.

ABSTRACT Background and Aims It remains unclear whether different factors influence 30‐ and 90‐day readmission rates. We identified predictors of 90‐day mortality in a global cohort of hospitalised patients with cirrhosis. Methods Variables associated with 30‐ and 90‐day post‐discharge readmission, liver transplantation (LT), and mortality were compared in prospectively enrolled, non‐electively hospitalised adult patients with cirrhosis. Results We enrolled 4208 patients from 125 centres across 37 countries. Alcohol‐related liver disease was the most common aetiology of cirrhosis (40%), followed by hepatitis B virus infection (21%); 47% were readmitted, 8% received liver transplantation (LT), and 23% died within 90 days post‐discharge. In multivariable analysis, country income was significantly associated with both 30‐ and 90‐day readmission rates, the receipt of LT, and mortality within 30 and 90 days post‐discharge (all p ‐values < 0.001). Independent predictors of higher rates of 30‐ and 90‐day readmissions, respectively, were disease severity ( p < 0.001, p = 0.003), prior ascites ( p = 0.025, p = 0.016), prior hospitalisation ( p < 0.001), hyponatremia ( p < 0.001, p = 0.008), and in‐hospital vasopressor use ( p = 0.002, p = 0.011). Prior variceal bleeding independently predicted higher 90‐day readmission rates, whereas age, disease severity, mechanical ventilation, and in‐hospital vasopressor use (all, p < 0.001) increased odds of 30‐ and 90‐day post‐discharge death. Additionally, admission ( p = 0.011) and nosocomial ( p = 0.022) infections increased the odds of 90‐day post‐discharge death. Conclusion In a global cohort of hospitalised patients with cirrhosis, patients in high‐income countries exhibited the highest rates of 30‐ and 90‐day readmission, alongside lower mortality rates and a higher incidence of LT. Variables that predict 30‐day readmission also predicted 90‐day readmission. Understanding these disparities is essential for achieving health equity.

Background and Aims: Prognosticating outcomes such as hospitalizations in outpatients with cirrhosis is challenging, especially with changing etiologies and demographics. This study aims to determine the impact of multi-omic strategies on outcome prediction. Approach and Results: NACSELD3 enrolls outpatients with cirrhosis with controlled/eradicated etiologies from 10 centers and follows them systematically. At baseline, clinical/demographic and cirrhosis details were recorded and saliva and serum samples were collected for microbiome and metabolome analysis, respectively. Multi-omic bioinformatic studies to determine the interaction of microbiota and metabolites with the clinical prediction of 6-month hospitalizations were performed. Five hundred sixty-five patients (60.2 y, 68% men, 35% alcohol, 33% metabolic dysfunction-associated steatohepatitis, 21%, eradicated HCV with MELD 3.0 12) were enrolled. One hundred sixty-three (29%) required 6-month hospitalizations; most (75%) were liver-related. Those hospitalized had worse cirrhosis severity and comorbidity indices but similar demographics and oral health variables. Salivary microbiome alpha-diversity was lower (1.96±0.48 vs. 2.09±0.45, p =0.018) with greater pathobionts ( Streptococcus , Treponema, Enterococcaceae) and lower commensal genhospitalized/noera ( Veillonella, Prevotella, Haemophilus , Lachnospiraceae spp) at baseline. Serum metabolomics showed significant separation at baseline between hospitalized/non-hospitalized patients using supervised analyses with microbial-origin (phenyllactate, secondary bile acids, indoles), choline moieties, and polyamine/GABA (3-ureidopropionate/spermidine) metabolites being most prominent. Area under the curve using random forest for clinical, microbial, and metabolomic variables was higher than that of these individually. Latent factor analysis showed clinical variables (MELD 3.0, hemoglobin, and albumin) with the greatest impact, followed by salivary microbiota and then serum microbiome for hospitalization prediction. Conclusions: In a multicenter North American outpatient cirrhosis cohort with controlled etiologies, serum metabolomics and salivary microbiome add to clinical variables to prognosticate 6-month hospitalization.

Background and Aims: Reducing avoidable readmissions in cirrhosis is challenging. Enhanced engagement using health information technology (HIT) interventions and caregivers lowered readmissions in an open-label study of the Patient Buddy App (PBA). Aim : Multicenter trial of PBA versus standard of care (SOC) to reduce avoidable readmissions. Approach and Results: An open-label, randomized clinical trial was performed at 3 sites to study the effect of PBA (HIT) versus SOC in cirrhosis inpatients with adult caregivers (dyads). Initial randomization was 1:1:1 between SOC, HIT only, and HIT+ visits. However, due to COVID-19, an unplanned study redesign required a combined HIT versus SOC. Primary outcome : Avoidable readmissions (decided by a blinded monitoring board). Secondary outcomes were all-cause readmission and stakeholder input. PBA focused on medication adherence, cognitive testing, and symptoms, and was remotely monitored by study staff. In all, 464 subjects (232 dyads) were enrolled [Virginia Commonwealth University (VCU): 120, Mayo: 40, Department of Veterans Affairs (VA): 72; 116 dyads/group]. Avoidable readmissions were significantly higher in SOC versus HIT (19.8% vs. 10.3%, p =0.04) with OR of 2.14 (95% CI 1.01–4.54) and remained significant even after removing pre-COVID HIT+ visits patients (19.8% vs. 9.3%, p =0.040) with OR of 2.41 (95% CI 1.02–5.69). All-cause readmissions were higher in SOC versus HIT (48% vs. 30%, p =0.005). App evaluation/engagemen t : 1660 alerts were sent; mostly related to HE. Most dyads were satisfied with the app. Conclusions: In a multicenter randomized clinical trial of 464 cirrhosis inpatients and their CGs across several practice settings, the PBA was associated with lower avoidable readmissions at 30 days post-discharge compared to SOC.

INTRODUCTION: Perception of the ascites burden and its effects on quality of life may be different between sexes. This study assessed sex differences in perception of ascites burden and its impact on health-related quality of life (HRQoL) in patients with recurrent or refractory ascites. METHODS: The North American Consortium for the Study of End-stage Liver Disease prospectively enrolled outpatients with cirrhosis and large ascites requiring repeat large volume paracenteses. Demographics, laboratory results, comorbidities, medications, frailty measurements, and self-reported questionnaires related to functional status, physical activities, and HRQoL (generic = Short Form 36 and ascites specific = Ascites Questionnaire) were compared between sexes. RESULTS: In total, 392 men (59.6 ± 10.7 years) and 184 women (59.5 ± 11.1 years) with predominantly alcohol-related liver disease (51% and 43%, respectively) and median Model for End-Stage Liver Disease-Na: 13 were enrolled. Both groups had similar comorbidities and cirrhosis complications, ascites duration and severity, and frailty scores ( P = 0.94). Women had more symptoms related to their ascites (Ascites Questionnaire score = 66 ± 21 vs 60 ± 21 in men, P = 0.001) (higher value = feeling worse). 35% of women felt depressed vs 22% of men ( P = 0.0009), with lower mental but not physical functioning components of Short Form 36 ( P = 0.019). Women continued to conduct their daily activities as adequately as men as indicated by Duke Status Activity Index ( P = 0.27) and Godin Leisure Activity Index ( P = 0.47). DISCUSSION: Women with cirrhosis and ascites experienced worse emotional HRQoL than men without difference in daily function. Our analyses underscore the differences in the lived experience of women vs men with cirrhosis and highlight the need for patient-reported metrics to provide patient-centered care.

INTRODUCTION: Hospitalized patients with cirrhosis can develop respiratory failure (RF), which is associated with a poor prognosis, but predisposing factors are unclear. METHODS: We prospectively enrolled a multicenter North American cirrhosis inpatient cohort and collected admission and in-hospital data (grading per European Association for the Study of Liver-Chronic Liver Failure scoring system, acute kidney injury [AKI], infections [admission/nosocomial], and albumin use) in an era when terlipressin was not available in North America. Multivariable regression to predict RF was performed using only admission day and in-hospital events occurring before RF. RESULTS: A total of 511 patients from 14 sites (median age 57 years, admission model for end-stage liver disease [MELD]-Na 23) were enrolled: RF developed in 15%; AKI occurred in 24%; and 11% developed nosocomial infections (NI). At admission, patients who developed RF had higher MELD-Na, gastrointestinal (GI) bleeding/AKI-related admission, and prior infections/ascites. During hospitalization, RF developers had higher NI (especially respiratory), albumin use, and other organ failures. RF was higher in patients receiving albumin (83% vs 59%, P < 0.0001) with increasing doses (269.5 ± 210.5 vs 208.6 ± 186.1 g, P = 0.01) regardless of indication. Admission for AKI, GI bleeding, and high MELD-Na predicted RF. Using all variables, NI (odds ratio [OR] = 4.02, P = 0.0004), GI bleeding (OR = 3.1, P = 0.002), albumin use (OR = 2.93, P = 0.01), AKI (OR = 3.26, P = 0.008), and circulatory failure (OR = 3.73, P = 0.002) were associated with RF risk. DISCUSSION: In a multicenter inpatient cirrhosis study of patients not exposed to terlipressin, 15% of patients developed RF. RF risk was highest in those admitted with AKI, those who had GI bleeding on admission, and those who developed NI and other organ failures or received albumin during their hospital course. Careful volume monitoring and preventing nosocomial respiratory infections and renal or circulatory failures could reduce this risk.

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 and Aims Compared to other chronic diseases, patients with chronic liver disease (CLD) have significantly higher inpatient mortality; accurate models to predict inpatient mortality are lacking. Serum lactate (LA) may be elevated in patients with CLD due to both tissue hypoperfusion as well as decreased LA clearance. We hypothesized that a parsimonious model consisting of Model for End‐Stage Liver Disease (MELD) and LA at admission may predict inpatient mortality in patients with CLD. Approach and Results We examined all patients with CLD in two large and diverse health care systems in Texas (North Texas [NTX] and Central Texas [CTX]) between 2010 and 2015. We developed (n = 3,588) and validated (n = 1,804) a model containing MELD and LA measured at the time of hospitalization. We further validated the model in a second cohort of 14 tertiary care hepatology centers that prospectively enrolled nonelective hospitalized patients with cirrhosis (n = 726). MELD‐LA was an excellent predictor of inpatient mortality in development (concordance statistic [C‐statistic] = 0.81, 95% confidence interval [CI] 0.79‐0.82) and both validation cohorts (CTX cohort, C‐statistic = 0.85, 95% CI 0.78‐0.87; multicenter cohort C‐statistic = 0.82, 95% CI 0.74‐0.88). MELD‐LA performed especially well in patients with specific cirrhosis diagnoses (C‐statistic = 0.84, 95% CI 0.81‐0.86) or sepsis (C‐statistic = 0.80, 95% CI 0.78‐0.82). For MELD score 25, inpatient mortality rates were 11.2% (LA = 1 mmol/L), 19.4% (LA = 3 mmol/L), 34.3% (LA = 5 mmol/L), and >50% (LA > 8 mmol/L). A linear increase (P < 0.01) was seen in MELD‐LA and increasing number of organ failures. Overall, use of MELD‐LA improved the risk prediction in 23.5% of patients compared to MELD alone. Conclusions MELD‐LA (bswh.md/meldla) is an early and objective predictor of inpatient mortality and may serve as a model for risk assessment and guide therapeutic options.