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Background Severe alcohol-associated hepatitis (sAH) is a life-threatening condition with high mortality, where corticosteroid use is the only treatment that has shown short-term benefits. Pentoxifylline, an anti-tumour necrosis factor-alpha agent, has been proposed for its potential to improve outcomes, especially in patients with acute kidney injury (AKI). We aimed to evaluate the impact of pentoxifylline on mortality in patients with sAH and AKI in a well-characterised global cohort. Methods We conducted a retrospective, registry-based study including patients meeting the National Institute on Alcohol Abuse and Alcoholism clinical criteria for sAH and AKI. Mortality was the primary endpoint, with liver transplantation as a competing risk. Statistical analysis included Cox regression and Kaplan-Meier survival estimates. Results We included 525 patients from 20 centres across eight countries. The median age was 48 years, with 26.1% females, and 76.9% had a history of cirrhosis. Multivariable Cox regression models showed that pentoxifylline use was not associated with survival (HR 1.20, 95% CI 0.85 to 1.69, p=0.291). Factors associated with mortality included age (HR 1.23, 95% CI 1.10 to 1.36, p<0.001), Model for End-Stage Liver Disease score at admission (HR 1.06, 95% CI 1.04 to 1.08, p<0.001) and renal replacement therapy use (HR 1.39, 95% CI 1.05 to 1.84, p=0.019). The main causes of death were multiple organ failure (42%), infections (10%), oesophageal varices bleeding (7%) and renal failure (6%). Conclusion Pentoxifylline showed no significant benefit on mortality in patients with sAH and AKI. Further studies are needed to refine treatment strategies for this high-risk group.

Abstract Background: Obstructive sleep apnea (OSA) is associated with obesity and cardiovascular risk. Results from the SURMOUNT-OSA studies demonstrated significant reduction of a number of cardiometabolic risk measures in participants with OSA and obesity following treatment with tirzepatide. Objectives: Systematically analyze all tirzepatide treatment related changes of cardiometabolic risk measures in SURMOUNT-OSA studies and investigate to what extent these changes were mediated by reductions in weight and/or OSA-related measures. Methods: The SURMOUNT-OSA master protocol comprised two 52-week, randomized, double-blind, placebo-controlled Phase 3 studies. Study 1 (N=234) and 2 (N=235) included participants with moderate-to-severe OSA and obesity, who at screening reported not using, or using positive airway pressure (PAP) therapy, respectively. The design and rationale of the studies have been published previously (1). The current analysis investigated the association of treatment with tirzepatide or placebo with SURMOUNT-OSA pre-specified cardiometabolic risk measures including blood pressure, and levels of high-sensitivity C-reactive protein (hsCRP), HDL-C, non-HDL-C, triglycerides, fasting insulin. Further analyses included change in HOMA-IR with tirzepatide compared to placebo, and mediation analysis to determine the proportion of observed changes attributable to reductions in body weight, apnea-hypopnea index (AHI) and sleep apnea-specific hypoxic burden (SASHB). Results: In Study 1, participants experienced greater difference in change from baseline with tirzepatide compared to placebo in SBP (-7.9 mmHg; 95% CI -11.0 to -4.9; p<0.001), and DBP (-3.2 mmHg; 95% CI -5.4 to -1.0; p=0.005) at 48 weeks, and hsCRP (-28.9±8.2%; p=0.003), HDL-C (7.2±2.1%; p<0.001), non-HDL-C (-13.0±3.1%; p<0.001), triglycerides (-32.2±3.7%; p<0.001), fasting insulin (-41.4±5.0%; p<0.001), and HOMA-IR (-48.0±4.9%; p<0.001) at 52 weeks. In Study 2, participants experienced greater differences in change from baseline following tirzepatide compared to placebo in SBP (-4.3 mmHg; 95% CI -7.3 to -1.2; p=0.007) at 48 weeks, and hsCRP (-45.1±8.0%; p<0.001), HDL-C (10.0±2.8%; p<0.001), non-HDL-C (-14.3±2.5%; p<0.001), triglycerides (-31.5±3.7%; p<0.001), fasting insulin (-45.4±5.4%; p<0.001), and HOMA-IR (-54.8±5.0%; p<0.001) at 52 weeks. Statistically significant independent mediation effect of changes in OSA metrics was observed on hsCRP, HOMA-IR and triglycerides. In addition, the combination of changes in weight and OSA metrics, but not weight or OSA improvements alone, had a significant mediation effect on non-HDL-C. There was no significant mediation effect of weight or OSA metrics observed on DBP. Conclusions: In the SURMOUNT-OSA program trials, tirzepatide appeared to have significant cardiometabolic benefits for patients with moderate-to-severe OSA and obesity. Based on the mediation analysis, treating both sleep-disordered breathing and obesity is likely required to optimize cardiometabolic benefits for patients with OSA and obesity.

INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2. METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) formed an ensemble model. RESULTS: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% confidence interval [CI]: 0.764–0.819), outperformed VCTE alone (0.745, 95% CI 0.717–0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679–0.748, P < 0.0001), and Agile-3 model (0.740, 0.710–0.770, P < 0.0001) regarding the AUC, decision curve analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668–0.749) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores. DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE (https://aihepatology.shinyapps.io/ALADDIN1) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab (https://aihepatology.shinyapps.io/ALADDIN2) offers an effective alternative when VCTE is unavailable.

Background: Severe alcohol-associated hepatitis (sAH) is a well-characterized disease with high short-term mortality. However, there is limited research on those with a “less severe condition” (moderate AH). This study aims to characterize in-depth patients with moderate AH (mAH), including the performance of mortality scoring systems, key prognostic factors, and survival over time. Methods: A multicenter retrospective cohort study (2009–2019) included patients with mAH (MELD score ≤20 at admission). Cox regression and receiver operating characteristic curves with AUC were used for analysis. Results: We included 1845 patients with AH (20 centers, 8 countries) between 2009 and 2019. mAH was defined as a MELD score ≤20 at admission. Twenty-four percent met the criteria for an mAH episode. Patients with mAH tend to be older and have a higher proportion of females, with a median MELD of 17 (15–19), Maddrey discriminant function (mDF) of 33 (22–40), the trajectory of serum bilirubin of 0.83 (0.60–1.21), and neutrophil-to-lymphocyte ratio (NLR) of 5 (2.96–8.60). The primary causes of death in mAH included multiple organ failure (34.1%) and infections (16.6%). The cumulative survival rates at 30, 90, and 180 days were 94.3%, 90.4%, and 88.2%, respectively. In multivariable analysis, age was the only significant predictor of 30-day mortality (HR 1.49, 95% CI: 1.27–1.76, p <0.001). Mortality prediction models showed poor performance, with AUC for MELD (0.671), mDF (0.726), trajectory of serum bilirubin (0.733), and NLR (0.697). Conclusions: Patients with moderate AH exhibited a mortality of 11.8% at 6 months, primarily driven by multiple organ failure and infections. These patients also exhibit a different clinical profile compared to those with sAH. Tailored models and therapeutic strategies are needed to improve long-term outcomes in mAH.

ABSTRACTObjectivesCeliac disease (CeD), an autoimmune disorder triggered by gluten ingestion, induces intestinal inflammation and varied symptoms. Treatment entails a strict gluten‐free diet (GFD), posing challenges for students, especially in schools with limited food choices. Nonadherence worsens symptoms, yet research on CeD's impact on students is scarce.MethodsThe CeliacKIDS study, conducted across 11 United States academic medical centers, evaluated gluten exposure risk in pediatric CeD patients via a cross‐sectional survey from August 2020 to August 2021. Participants recruited from treating institutions were approved by respective Institutional Review Boards.ResultsOne hundred and sixty children aged 5–18 (65% female, 34% male, 1% other) participated. Only 12% had GF food options at school, 31% brought their own for celebrations, and 41% lacked gluten free (GF) snacks after school. Thirty‐six percent lacked a 504 plan, with 5% misinformed. Hand hygiene concerns included 24% using sanitizer and 10% rarely washing hands before eating. Sixty‐two percent disclosed CeD, 35% when prompted, and 3% refused, mainly 13‐year‐old males. Two percent hesitated to request GF options, and 2% consumed potentially gluten‐containing food from friends.ConclusionMany US schools provide GF accommodations under the Americans with Disabilities Act (ADA) but lack national standards. Diverse GF options and education on GF‐safe practices are crucial for GFD adherence. Discrepancies in parent–child perceptions emphasize the need for better communication. Adolescents, particularly females aged 12–13 with 2+ years on a GF diet, face higher risks. Transparent family–school communication is vital for optimizing the school experience and ensuring GFD adherence. Comprehensive nationwide school training is essential for celiac patients' well‐being.

ABSTRACTSeveral dynamic models predict mortality and corticosteroid response in alcohol‐associated hepatitis (AH), yet no consensus exists on the most effective model. This study aimed to assess predictive models for corticosteroid response and short‐term mortality in severe AH within a global cohort. We conducted a multi‐national study of patients with severe AH treated with corticosteroids for at least 7 days, enrolled between 2009 and 2019. Dynamic models—Lille‐4, Lille‐7, trajectory of serum bilirubin (TSB), and neutrophil‐to‐lymphocyte ratio (NLR)—were used to estimate 30‐ and 90‐day mortality. Lille‐7 demonstrated the highest accuracy for both 30‐ and 90‐day mortality.

Abstract Background Despite advances in our understanding of the molecular underpinnings of meningioma progression and innovations in systemic and local treatments, recurrent meningiomas remain a substantial therapeutic challenge. The objective of this systematic review and meta-analysis is to provide a historical baseline, contemporary analysis, and propose a “rate of probable interest” to inform future clinical trial design and development on behalf of the Response Assessment in Neuro-Oncology meningioma group. Methods PubMed, ClinicalTrials.gov, and ASCOpubs databases were screened for clinical trials evaluating the activity of systemic therapies for adults with recurrent meningiomas. The pooled progression-free survival at 6-months and 1-year (PFS-6 and PFS-1 year) values were calculated using the random effects technique with I2 indices. Results The pooled PFS-6 and PFS-1 year rates for recurrent WHO grade 1 meningiomas were 43.6% (95% CI: 22.7-67.0%, I2 = 80%) and 21.7% (95% CI: 6.2-53.9%, I2 = 76%), and for grades 2-3 meningiomas, the PFS-6 was 38.0% (95% CI: 28.3-48.8%, I2 = 68%). In the targeted therapy group, PFS-6 and PFS-1 year rates stood at 62.0% (I2 = 58%) and 49.0% (I2 = 63%) for grade 1, while for grades 2-3 tumors, the PFS-6 rates with targeted therapy and immunotherapy were 42.1% (I² = 60%) and 46.0% (I² = 0%), respectively. The benchmarks were set at 67% and 54% for PFS-6 and PFS-1 year for grade 1 tumors, and PFS-6 of 49% for grades 2-3 tumors. Conclusions Several studies have reported outcomes in patients with recurrent meningiomas testing a variety of agents with modest, but variable and progressively increasing activity. In this context, we recommend new benchmarks for future trials to define efficacy of future investigational therapies.

AbstractBackgroundTwo main risk factors of Alzheimer’s disease (AD) are aging and APOE‐ε4. However, some individuals remain cognitively normal despite having these risk factors. They are considered “cognitively resilient”. This study aimed to identify molecular factors that confer cognitive resilience in APOE‐ε4 carriers ≥ 80 years of age and may serve as biomarkers.MethodWe applied weighted gene co‐expression network analysis (WGCNA) to generate consensus co‐expression networks from blood of participants in two antemortem cohorts, the Mayo Clinic Study of Aging (MCSA, n=105), and the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n=91), using RNA‐sequencing and microarray data, respectively. We associated these networks with resilience (resilient vs non‐resilient), cognitive endophenotypes and hippocampal volume. Preservation between consensus networks from blood and those derived from postmortem brain tissues of AD and control donors from AMP‐AD (n=1174) was evaluated. We validated the human findings in four AD mouse models. Finally, machine learning models were utilized to discriminate cases (AD+mild cognitive impairment (MCI)) from controls in MCSA, ADNI and ANMerge antemortem cohorts.ResultFour consensus networks were significantly correlated with a memory phenotype (logical memory delayed recall=LMDR) and hippocampal volume in both MCSA and ADNI. Among these, blood expression module M3 was most preserved with the brain transcriptome. M3 was enriched with NDUF hub genes that are involved in the mitochondrial respiratory chain. Expression levels of M3 and many blood NDUFs had significant associations with better LMDR and hippocampal volume. In brain, NDUFs were upregulated in controls compared to AD, and their expression levels were associated with better global cognition and decreased AD neuropathology. Many NDUFs were significantly downregulated in the hippocampus or cortex of AD mice compared to wild‐types. Lastly, models that included blood NDUFs improved diagnostic accuracy of AD+MCI compared to models that only included demographic and risk variables (age, sex, APOE‐ε4) in MCSA, ADNI and ANMerge. In MCSA and ADNI, adding NDUFs’ expression to models that included established blood biomarkers (Aβ42/40, ptau181, NFL) further improved diagnostic accuracy.ConclusionOur results suggest that mitochondrial NDUFs are centrally‐linked peripheral molecular signatures that may be resilience factors against AD and serve as both therapeutic targets and novel diagnostic biomarkers.

Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier).