Efficacy and Safety of Sphingosine 1-Phosphate Receptor Modulators for Ulcerative Colitis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND AND AIMS: Sphingosine 1-phosphate receptor modulators (S1PRMs) are an effective treatment for ulcerative colitis (UC). This review summarizes all available randomized trial data on the efficacy and safety of S1PRM therapy. METHODS: Multiple publication databases were systematically searched for randomized control trials (RCTs) of adults with moderate to severe UC treated with S1PRMs. Random effects meta-analysis was performed. The risk of bias was assessed using the Cochrane Risk-of-Bias 2 tool, and the overall quality of evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. RESULTS: We identified 7 RCTs (1737 patients) involving the use of S1PRMs for moderate to severe UC. During induction, S1PRM therapy was efficacious when compared with placebo for clinical remission [RR: 2.65 (95% CI: 2.00, 3.53)], clinical response [RR: 1.68 (95% CI: 1.48, 1.91)], endoscopic improvement [RR: 2.17 (95% CI: 1.76, 2.68)], endoscopic normalization [RR: 2.56 (95% CI: 1.58, 3.83)], mucosal healing [RR: 2.88 (95% CI: 1.94, 4.26)], and histologic remission [RR: 2.42 (95% CI: 1.60, 3.66)]. Similar results were seen throughout the maintenance peroid, although fewer data were available to pool; notably, both sustained [RR: 3.57 (95% CI: 1.23, 10.35)] and steroid-free [RR: 2.92 (95% CI: 1.35, 6.33)] remission were significantly increased by S1PRM. There were no significant differences in adverse events [RR: 1.02 (95% CI: 0.90, 1.15)] and infections [RR: 1.15 (95% CI: 0.82, 1.60)] between S1PRM and placebo. CONCLUSION: Pooling of RCT data confirms that S1PRM therapy is both effective and safe for patients with moderate to severe UC.

Are adverse events higher among patients with acute optic neuritis prescribed glucocorticoids? A retrospective, longitudinal cohort study.

OBJECTIVE: Optic neuritis (ON) is an acute focal inflammation of the optic nerve routinely treated with glucocorticoids. We aimed to compare adverse events (AE) among glucocorticoid-treated and untreated patients in the real world to guide clinical decision making about treatment tradeoffs. DESIGN: Retrospective, longitudinal cohort study. SETTING: Claims study from a large, private insurer in the USA (2005-2019). PARTICIPANTS: Adults≥18 years old with ≥1 ICD9/10 ON diagnosis with an evaluation/management visit code, and ≥6 months continuous enrolment prior to and following ON diagnosis. INTERVENTION: Glucocorticoid prescription exposure. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome was any AE within 90 days of glucocorticoid prescription. Secondary outcome was AE assessment by severity. Generalised estimating equations with logit link assessed relationships between glucocorticoid prescription and AEs. High-dimensional propensity score analyses accounted for potential confounding (eg, sociodemographics and comorbidities). Sensitivity analyses restricted the cohort to high-dose prescriptions (≥100 mg prednisone equivalent, injection/infusion), AEs within 30 days, highly specific ON definition and traditional propensity score match. RESULTS: Of the 14 311 people with 17 404 ON claims, 66.3% were women (n=9481), predominantly White (78.2%; n=9940), with median age (IQR)=48 (37,60) years. Within 90 days of the claim, 15.7% (n=2733/17 404) were prescribed glucocorticoids. The median (IQR) prescription duration=10 (6,20) days. Any and severe AEs were higher among patients prescribed glucocorticoids versus none (any AEs: n=437/2733 (16.0%) vs n=1784/14 671 (12.2%), adjusted OR 1.33 (95% CI: 1.18 to 1.50); severe AEs: n=72/2733 (2.6%) vs n=273/14 671 (1.9%), adjusted OR 1.82 (95% CI: 1.37 to 2.35)). Sensitivity analyses were similar. CONCLUSIONS: Real-world glucocorticoid prescriptions among ON patients were short-term, associated with a 30% relative increase in potentially serious AEs captured within healthcare encounters, including those not previously observed, such as VTE. These results can inform treatment decisions, particularly for ON patients likely to experience only marginal benefits.

Impact of lung cancer screening on stage migration and mortality among the national Veterans Health Administration population with lung cancer.

BACKGROUND: Despite randomized trials demonstrating a mortality benefit to low-dose computed tomography screening to detect lung cancer, uptake of lung cancer screening (LCS) has been slow, and the benefits of screening remain unclear in clinical practice. METHODS: This study aimed to assess the impact of screening among patients in the Veterans Health Administration (VA) health care system diagnosed with lung cancer between 2011 and 2018. Lung cancer stage at diagnosis, lung cancer-specific survival, and overall survival between patients with cancer who did and did not receive screening before diagnosis were evaluated. We used Cox regression modeling and inverse propensity weighting analyses with lead time bias adjustment to correlate LCS exposure with patient outcomes. RESULTS: Of 57,919 individuals diagnosed with lung cancer in the VA system between 2011 and 2018, 2167 (3.9%) underwent screening before diagnosis. Patients with screening had higher rates of stage I diagnoses (52% vs. 27%; p ≤ .0001) compared to those who had no screening. Screened patients had improved 5-year overall survival rates (50.2% vs. 27.9%) and 5-year lung cancer-specific survival (59.0% vs. 29.7%) compared to unscreened patients. Among screening-eligible patients who underwent National Comprehensive Cancer Network guideline-concordant treatment, screening resulted in substantial reductions in all-cause mortality (adjusted hazard ratio [aHR], 0.79; 95% confidence interval [CI], 0.67-0.92; p = .003) and lung-specific mortality (aHR, 0.61; 95% CI, 0.50-0.74; p < .001). CONCLUSIONS: While LCS uptake remains limited, screening was associated with earlier stage diagnoses and improved survival. This large national study corroborates the value of LCS in clinical practice; efforts to widely adopt this vital intervention are needed.

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE2AT Center study protocol.

INTRODUCTION: African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat-health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards. METHODS AND ANALYSIS: The research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat-health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques. ETHICS AND DISSEMINATION: The study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations.

Legalization of Cannabis Does Not Reduce Opioid Prescribing in Patients With Inflammatory Bowel Disease: A Difference-in-Difference Analysis.

INTRODUCTION: Cannabis may provide inflammatory bowel disease (IBD) patients with an alternative to opioids for pain. METHODS: We conducted a difference-in-difference analysis using MarketScan. Changes over time in rates of opioid prescribing were compared in states with legalized cannabis to those without. RESULTS: We identified 6,240 patients with IBD in states with legalized cannabis and 79,272 patients with IBD in states without legalized cannabis. The rate of opioid prescribing decreased over time in both groups and were not significantly different (attributed differential = 0.34, confidence interval -13.02 to 13.70, P = 0.96). DISCUSSION: Opioid prescribing decreased from 2009 to 2016 among patients with IBD in both states with legalized and state without legalized cannabis, similar to what has been observed nationally across a variety of diseases. Cannabis legalization was not associated with a lower rate of opioid prescribing for patients with IBD.

Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures.

Background: Serum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels. Methods: We mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors. Results: We show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors. Conclusions: Our findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we used artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease. APPROACH AND RESULTS: Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory, and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 and 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. In all, 4614 patients were included. We found that the eCTP Score had a Concordance index of 0.64 for the prediction of overall mortality while the imaging-based model alone or with eCTP Score performed significantly better [Concordance index of 0.72 and 0.73 ( p <0.001)]. For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance index for predicting future decompensation was 0.67, 0.79, and 0.80 for eCTP Score, imaging alone, or combined, respectively. CONCLUSIONS: This proof of concept demonstrates that the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease.