Comparative efficacy of pharmacologic therapies for MASH in reducing liver fat content: Systematic review and network meta-analysis.

BACKGROUND AND AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of liver disease. Dynamic changes in MRI proton-density-fat fraction (PDFF) are associated with MASH resolution. We aimed to determine the relative efficacy of therapeutic agents for reducing hepatic fat, assessed by MRI-PDFF. APPROACH AND RESULTS: In this systematic review and network meta-analysis, we searched MEDLINE and Embase from inception until December 26, 2023, for published randomized controlled trials comparing pharmacological interventions in patients with MASH that assessed changes in MRI-PDFF. The primary outcome was the absolute change in MRI-PDFF. The secondary outcome was a ≥30% decline in MRI-PDFF. A surface under-the-curve cumulative ranking probabilities (SUCRA) analysis was performed. Of 1550 records, a total of 39 randomized controlled trials (3311 participants) met the inclusion criteria. For MRI-PDFF decline at 24 weeks, aldafermin (SUCRA: 83.65), pegozafermin (SUCRA: 83.46), and pioglitazone (SUCRA: 71.67) were ranked the most effective interventions. At 24 weeks, efinopegdutide (SUCRA: 67.02), semaglutide + firsocostat (SUCRA: 62.43), and pegbelfermin (SUCRA: 61.68) were ranked the most effective interventions for achieving a ≥30% decline in MRI-PDFF. CONCLUSIONS: This study provides an updated, relative rank-order efficacy of therapies for MASH in reducing hepatic fat. These data may help inform the design and sample size calculation of future clinical trials and assist in the selection of combination therapy.

Sex and Race-Ethnic Disparities in Metabolic Dysfunction-Associated Steatotic Liver Disease: An Analysis of 40,166 Individuals.

BACKGROUND: To overcome the limitations of the term "non-alcoholic fatty liver disease" (NAFLD), the term metabolic-associated steatotic liver disease (MASLD) was introduced. While epidemiologic studies have been conducted on MASLD, there is limited evidence on its associated sex and ethnic variations. AIMS: This study assesses the differences across sex and race-ethnicity on the prevalence, associated risk factors and adverse outcomes in individuals with MASLD. METHODS: Data retrieved from the National Health and Nutrition Examination Survey between 1999 to 2018 was analyzed. Prevalence, clinical characteristics, and outcomes were evaluated according to sex and race-ethnicity. Adverse outcomes and mortality events were analyzed using multivariate analyses. RESULTS: Of 40,166 individuals included, 37.63% had MASLD. There was a significant increase in MASLD prevalence from 1999 to 2018 among Mexican Americans (Annual Percentage Change [APC] + 1.889%, p < 0.001), other Hispanics (APC + 1.661%, p = 0.013), NH Whites (APC + 1.084%, p = 0.018), NH Blacks (APC + 1.108%, p = 0.007), and females (APC + 0.879%, p = 0.030), but not males. Females with MASLD were at lower risk of all-cause (HR: 0.766, 95%CI 0.711 to 0.825, p < 0.001), cardiovascular disease-related (CVD) (SHR: 0.802, 95% CI 0.698 to 0.922, p = 0.002) and cancer-related mortality (SHR: 0.760, 95% CI 0.662 to 0.873, p < 0.001). Significantly, NH Blacks have the highest risk of all-cause and CVD-related mortality followed by NH Whites then Mexican Americans. CONCLUSION: There has been an increase in prevalence in most race-ethnicities over time. While the change in definition shows no significant differences in previous associations found in NAFLD, the increased mortality in NH Whites relative to Mexican Americans remains to be explored.

Improving the Interpretation of Afternoon Cortisol Levels and SSTs to Prevent Misdiagnosis of Adrenal Insufficiency.

BACKGROUND: Adrenal Insufficiency (AI), especially iatrogenic-AI, is a treatable cause of mortality. The difficulty in obtaining 9 am cortisol levels means samples are taken at suboptimal times, including a substantial proportion in the afternoon. Low afternoon cortisol levels often provoke short Synacthen tests (SSTs). It is important that this does not lead to patients misdiagnosed with AI, exposing them to the excess mortality and morbidity of inappropriate steroid replacement therapy. METHODS: This retrospective study collected 60 178 cortisol results. Medical records, including subsequent SSTs of initial cortisol results measured after midday were reviewed. RESULTS: Receiver operating characteristic analysis (area under the curve: 0.89) on 6531 suitable cortisol values showed that a limit of <201.5 nmol/L achieved a sensitivity and specificity of 95.6% and 72.6%, while a limit of <234 nmol/L had a sensitivity of 100% and a specificity of 59.5%. Out of 670 SSTs, 628 patients passed. Of these, 140 would have otherwise failed if only their 30-min cortisol was assessed without the 60-min value. A 30- and 60-min SST cortisol cutoff of 366.5 nmol/L and 418.5 nmol/L, respectively, can achieve a sensitivity of >95% on the Abbott analyser platform. CONCLUSION: An afternoon cortisol >234 nmol/L excludes AI on Abbott analyser platforms. In patients who have an afternoon cortisol <234 nmol/L, including both 30- and 60-min SST cortisol values prevents unnecessary glucocorticoid replacement therapy in 22.3% of individuals in this study. The Abbott analyser SST cortisol cutoffs used to define AI should be 366.5 nmol/L and 418.5 nmol/L at 30 and 60 min, respectively. All patients remained well subsequently with at least 1-year longitudinal follow-up.

Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition.

The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency.

Data management for intervention effectiveness research: comparing deductive and inductive approaches.

Management approaches are needed to prepare intervention data sets for research. We identified four management approaches and applied them to Omaha System intervention data from 15 home care agencies (621,385 interventions provided to 2,862 patients). Classifying intervention data created differing numbers of distinct groups for deductive approaches labeled as action category (four groups), theoretical (5), and clinical expert consensus (23). One inductive, data-driven approach generated 150 groups of interventions, of which 24 were meaningful and unique. Interventions in deductive groups were mutually exclusive, and approaches mapped readily according to intervention action terms. The novel, overlapping, inductive groups consisted of diverse actions for multiple problems. The four management approaches created meaningful intervention groups to be employed in future outcomes evaluation studies.