AIM: To re-examine whether hepatic vein thrombosis (HVT) (classical Budd-Chiari syndrome) and hepatic vena cava-Budd Chiari syndrome (HVC-BCS) are the same disorder. METHODS: A systematic review of observational studies conducted in adult subjects with primary BCS, hepatic vein outflow tract obstruction, membranous obstruction of the inferior vena cava (IVC), obliterative hepatocavopathy, or HVT during the period of January 2000 until February 2015 was conducted using the following databases: Cochrane Library, CINAHL, MEDLINE, PubMed and Scopus. RESULTS: Of 1299 articles identified, 26 were included in this study. Classical BCS is more common in women with a pure hepatic vein obstruction (49%-74%). HVC-BCS is more common in men with the obstruction often located in both the inferior vena cava and hepatic veins (14%-84%). Classical BCS presents with acute abdominal pain, ascites, and hepatomegaly. HVC-BCS presents with chronic abdominal pain and abdominal wall varices. Myeloproliferative neoplasms (MPN) are the most common etiology of classical BCS (16%-62%) with the JAK2V617-F mutation found in 26%-52%. In HVC-BCS, MPN are found in 4%-5%, and the JAK2V617-F mutation in 2%-5%. Classical BCS responds well to medical management alone and 1^st line management of HVC-BCS involves percutaneous recanalization, with few managed with medical management alone. CONCLUSION: Systematic review of recent data suggests that classical BCS and HVC-BCS may be two clinically different disorders that involve the disruption of hepatic venous outflow.

Update on endoscopic ultrasound-guided liver biopsy

Endoscopic management of large bile duct stones may be challenging and refractory to standard endoscopic retrograde cholangiopancreatography (ERCP) techniques. To this end, per-oral cholangioscopy (POC)-guided electrohydraulic lithotripsy (EHL) or laser lithotripsy (LL) has been increasingly utilized during ERCP. There are limited data, however, comparing EHL and LL in the management of choledocholithiasis. Therefore, the aim was to analyze and compare the efficacy of POC-guided EHL and LL for the treatment of choledocholithiasis. A database search on PubMed was performed selecting prospective English-language articles published by September 20th, 2022, in accordance with PRISMA guidelines. Studies selected included bile duct clearance as an outcome. A total of 21 prospective studies (15 using LL, 4 using EHL, and 2 both) including 726 patients were included for analysis. Complete ductal clearance was achieved in 639 (88%) patients with 87 (12%) patients having incomplete ductal clearance. Patients treated with LL had an overall median stone clearance success rate of 91.0% (IQR, 82.7-95.5), whereas EHL achieved a median stone clearance success rate of 75.8% (IQR, 74.0-82.4), [p = .03]. LL is a highly effective form of POC-guided lithotripsy for the treatment of large bile duct stones, particularly when compared to EHL. However, direct, head-to-head randomized trials are needed to identify the most effective form of lithotripsy for treating refractory choledocholithiasis.

Background: An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient triage) and to advance personalized medicine. However, such a prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. However, methodologic and reporting quality has received little attention. High-quality reporting and study methodology are critical for model validity, reproducibility, and clinical implementation. In collaboration with content experts in ML methodology, we performed a systematic review critically appraising the quality of methodology and reporting of recently published ML AP prognostic models. Methods/findings: Using a validated search strategy, we identified ML AP studies from the databases MEDLINE and EMBASE published between January 2021 and December 2023. We also searched pre-print servers medRxiv, bioRxiv, and arXiv for pre-prints registered between January 2021 and December 2023. Eligibility criteria included all retrospective or prospective studies that developed or validated new or existing ML models in patients with AP that predicted an outcome following an episode of AP. Meta-analysis was considered if there was homogeneity in the study design and in the type of outcome predicted. For risk of bias (ROB) assessment, we used the Prediction Model Risk of Bias Assessment Tool. Quality of reporting was assessed using the Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis—Artificial Intelligence (TRIPOD+AI) statement that defines standards for 27 items that should be reported in publications using ML prognostic models. The search strategy identified 6,480 publications of which 30 met the eligibility criteria. Studies originated from China (22), the United States (4), and other (4). All 30 studies developed a new ML model and none sought to validate an existing ML model, producing a total of 39 new ML models. AP severity (23/39) or mortality (6/39) were the most common outcomes predicted. The mean area under the curve for all models and endpoints was 0.91 (SD 0.08). The ROB was high for at least one domain in all 39 models, particularly for the analysis domain (37/39 models). Steps were not taken to minimize over-optimistic model performance in 27/39 models. Due to heterogeneity in the study design and in how the outcomes were defined and determined, meta-analysis was not performed. Studies reported on only 15/27 items from TRIPOD+AI standards, with only 7/30 justifying sample size and 13/30 assessing data quality. Other reporting deficiencies included omissions regarding human–AI interaction (28/30), handling low-quality or incomplete data in practice (27/30), sharing analytical codes (25/30), study protocols (25/30), and reporting source data (19/30). Conclusions: There are significant deficiencies in the methodology and reporting of recently published ML based prognostic models in AP patients. These undermine the validity, reproducibility, and implementation of these prognostic models despite their promise of superior predictive accuracy. Registration: Research Registry (reviewregistry1727)