Background and Objectives Bariatric surgery is a highly effective obesity treatment, yet it may predispose individuals to alcohol-related liver injury. While altered ethanol metabolism following procedures like Roux-en-Y gastric bypass (RYGB) is well described, the long-term hepatic consequences, particularly the risk of portal hypertension in patients who develop alcohol-related hepatitis (AH,) remain poorly defined. Methods Using the TriNetX US Collaborative Network, we identified adult patients diagnosed with AH or alcohol-related cirrhosis. We compared outcomes between patients with a history of RYGB or sleeve gastrectomy (SG) who subsequently developed AH (Bariatric+AH group) and those with AH and no history of bariatric surgery (AH-only group). Propensity score matching was performed on over 44 demographic, clinical, and laboratory variables. Cox proportional hazards models and Kaplan-Meier survival curves were used to estimate the risk of clinically significant portal hypertension (PH) events, liver transplantation, and all-cause mortality at three-, five-, and seven-year follow-ups. Results After matching, 772 patients were included in each cohort. At 7 years post-index event, the Bariatric + AH group exhibited a significantly higher risk of PH-related complications compared to the AH-only group (HR 1.519; 95% CI, 1.15-2.005; p = 0.003). No significant differences were observed in liver transplantation (HR 1.412; 95% CI, 0.850-2.346; p = 0.181) or all-cause mortality (HR 1.085; 95% CI, 0.904-1.303; p = 0.381). These findings were consistent across all follow-up intervals. Conclusion Bariatric surgery is associated with an increased long-term risk of portal hypertension in patients who develop alcohol-related hepatitis despite similar mortality and transplantation rates. These findings underscore the need for targeted postoperative counseling, liver-focused surveillance strategies, and integration of hepatologic risk assessment into metabolic surgery care pathways.
Real-world federated learning for brain imaging scientists
BackgroundFederated learning (FL) has the potential to boost deep learning in neuroimaging but is rarely deployed in real-world scenarios, where its true potential lies. We


