Chronic liver disease has been associated with pulmonary dysfunction both before and after liver transplantation. Post-liver transplantation pulmonary complications can affect both morbidity and mortality often necessitating intensive care during the immediate postoperative period. The major pulmonary complications include pneumonia, pleural effusions, pulmonary edema, and atelectasis. Poor clinical outcomes have been known to be associated with age, severity of liver dysfunction, and preexisting lung disease as well as perioperative events related to fluid balance, particularly transfusion and fluid volumes. Delineating each and every one of these pulmonary complications and their associated risk factors becomes paramount in guiding specific therapeutic strategies.

BACKGROUND Liver cirrhosis is the late stage of hepatic fibrosis and is characterized by portal hypertension that can clinically lead to decompensation in the form of ascites, esophageal/gastric varices or encephalopathy. The most common sequelae associated with liver cirrhosis are neurologic and neuropsychiatric impairments labeled as hepatic encephalopathy (HE). Well established triggers for HE include infection, gastrointestinal bleeding, constipation, and medications. Alterations to the gut microbiome is one of the leading ammonia producers in the body, and therefore may make patients more susceptible to HE. AIM To investigate the relationship between the use of proton pump inhibitors (PPIs) and HE in patients with cirrhosis. METHODS This is a single center, retrospective analysis. Patients were included in the study with an admitting diagnosis of HE. The degree of HE was determined from subjective and objective portions of hospital admission notes using the West Haven Criteria. The primary outcome of the study was to evaluate the grade of HE in PPI users versus non-users at admission to the hospital and throughout their hospital course. Secondary outcomes included rate of infection, gastrointestinal bleeding within the last 12 mo, mean ammonia level, and model for end-stage liver disease scores at admission. RESULTS The HE grade at admission using the West Haven Criteria was 2.3 in the PPI group compared to 1.7 in the PPI nonuser group ( P = 0.001). The average length of hospital stay in PPI group was 8.3 d compared to 6.5 d in PPI nonusers ( P = 0.046). Twenty-seven (31.8%) patients in the PPI user group required an Intensive Care Unit admission during their hospital course compared to 6 in the PPI nonuser group (16.7%) ( P = 0.138). Finally, 10 (11.8%) patients in the PPI group expired during their hospital stay compared to 1 in the PPI nonuser group (2.8%) ( P = 0.220). CONCLUSION Chronic PPI use in cirrhotic patients is associated with significantly higher average West Haven Criteria for HE compared to patients that do not use PPIs.

Introduction The occurrence of false positive (FP) alarms is an important outcome measure in computer-aided colon polyp detection (CADe) studies. However, there is no consensus definition for FPs in clinical trials evaluating CADe in colonoscopy. We aimed to study the diagnostic performance of CADe based on different threshold definitions for FP alarms. Methods A previously validated CADe system was applied to screening/surveillance colonoscopy videos. Different thresholds for FP alerts were defined based on the time an alarm box was continuously traced by the system. Primary outcomes were FP results and specificity using differing FP thresholds. Results 62 colonoscopies were analyzed. CADe specificity and accuracy were 93.2% and 97.8% respectively for a FP threshold definition of > 0.5 seconds, 98.6 % and 99.5% for a FP threshold > 1 second, and 99.8% and 99.9% for a FP threshold > 2 seconds. Conclusion Our analysis demonstrates how different threshold definitions for false positives can impact the reported diagnostic performance of CADe for colon polyp detection.

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations.

These data help improve our knowledge of AEs regarding risk factors for rebleeding, and utilizes a novel small bowel transit time-based quartile localization method that may simplify future research and comparisons of anatomic distribution and behavior of small bowel AEs.