Natural history of covert hepatic encephalopathy: An observational study of 366 cirrhotic patients

AIM To explore the natural history of covert hepatic encephalopathy(CHE) in absence of medication intervention.METHODS Consecutive outpatient cirrhotic patients in a Chinese tertiary care hospital were enrolled and evaluated for CHE diagnosis. They were followed up for a mean of 11.2 ± 1.3 mo. Time to the first cirrhosis-related complications requiring hospitalization, including overt HE(OHE), resolution of CHE and death/transplantation, were compared between CHE and no-CHE patients. Predictors for complication(s) and death/transplantation were also analyzed.RESULTS A total of 366 patients(age: 47.2 ± 8.6 years, male: 73.0%) were enrolled. CHE was identified in 131 patients(35.8%). CHE patients had higher rates of death and incidence of complications requiring hospitalization, including OHE, compared to unimpaired patients. Moreover, 17.6% of CHE patients developed OHE, 42.0% suffered persistent CHE, and 19.8% of CHE spontaneously resolved. In CHE patients, serum albumin < 30 g/L(HR = 5.22, P = 0.03) was the sole predictor for developing OHE, and blood creatinine > 133 μmol/L(HR = 4.75, P = 0.036) predicted mortality. Child-Pugh B/C(HR = 0.084, P < 0.001) and OHE history(HR = 0.15, P = 0.014) were predictors of spontaneous resolution of CHE.CONCLUSION CHE exacerbates, persists or resolves without medication intervention in clinically stable cirrhosis. Triage of patients based on these predictors will allow for more cost-effect management of CHE.

Duodenal variceal bleeding secondary to idiopathic portal hypertension treated with transjugular intra-hepatic portosystemic shunt plus embolization: A case report

BACKGROUND Duodenal varices are a lesser-known complication with non-cirrhotic portal hypertension. We report a circuitous route from missed diagnosis of duodenal varices to correction. An extremely rare case of duodenal variceal bleeding secondary to idiopathic portal hypertension(IPH) is expounded in this study, which was controlled by transjugular intra-hepatic porto-systemic shunt(TIPS) plus embolization. CASE SUMMARY A 46-year-old woman with anemia for two years was frequently admitted to the local hospital. Upon examination, anemia was attributed to gastrointestinal tract bleeding, which resulted from duodenal variceal bleeding detected by repeated esophagogastroduodenoscopy. At the end of a complete workup, IPH leadingto duodenal varices was diagnosed. Portal venography revealed that the remarked duodenal varices originated from the proximal superior mesenteric vein. TIPS plus embolization with coils and Histoacryl was performed to obliterate the rupture of duodenal varices. The anemia resolved, and the duodenal varices completely vanished by 2 mo after the initial operation. CONCLUSION TIPS plus embolization may be more appropriate to treat the bleeding of large duodenal varices.

Deep learning for automatically predicting early haematoma expansion in Chinese patients

Background and purpose Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage(ICH)patients.The aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning model and validate its prediction accuracy.Methods Data of this study were obtained from a prospectively enrolled cohort of patients with primary supratentorial ICH from our centre.We developed a deep learning model to predict haematoma expansion and compared its performance with conventional non-contrast CT(NCCT)markers.To evaluate the predictability of this model,it was also compared with a logistic regression model based on haematoma volume or the BAT score.Results A total of 266 patients were finally included for analysis,and 74(27.8%)of them experienced early haematoma expansion.The deep learning model exhibited highest C statistic as 0.80,compared with 0.64,0.65,0.51,0.58 and 0.55 for hypodensities,black hole sign,blend sign,fluid level and irregular shape,respectively.While the C statistics for swirl sign(0.70;p=0.211)and heterogenous density(0.70;p=0.141)were not significantly higher than that of the deep learning model.Moreover,the predictive value for the deep learning model was significantly superior to that of the logistic model of haematoma volume(0.62;p=0.042)and the BAT score(0.65;p=0.042).Conclusions Compared with the conventional NCCT markers and BAT predictive model,the deep learning algorithm showed superiority for predicting early haematoma expansion in ICH patients.