[A nomogram prediction model for individualized prediction of the risk of covert (minimal) hepatic encephalopathy occurrence in patients with liver cirrhosis].

To construct an individualized nomogram prediction model for predicting the risk of the occurrence of covert hepatic encephalopathy (CHE) in patients with liver cirrhosis. 325 cases of liver cirrhosis admitted from January 2020 to December 2022 were selected as the study subjects. Patients were divided into training (=213) and validation (=112) sets using a cluster randomization method. The risk factors for CHE occurrence in patients with cirrhosis in the training set were analyzed by univariate and multivariate logistic regression. A prediction model related to the nomogram was established. Independent risk factors for the occurrence of CHE in patients with cirrhosis were a history of hepatic encephalopathy, co-infection, gastrointestinal bleeding, severe ascites, prothrombin time ≥16 seconds, high total bilirubin, and high blood ammonia levels (<0.05). Nomogram model validation results: The model had a net benefit for the training and validation sets, with C-indices of 0.830 (95%: 0.802-0.858) and 0.807 (95%: 0.877-0.837), respectively, within the range of 0-96%. The calibration curves of both sets were evenly close to the ideal curves. The AUCs for the ROC curves in both sets were 0.827 (95%: 0.796-0.858) and 0.811 (95%: 0.787-0.836), respectively. Patients with cirrhosis have many risk factors for CHE occurrence. The nomogram model constructed based on these risk factors possesses a good predictive value for assessing CHE occurrence in cirrhotic patients.