Risk Prediction Models for Hepatic Encephalopathy Post-Transjugular Intrahepatic Portosystemic Shunt: a Systematic...

Transjugular Intrahepatic Portosystemic Shunt (TIPS) is an established treatment for portal hypertension (PH) with expanding indications. Hepatic encephalopathy (HE) is the main complication, affecting up to 50% of patients. Accurate HE-risk prediction is necessary for patient selection. Hence, we aimed to provide an overview of prediction models for post-TIPS HE.

We conducted a systematic review of studies developing or validating prediction models for post-TIPS HE, which included ≥ 100 patients. The search, conducted up to December 2025, covered databases such as Embase and Medline. Outcomes assessed included AUCs/C-indices and bias risk, evaluated using PROBAST.

Twenty-seven studies were included, with 24 developing prediction models. All used retrospective cohorts of cirrhotic patients (population size: 106-621), with 25 cohorts from Asia (93%). Models varied in HE severity and prediction time frame, with 11 studies (41%) not reporting any. Discrimination was variable across studies. Child-Pugh score (CPS) and Model for End-Stage Liver Disease, evaluated in nine studies, showed moderate performance (AUCs/C-indices: 0.60-0.75). Models that included imaging predictors (42%) reported higher discrimination than clinical models alone (0.73-0.97 vs. 0.61-0.86). Common clinical predictors were age (68%), CPS (40%), and creatinine (28%). Models scored a high bias risk in PROBAST analysis (96%). Methodological concerns included inappropriate patient exclusion (63%), low event-per-variable rate (81%), inadequate validation (79%), and neglecting competing risks (93%). Applicability concerns involved using highly selected populations (44%) and specialized imaging predictors (33%).

Current prediction models for post-TIPS HE, despite strong discrimination, cannot support clinical decision-making due to methodological and applicability concerns. Future research should prioritize models with accessible parameters, broad patient representation, competing risks, and rigorous validation to address overfitting.

Despite numerous studies, there remains no consensus on whether prediction models for post-TIPS HE can be integrated into clinical practice. This paper provides a comprehensive overview of existing models by highlighting their strengths and limitations. In addition, it identifies recurrent predictors across models and proposes a framework for future research aimed at improving the prediction of this challenging complication.