Preoperative prediction of overt hepatic encephalopathy caused by transjugular intrahepatic portosystemic shunt. Issue 154 (September 2022) - Yang, Yang; Liang, Xueqing; Yang, Shirui; He, Xiaofeng; Huang, Mingsheng; Shi, Wenfeng; Luo, Junyang; Duan, Chongyang; Feng, Xinghui; Fu, Sirui; Lu, Ligong
Graphical abstract: For patients classified as Child-Pugh class B with variceal bleeding, the appropriate selection of patients before transjugular intrahepatic portosystemic shunt (TIPS) is challenging. We constructed a clinical model (Model.C), a radiological model (Model.R), and a combined model (Model.CR) to predict post-TIPS overt hepatic encephalopathy (OHE) based on the clinical factors and the radiological characteristics. Then, we compared the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), Delong test, calibration curve, and decision curve analyses results to identify the best model. Highlights: Imaging factors also contribute to the prediction of overt hepatic encephalopathy. Identified isolated risk factors were integrated into combined model. Our combined model could be used for preoperative selection of patients before TIPS. Clinicians can calculate the quantitative score to predict the risk of post-TIPS OHE. Abstract: Purpose: Preoperative prediction of overt hepatic encephalopathy (OHE) should be performed in patients with variceal bleeding treated using the transjugular intrahepatic portosystemic shunt (TIPS) procedure. A reliable prediction tool is therefore required. Method: Patients with cirrhosis-related variceal bleeding treated using the TIPS procedure were screened at two hospitals. Patients classified as Child-Pugh Class B were identified. The least absolute shrinkage and selection operator method and the backward stepwise selection method were used to screen the clinical and radiological characteristics of participants. Then, models were constructed accordingly to predict OHE. Area under the receiver operating characteristic curves, calibration curves, and decision curves were performed to discover the optimal model. Finally, whether clinical factors influenced the performance of our optimal model was tested. Results: A total of 191 patients were included (training cohort: 127 cases; validation cohort: 64 cases). Three novel radiological independent risk factors were found. The combined model outperformed the models containing clinical factors or radiological characteristics alone. The areas under the curve for the training and validation cohorts were 0.901 and 0.903, respectively, with satisfactory calibration and decision curves. The Model for End-Stage Liver Disease score, serum sodium, albumin, total bilirubin, and age exhibited limited influence on the performance of the combined model. Conclusions: These radiological characteristics are also independent risk factors for post-TIPS OHE. Combining clinical factors and radiological characteristics was an effective means of predicting OHE. This study's model could be used for preoperative selection of appropriate patients before the TIPS procedure is performed.