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Machine Learning Insights: Predicting Hepatic Encephalopathy After TIPS Placement

  • Clinical Investigation
  • Tips
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CardioVascular and Interventional Radiology Aims and scope Submit manuscript

Abstract

Purpose

To develop and assess machine learning (ML) models' ability to predict post-procedural hepatic encephalopathy (HE) following transjugular intrahepatic portosystemic shunt (TIPS) placement.

Materials and Methods

In this retrospective study, 327 patients who underwent TIPS for hepatic cirrhosis between 2005 and 2019 were analyzed. Thirty features (8 clinical, 10 laboratory, 12 procedural) were collected, and HE development regardless of severity was recorded one month follow-up. Univariate statistical analysis was performed with numeric and categoric data, as appropriate. Feature selection is used with a sequential feature selection model with fivefold cross-validation (CV). Three ML models were developed using support vector machine (SVM), logistic regression (LR) and CatBoost, algorithms. Performances were evaluated with nested fivefold-CV technique.

Results

Post-procedural HE was observed in 105 (32%) patients. Patients with variceal bleeding (p = 0.008) and high post-porto-systemic pressure gradient (p = 0.004) had a significantly increased likelihood of developing HE. Also, patients having only one indication of bleeding or ascites were significantly unlikely to develop HE as well as Budd-Chiari disease (p = 0.03). The feature selection algorithm selected 7 features. Accuracy ratios for the SVM, LR and CatBoost, models were 74%, 75%, and 73%, with area under the curve (AUC) values of 0.82, 0.83, and 0.83, respectively.

Conclusion

ML models can aid identifying patients at risk of developing HE after TIPS placement, providing an additional tool for patient selection and management.

Graphical Abstract

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Data Availability

The computer code will be shared at the corresponding author’s github page after peer-review process.

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Funding

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Authors and Affiliations

  1. Department of Radiology, Medical School, University of Minnesota, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA

    Okan İnce, Mehmet Gençtürk & Jafar Golzarian

  2. Department of Radiology, Prof. Dr. Cemil TASCIOGLU City Hospital, Health Sciences University, Kaptanpaşa Mah, Daruleceze Cad. No: 25 Prof. Dr. Cemil Taşçıoğlu Şehir Hastanesi, Radyoloji Kliniği, 34384, Şişli, Istanbul, Turkey

    Hakan Önder

  3. Department of Radiology, College of Medicine, University of Arizona, 1501 N. Campbell Avenue, Tucson, AZ, 85724, USA

    Shamar Young

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  1. Okan İnce
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Correspondence to Okan İnce.

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İnce, O., Önder, H., Gençtürk, M. et al. Machine Learning Insights: Predicting Hepatic Encephalopathy After TIPS Placement. Cardiovasc Intervent Radiol 46, 1715–1725 (2023). https://doi.org/10.1007/s00270-023-03593-w

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