Department of Radiology, Medical School, University of Minnesota, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA.
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.
Cardiovasc Intervent Radiol. 2023 Dec;46(12):1715-1725. doi: 10.1007/s00270-023-03593-w. Epub 2023 Nov 17.
To develop and assess machine learning (ML) models' ability to predict post-procedural hepatic encephalopathy (HE) following transjugular intrahepatic portosystemic shunt (TIPS) placement.
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.
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.
ML models can aid identifying patients at risk of developing HE after TIPS placement, providing an additional tool for patient selection and management.
开发并评估机器学习（ML）模型预测经颈静脉肝内门体分流术（TIPS）后肝性脑病（HE）的能力。
在这项回顾性研究中，分析了 2005 年至 2019 年间接受 TIPS 治疗的 327 例肝硬化患者。收集了 30 项特征（8 项临床、10 项实验室、12 项手术），并在术后 1 个月记录 HE 的发生情况，无论严重程度如何。对数值和分类数据进行了适当的单变量统计分析。使用具有五重交叉验证（CV）的序贯特征选择模型进行特征选择。使用支持向量机（SVM）、逻辑回归（LR）和 CatBoost 算法开发了三种 ML 模型。使用嵌套五重 CV 技术评估性能。
105 例（32%）患者术后出现 HE。与未发生 HE 的患者相比，发生静脉曲张出血（p=0.008）和高门静脉-系统压力梯度（p=0.004）的患者发生 HE 的可能性显著增加。此外，仅存在出血或腹水一个适应证的患者发生 HE 的可能性明显降低，而发生布加氏综合征的可能性也降低（p=0.03）。特征选择算法选择了 7 个特征。SVM、LR 和 CatBoost 模型的准确率分别为 74%、75%和 73%，曲线下面积（AUC）值分别为 0.82、0.83 和 0.83。
ML 模型可以帮助识别 TIPS 术后发生 HE 的风险患者，为患者选择和管理提供额外的工具。
