预测胸腔血管内动脉瘤修复后的早期死亡率:基于机器学习的决策树分析。

IF 0.6 Q4 PERIPHERAL VASCULAR DISEASE
Annals of vascular diseases Pub Date : 2025-01-01 Epub Date: 2025-05-23 DOI:10.3400/avd.oa.25-00009
Masaki Kano, Toshiya Nishibe, Tsuyoshi Iwasa, Seiji Matsuda, Shinobu Akiyama, Toru Iwahashi, Shoji Fukuda, Yusuke Shimahara, Masayasu Nishibe
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引用次数: 0

摘要

目的:胸血管内动脉瘤修复术(TEVAR)为胸主动脉瘤(TAA)的治疗带来了革命性的变化,为开放性手术提供了一种侵入性更小的选择。本研究旨在使用基于机器学习的决策树分析(DTA)确定退行性TAA TEVAR后早期死亡的风险因素。方法:本回顾性观察性研究分析了79例选择性TEVAR患者,使用决策树分析确定早期死亡(2年内)的预测因素。数据集包括36个变量,包括年龄、性别、营养状况、合并症、炎症、免疫状态和手术细节。决策树分类器是使用Python 3.7和scikit-learn工具包开发和验证的。结果:DTA确定八十岁状态是早期死亡的最强预测因子,其次是营养状况不良、去分支手术和免疫力低下。该模型确定了7个终末淋巴结,早期死亡风险从0%到77.7%不等。准确度中等(65.8%),灵敏度较高(81.0%),特异性较低(60.3%),可有效识别高危患者。结论:基于机器学习的DTA确定了TEVAR术后早期死亡率的关键预测因素,包括八十岁高龄、营养状况不佳、免疫力低下和去分支手术。该模型提供了一种可解释的风险分层工具,但其临床适用性有待进一步验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Early Mortality after Thoracic Endovascular Aneurysm Repair: A Machine Learning-Based Decision Tree Analysis.

Objectives: Thoracic endovascular aneurysm repair (TEVAR) has revolutionized the treatment of thoracic aortic aneurysms (TAA) by providing a less invasive alternative to open surgery. This study aims to identify risk factors for early mortality following TEVAR for degenerative TAA using a machine learning-based decision tree analysis (DTA). Methods: This retrospective observational study analyzed 79 patients who underwent elective TEVAR to identify predictors of early mortality (within 2 years) using decision tree analysis. The dataset included 36 variables, covering age, sex, nutritional status, comorbidities, inflammation, immune status, and surgical details. The decision tree classifier was developed and validated using Python 3.7 with the scikit-learn toolkit. Results: DTA identified octogenarian status as the strongest predictor of early mortality, followed by poor nutritional status, debranching procedures, and compromised immunity. The model identified 7 terminal nodes, with early mortality risk ranging from 0% to 77.7%. It demonstrated moderate accuracy (65.8%) and high sensitivity (81.0%) but had relatively low specificity (60.3%), effectively identifying high-risk patients. Conclusions: Machine learning-based DTA identified key predictors of early mortality following TEVAR, including octogenarian status, poor nutritional status, compromised immunity, and debranching procedures. The model provides an interpretable risk stratification tool, but its clinical applicability requires further validation.

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来源期刊
Annals of vascular diseases
Annals of vascular diseases PERIPHERAL VASCULAR DISEASE-
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