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基于超声心动图特征的XGBoost算法预测二尖瓣疾病的手术入路

Prediction of Surgical Approach in Mitral Valve Disease by XGBoost Algorithm Based on Echocardiographic Features.

作者信息

Lin Xiaoxuan, Chen Lixin, Zhang Defu, Luo Shuyu, Sheng Yuanyuan, Liu Xiaohua, Liu Qian, Li Jian, Shi Bobo, Peng Guijuan, Zhong Xiaofang, Huang Yuxiang, Li Dagang, Qin Gengliang, Yin Zhiqiang, Xu Jinfeng, Meng Chunying, Liu Yingying

机构信息

Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China.

Department of Cardiovascular Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.

出版信息

J Clin Med. 2023 Feb 2;12(3):1193. doi: 10.3390/jcm12031193.

DOI:10.3390/jcm12031193
PMID:36769840
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9917697/
Abstract

In this study, we aimed to develop a prediction model to assist surgeons in choosing an appropriate surgical approach for mitral valve disease patients. We retrospectively analyzed a total of 143 patients who underwent surgery for mitral valve disease. The XGBoost algorithm was used to establish a predictive model to decide a surgical approach (mitral valve repair or replacement) based on the echocardiographic features of the mitral valve apparatus, such as leaflets, the annulus, and sub-valvular structures. The results showed that the accuracy of the predictive model was 81.09% in predicting the appropriate surgical approach based on the patient's preoperative echocardiography. The result of the predictive model was superior to the traditional complexity score (81.09% vs. 75%). Additionally, the predictive model showed that the three main factors affecting the choice of surgical approach were leaflet restriction, calcification of the leaflet, and perforation or cleft of the leaflet. We developed a novel predictive model using the XGBoost algorithm based on echocardiographic features to assist surgeons in choosing an appropriate surgical approach for patients with mitral valve disease.

摘要

在本研究中，我们旨在开发一种预测模型，以协助外科医生为二尖瓣疾病患者选择合适的手术方法。我们回顾性分析了总共143例接受二尖瓣疾病手术的患者。使用XGBoost算法建立了一个预测模型，该模型基于二尖瓣装置的超声心动图特征（如瓣叶、瓣环和瓣下结构）来决定手术方法（二尖瓣修复或置换）。结果显示，基于患者术前超声心动图，预测模型在预测合适手术方法方面的准确率为81.09%。预测模型的结果优于传统的复杂性评分（81.09%对75%）。此外，预测模型表明影响手术方法选择的三个主要因素是瓣叶受限、瓣叶钙化以及瓣叶穿孔或裂缺。我们基于超声心动图特征，使用XGBoost算法开发了一种新型预测模型，以协助外科医生为二尖瓣疾病患者选择合适的手术方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b380/9917697/229a91668efd/jcm-12-01193-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b380/9917697/229a91668efd/jcm-12-01193-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b380/9917697/6110a28059aa/jcm-12-01193-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b380/9917697/2d31e7019fea/jcm-12-01193-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b380/9917697/5c564a67a425/jcm-12-01193-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b380/9917697/47f20f639131/jcm-12-01193-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b380/9917697/67a25ddad475/jcm-12-01193-g011.jpg
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