Evaluation of the Factors Influencing Blood Transfusion during Minimally Invasive Direct Coronary Artery Bypass Surgery.

IF 1.9 4区医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Cardiology Pub Date : 2024-07-26 DOI:10.1159/000540349

Zhenmin Sun, Zhongqi Cui, Yan Xie, Lei Wang, Zhengqian Li, Xiaoyu Yang, Xiaoqing Zhang, Jun Wang

{"title":"Evaluation of the Factors Influencing Blood Transfusion during Minimally Invasive Direct Coronary Artery Bypass Surgery.","authors":"Zhenmin Sun, Zhongqi Cui, Yan Xie, Lei Wang, Zhengqian Li, Xiaoyu Yang, Xiaoqing Zhang, Jun Wang","doi":"10.1159/000540349","DOIUrl":null,"url":null,"abstract":"Introduction: The objective of this study was to analyze the blood transfusion factors of minimally invasive direct coronary artery bypass (MIDCAB) surgery using artificial intelligence.Methods: A retrospective analysis was performed for patients undergoing MIDCAB operations and no heart-lung machine was used from January 2017 to September 2022 in our hospital. The influencing factors of blood transfusion were used to build the artificial intelligence model. Eighty percent of the database was used as the training set, and twenty percent database was used as the testing set. To predict whether to use red blood cells during operation, we compared 104 artificial intelligence models. We aimed to assess whether which factors influence allogeneic transfusion in MIDCAB operations.Results: Of the 104 machine learning algorithms, the XGBoost model delivered the best performance, with an AUC of 0.726 in the testing set and an accuracy of 0.854 in the testing set. The artificial intelligence model showed preoperative hemoglobin less than 120 g/L, prothrombin time greater than 13.75, body mass index less than 22.7 kg/m2, coronary heart disease with additional comorbidities, a history of percutaneous coronary intervention, weight lower than 67 kg were the six major risk factors of allogeneic transfusion.Conclusion: The XGBoost model can predict transfusion or not transfusion in MIDCBA surgery with high accuracy.","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-12"},"PeriodicalIF":1.9000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000540349","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Introduction: The objective of this study was to analyze the blood transfusion factors of minimally invasive direct coronary artery bypass (MIDCAB) surgery using artificial intelligence.

Methods: A retrospective analysis was performed for patients undergoing MIDCAB operations and no heart-lung machine was used from January 2017 to September 2022 in our hospital. The influencing factors of blood transfusion were used to build the artificial intelligence model. Eighty percent of the database was used as the training set, and twenty percent database was used as the testing set. To predict whether to use red blood cells during operation, we compared 104 artificial intelligence models. We aimed to assess whether which factors influence allogeneic transfusion in MIDCAB operations.

Results: Of the 104 machine learning algorithms, the XGBoost model delivered the best performance, with an AUC of 0.726 in the testing set and an accuracy of 0.854 in the testing set. The artificial intelligence model showed preoperative hemoglobin less than 120 g/L, prothrombin time greater than 13.75, body mass index less than 22.7 kg/m2, coronary heart disease with additional comorbidities, a history of percutaneous coronary intervention, weight lower than 67 kg were the six major risk factors of allogeneic transfusion.

Conclusion: The XGBoost model can predict transfusion or not transfusion in MIDCBA surgery with high accuracy.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

评估微创直接冠状动脉搭桥手术中输血的影响因素。

简介：目的利用人工智能分析微创冠状动脉直接搭桥术（MIDCAB）的输血因素：对我院2017年1月至2022年9月接受MIDCAB手术且未使用心肺机的患者进行回顾性分析。输血的影响因素被用于建立人工智能模型。80%的数据库作为训练集，20%的数据库作为测试集。为了预测手术中是否使用红细胞，我们比较了 104 个人工智能模型。我们的目的是评估哪些因素会影响 MIDCAB 手术中的异体输血：结果：在 104 种机器学习算法中，XGBoost 模型的性能最佳，测试集的 AUC 为 0.726，准确率为 0.854。人工智能模型显示，术前血红蛋白（Hb）小于 120 g/L、凝血酶原时间（PT）大于 13.75、体重指数（BMI）小于 22.7 kg/m2、冠心病伴有其他合并症、有经皮冠状动脉介入治疗（PCI）史、体重低于 67 kg 是异体输血的六大风险因素：XGBoost模型能高度准确地预测MIDCBA手术中是否需要输血。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Cardiology 医学-心血管系统

CiteScore

3.40

自引率

5.30%

发文量

审稿时长

1.5 months

期刊介绍： ''Cardiology'' features first reports on original clinical, preclinical and fundamental research as well as ''Novel Insights from Clinical Experience'' and topical comprehensive reviews in selected areas of cardiovascular disease. ''Editorial Comments'' provide a critical but positive evaluation of a recent article. Papers not only describe but offer critical appraisals of new developments in non-invasive and invasive diagnostic methods and in pharmacologic, nutritional and mechanical/surgical therapies. Readers are thus kept informed of current strategies in the prevention, recognition and treatment of heart disease. Special sections in a variety of subspecialty areas reinforce the journal''s value as a complete record of recent progress for all cardiologists, internists, cardiac surgeons, clinical physiologists, pharmacologists and professionals in other areas of medicine interested in current activity in cardiovascular diseases.