肺切除术前的心肺运动测试:还需要吗?使用机器学习评估预测效用

IF 7.7 1区 医学 Q1 RESPIRATORY SYSTEM
Thorax Pub Date : 2025-10-02 DOI:10.1136/thorax-2024-221485
Ákos Filakovszky, Kristian Brat, Thomas Tschoellitsch, Stepan Bartos, Andrej Mazur, Jens Meier, Lyle Olson, Ivan Cundrle
{"title":"肺切除术前的心肺运动测试:还需要吗?使用机器学习评估预测效用","authors":"Ákos Filakovszky, Kristian Brat, Thomas Tschoellitsch, Stepan Bartos, Andrej Mazur, Jens Meier, Lyle Olson, Ivan Cundrle","doi":"10.1136/thorax-2024-221485","DOIUrl":null,"url":null,"abstract":"Rationale Despite significant advances in patient care and outcomes, criteria for cardiopulmonary exercise testing (CPET) in risk stratification guidelines for lung resection have not been updated in over a decade. We hypothesised that CPET no longer holds additional predictive value for postoperative complications. Methods In this secondary analysis, we included lung resection candidates from two prospective, multicentre studies eligible for CPET and assessed with preoperative pulmonary function tests (PFTs) and arterial blood gas analysis. Postoperative pulmonary (PPCs) and cardiovascular complications (PCCs) were documented during hospitalisation. We trained five types of machine learning models applying nested cross-validation to predict complications and compared predictive performance based on four metrics, including area under the receiver operating characteristic curve (AUC-ROC). Results A total of 497 patients were included. PPCs developed in 71 (14%) patients. Adding CPET parameters to PFTs and baseline clinical data did not improve the ability of models to predict PPCs in unselected patients (AUC-ROC=0.72–0.78; p=0.47), nor in those meeting American College of Chest Physicians (ACCPs) (n=236; AUC-ROC=0.64–0.78; p=0.70) or European Respiratory Society/European Society of Thoracic Surgery (ERS/ESTS) criteria (n=168; AUC-ROC=0.59–0.76; p=0.92). PCCs developed in 90 (18%) patients. CPET parameters likewise did not improve model performance for the prediction of PCCs in unselected patients (AUC-ROC=0.65–0.73; p=0.96), nor in the ACCP (AUC-ROC=0.61–0.73; p=0.82) or ERS/ESTS subgroups (AUC-ROC=0.62–0.69; p=0.87). Conclusions In contemporary surgical practice, CPET did not improve the predictive performance of machine learning models for PPCs or PCCs in patients with an indication based on established guidelines or in those without. The role of CPET in preoperative risk stratification for lung resection should be re-evaluated. Trial registration number [NCT03498352][1], [NCT04826575][2]. Data are available on reasonable request. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03498352&atom=%2Fthoraxjnl%2Fearly%2F2025%2F10%2F02%2Fthorax-2024-221485.atom [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT04826575&atom=%2Fthoraxjnl%2Fearly%2F2025%2F10%2F02%2Fthorax-2024-221485.atom","PeriodicalId":23284,"journal":{"name":"Thorax","volume":"54 1","pages":""},"PeriodicalIF":7.7000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning\",\"authors\":\"Ákos Filakovszky, Kristian Brat, Thomas Tschoellitsch, Stepan Bartos, Andrej Mazur, Jens Meier, Lyle Olson, Ivan Cundrle\",\"doi\":\"10.1136/thorax-2024-221485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rationale Despite significant advances in patient care and outcomes, criteria for cardiopulmonary exercise testing (CPET) in risk stratification guidelines for lung resection have not been updated in over a decade. We hypothesised that CPET no longer holds additional predictive value for postoperative complications. Methods In this secondary analysis, we included lung resection candidates from two prospective, multicentre studies eligible for CPET and assessed with preoperative pulmonary function tests (PFTs) and arterial blood gas analysis. Postoperative pulmonary (PPCs) and cardiovascular complications (PCCs) were documented during hospitalisation. We trained five types of machine learning models applying nested cross-validation to predict complications and compared predictive performance based on four metrics, including area under the receiver operating characteristic curve (AUC-ROC). Results A total of 497 patients were included. PPCs developed in 71 (14%) patients. Adding CPET parameters to PFTs and baseline clinical data did not improve the ability of models to predict PPCs in unselected patients (AUC-ROC=0.72–0.78; p=0.47), nor in those meeting American College of Chest Physicians (ACCPs) (n=236; AUC-ROC=0.64–0.78; p=0.70) or European Respiratory Society/European Society of Thoracic Surgery (ERS/ESTS) criteria (n=168; AUC-ROC=0.59–0.76; p=0.92). PCCs developed in 90 (18%) patients. CPET parameters likewise did not improve model performance for the prediction of PCCs in unselected patients (AUC-ROC=0.65–0.73; p=0.96), nor in the ACCP (AUC-ROC=0.61–0.73; p=0.82) or ERS/ESTS subgroups (AUC-ROC=0.62–0.69; p=0.87). Conclusions In contemporary surgical practice, CPET did not improve the predictive performance of machine learning models for PPCs or PCCs in patients with an indication based on established guidelines or in those without. The role of CPET in preoperative risk stratification for lung resection should be re-evaluated. Trial registration number [NCT03498352][1], [NCT04826575][2]. Data are available on reasonable request. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03498352&atom=%2Fthoraxjnl%2Fearly%2F2025%2F10%2F02%2Fthorax-2024-221485.atom [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT04826575&atom=%2Fthoraxjnl%2Fearly%2F2025%2F10%2F02%2Fthorax-2024-221485.atom\",\"PeriodicalId\":23284,\"journal\":{\"name\":\"Thorax\",\"volume\":\"54 1\",\"pages\":\"\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2025-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Thorax\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/thorax-2024-221485\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RESPIRATORY SYSTEM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Thorax","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/thorax-2024-221485","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0

摘要

尽管在患者护理和预后方面取得了重大进展,但肺切除术风险分层指南中的心肺运动试验(CPET)标准在十多年内没有更新。我们假设CPET对术后并发症不再具有额外的预测价值。在这一次要分析中,我们纳入了两项符合CPET条件的前瞻性多中心研究的肺切除术候选人,并通过术前肺功能试验(PFTs)和动脉血气分析进行评估。住院期间记录了术后肺部(PPCs)和心血管并发症(PCCs)。我们训练了五种类型的机器学习模型,应用嵌套交叉验证来预测并发症,并基于四个指标比较预测性能,包括受试者工作特征曲线下面积(AUC-ROC)。结果共纳入497例患者。71例(14%)患者出现PPCs。将CPET参数添加到PFTs和基线临床数据中并不能提高模型对未选择患者PPCs的预测能力(AUC-ROC= 0.72-0.78; p=0.47),也不能提高模型对符合美国胸科医师学会(ACCPs) (n=236; AUC-ROC= 0.64-0.78; p=0.70)或欧洲呼吸学会/欧洲胸外科学会(ERS/ESTS)标准的患者(n=168; AUC-ROC= 0.59-0.76; p=0.92)的预测能力。90例(18%)患者出现PCCs。同样,CPET参数对未选择患者的PCCs预测(AUC-ROC= 0.65-0.73; p=0.96)、ACCP (AUC-ROC= 0.61-0.73; p=0.82)或ERS/ESTS亚组(AUC-ROC= 0.62-0.69; p=0.87)也没有改善模型的性能。结论:在当前的外科实践中,CPET并没有提高机器学习模型对PPCs或PCCs患者的预测性能,这些患者有基于既定指南的指征或没有指征。CPET在肺切除术术前风险分层中的作用有待重新评估。试验注册号[NCT03498352][1], [NCT04826575][2]。如有合理要求,可提供资料。[1]: /查找/ external-ref ? link_type = CLINTRIALGOV&access_num = NCT03498352&atom = % 2 fthoraxjnl % 2恐惧% 2 f2025 % 2 f10 % 2 f02 % 2 fthorax - 2024 - 221485。link_type=CLINTRIALGOV&access_num=NCT04826575&atom=%2Fthoraxjnl% 2F2025%2F10%2F02% 2fthoraxl -2024-221485.atom
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning
Rationale Despite significant advances in patient care and outcomes, criteria for cardiopulmonary exercise testing (CPET) in risk stratification guidelines for lung resection have not been updated in over a decade. We hypothesised that CPET no longer holds additional predictive value for postoperative complications. Methods In this secondary analysis, we included lung resection candidates from two prospective, multicentre studies eligible for CPET and assessed with preoperative pulmonary function tests (PFTs) and arterial blood gas analysis. Postoperative pulmonary (PPCs) and cardiovascular complications (PCCs) were documented during hospitalisation. We trained five types of machine learning models applying nested cross-validation to predict complications and compared predictive performance based on four metrics, including area under the receiver operating characteristic curve (AUC-ROC). Results A total of 497 patients were included. PPCs developed in 71 (14%) patients. Adding CPET parameters to PFTs and baseline clinical data did not improve the ability of models to predict PPCs in unselected patients (AUC-ROC=0.72–0.78; p=0.47), nor in those meeting American College of Chest Physicians (ACCPs) (n=236; AUC-ROC=0.64–0.78; p=0.70) or European Respiratory Society/European Society of Thoracic Surgery (ERS/ESTS) criteria (n=168; AUC-ROC=0.59–0.76; p=0.92). PCCs developed in 90 (18%) patients. CPET parameters likewise did not improve model performance for the prediction of PCCs in unselected patients (AUC-ROC=0.65–0.73; p=0.96), nor in the ACCP (AUC-ROC=0.61–0.73; p=0.82) or ERS/ESTS subgroups (AUC-ROC=0.62–0.69; p=0.87). Conclusions In contemporary surgical practice, CPET did not improve the predictive performance of machine learning models for PPCs or PCCs in patients with an indication based on established guidelines or in those without. The role of CPET in preoperative risk stratification for lung resection should be re-evaluated. Trial registration number [NCT03498352][1], [NCT04826575][2]. Data are available on reasonable request. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03498352&atom=%2Fthoraxjnl%2Fearly%2F2025%2F10%2F02%2Fthorax-2024-221485.atom [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT04826575&atom=%2Fthoraxjnl%2Fearly%2F2025%2F10%2F02%2Fthorax-2024-221485.atom
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Thorax
Thorax 医学-呼吸系统
CiteScore
16.10
自引率
2.00%
发文量
197
审稿时长
1 months
期刊介绍: Thorax stands as one of the premier respiratory medicine journals globally, featuring clinical and experimental research articles spanning respiratory medicine, pediatrics, immunology, pharmacology, pathology, and surgery. The journal's mission is to publish noteworthy advancements in scientific understanding that are poised to influence clinical practice significantly. This encompasses articles delving into basic and translational mechanisms applicable to clinical material, covering areas such as cell and molecular biology, genetics, epidemiology, and immunology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信