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. 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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
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.