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 i
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APA
Filakovszky Á, Brat K, et al. (2026). Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning.. Thorax, 81(5), 474-482. https://doi.org/10.1136/thorax-2024-221485
MLA
Filakovszky Á, et al.. "Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning.." Thorax, vol. 81, no. 5, 2026, pp. 474-482.
PMID
41043965
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, NCT04826575.
[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, NCT04826575.
MeSH Terms
Humans; Exercise Test; Male; Female; Machine Learning; Pneumonectomy; Middle Aged; Postoperative Complications; Prospective Studies; Aged; Predictive Value of Tests; Risk Assessment; Respiratory Function Tests; ROC Curve; Preoperative Care; Lung Neoplasms