Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning.

Filakovszky A; Department of Anesthesiology and Critical Care Linz, Austria.
Brat K; Tschoellitsch T; Bartos S; Mazur A; Meier J; Olson L;
Cundrle I

Thorax. 81(5):474-482, 2026 Apr 16.

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.