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

Clinical Snapshot

Key Findings

• P Value: Non-significant p-values ranging from 0.47 to 0.96 for CPET parameter addition

• Effect Size: No significant improvement in predictive performance when adding CPET parameters

• Primary Outcome: Postoperative pulmonary complications occurred in 71 (14%) patients; postoperative cardiovascular complications in 90 (18%) patients

• Nnt Or Sensitivity: AUC-ROC 0.72-0.78 for PPC prediction, 0.65-0.73 for PCC prediction in unselected patients

• Confidence Interval: AUC-ROC ranges: 0.59-0.78 across different models and subgroups

Clinical Application

High feasibility as questions current resource-intensive practice of routine CPET in lung resection candidates Highly relevant to Australian thoracic surgery practice. May inform TGA device utilisation guidelines and influence PBS funding decisions for CPET. Aligns with RACGP emphasis on evidence-based resource allocation in specialist referrals Patients being considered for lung resection surgery, particularly those meeting current ACCP or ERS/ESTS criteria for CPET

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.

References

1. 1.Filakovszky, Á., Brat, K., Tschoellitsch, T., Bartos, S., Mazur, A., Meier, J., Olson, L., & Cundrle, I. (2026). Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning. Thorax. https://doi.org/10.1136/thorax-2024-221485