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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F26%3A00082383" target="_blank" >RIV/00159816:_____/26:00082383 - isvavai.cz</a>

Result on the web

<a href="https://thorax.bmj.com/content/81/5/474" target="_blank" >https://thorax.bmj.com/content/81/5/474</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1136/thorax-2024-221485" target="_blank" >10.1136/thorax-2024-221485</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30212 - Surgery

Project

<a href="/en/project/NU21-06-00086" target="_blank" >NU21-06-00086: High intensity respiratory muscle training as a pre-habilitation in lung surgery candidates</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2026

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

THORAX

ISSN

0040-6376

e-ISSN

1468-3296

Volume of the periodical

81

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

"474–482"

UT code for WoS article

001587262500001

EID of the result in the Scopus database

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