Evidence map›Paper›PMID 41043965›Full record

ArticleThorax2026

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

Ákos Filakovszky, Kristian Brat, Thomas Tschoellitsch, Stepan Bartos, Andrej Mazur, Jens Meier, Lyle Olson, Ivan Cundrle

2 registry-linked trialsAbstract readMulticenter Study
In one paragraph

Article in Thorax, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03498352 completednot on this map

Rest Ventilatory Parameters Predict Morbidity and Mortality in Patients Undergoing Thoracic Surgery

TypeobservationalSponsorSt. Anne's University Hospital Brno, Czech RepublicRan2017 to 2021Enrolled366ConditionsPulmonary Complication, Cardiovascular ComplicationArmsThoracic surgery
NCT04826575 nacompletednot on this map

High Intensity Respiratory Muscle Training as a Pre-habilitation in Lung Surgery

TypeinterventionalSponsorSt. Anne's University Hospital Brno, Czech RepublicRan2021 to 2024Enrolled150ConditionsLung Cancer, Post-Op ComplicationArmsHigh intensity inspiratory and expiratory muscle training
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ákos FilakovszkyDepartment of Anesthesiology and Critical Care Medicine, Johannes Kepler University and Kepler University Hospital, Linz, Austria.ORCID 0009-0003-9166-4215
Kristian BratMasaryk Memorial Cancer Institute, Brno, Czech Republic.
Thomas TschoellitschDepartment of Anesthesiology and Critical Care Medicine, Johannes Kepler University and Kepler University Hospital, Linz, Austria.
Stepan BartosFaculty of Medicine, Masaryk University, Brno, Czech Republic.
Andrej MazurFaculty of Medicine, Masaryk University, Brno, Czech Republic.
Jens MeierDepartment of Anesthesiology and Critical Care Medicine, Johannes Kepler University and Kepler University Hospital, Linz, Austria.
Lyle OlsonDepartment of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA.
Ivan CundrleFaculty of Medicine, Masaryk University, Brno, Czech Republic Ivan.Cundrle@seznam.cz.ORCID 0000-0002-7709-8021

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

rationaleDespite 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.

methodsIn 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).

resultsA 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).

conclusionsIn 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.

Indexed as

Exercise TestLung NeoplasmsMachine LearningPneumonectomyPostoperative ComplicationsAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsPreoperative CareProspective StudiesRespiratory Function TestsRisk AssessmentROC CurveExerciseLung Cancer

Identifiers

PMID41043965
PMCPMC13151524

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LicenceCC BY-NC
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.