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Is the Parsimonious Eurolung risk model a good predictor for all populations? An external validation study
Filipe Leite1,2,3, Ana Margarida Silva4, Gonçalo Paupério4
1University of Beira Interior - Health Sciences Faculty, 6200-506, Covilhã, Portugal. filipe.miguel.leite@outlook.com.
Journal of Cardiothoracic Surgery
|December 5, 2025
Summary
The Parsimonious Eurolung 1 score moderately predicts morbidity in Portuguese lung cancer patients, but the Eurolung 2 score is not validated for mortality prediction in this cohort, indicating a need for population-specific models.
Area of Science:
- Thoracic surgery outcomes research
- Clinical risk prediction modeling
- Health services research
Background:
- Numerous models exist for predicting postoperative mortality in thoracic surgery.
- The Parsimonious Eurolung score (2019) predicts perioperative morbidity (EuroLung1) and mortality (EuroLung2) using ESTS data.
- Previous validation was limited to European populations, necessitating external validation in diverse cohorts.
Purpose of the Study:
- To externally validate the Parsimonious Eurolung risk models.
- To assess model performance in a Portuguese cohort undergoing lung cancer resection.
Main Methods:
- Retrospective analysis of 410 non-small cell lung cancer (NSCLC) patients undergoing resection (2018-2021).
- Utilized Parsimonious Eurolung 1 (morbidity) and 2 (mortality) models for prediction.
- Evaluated model calibration, discrimination, and clinical usefulness.
Main Results:
- The Parsimonious Eurolung 1 (morbidity) model showed moderate applicability (AUC 0.668) but with calibration issues.
- The Parsimonious Eurolung 2 (mortality) model was not validated (AUC 0.437), indicating significant underestimation.
- Pneumonectomy was associated with the highest morbidity rates.
Conclusions:
- The Parsimonious Eurolung 1 score demonstrates moderate utility for predicting morbidity.
- The Parsimonious Eurolung 2 score is unreliable for 30-day mortality prediction in this Portuguese cohort.
- Findings highlight the need for population-specific risk models and improved data collection in thoracic surgery.
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