Parsimonious Eurolung risk models to predict cardiopulmonary morbidity and mortality following anatomic lung resections: an updated analysis from the European Society of Thoracic Surgeons database

OBJECTIVES: To develop a simplified version of the Eurolung risk model to predict cardiopulmonary morbidity and 30-day mortality after lung resection from the ESTS database. METHODS: A total of 82 383 lung resections (63 681 lobectomies, 3617 bilobectomies, 7667 pneumonectomies and 7418 segmentectomies) recorded in the ESTS database (January 2007-December 2018) were analysed. Multiple imputations with chained equations were performed on the predictors included in the original Eurolung models. Stepwise selection was then applied for determining the best logistic model. To develop the parsimonious models, different models were tested eliminating variables one by one starting from the less significant. The models' prediction power was evaluated estimating area under curve (AUC) with the 10-fold cross-validation technique. RESULTS: Cardiopulmonary morbidity model (Eurolung1): the best parsimonious Eurolung1 model contains 5 variables. The logit of the parsimonious Eurolung1 model was as follows: -2.852 + 0.021 × age + 0.472 × male -0.015 × ppoFEV1 + 0.662×thoracotomy + 0.324 × extended resection. Pooled AUC is 0.710 [95% confidence interval (CI) 0.677-0.743]. Mortality model (Eurolung2): the best parsimonious model contains 6 variables. The logit of the parsimonious Eurolung2 model was as follows: -6.350 + 0.047 × age + 0.889 × male -0.055 × BMI -0.010 × ppoFEV1 + 0.892 × thoracotomy + 0.983 × pneumonectomy. Pooled AUC is 0.737 (95% CI 0.702-0.770). An aggregate parsimonious Eurolung2 was also generated by repeating the logistic regression after categorization of the numeric variables. Patients were grouped into 7 risk classes showing incremental risk of mortality (P < 0.0001). CONCLUSIONS: We were able to develop simplified and updated versions of the Eurolung risk models retaining the predictive ability of the full original models. They represent a more user-friendly tool designed to inform the multidisciplinary discussion and shared decision-making process of lung resection candidates.

Publication year:2020

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:2

CSS-citation score:1

Authors:International

Authors from:Higher Education

Accessibility:Closed