Alessandro Brunelli1, Silvia Cicconi2, Herbert Decaluwe3, Zalan Szanto4, Pierre Emmanuel Falcoz5. 1. Department of Thoracic Surgery, St. James's University Hospital, Leeds, UK. 2. Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK. 3. Department of Thoracic Surgery, University Hospital Leuven, Leuven, Belgium. 4. Department of Thoracic Surgery, University of Pecs, Pecs, Hungary. 5. Department of Thoracic Surgery, University Hospital Strasbourg, Strasbourg, France.
Abstract
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.
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.
Authors: Marcus Taylor; Syed F Hashmi; Glen P Martin; Michael Shackcloth; Rajesh Shah; Richard Booton; Stuart W Grant Journal: Interact Cardiovasc Thorac Surg Date: 2021-04-08
Authors: Fabian M Troschel; Qianna Jin; Florian Eichhorn; Thomas Muley; Till D Best; Konstantin S Leppelmann; Chi-Fu Jeffrey Yang; Amelie S Troschel; Hauke Winter; Claus P Heußel; Henning A Gaissert; Florian J Fintelmann Journal: Cancer Med Date: 2021-08-19 Impact factor: 4.452