Surgical Risk Following Anatomic Lung Resection in Thoracic Surgery: A Prediction Model Derived From a Spanish Multicenter Database

Introduction: The aim of this study was to develop a surgical risk prediction model in patients undergoing anatomic lung resections from the registry of the Spanish Video-Assisted Thoracic Surgery Group (GEVATS). Methods: Data were collected from 3533 patients undergoing anatomic lung resection for any diagnosis between December 20, 2016 and March 20, 2018. We defined a combined outcome variable: death or Clavien-Dindo grade IV complication at 90 days after surgery. Univariate and multivariate analyses were performed by logistic regression. Internal validation of the model was performed using resampling techniques. Results: The incidence of the outcome variable was 4.29% (95% CI 3.6-4.9). The variables remaining in the final logistic model were: age, sex, previous lung cancer resection, dyspnea (mMRC), right pneumonectomy, and ppo DLCO. The performance parameters of the model adjusted by resampling were: C-statistic 0.712 (95% CI 0.648-0.750), Brier score 0.042 and bootstrap shrinkage 0.854. Conclusions: The risk prediction model obtained from the GEVATS database is a simple, valid, and reliable model that is a useful tool for establishing the risk of a patient undergoing anatomic lung resection. (C) 2021 SEPAR. Published by Elsevier Espana, S.L.U. All rights reserved.

Automated summary

The aim of this study was to develop a surgical risk prediction model for patients undergoing anatomic lung resections. By analyzing a large dataset, the researchers identified relevant variables and developed a simple and effective model. This model can assist doctors in assessing the risk for patients undergoing surgery.