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

Abstract

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 3,533 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.

Keywords: Anatomic lung resection; Cirugía mínimamente invasiva; Cirugía torácica; Minimally invasive surgery; Modelo predictivo de riesgo; Morbimortalidad posquirúrgica; Post-surgical morbidity and mortality; Predictive risk model; Resección pulmonar anatómica; Riesgo quirúrgico; Surgical risk; Thoracic surgery.