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Updated: Nov 11, 2025

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

David Gómez de Antonio1, Silvana Crowley Carrasco1, Alejandra Romero Román1

  • 1Servicio de Cirugía Torácica, Hospital Universitario Puerta de Hierro Majadahonda. Madrid, España.

Archivos De Bronconeumologia
|March 23, 2021
PubMed

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Summary

A new surgical risk model for anatomic lung resections was developed using GEVATS data. This tool helps predict patient risk for death or severe complications after lung surgery.

Area of Science:

  • Thoracic Surgery
  • Surgical Oncology
  • Predictive Analytics

Background:

  • Anatomic lung resections are complex procedures with inherent risks.
  • Accurate surgical risk prediction is crucial for patient management and informed consent.

Purpose of the Study:

  • To develop and validate a surgical risk prediction model for patients undergoing anatomic lung resections.
  • To identify key factors influencing postoperative outcomes in this patient cohort.

Main Methods:

  • Utilized data from 3,533 patients in the Spanish Video-Assisted Thoracic Surgery Group (GEVATS) registry.
  • Defined a composite outcome of death or Clavien Dindo grade IV complication within 90 days.
  • Employed logistic regression for analysis and resampling techniques for internal validation.
Keywords:
Anatomic lung resectionCirugía mínimamente invasivaCirugía torácicaMinimally invasive surgeryModelo predictivo de riesgoMorbimortalidad posquirúrgicaPost-surgical morbidity and mortalityPredictive risk modelResección pulmonar anatómicaRiesgo quirúrgicoSurgical riskThoracic surgery

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Main Results:

  • The overall incidence of the composite outcome was 4.29%.
  • Key predictors identified include age, sex, prior lung cancer resection, dyspnea (mMRC), right pneumonectomy, and predicted postoperative diffusing capacity of the lungs for carbon monoxide (ppl DLCO).
  • The validated model demonstrated good performance with a C-statistic of 0.712.

Conclusions:

  • The developed risk prediction model is simple, valid, and reliable.
  • It serves as a valuable tool for assessing patient risk prior to anatomic lung resection.
  • The model can aid clinicians in surgical decision-making and patient counseling.