Surgical Risk Following Anatomic Lung Resection in Thoracic Surgery: A Prediction Model Derived from a Spanish Multicenter Database.
Por:
Gómez de Antonio D, Crowley Carrasco S, Romero Román A, Royuela A, Sánchez Calle Á, Obiols Fornell C, Call Caja S, Embún R, Royo Í, Recuero JL, Cabañero A, Moreno N, Bolufer S, Congregado M, Jimenez MF, Aguinagalde B, Amor-Alonso S, Arrarás MJ, Blanco Orozco AI, Boada M, Cal I, Cilleruelo Ramos Á, Fernández-Martín E, García-Barajas S, García-Jiménez MD, García-Prim JM, Garcia-Salcedo JA, Gelbenzu-Zazpe JJ, Giraldo-Ospina CF, Gómez Hernández MT, Hernández J, Illana Wolf JD, Jáuregui Abularach A, Jiménez U, López Sanz I, Martínez-Hernández NJ, Martínez-Téllez E, Milla Collado L, Mongil Poce R, Moradiellos-Díez FJ, Moreno-Basalobre R, Moreno Merino SB, Quero-Valenzuela F, Ramírez-Gil ME, Ramos-Izquierdo R, Rivo E, Rodríguez-Fuster A, Rojo-Marcos R, Sanchez-Lorente D, Moreno LS, Simón C, Trujillo-Reyes JC, López García C, Fibla Alfara JJ, Sesma Romero J and Hernando Trancho F
Publicada:
1 may 2022
Ahead of Print:
24 feb 2021
Resumen:
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.
Open Access
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