Abstract

ObjectiveExisting evidence demonstrates some benefit of regionalization on early postoperative outcomes following lung cancer resection, but data regarding the persistence of this effect in long-term mortality are lacking. We investigated whether previously reported improvements in short-term outcomes translated to long-term survival benefit. MethodsWe retrospectively reviewed patients undergoing major pulmonary resection (lobectomy, bilobectomy, or pneumonectomy) for cancer within our integrated health care system before (2011-2013; n = 782) and after (2015-2017; n = 845) thoracic surgery regionalization. Overall survival was compared by Kaplan–Meier analysis, and 1- and 3-year mortality was compared by the by χ2 or Fisher exact test. Multivariable Cox regression models evaluated the effect of regionalization on mortality adjusted for relevant factors. ResultsKaplan–Meier curves showed that overall survival was better among patients undergoing surgery postregionalization (log-rank test, P < .0001). Both 1- and 3-year mortality were decreased after regionalization: to 5.7% from 11.1% (P < .0001) for 1 year and to 17.0% from 25.5% (P = .0002) for 3 years. The multivariable adjusted Cox regression analysis revealed that only regionalization (hazard ratio [HR], 0.57; 95% confidence interval [CI], 0.42-0.76), age (HR, 1.03; 95% CI, 1.02-1.04), cancer stage (HR, 1.72, 1.83, and 2.56 for stages II, III, and IV, respectively), and Charlson comorbidity index (HR, 1.80 for 1-2; 2.05 for ≥3) were independent predictors of mortality. ConclusionsWe found that overall mortality as well as 1- and 3-year mortality for lung cancer resection were lower after thoracic surgery regionalization. The association between regionalization and reduced mortality was significant even after adjusting for other related factors in a multivariable Cox analysis. Notably, surgeon volume, facility volume, surgeon specialty, neoadjuvant treatment, and video-assisted thoracoscopic surgery approach did not significantly affect mortality in the adjusted model.

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