Published in:
01-11-2019 | NSCLC | Original Scientific Report
Impact of Timeliness of Surgical Treatment on the Outcomes of Patients with Non-metastatic Non-small Cell Lung Cancer: Findings From the PLCO Trial
Author:
Omar Abdel-Rahman
Published in:
World Journal of Surgery
|
Issue 11/2019
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Abstract
Background
This study aimed to assess the impact of timeliness of surgical resection among patients with non-metastatic non-small cell lung cancer (NSCLC) treated with upfront surgery.
Methods
Cases with confirmed non-metastatic NSCLC diagnosis treated with upfront surgery within the cohort of participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial were included in the current study. Multivariate logistic regression analysis was used to assess factors predicting time from diagnosis to surgical resection. Multivariate Cox regression analysis was used to assess factors affecting lung cancer-specific survival.
Results
A total of 1022 patients were included in the current analysis. A total of 873 patients underwent surgical resection within 30 days of diagnosis, while a total of 149 patients underwent surgical resection after 30 days from diagnosis. Through multivariate logistic regression analysis, the following factors were predictive for longer time to surgical resection: older age (odds ratio 1.077; 1.043–1.112; P < 0.001) and advanced stage at presentation (odds ratio 1.923; 1.056–3.502; P = 0.033). Through multivariate Cox regression analysis, time to surgical resection (≤30 days vs. >30 days) did not affect lung cancer-specific survival (hazard ratio 0.999; 0.739–1.350; P = 0.994). When the same multivariate analysis was repeated using time to surgical resection as a continuous variable, there was no impact on lung cancer-specific survival (hazard ratio 1.002; 0.997–1.007; P = 0.383).
Conclusions
Time to surgical resection did not affect survival outcomes of non-metastatic NSCLC patients. Current therapy timeline targets need to be reviewed in our healthcare systems in order to redirect and prioritize the existing resources.