Frontiers in Surgery (Jul 2022)

Identifying octogenarians with non-small cell lung cancer who could benefit from surgery: A population-based predictive model

  • Ce Chao,
  • Dongmei Di,
  • Min Wang,
  • Yang Liu,
  • Bin Wang,
  • Yongxiang Qian

DOI
https://doi.org/10.3389/fsurg.2022.972014
Journal volume & issue
Vol. 9

Abstract

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BackgroundAs the population ages, there will be an increasing number of octogenarian patients with non-small cell lung cancer (NSCLC). In carefully selected elderly patients, surgery can improve long-term survival. To identify candidates who would benefit from surgery, we performed this study and built a predictive model.Materials and methodsData from NSCLC patients over 80 years old were obtained from the Surveillance, Epidemiology and End Results database. A 1:1 propensity score matching was performed to balance the clinicopathological features between the surgery and non-surgery groups. Kaplan-Meier analyses and log-rank tests were used to assess the significance of surgery to outcome, and Cox proportional-hazards regression and competing risk model were conducted to determine the independent prognostic factors for these patients. A nomogram was built using multivariable logistic analyses to predict candidates for surgery based on preoperative factors.ResultsThe final study population of 31,462 patients were divided into surgery and non-surgery groups. The median cancer-specific survival time respectively was 53 vs. 13 months. The patients’ age, sex, race, Tumor, Node, Metastasis score, stage, chemotherapy use, tumor histology and nuclear grade were independent prognostic factors. Apart from race and chemotherapy, other variates were included in the predictive model to distinguish the optimal surgical octogenarian candidates with NSCLC. Internal and external validation confirmed the efficacy of this model.ConclusionSurgery improved the survival time of octogenarian NSCLC patients. A novel nomogram was built to help clinicians make the decision to perform surgery on elderly patients with NSCLC.

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