医学
比例危险模型
内科学
危险系数
多元分析
肺癌
观察研究
肿瘤科
子群分析
生存分析
阶段(地层学)
癌症
病历
队列
回顾性队列研究
置信区间
生物
古生物学
作者
Xin Li,Xiaofei Li,Xiangning Fu,Lunxu Liu,Yang Liu,Heng Zhao,Yin Li,Jian Hu,Lin Xu,Deruo Liu,Haiying Yang,Qian Zhang
出处
期刊:Ejso
[Elsevier BV]
日期:2019-12-18
卷期号:46 (10): 1874-1881
被引量:16
标识
DOI:10.1016/j.ejso.2019.12.015
摘要
Introduction N2 non-small cell lung cancer (NSCLC) without N1 involvement, also known as skip metastases (pN0N2), has been suggested as a subgroup of heterogeneous N2 disease with better survival. This real-world observational study aimed to elucidate the prognostic impacts of skip N2 metastases using a large number of pathologic N2 NSCLC from 10 participating centers in China. Materials and methods Medical records of pN2 NSCLC patients after surgical resection at 10 thoracic surgery centers between January 2014 and September 2017 were retrospectively reviewed based on the LinkDoc database. Clinical data on patient demographics, tumor characteristics, treatments and clinical outcomes were collected. Overall survival of patients with and without skip metastases was evaluated and compared by Kaplan-Meier method and Log-rank test. Cox proportional hazard model was established to identify potential prognostic predictors. Subgroup analysis was carried out to further explore the prognostic significance of skip metastases. Results Among 2653 surgically resected N2 patients, 881 (33.2%) had skip metastases. Patients with skip N2 had a significant better overall survival (P = 0.0019). Multivariate COX regression analysis showed borderline significance of skip metastases (HR = 0.81, 95%CI: 0.645–1.017, P = 0.0698) after adjustment for other covariates. Other independent prognostic predictors included smoking history, tumor location, stage and N2 station involved (P < 0.05). Subgroup analysis demonstrated significant survival benefits of skip N2 in most subpopulations. Conclusions This study suggested a prognostic benefit of skip N2 metastases in real world practice. Further subdivision of N2 disease is warranted for better patient management and prognostic prediction (NCT 03429192).
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