Impact of coexistent preserved ratio impaired spirometry on the survival of patients with lung cancer: Analysis of data from the Korean Association for Lung Cancer Registry

肺功能正常但肺活量测定受损对肺癌患者生存率的影响:来自韩国肺癌登记协会的数据分析

阅读:1

Abstract

BACKGROUND: Preserved ratio impaired spirometry (PRISm) is a common spirometric pattern that is associated with respiratory symptoms and higher mortality rates. However, the relationship between lung cancer and PRISm remains unclear. This study investigated the clinical characteristics of lung cancer patients with PRISm and the potential role of PRISm as a prognostic factor. METHODS: We retrospectively reviewed data collected from 2014 to 2015 in the Korean Association for Lung Cancer Registry. We classified all patients into three subgroups according to lung function as follows: normal lung function; PRISm (forced expiratory volume in 1 s [FEV(1) ] < 80% predicted and FEV(1) /forced vital capacity [FVC] ≥ 0.7); and chronic obstructive pulmonary disease (COPD; FEV1/FVC < 0.7). In non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), the overall survival period was compared among the three subgroups. The prognostic factors were investigated using Cox regression analysis. RESULTS: Of the 3763 patients, 38.6%, 40.1%, and 21.3% had normal lung function, COPD, and PRISm, respectively. Patients with PRISm had poorer overall survival than those with COPD or normal lung function in NSCLC and SCLC (Mantel-Cox log-rank test, p < 0.05). In the risk-adjusted analysis, overall survival was independently associated with COPD (hazard ratio [HR] 1.209, p = 0.027) and PRISm (HR 1.628, p < 0.001) in NSCLC, but was only associated with PRISm (HR 1.629, p = 0.004) in SCLC. CONCLUSIONS: PRISm is a significant pattern of lung function in patients with lung cancer. At the time of lung cancer diagnosis, pre-existing PRISm should be considered a predictive factor of poor prognosis.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。