First-line PD-1/PD-L1 inhibitors plus chemotherapy versus bevacizumab plus chemotherapy for advanced non-squamous non-small cell lung cancer: A Bayesian network meta-analysis of randomized controlled trials

一线PD-1/PD-L1抑制剂联合化疗与贝伐单抗联合化疗治疗晚期非鳞状非小细胞肺癌:一项基于随机对照试验的贝叶斯网络荟萃分析

阅读:1

Abstract

Chemotherapy in combination with immune checkpoint inhibitor (ICI) or bevacizumab has demonstrated a superior effect for non-squamous non-small cell lung cancer (NS-NSCLC). There are still few randomized controlled trials (RCTs) investigating the differences between ICI plus chemotherapy (ICI-chemotherapy) and bevacizumab plus chemotherapy (Bev-chemotherapy) in first-line treatment of NS-NSCLC. We identified RCTs in databases and conference abstracts presented at international conferences by Sep 1, 2021. Bayesian network meta-analysis was performed using randomized effect consistency model to estimate hazard ratio (HR) and odds ratio (OR). The outcomes included overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and grade ≥ 3 treatment-related adverse events (TRAEs). Fifteen RCTs (17 articles) of 6561 advanced NS-NSCLC patients receiving ICI-chemotherapy, Bev-chemotherapy, or chemotherapy at first-line were eligible for analysis. NMA results showed that first-line ICI-chemotherapy prolonged OS (HR 0.79, 0.66-0.94) in patients with advanced NS-NSCLC compared with Bev-chemotherapy, while no differences were in PFS, ORR, and grade ≥ 3 TRAEs (p > 0.05). Ranking plots suggested that ICI-chemotherapy had the most probability to offer the best OS (probability 0.993), PFS (probability 0.658), and ORR (probability 0.565), and Bev-chemotherapy had the most risks of grade ≥ 3 TRAEs (probability 0.833). Therefore, our findings showed that first-line ICI-chemotherapy was associated with better OS than Bev-chemotherapy in patients with advanced NS-NSCLC, and more clinical trials are warranted to confirm these results.

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。