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. 2018 Aug 20:11:4913-4944.
doi: 10.2147/OTT.S167422. eCollection 2018.

The effects of beta-blocker use on cancer prognosis: a meta-analysis based on 319,006 patients

Affiliations

The effects of beta-blocker use on cancer prognosis: a meta-analysis based on 319,006 patients

Zhijing Na et al. Onco Targets Ther. .

Abstract

Background: Beta-blockers are antihypertensive drugs and have shown potential in cancer prognosis. However, this benefit has not been well defined due to inconsistent results from the published studies.

Methods: To investigate the association between administration of beta-blocker and cancer prognosis, we performed a meta-analysis. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was conducted to identify all relevant studies published up to September 1, 2017. Thirty-six studies involving 319,006 patients were included. Hazard ratios were pooled using a random-effects model. Subgroup analyses were conducted by stratifying ethnicity, duration of drug use, cancer stage, sample size, beta-blocker type, chronological order of drug use, and different types of cancers.

Results: Overall, there was no evidence to suggest an association between beta-blocker use and overall survival (HR=0.94, 95% CI: 0.87-1.03), all-cause mortality (HR=0.99, 95% CI: 0.94-1.05), disease-free survival (HR=0.59, 95% CI: 0.30-1.17), progression-free survival (HR=0.90, 95% CI: 0.79-1.02), and recurrence-free survival (HR=0.99, 95% CI: 0.76-1.28), as well. In contrast, beta-blocker use was significantly associated with better cancer-specific survival (CSS) (HR=0.78, 95% CI: 0.65-0.95). Subgroup analysis generally supported main results. But there is still heterogeneity among cancer types that beta-blocker use is associated with improved survival among patients with ovarian cancer, pancreatic cancer, and melanoma.

Conclusion: The present meta-analysis generally demonstrates no association between beta-blocker use and cancer prognosis except for CSS in all population groups examined. High-quality studies should be conducted to confirm this conclusion in future.

Keywords: beta-blocker; cancer; meta-analysis; prognosis.

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Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
PRISMA flowchart of article selection for this meta-analysis. Abbreviation: PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Figure 2
Figure 2
Forest plots showing the effects of beta-blocker use on OS (A), all-cause mortality (B), CSS (C), DFS (D), PFS (E), and RFS (F). Notes: Weights are from random-effects analysis. The numbers in parentheses indicate the different included studies in the same year. Abbreviations: OS, overall survival; CSS, cancer-specific survival; DFS, disease-free survival; PFS, progression-free survival; RFS, recurrence-free survival.
Figure 3
Figure 3
Sensitivity analysis of beta-blocker use on OS (A), all-cause mortality (B), CSS (C), DFS (D), PFS (E), and RFS (F). Abbreviations: OS, overall survival; CSS, cancer-specific survival; DFS, disease-free survival; PFS, progression-free survival; RFS, recurrence-free survival.
Figure 4
Figure 4
Funnel plot of Begg’s test of beta blocker use on OS (A), all-cause mortality (B), CSS (C), DFS (D), PFS (E), and RFS (F). Abbreviations: OS, overall survival; CSS, cancer-specific survival; DFS, disease-free survival; PFS, progression-free survival; RFS, recurrence-free survival; SE, standard error.

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