Background: To investigate the association between post-diagnostic beta-blocker usage and risk of cancer-specific mortality in a large population-based cohort of female breast cancer patients.
Methods: A nested case-control study was conducted within a cohort of breast cancer patients identified from cancer registries in England(using the National Cancer Data repository) and diagnosed between 1998 and 2007. Patients who had a breast cancer-specific death(ascertained from Office of National Statistics death registration data) were each matched to four alive controls by year and age at diagnosis. Prescription data for these patients were available through the Clinical Practice Research Datalink. Conditional logistic regression models were used to investigate the association between breast cancer-specific death and beta-blocker usage.
Results: Post-diagnostic use of beta-blockers was identified in 18.9% of 1435 breast cancer-specific deaths and 19.4% of their 5697 matched controls,indicating little evidence of association between beta-blocker use and breast cancer-specific mortality [odds ratio (OR) = 0.97,95% confidence interval (CI) 0.83, 1.13]. There was also little evidence of an association when analyses were restricted to cardio non-selective beta-blockers (OR = 0.90, 95% CI 0.69, 1.17). Similar results were observed in analyses of drug dosage frequency and duration, and beta-blocker type.
Conclusions: In this large UK population-based cohort of breast cancer patients,there was little evidence of an association between post-diagnostic beta-blocker usage and breast cancer progression. Further studies which include information on tumour receptor status are warranted to determine whether response to beta-blockers varies by tumour subtypes.
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http://dx.doi.org/10.1093/ije/dyt196 | DOI Listing |
Sci Rep
December 2024
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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