Beta-blocker use and breast cancer outcomes: a meta-analysis.

PURPOSE: Beta blockers (BBs) are commonly used cardiovascular medications, and their association with breast cancer outcomes has been examined in several previous observational studies and meta-analyses. In this study, an updated meta-analysis was undertaken to ascertain the association between BBs and both breast cancer death (BCD) and breast cancer recurrence (BCR). METHODS: Articles were sourced from various databases up until the 14th of August 2023. Effect estimates were pooled using the random effects model, and the Higgins I2 statistic was computed to ascertain heterogeneity. Subgroup analyses were conducted by the potential for immortal time bias (ITB), the exposure period (prediagnosis vs postdiagnosis), and type of BB (selective vs non-selective). Publication bias was assessed using funnel plots and Egger's regression tests. RESULTS: Twenty-four studies were included. Pooled results showed that there was no statistically significant association between BB use and both BCD (19 studies, hazard ratio = 0.90, 95% CI 0.78-1.04) and BCR (16 studies, HR = 0.87, 95% CI 0.71-1.08). After removing studies with ITB, the associations were attenuated towards the null. There was no effect modification for either outcome when stratifying by the exposure period or type of BB. There was clear evidence of publication bias for both outcomes. CONCLUSION: In this meta-analysis, we found no evidence of an association between BB use and both BCD and BCR. Removing studies with ITB attenuated the associations towards the null, but there was no effect modification by the exposure period or type of BB.

Post-diagnostic statin use and breast cancer-specific mortality: a population-based cohort study.

PURPOSE: Statins are the most widely prescribed cholesterol lowering medications and have been associated with both improved and unchanged breast cancer outcomes in previous studies. This study examines the association between the post-diagnostic use of statins and breast cancer outcomes (death and recurrence) in a large, representative sample of New Zealand (NZ) women with breast cancer. METHODS: Women diagnosed with a first primary breast cancer between 2007 and 2016 were identified from four population-based regional NZ breast cancer registries and linked to national pharmaceutical data, hospital discharges, and death records. Cox proportional hazard models were used to estimate the hazard of breast cancer-specific death (BCD) associated with any post-diagnostic statin use. RESULTS: Of the 14,976 women included in analyses, 27% used a statin after diagnosis and the median follow up time was 4.51 years. Statin use (vs non-use) was associated with a statistically significant decreased risk of BCD (adjusted hazard ratio: 0.74; 0.63-0.86). The association was attenuated when considering a subgroup of 'new' statin users (HR: 0.91; 0.69-1.19), however other analyses revealed that the protective effect of statins was more pronounced in estrogen receptor positive patients (HR: 0.77; 0.63-0.94), postmenopausal women (HR: 0.74; 0.63-0.88), and in women with advanced stage disease (HR: 0.65; 0.49-0.84). CONCLUSION: In this study, statin use was associated with a statistically significant decreased risk of breast cancer death, with subgroup analyses revealing a more protective effect in ER+ patients, postmenopausal women, and in women with advanced stage disease. Further research is warranted to determine if these associations are replicated in other clinical settings.

Development and validation of a new predictive model for breast cancer survival in New Zealand and comparison to the Nottingham prognostic index.

BACKGROUND: The only available predictive models for the outcome of breast cancer patients in New Zealand (NZ) are based on data in other countries. We aimed to develop and validate a predictive model using NZ data for this population, and compare its performance to a widely used overseas model, the Nottingham Prognostic Index (NPI). METHODS: We developed a model to predict 10-year breast cancer-specific survival, using data collected prospectively in the largest population-based regional breast cancer registry in NZ (Auckland, 9182 patients), and assessed its performance in this data set (internal validation) and in an independent NZ population-based series of 2625 patients in Waikato (external validation). The data included all women with primary invasive breast cancer diagnosed from 1 June 2000 to 30 June 2014, with follow up to death or Dec 31, 2014. We used multivariate Cox proportional hazards regression to assess predictors and to calculate predicted 10-year breast cancer mortality, and therefore survival, probability for each patient. We assessed observed survival by the Kaplan Meier method. We assessed discrimination by the C statistic, and calibration by comparing predicted and observed survival rates for patients in 10 groups ordered by predicted 10-year survival. We compared this NZ model with the Nottingham Prognostic Index (NPI) in this validation data set. RESULTS: Discrimination was good: C statistics were 0.84 for internal validity and 0.83 for an independent external validity. For calibration, for both internal and external validity the predicted 10-year survival probabilities in all groups of patients, ordered by predicted survival, were within the 95% confidence intervals (CI) of the observed Kaplan-Meier survival probabilities. The NZ model showed good discrimination even within the prognostic groups defined by the NPI. CONCLUSIONS: These results for the New Zealand model show good internal and external validity, transportability, and potential clinical value of the model, and its clear superiority over the NPI. Further research is needed to assess other potential predictors, to assess the model's performance in specific subgroups of patients, and to compare it to other models, which have been developed in other countries and have not yet been tested in NZ.