Competing mortality risks: predicted cardiovascular disease risk versus predicted risk of breast cancer mortality in patients receiving adjuvant chemotherapy in a single Irish center.

Background: Due to advances in care, most women diagnosed with breast cancer do not die from the disease itself. Instead, cardiovascular disease (CVD) remains the most frequent cause of death. Many breast cancer patients are older and have established CVD risk factors. They are at further risk due to exposure to anthracyclines, HER2 targeted agents, endocrine therapy and radiotherapy. In this study, we compared the 10-year predicted risk of breast cancer mortality versus that of cardiovascular (CV) morbidity/mortality in breast cancer patients receiving adjuvant chemotherapy using online predictive risk calculators. Furthermore, we evaluated the predicted outcome of CV risk factor optimisation on their overall CV risk.

Methods: This was a cross sectional study. All patients with resected Stage I-III breast cancer who received adjuvant chemotherapy at our centre from September 2015 to November 2016 were identified. Data recorded included demographics, tumor characteristics, treatments and CV risk factors. To calculate predicted 10-year risk of CVD and impact of lifestyle changes, we used the JBS3 (Joint British Society) online risk calculator. To calculate the predicted 10-year risk of breast cancer mortality, we used the PREDICT calculator. Biostatistical methods included Wilcoxon signed rank test for predicted CVD risk pre and post cardiovascular risk optimization.

Results: We identified 102 patients. Of this cohort, 76 patients were ≥ 50 years & 26 were < 50 years of age. The group had significant baseline cardiovascular risk factors: increased BMI (68 %, n = 70), ex-smoking (34 %, n = 35), current smoking (13 %, n = 13), hypertension (47 %, n = 47) and dyslipidemia (57 %). Of the total group, 48 % had a high (> 20 %) and 37 % had a moderate (10-20 %) 10-year predicted breast cancer mortality risk. Regarding 10-year predicted risk of CVD, 11 % and 22 % fell into the high (> 20 %) and moderate (10-20 %) risk categories, respectively. Assuming CV risk factor optimisation, there was a predicted improvement in median 10-year CV risk from 26.5 to 9.9 % (p = .005) in the high CVD risk group and from 14.0 to 6.6 % (p < .001) in the moderate CVD risk group.

Conclusions: Benefits predicted with a CVD risk intervention model indicates that this should be incorporated into routine breast oncology care.