Independent risk factors for CAC include older age, lower hemoglobin levels, hypertension, diabetes
TUESDAY, Sept. 17, 2024 (HealthDay News) — For postoperative patients with breast cancer, the duration of aromatase inhibitor (AI) treatment is not associated with the risk for coronary artery calcium (CAC), according to a study published online Sept. 17 in the Canadian Journal of Cardiology.
Yu Hiasa, M.D., from the Ehime University Graduate School of Medicine in Toon, Japan, and colleagues examined the potential association between duration of AI treatment and severity of CAC among outpatients who initiated adjuvant endocrine therapy with AIs for breast cancer.
The researchers found that 44.8 percent of the 357 patients exhibited CAC. The groups had no significant difference in AI treatment duration. Compared with those without CAC, patients with CAC were older (74.39 versus 63.06 years), had lower hemoglobin levels (12.60 versus 13.20 g/dL), and had a reduced estimated glomerular filtration rate (62.80 versus 72.00 mL/min/1.73 m2). Patients with CAC had significantly higher prevalence rates of hypertension, diabetes mellitus, and dyslipidemia. There was no correlation observed between AI treatment duration and CAC score. Older age, lower hemoglobin levels, and presence of hypertension and diabetes were independent risk factors for CAC in postoperative patients with breast cancer.
“Although there is an ongoing discussion on the optimal duration of aromatase inhibitor therapy (five years or 10 years), our data suggest that longer aromatase inhibitor use (as often used to prevent or suppress late recurrences or spread of breast cancer) is safe, at least in regard to coronary artery calcification,” Hiasa said in a statement.
Addition of CAC to PREVENT-ASCVD Equations May Improve Predictive Utility in Certain Patients
Chemo-Free Combo of Four Targeted Therapies Shows Promise in Metastatic Breast Cancer
AMH Predicts Benefit of Chemo + Endocrine Therapy in Under 55s With Breast Cancer
Warning Symptoms, Clinical History Can Predict Sudden Cardiac Arrest
2011 to 2021 Saw Heart Failure-Related Hospitalization Rise in CKD
Machine Learning Model IDs Heart Failure With Reduced Ejection Fraction Using Routine Lab Indicators
Emergency Diagnosis Linked to Worse Outcomes for Range of Conditions
Small Change in Hours Benefits Emergency Physicians Working Night Shifts