Breast cancer prevention in older women: an algorithm to choose an optimal preventive agent

Breast cancer is a significant cause of morbidity and mortality in older women. The current study presents new, comprehensive guidelines for providing chemoprevention to older women.

The objective of this study was to develop and pilot test a chemopreventive choice algorithm to assess its feasibility for older women at high risk of breast cancer.

The study observed outcomes of 23 older adult females being treated with one of the four different chemopreventive agents. A novel algorithm protocol was utilized for individualized chemopreventive selection.

The study was conducted in a high-risk outpatient clinic for older women.

Older outpatient females at high risk (N=23) were offered chemopreventive options based on individual criteria.

Literature review for breast cancer chemopreventive agents informed our development of a logic-based algorithm to guide treatment protocol and chemopreventive choice optimization. Selective estrogen receptive modulators (SERMs) were avoided in women with endometrial cancer risk (ie, pre-hysterectomy individuals), but used in women with low thromboembolic event (TE) risk. Raloxifene was used with osteoporotic women. Aromatase inhibitors (AIs) were used in women with high TE risk. Women without TE risks are advised to take SERMs. When bone density decreased due to AI use, women were switched to raloxifene.

Of 23 participants of age ranging from 59 to 80 years (mean=72.6), two women developed estrogen receptor-positive breast cancer. Two participants, one who declined chemoprevention and one treated with an AI, developed breast cancer. All initial chemopreventive agents were selected according to the algorithm. Although minor adverse events occurred, each was managed by discontinuation or replacement of the chemopreventive agent. Discontinuation was most commonly due to side effect concerns or cost rather than experienced side effects.

Outcomes of the initial utilization of the chemopreventive agent choice algorithm support the viability of the protocol, but further evaluation with a larger and more diverse sample is required.