The model uses sociodemographic factors to predict the risk of non-adherence to aromatase inhibitors

The new risk model uses basic sociodemographic and financial measures to predict which patients prescribed long-term aromatase inhibitor therapy for breast cancer are most at risk of stopping that treatment early (non-adherence).

The work was presented at a poster spotlight discussion session at 2023 San Antonio Breast Cancer Symposium (SABCS) on Wednesday, December 6.

Researchers analyzed data from the SWOG S1105 clinical trial, including measures of patient adherence to aromatase inhibitor (AI) treatment for hormone-sensitive breast cancer. They created a model consisting of individual sociodemographic and financial factors – as well as measures of deprivation at the area level and rural/urban location – that were statistically significantly associated with adherence.

The presence of each additional risk factor increased the risk of nonadherence by 47%. Those with more than two risk factors had a 64% greater likelihood of nonadherence to AI.

The lead author of the abstract is Dawn L. Hershman, MD, MS, who presented the work at SABCS.

“We recognize that nonadherence to endocrine therapy is multifactorial,” said Hirschman, an American Cancer Society professor of medicine and epidemiology at Columbia University Irving Medical Center. “Predicting who is at risk will help us target personalized interventions to appropriate patients.” Deputy Director of the Herbert Irving Comprehensive Cancer Center, and Co-Chair-elect of the SWOG Cancer Research Network.

About two-thirds of patients treated for breast cancer have hormone receptor-positive disease, meaning the growth of their tumors is driven by estrogen or progesterone. Women with this type of cancer are often prescribed medications that reduce hormone production, such as aromatase inhibitors (AIs).

Clinical trials have shown that taking AI daily for several years can significantly reduce the chance of breast cancer coming back. But for several reasons, including common side effects such as bone pain and hot flashes, many patients stop taking AI early.

The risk model presented at SABCS was developed using data from the SWOG S1105 clinical trial, a randomized study that enrolled more than 700 postmenopausal women to test whether text message reminders can improve adherence to AI treatment. All patients were prescribed AI treatment for breast cancer, and were evaluated in the study every three months to continue using their AI pills. Preliminary results of the experiment, led by Hirschman, were published in the journal Journal of Clinical Oncology In 2020.

To develop the predictive model, researchers analyzed a range of demographic and financial metrics collected when patients joined the S1105 trial. They found that four of these measures had statistically significant associations with increased nonadherence to AI treatment: younger age, lower education, lower direct costs, and living in urban areas. In their data, race and ethnicity were not associated with nonadherence.

“These findings provide further evidence that an individual’s socioeconomic background can contribute vital information in predicting their course of treatment,” said lead author Joseph Unger, Ph.D., an associate professor at the Fred Hutchinson Cancer Center and an expert in biostatistics and health. Service Researcher at SWOG Cancer Research Network.

“This recognition is important for early identification of patients most at risk of non-adherence to long-term AI treatment, which may allow for more effective targeting of interventions.”

The researchers concluded that such interventions, in addition to steps to relieve symptoms of side effects, should focus on structural barriers in patients most at risk.

Provided by SWOG Cancer Research Network