New York—The best way to determine how adherent breast cancer patients will be to aromatase inhibitors might be to just ask them.
That’s according to a presentation at the San Antonio Breast Cancer Symposium. New York-Presbyterian and Columbia University–led researchers said looking at responses to the health questions patients commonly answer in cancer clinical trials can offer important insights on whether they will stick with the therapy.
The surveys are used in clinical trials to provide a patient perspective on a treatment and its effect on quality of life. Questions might be: Was the drug easy to take? What are your symptoms? Are you in pain? Are you able to work? Are you getting family support?
The study suggests the answers also can predict who is likely to stop taking aromatase inhibitors (AIs). These drugs are used by tens of thousands of postmenopausal women each year to treat their hormone-sensitive breast cancers, according to new research led by SWOG Cancer Research Network Vice Chair Dawn Hershman, MD.
According to the results, those more likely to stop taking their medication:
• Reported a poorer quality of life than their peers on the trial
• Had higher levels of both pain and endocrine symptoms
• Had negative beliefs about whether medication works
• Indicated uncertainty whether they need it to keep their cancer from returning
“We found a clear association between women who started the trial who were struggling with pain, poor quality of life, and endocrine symptoms—as well as beliefs that the medicine isn’t helping—and those not taking the medication 36 months later,” said Dr. Hershman, director of the Breast Cancer Program at New York-Presbyterian and Columbia University Irving Medical Center’s Herbert Irving Comprehensive Cancer Center. “In this way, patient-reported outcomes can act like biomarkers. They can predict an outcome—and be used to personalize treatments.”
The study points out that nonadherence to AIs for breast cancer is common and increases risk of recurrence. Yet, the authors add, few prospective studies have systematically evaluated reasons for adherence failure.
Researchers analyzed baseline characteristics to evaluate factors associated with nonadherence prospectively over 3 years. Participants were patients enrolled in a SWOG study S1105 that randomized women to receive text-message reminders or usual care, with no effect of the intervention observed on the rates of adherence failure. Those patients were required to have been on an AI for 30 or more days at enrollment and were assessed for nonadherence of AIs every 3 months for 36 months, with nonadherence defined as urine AI metabolite assay results satisfying any of the following: <10 [units], undetectable, specimen submitted outside of the ± 21 day follow-up appointment window, or no submitted specimen.
At baseline, all patients were asked about insurance, pill number dispensed (30 vs. 90) and medication cost, and they also completed patient-reported outcomes (PROs) focused on pain and endocrine symptoms (BPI [Brief Pain Inventory]), FACT-ES (Endocrine Symptoms), quality of life (FACT-G), as well as their beliefs about medications (TSQM [Treatment Satisfaction Questionnaire for Medicine] and BMQ [Brief Medication Questionnaire]).
The more recent study examined the association of baseline factors, looking at both adherence failure at 36 months and time-to-adherence failure (TTAF) in the 724 patients who were registered from 40 institutions between May 2012 and September 2013. Median age was 60.9 years, and 64.5% were on AI less than 12 months prior to registration.
Results indicate that observed adherence at 36 months was 55.4% (41.2% in-window, 14.2% out-of-window), with neither age, race, nor ethnicity associated with long-term adherence.
Of the 29 baseline factors analyzed, researchers determined that 12 were associated with nonadherence at 36 months, including symptoms ([FACT-ES total: OR, 1.64; 95% CI,1.2-2.22; P = .002] [BPI Interference: OR, 1.68; 95% CI,1.23-2.28; P = .001] [FACT G: OR,1.70; 95% CI, 1.25-2.31; P <.001]); satisfaction with medications (TSQM global: OR, 1.55; 95% CI,1.1-2.17; P = .01) and beliefs about medication (BMQ: OR, 1.51; 95% CI,1.11- 2.05 P = .009).
At the same time, nine of 29 baseline factors were associated with TTAF, including lack of private insurance (HR, 1.20, 95% CI, 1.01-1.41; P = .04) and 30 day versus 90-day pill supply (HR, 1.2; 95% CI, 1.01-1.42; P = .04).
“Baseline endocrine symptoms, pain, medication satisfaction, beliefs and concerns about side effects are strongly associated with long-term AI adherence,” the authors conclude. “Targeted behavioral and symptom management interventions to improve adherence should be focused on women at the highest risk of adherence failure.”
“This means we can look at baseline PRO data and identify—early on—which patients are at risk of non-adherence,” Dr. Hershman explained. “Consistently taking a medication over time isn’t as simple as being given a reminder. There are a host of factors that affect why patients may stop. We can use PROs data to tailor interventions so they’re more likely to continue taking these potentially life-saving medications. That’s good to know.”
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That’s according to a presentation at the San Antonio Breast Cancer Symposium. New York-Presbyterian and Columbia University–led researchers said looking at responses to the health questions patients commonly answer in cancer clinical trials can offer important insights on whether they will stick with the therapy.
The surveys are used in clinical trials to provide a patient perspective on a treatment and its effect on quality of life. Questions might be: Was the drug easy to take? What are your symptoms? Are you in pain? Are you able to work? Are you getting family support?
The study suggests the answers also can predict who is likely to stop taking aromatase inhibitors (AIs). These drugs are used by tens of thousands of postmenopausal women each year to treat their hormone-sensitive breast cancers, according to new research led by SWOG Cancer Research Network Vice Chair Dawn Hershman, MD.
According to the results, those more likely to stop taking their medication:
• Reported a poorer quality of life than their peers on the trial
• Had higher levels of both pain and endocrine symptoms
• Had negative beliefs about whether medication works
• Indicated uncertainty whether they need it to keep their cancer from returning
“We found a clear association between women who started the trial who were struggling with pain, poor quality of life, and endocrine symptoms—as well as beliefs that the medicine isn’t helping—and those not taking the medication 36 months later,” said Dr. Hershman, director of the Breast Cancer Program at New York-Presbyterian and Columbia University Irving Medical Center’s Herbert Irving Comprehensive Cancer Center. “In this way, patient-reported outcomes can act like biomarkers. They can predict an outcome—and be used to personalize treatments.”
The study points out that nonadherence to AIs for breast cancer is common and increases risk of recurrence. Yet, the authors add, few prospective studies have systematically evaluated reasons for adherence failure.
Researchers analyzed baseline characteristics to evaluate factors associated with nonadherence prospectively over 3 years. Participants were patients enrolled in a SWOG study S1105 that randomized women to receive text-message reminders or usual care, with no effect of the intervention observed on the rates of adherence failure. Those patients were required to have been on an AI for 30 or more days at enrollment and were assessed for nonadherence of AIs every 3 months for 36 months, with nonadherence defined as urine AI metabolite assay results satisfying any of the following: <10 [units], undetectable, specimen submitted outside of the ± 21 day follow-up appointment window, or no submitted specimen.
At baseline, all patients were asked about insurance, pill number dispensed (30 vs. 90) and medication cost, and they also completed patient-reported outcomes (PROs) focused on pain and endocrine symptoms (BPI [Brief Pain Inventory]), FACT-ES (Endocrine Symptoms), quality of life (FACT-G), as well as their beliefs about medications (TSQM [Treatment Satisfaction Questionnaire for Medicine] and BMQ [Brief Medication Questionnaire]).
The more recent study examined the association of baseline factors, looking at both adherence failure at 36 months and time-to-adherence failure (TTAF) in the 724 patients who were registered from 40 institutions between May 2012 and September 2013. Median age was 60.9 years, and 64.5% were on AI less than 12 months prior to registration.
Results indicate that observed adherence at 36 months was 55.4% (41.2% in-window, 14.2% out-of-window), with neither age, race, nor ethnicity associated with long-term adherence.
Of the 29 baseline factors analyzed, researchers determined that 12 were associated with nonadherence at 36 months, including symptoms ([FACT-ES total: OR, 1.64; 95% CI,1.2-2.22; P = .002] [BPI Interference: OR, 1.68; 95% CI,1.23-2.28; P = .001] [FACT G: OR,1.70; 95% CI, 1.25-2.31; P <.001]); satisfaction with medications (TSQM global: OR, 1.55; 95% CI,1.1-2.17; P = .01) and beliefs about medication (BMQ: OR, 1.51; 95% CI,1.11- 2.05 P = .009).
At the same time, nine of 29 baseline factors were associated with TTAF, including lack of private insurance (HR, 1.20, 95% CI, 1.01-1.41; P = .04) and 30 day versus 90-day pill supply (HR, 1.2; 95% CI, 1.01-1.42; P = .04).
“Baseline endocrine symptoms, pain, medication satisfaction, beliefs and concerns about side effects are strongly associated with long-term AI adherence,” the authors conclude. “Targeted behavioral and symptom management interventions to improve adherence should be focused on women at the highest risk of adherence failure.”
“This means we can look at baseline PRO data and identify—early on—which patients are at risk of non-adherence,” Dr. Hershman explained. “Consistently taking a medication over time isn’t as simple as being given a reminder. There are a host of factors that affect why patients may stop. We can use PROs data to tailor interventions so they’re more likely to continue taking these potentially life-saving medications. That’s good to know.”
« Click here to return to Weekly News Update.
