Dataset Information

Strategies to Identify and Recruit Women at High Risk for Breast Cancer to a Randomized Controlled Trial of Web-based Decision Support Tools.

ABSTRACT: We evaluated strategies to identify and recruit a racially/ethnically diverse cohort of women at high-risk for breast cancer to a randomized controlled trial (RCT). We enrolled 300 high-risk women and 50 healthcare providers to a RCT of standard educational materials alone or in combination with web-based decision support tools. We implemented five strategies to identify high-risk women: (i) recruitment among patients previously enrolled in a study evaluating breast cancer risk; (ii) automated breast cancer risk calculation using information extracted from the electronic health record (EHR); (iii) identification of women with atypical hyperplasia or lobular carcinoma in situ (LCIS) using International Classification of Diseases (ICD)-9/10 diagnostic codes; (iv) clinical encounters with enrolled healthcare providers; (v) recruitment flyers/online resources. Breast cancer risk was calculated using either the Gail or Breast Cancer Surveillance Consortium (BCSC) models. We identified 6,229 high-risk women and contacted 3,459 (56%), of whom 17.2% were identified from prior study cohort, 37.5% through EHR risk information, 14.8% with atypical hyperplasia/LCIS, 29.0% by clinical encounters, and 1.5% through recruitment flyers. Women from the different recruitment sources varied by age and 5-year invasive breast cancer risk. Of 300 enrolled high-risk women, 44.7% came from clinical encounters and 27.3% from prior study cohort. Comparing enrolled with not-enrolled participants, there were significant differences in mean age (57.2 vs. 59.1 years), proportion of non-Whites (41.5% vs. 54.8%), and mean 5-year breast cancer risk (3.0% vs. 2.3%). We identified and successfully recruited diverse high-risk women from multiple sources. These strategies may be implemented in future breast cancer chemoprevention trials.

Prevention relevance

We describe five strategies to identify and successfully recruit a large cohort of racially/ethnically diverse high-risk women from multiple sources to a randomized controlled trial evaluating interventions to increase chemoprevention uptake. Findings could inform recruitment efforts for future breast cancer prevention trials to increase recruitment yield of high-risk women.

SUBMITTER: McGuinness JE

PROVIDER: S-EPMC9167698 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Strategies to Identify and Recruit Women at High Risk for Breast Cancer to a Randomized Controlled Trial of Web-based Decision Support Tools.

McGuinness Julia E JE Bhatkhande Gauri G Amenta Jacquelyn J Silverman Thomas T Mata Jennie J Guzman Ashlee A He Ting T Dimond Jill J Jones Tarsha T Kukafka Rita R Crew Katherine D KD

Cancer prevention research (Philadelphia, Pa.) 20220601 6

We evaluated strategies to identify and recruit a racially/ethnically diverse cohort of women at high-risk for breast cancer to a randomized controlled trial (RCT). We enrolled 300 high-risk women and 50 healthcare providers to a RCT of standard educational materials alone or in combination with web-based decision support tools. We implemented five strategies to identify high-risk women: (i) recruitment among patients previously enrolled in a study evaluating breast cancer risk; (ii) automated b ...[more]

PMID: 35412592

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Project description:Significant underutilization of breast cancer chemoprevention remains, despite guidelines stating that physicians should recommend chemoprevention with antiestrogen therapy to high-risk women. We randomized women, ages 35 to 75 years, who met high-risk criteria for breast cancer, without a personal history of breast cancer or prior chemoprevention use, to standard educational materials alone or combined with a web-based decision aid. All healthcare providers, including primary care providers and breast specialists, were given access to a web-based decision support tool. The primary endpoint was chemoprevention uptake at 6 months. Secondary outcomes included decision antecedents (perceived breast cancer risk/worry, chemoprevention knowledge, self-efficacy) and decision quality (decision conflict, chemoprevention informed choice) based upon patient surveys administered at baseline, 1 and 6 months after randomization. Among 282 evaluable high-risk women enrolled from November 2016 to March 2020, mean age was 57 years (SD, 9.9) and mean 5-year invasive breast cancer risk was 2.98% (SD, 1.42). There was no significant difference in chemoprevention uptake at 6 months between the intervention and control groups (2.1% vs. 3.5%). Comparing the intervention and control arms at 1 month, there were significant differences among high-risk women in accurate breast cancer risk perceptions (56% vs. 39%, P = 0.017), adequate chemoprevention knowledge (49% vs. 27%, P < 0.001), mean decision conflict (34.0 vs. 47.0, P < 0.001), and informed choice (41% vs. 23%, P = 0.003). These differences were no longer significant at 6 months. Although our decision support tools did not result in a significant increase in chemoprevention uptake, we did observe improvements in decision antecedents and decision quality measures.Prevention relevanceIn this randomized controlled trial of decision support for 300 high-risk women and 50 healthcare providers, we did not observe a significant increase in chemoprevention uptake, which remained low at under 5%. However, these decision support tools may increase knowledge and informed choice about breast cancer chemoprevention.

Project description:BackgroundDuring the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs' underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree's nodes step-by-step in an interactive manner.ObjectiveThis study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts.MethodsWe developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention's guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants' accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants' ratings of the DSTs' usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants' perception of the tools. We used ANOVAs and t tests to assess statistical significance.ResultsOur survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P<.001 for all 4 t tests). ANOVA detected no significant differences regarding mental effort between the 3 study groups. Differences between the 2 intervention groups were of small effect sizes and nonsignificant for all 3 measures of the quality of decision support and most measures of participants' perception of the DSTs.ConclusionsWhen the decision space is limited, as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more with 1 type of user interface than the other.Trial registrationDeutsches Register Klinischer Studien DRKS00028136; https://tinyurl.com/4bcfausx (retrospectively registered).

Project description:BACKGROUND:Breast cancer chemoprevention can reduce breast cancer incidence in high-risk women; however, chemoprevention is underutilized in the primary care setting. We conducted a pilot study of decision support tools among high-risk women and their primary care providers (PCPs). METHODS:The intervention included a decision aid (DA) for high-risk women, RealRisks, and a provider-centered tool, Breast Cancer Risk Navigation (BNAV). Patients completed validated surveys at baseline, after RealRisks and after their PCP clinical encounter or at 6-months. Referral for high-risk consultation and chemoprevention uptake were assessed via the electronic health record. The primary endpoint was accuracy of breast cancer risk perception at 6-months. RESULTS:Among 40 evaluable high-risk women, median age was 64.5 years and median 5-year breast cancer risk was 2.19%. After exposure to RealRisks, patients demonstrated an improvement in accurate breast cancer risk perceptions (p = 0.02), an increase in chemoprevention knowledge (p < 0.01), and 24% expressed interest in taking chemoprevention. Three women had a high-risk referral, and no one initiated chemoprevention. Decisional conflict significantly increased from after exposure to RealRisks to after their clinical encounter or at 6-months (p < 0.01). Accurate breast cancer risk perceptions improved and was sustained at 6-months or after clinical encounters. We discuss the side effect profile of chemoprevention and the care pathway when RealRisks was introduced to understand why patients experienced increased decision conflict. CONCLUSION:Future interventions should carefully link the use of a DA more proximally to the clinical encounter, investigate timed measurements of decision conflict and improve risk communication, shared decision making, and chemoprevention education for PCPs. Additional work remains to better understand the impact of decision aids targeting both patients and providers. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02954900 November 4, 2016 Retrospectively registered.

Dataset Information

Strategies to Identify and Recruit Women at High Risk for Breast Cancer to a Randomized Controlled Trial of Web-based Decision Support Tools.

Prevention relevance

Publications

Strategies to Identify and Recruit Women at High Risk for Breast Cancer to a Randomized Controlled Trial of Web-based Decision Support Tools.

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