Trial reportCancer prevention research (Philadelphia, Pa.)2022
Strategies to Identify and Recruit Women at High Risk for Breast Cancer to a Randomized Controlled Trial of Web-based Decision Support Tools.
Trial report in Cancer prevention research (Philadelphia, Pa.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Patient and Provider Web-Based Decision Support for Breast Cancer Chemoprevention: A Randomized Controlled Trial.Cancer prevention research (Philadelphia, Pa.) · 2022Trial
- Fragmented ownership and multilevel barriers to breast MRI screening among high-risk women: A qualitative study informing development of clinical decision support.Research square · 2026Article
- Strategies for optimizing clinical trial recruitment: perspectives among patients with breast cancer.Cancer causes & control : CCC · 2026Article
- Strategies for recruiting participants underrepresented in clinical research: A scoping review.Social science & medicine (1982) · 2025Article
- Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials.LIPIcs : Leibniz international proceedings in informatics · 2025Article
- Strategies for Identifying and Recruiting Women at High Risk for Breast Cancer for Research Outside of Clinical Settings: Observational Study.Journal of medical Internet research · 2024Observational
- Making Informed Choices On Incorporating Chemoprevention into carE (MiCHOICE, SWOG 1904): Design and methods of a cluster randomized controlled trial.Contemporary clinical trials · 2024Article
- User Comprehension and EHR Integration of theAMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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Authors and funding
11 authors.
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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.
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