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

Author:

McGuinness Julia E.12ORCID,Bhatkhande Gauri3ORCID,Amenta Jacquelyn12ORCID,Silverman Thomas4,Mata Jennie12,Guzman Ashlee12,He Ting5,Dimond Jill6,Jones Tarsha7,Kukafka Rita248,Crew Katherine D.123ORCID

Affiliation:

1. 1Division of Hematology and Oncology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

2. 2Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York.

3. 3Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.

4. 4Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

5. 5Department of Biomedical Informatics and Data Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.

6. 6Sassafras Tech Collective, Ann Arbor, Michigan.

7. 7Christine E Lynn College of Nursing, Florida Atlantic University, Boca Raton, Florida.

8. 8Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, New York.

Abstract

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.

Funder

NIH NCI

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology

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