RealRisks for Breast Cancer Risk Assessment

(FHIR Trial)

Overseen By Katherine Crew, MD, MS

Age: 18+

Sex: Female

Trial Phase: Academic

Sponsor: Columbia University

Disqualifiers: History of breast cancer, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

55 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

How does the RealRisks treatment for breast cancer risk assessment differ from other treatments?

The RealRisks treatment is unique because it focuses on assessing individual breast cancer risk using various models, which helps guide prevention and screening decisions. Unlike standard treatments that directly target cancer, this approach emphasizes personalized risk evaluation to inform potential preventive measures. ^{1 2 3 4 5}

What is the purpose of this trial?

Electronic health records (EHRs) are an increasingly common source for populating risk models, but whether used to populate validated risk assessment models or to de-facto build risk prediction models, EHR data presents several challenges. The purpose of this study is to assess how the integration of patient generated health data (PGHD) and EHR data can generate more accurate risk prediction models, advance personalized cancer prevention, improve digital access to health data in an equitable manner, and advance policy goals for Patient Generated Health Data (PGHD) and EHR interoperability.

Research Team

Rita Kukafka, DrPH, MA

Principal Investigator

Columbia University

Eligibility Criteria

This trial is for women aged 35-74 who are at high risk of breast cancer, with a predicted 5-year invasive risk of ≥1.7% or lifetime risk ≥20%. Participants must speak English or Spanish and be able to give informed consent. Women with a personal history of breast cancer or those who took part in a related sub-study cannot join.

Inclusion Criteria

I am a woman aged between 35 and 74.

My risk of developing invasive breast cancer is high according to risk models.

Able to sign informed consent.

Exclusion Criteria

I was part of the initial study group for this research.

I have had breast cancer before.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Pre-Study Evaluation

Participants undergo pre-study evaluations including user evaluations to refine FHIR-enhanced RealRisks

2 weeks

Intervention

Participants self-administer FHIR-enhanced RealRisks with access to risk communication games, family history pedigree, and modules on chemoprevention and genetics testing

2 weeks

1 visit (virtual)

Follow-up

Participants are monitored for accuracy of breast cancer risk perception and patient activation

2 weeks

Treatment Details

Interventions

• RealRisks

Trial Overview The study is testing 'RealRisks', which combines patient health data from electronic records and patient-generated info to improve the accuracy of breast cancer risk assessments, aiming to personalize prevention strategies and enhance digital health data access.

Participant Groups

Group I: FHIR-Enhanced RealRisks Experimental Treatment 1 Intervention

Participants will self-administer FHIR-enhanced RealRisks with access to risk communication games, family history pedigree and modules on chemoprevention and genetics testing, if relevant to them based on their risk and family history. The investigators are interested in gaining short-term feedback on patient activation and other patient reported outcomes, which will be assessed before and within 2 weeks after using RealRisks.

Who Is Running the Clinical Trial?

Columbia University

Lead Sponsor

Trials

1,529

Recruited

2,832,000+

National Institute on Minority Health and Health Disparities (NIMHD)

Collaborator

Trials

473

Recruited

1,374,000+

Findings from Research

In a study of 35,921 women aged 40-84 who underwent mammography screening, the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models showed similar moderate predictive accuracy for breast cancer risk over 6 years, with the Gail model performing slightly better.

All models had lower predictive accuracy for aggressive breast cancer subtypes, suggesting that incorporating more genetic and nongenetic factors could enhance risk prediction for these specific types.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort. McCarthy, AM., Guan, Z., Welch, M., et al.[2021]

In a study involving 15,732 women with a median follow-up of 11.1 years, the BOADICEA and IBIS models were found to be well-calibrated for predicting breast cancer risk, while the BRCAPRO and BCRAT models significantly underpredicted risk.

The results indicate that models incorporating multigenerational family history, like BOADICEA and IBIS, are more effective in predicting breast cancer risk, suggesting potential for improved accuracy with hybrid models that combine different risk factors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

10-year performance of four models of breast cancer risk: a validation study. Terry, MB., Liao, Y., Whittemore, AS., et al.[2020]

The manual adaptation of Claus tables for breast cancer risk assessment was validated with a study of 8,824 women over an average follow-up of 9.71 years, showing accurate predictions of breast cancer incidence with an observed-to-expected ratio of 1.05.

This method provided reliable risk predictions across all age groups, including women in their forties, indicating its effectiveness for individualized risk assessment in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme. Evans, DG., Ingham, S., Dawe, S., et al.[2022]

Breast cancer risk assessment helps estimate the likelihood of developing the disease and can identify specific genetic risks, such as BRCA1 or BRCA2 mutations, which is crucial for guiding management strategies.

Recent advancements in risk assessment have led to effective prevention options like Tamoxifen chemoprevention and tailored management for mutation carriers, emphasizing the need for a combination of models and clinical judgment for accurate counseling.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Application of breast cancer risk prediction models in clinical practice. Domchek, SM., Eisen, A., Calzone, K., et al.[2016]

The new CARE model for estimating breast cancer risk in African American women provided higher risk estimates compared to the widely used Breast Cancer Risk Assessment Tool, suggesting it may be more accurate for this population.

In a comparison using eligibility data from the STAR trial, the CARE model identified 30.3% of African American women at a 5-year invasive breast cancer risk of at least 1.66%, significantly higher than the 14.5% identified by the Breast Cancer Risk Assessment Tool.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Projecting individualized absolute invasive breast cancer risk in African American women. Gail, MH., Costantino, JP., Pee, D., et al.[2022]

References

1. United States pubmed.ncbi.nlm.nih.gov

Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort. [2021]

2. England pubmed.ncbi.nlm.nih.gov

10-year performance of four models of breast cancer risk: a validation study. [2020]

3. Netherlands pubmed.ncbi.nlm.nih.gov

Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme. [2022]

4. United States pubmed.ncbi.nlm.nih.gov

Application of breast cancer risk prediction models in clinical practice. [2016]

5. United States pubmed.ncbi.nlm.nih.gov

Projecting individualized absolute invasive breast cancer risk in African American women. [2022]