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Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study

NCT ID: NCT05222464

Last Updated: 2025-12-18

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

PHASE4

Total Enrollment

56 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-02-25

Study Completion Date

2022-09-22

Brief Summary

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Vasomotor symptoms (VMS) are a common consequence of systemic therapies for breast cancer. Breast cancer treatments can cause VMS in approximately 30% of postmenopausal women and 95% of premenopausal women with early stage breast cancer (EBC). There are many non-estrogen-based interventions available to manage VMS, including; lifestyle modifications, complementary and alternative medicine (CAM) therapies. However, a recent systematic review and meta-analysis of pharmacological and CAM interventions conducted by our team, found no single optimal treatment for VMS management in breast cancer patients. Given the complex patient, cancer and treatment variables influencing the experience of VMS, the numerous potentially effective VMS interventions available and the varying expectations for an effective intervention, the investigators believe Machine Learning (ML) is ideally suited to the analysis of this common and bothersome treatment related toxicity. The EPIC electronic medical record, and MyChart application has provided both clinicians and patients with increased tools for the documentation of health related outcomes. The investigators believe that the MyChart platform, and ML techniques can be utilized to collect, and analyze outcome data for breast cancer patients experiencing VMS.

Detailed Description

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Vasomotor symptoms (VMS) are a common consequence of systemic therapies for breast cancer. Breast cancer treatments can cause VMS in approximately 30% of postmenopausal women and 95% of premenopausal women with early stage breast cancer (EBC). In addition to their negative impact on quality of life, unmanaged VMS are the most common reason for discontinuation of potentially curative treatment in 25-60% of EBC patients. Estrogen replacement is a common treatment for VMS in the general population, however, it is contraindicated in breast cancer patients. There are many non-estrogen-based interventions available to manage VMS, including; lifestyle modifications, complementary and alternative medicine (CAM) therapies. However, a recent systematic review and meta-analysis of pharmacological and CAM interventions conducted by our team, found no single optimal treatment for VMS management in breast cancer patients. The investigators recently conducted a survey in 373 patients with EBC which found that while the majority of patients were interested in receiving an intervention to mitigate their symptoms, only 18% received a treatment for this problem. In addition, more than one third of patients experiencing VMS report that they are not routinely asked about their symptoms in routine follow up. Given the complex patient, cancer and treatment variables influencing the experience of VMS, the numerous potentially effective VMS interventions available and the varying expectations for an effective intervention, the investigators believe Machine Learning (ML) is ideally suited to the analysis of this common and bothersome treatment related toxicity. Prior breast cancer studies have successfully applied to ML models to examine risk of developing breast cancer, as well as breast cancer prognosis. The EPIC electronic medical record, and MyChart application has provided both clinicians and patients with increased tools for the documentation of health related outcomes. The investigators believe that the MyChart platform, and ML techniques can be utilized to collect, and analyze outcome data for breast cancer patients experiencing VMS.

Conditions

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Breast Cancer

Keywords

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Breast Cancer Vasomotor Symptoms Machine Learning

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

SUPPORTIVE_CARE

Blinding Strategy

NONE

Study Groups

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Standard of Care Intervention

Standard of care intervention - The intervention will consist of 4 classes of standard of care treatments, namely, lifestyle modifications, complementary and alternative medicine (CAM) therapies, prescription medications, or adjustment of anti-cancer therapy.

Group Type OTHER

Standard of care treatments

Intervention Type OTHER

Interventions will consist of 4 classes of standard of care treatments, namely, lifestyle modifications, complementary and alternative medicine (CAM) therapies, prescription medications, or adjustment of anti-cancer therapy.

Interventions

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Standard of care treatments

Interventions will consist of 4 classes of standard of care treatments, namely, lifestyle modifications, complementary and alternative medicine (CAM) therapies, prescription medications, or adjustment of anti-cancer therapy.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Patients over the age of 18 who have histologically confirmed breast cancer, of any stage
* Patients experiencing vasomotor symptoms
* While the study is intended to evaluate the feasibility of the MyChart platform, patients without a MyChart account, who are interested in participating in the study, will have access to a paper or electronic email version. As participation in the MyChart program has benefits outside of this intended study, all patients without a MyChart account will be encouraged to sign up for the service

Exclusion Criteria

* Those who are unable to complete questionnaires in English
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Ottawa Hospital Research Institute

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Sharon McGee, MD

Role: PRINCIPAL_INVESTIGATOR

The Ottawa Hospital Cancer Centre

Locations

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The Ottawa Hospital Cancer Centre

Ottawa, Ontario, Canada

Site Status

Countries

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Canada

References

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Cole KM, Clemons M, McGee S, Alzahrani M, Larocque G, MacDonald F, Liu M, Pond GR, Mosquera L, Vandermeer L, Hutton B, Piper A, Fernandes R, Emam KE. Using machine learning to predict individual patient toxicities from cancer treatments. Support Care Cancer. 2022 Sep;30(9):7397-7406. doi: 10.1007/s00520-022-07156-6. Epub 2022 May 25.

Reference Type BACKGROUND
PMID: 35614153 (View on PubMed)

Hutton B, Hersi M, Cheng W, Pratt M, Barbeau P, Mazzarello S, Ahmadzai N, Skidmore B, Morgan SC, Bordeleau L, Ginex PK, Sadeghirad B, Morgan RL, Cole KM, Clemons M. Comparing Interventions for Management of Hot Flashes in Patients With Breast and Prostate Cancer: A Systematic Review With Meta-Analyses. Oncol Nurs Forum. 2020 Jul 1;47(4):E86-E106. doi: 10.1188/20.ONF.E86-E106.

Reference Type BACKGROUND
PMID: 32555553 (View on PubMed)

Cole KM, Clemons M, Alzahrani M, Larocque G, MacDonald F, Vandermeer L, Hutton B, Piper A, Pond G, McGee S. Developing patient-centred strategies to optimize the management of vasomotor symptoms in breast cancer patients: a survey of health care providers. Breast Cancer Res Treat. 2021 Jul;188(2):343-350. doi: 10.1007/s10549-021-06186-8. Epub 2021 Jun 22.

Reference Type BACKGROUND
PMID: 34159473 (View on PubMed)

Cole KM, McGee S, Clemons M, Liu M, MacDonald F, Vandermeer L, Ng TL, Pond G, Emam KE. Development and application of a weighted change score to evaluate interventions for vasomotor symptoms in patients with breast cancer using regression trees: a cohort study. Breast Cancer Res Treat. 2024 Sep;207(2):313-321. doi: 10.1007/s10549-024-07360-4. Epub 2024 May 19.

Reference Type RESULT
PMID: 38763972 (View on PubMed)

Related Links

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https://react.ohri.ca/

The Rethinking Clinical Trials (REaCT) website

Other Identifiers

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REaCT-Hot Flashes Pilot

Identifier Type: -

Identifier Source: org_study_id