Breast CancerPhase 4NCT05222464
Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study
A Phase 4 study of Breast Cancer, run by Ottawa Hospital Research Institute at 1 site in Canada.
Trial summary
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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Breast Cancer
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 56 participants
- Where
- 1 siteCanada
- Study dates
- 25 Feb 2022to 22 Sep 2022
- Phase
- Phase 4
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Those who are unable to complete questionnaires in English
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study (REaCT-Hot Flashes Pilot)
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.
Read the detailed description
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
- Breast Cancer
- Being tested
- Standard of care treatments
- Keywords
- Breast Cancer, Vasomotor Symptoms, Machine Learning
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Standard of care treatmentsOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Standard of Care Intervention | Other | 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. Receives: Other: Standard of care treatments |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient Engagement (MyChart Accessibility and User Experience)
Patient engagement will be defined by 60% of patients approached agreeing to participate in the study.
Measured: 3 Months
Physician Engagement (MyChart Accessibility and User Experience)
Physician engagement will be defined by 60% of those completing the study log to approach patients for participation in study.
Measured: 3 Months
Patient Accrual (MyChart Accessibility and User Experience)
Patient accrual will be defined by accruing 50 participants within 3 months.
Measured: 3 Months
MyChart Utilization
MyChart utilization will be defined as 85% of participants completing both questionnaires (the Hot Flash Problem Score and the Composite Hot Flash Score) on the MyChart interface, and 50% of enrolled participants completing both questionnaires as per study protocol.
Measured: Baseline and 6 weeks
Secondary outcomes (5)
Hot Flash Severity (MyChart Feasibility)
Hot flash severity (MyChart feasibility) will be assessed by the Hot Flash Problem Score, a composite score of the perceived distress, interference, and problematic nature of vasomotor symptoms (VMS) in daily life and by the composite hot flash score (assess hot flashes on a daily basis for 7 days). The researchers will assess the feasibility of using MyChart to complete hot flash severity assessments by determining the percentage of participants who complete the tools as per protocol, including the percentage of patients who complete daily assessments over the 7 day period.
Measured: 3 Months
MyChart Feasibility in assessing effectiveness of interventions for VMS
The investigators will assess the effectiveness of an intervention by assessing change in hot flash severity scores using the Hot Flash Problem Score, and composite hot flash score from baseline to 6 weeks post intervention.
Measured: 3 months
Effectiveness of Interventions for VMS - Traditional Statistical Modeling
Analyze MyChart questionnaire response data, using traditional statistical modelling (including linear and logistic regression models) to predict change in hot flash severity outcomes in response to interventions for VMS. The severity outcomes will be based on two validated clinical tools. These tools consist of the Hot Flash Problem Score (a composite score of the perceived distress, interference, and problematic nature of VMS in daily life), and Composite Hot Flash Score (this assess hot flashes on a daily basis for 7 days).
Measured: 3 Months
Effectiveness of interventions for VMS (MyChart feasibility)
Effectiveness of interventions for VMS (MyChart feasibility) will be assessed by frequency of nocturnal awakenings, and toxicity data. Data will be analyzed using traditional statistics and machine learning techniques to create a preliminary model predicting VMS treatment response in individuals.
Measured: 3 Months
Predicting effectiveness of interventions for VMS - machine learning
Utilize machine learning models, including classification and regression trees, with comparison against standard regression models, to assess for improvements in predictive power for hot flash severity. The researchers will use model explainability techniques, such as conditional dependence plots, to study the impact of specific features on the hot flash severity outcomes.
Measured: 3 Months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Feb 2022
- Study start25 Feb 2022
- Primary completion22 Jul 2022
- Study completion22 Sep 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- More trials run by Ottawa Hospital Research InstituteThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 4
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 56 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ottawa Hospital Research Institute
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Sharon McGee, MD, Principal investigator, The Ottawa Hospital Cancer Centre
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05222464
- Sponsor's protocol ID
- REaCT-Hot Flashes Pilot
- Status last verified
- Dec 2025
- First submitted
- 15 Dec 2021
- Last updated
- 18 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 35614153Hutton 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.
PubMed 32555553Cole 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.
PubMed 34159473Cole 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.
PubMed 38763972
Study at a glance
From the registry record, last updated by the sponsor on 18 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study (REaCT-Hot Flashes Pilot)
- ClinicalTrials.gov ID
- NCT05222464
- Acronym / sponsor ID
- REaCT-Hot Flashes Pilot
- Conditions
- Breast Cancer
- Keywords
- Breast Cancer, Vasomotor Symptoms, Machine Learning
- Phase
- Phase 4
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 56 participants (actual)
- Lead sponsor
- Ottawa Hospital Research InstituteUniversity, hospital or other
- Study start
- 25 Feb 2022 (actual)
- Primary completion
- 22 Jul 2022 (actual)
- Study completion
- 22 Sep 2022 (actual)
- First posted
- 3 Feb 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Standard of Care Intervention | Other | Other: Standard of care treatmentsStandard 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- Those who are unable to complete questionnaires in English
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Standard of care treatmentsInterventions 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. | Other | Standard of Care Intervention |
Primary outcome
Patient Engagement (MyChart Accessibility and User Experience)
Time frame: 3 Months
Patient engagement will be defined by 60% of patients approached agreeing to participate in the study.
Physician Engagement (MyChart Accessibility and User Experience)
Time frame: 3 Months
Physician engagement will be defined by 60% of those completing the study log to approach patients for participation in study.
Patient Accrual (MyChart Accessibility and User Experience)
Time frame: 3 Months
Patient accrual will be defined by accruing 50 participants within 3 months.
MyChart Utilization
Time frame: Baseline and 6 weeks
MyChart utilization will be defined as 85% of participants completing both questionnaires (the Hot Flash Problem Score and the Composite Hot Flash Score) on the MyChart interface, and 50% of enrolled participants completing both questionnaires as per study protocol.
5 secondary outcomes
Hot Flash Severity (MyChart Feasibility)
Time frame: 3 Months
MyChart Feasibility in assessing effectiveness of interventions for VMS
Time frame: 3 months
Effectiveness of Interventions for VMS - Traditional Statistical Modeling
Time frame: 3 Months
Effectiveness of interventions for VMS (MyChart feasibility)
Time frame: 3 Months
Predicting effectiveness of interventions for VMS - machine learning
Time frame: 3 Months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Dec 2025.
- Background: PubMed 35614153Cole 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.
- Background: PubMed 32555553Hutton 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.
- Background: PubMed 34159473Cole 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.
- Results publication: PubMed 38763972Cole 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.
- The Rethinking Clinical Trials (REaCT) websiteLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Sharon McGee, MD principal investigator
The Ottawa Hospital Cancer Centre
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A Phase 4 study of Breast Cancer, run by Ottawa Hospital Research Institute at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Phase 4 studies take place after a treatment is approved, to learn more about its long-term benefits and risks in everyday use.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient Engagement (MyChart Accessibility and User Experience) (time frame: 3 Months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05222464, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Dec 2025.