Clinical trial NCT05222464: Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study
Official title: Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study (REaCT-Hot Flashes Pilot)
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: PHASE4
Conditions: Breast Cancer
Keywords: Breast Cancer
Vasomotor Symptoms
Machine Learning
Lead sponsor: Ottawa Hospital Research Institute
Sponsor class: OTHER
First posted: 2022-02-03
Enrollment: 56
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: SUPPORTIVE_CARE

Brief 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). 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.

Eligibility criteria: 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

Primary outcomes:
measure: Patient Engagement (MyChart Accessibility and User Experience)
timeFrame: 3 Months
measure: Physician Engagement (MyChart Accessibility and User Experience)
timeFrame: 3 Months
measure: Patient Accrual (MyChart Accessibility and User Experience)
timeFrame: 3 Months
measure: MyChart Utilization
timeFrame: Baseline and 6 weeks
Secondary outcomes:
measure: Hot Flash Severity (MyChart Feasibility)
timeFrame: 3 Months
measure: MyChart Feasibility in assessing effectiveness of interventions for VMS
timeFrame: 3 months
measure: Effectiveness of Interventions for VMS - Traditional Statistical Modeling
timeFrame: 3 Months
measure: Effectiveness of interventions for VMS (MyChart feasibility)
timeFrame: 3 Months
measure: Predicting effectiveness of interventions for VMS - machine learning
timeFrame: 3 Months
Other outcomes:
Locations:
city: Ottawa
country: Canada
facility: The Ottawa Hospital Cancer Centre
state: Ontario
Study officials:
affiliation: The Ottawa Hospital Cancer Centre
name: Sharon McGee, MD
role: PRINCIPAL_INVESTIGATOR
References: