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Completed

NCT Number: NCT05222464

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

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Phase 4

Primary location

The Ottawa Hospital Cancer Centre

Ottawa, Ontario, Canada

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Standard of care treatments

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.

Primary outcomes

  1. 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.

  2. 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.

  3. Patient Accrual (MyChart Accessibility and User Experience)

    Time frame: 3 Months

    Patient accrual will be defined by accruing 50 participants within 3 months.

  4. 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.

Secondary outcomes

  1. Hot Flash Severity (MyChart Feasibility)

    Time frame: 3 Months

    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.

  2. MyChart Feasibility in assessing effectiveness of interventions for VMS

    Time frame: 3 months

    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.

  3. Effectiveness of Interventions for VMS - Traditional Statistical Modeling

    Time frame: 3 Months

    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).

  4. Effectiveness of interventions for VMS (MyChart feasibility)

    Time frame: 3 Months

    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.

  5. Predicting effectiveness of interventions for VMS - machine learning

    Time frame: 3 Months

    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.

Sponsors and collaborators

Lead sponsor

Ottawa Hospital Research Institute

Other

Registry information

Official study title

Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study (REaCT-Hot Flashes Pilot)

Important dates

Study start
2022
Primary completion
2022
Study completion
2022
First posted
Feb 3, 2022
Registry last updated
Dec 18, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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