Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study (REaCT-Hot Flashes Pilot)
试验速览
- 阶段
- 4 期
- 状态
- 已完成
- 入组人数
- 56
- 试验地点
- 1
- 主要终点
- Physician Engagement (MyChart Accessibility and User Experience)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Supportive Care
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •Those who are unable to complete questionnaires in English
结局指标
主要结局
Physician Engagement (MyChart Accessibility and User Experience)
时间窗: 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)
时间窗: 3 Months
Patient accrual will be defined by accruing 50 participants within 3 months.
MyChart Utilization
时间窗: 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.
Patient Engagement (MyChart Accessibility and User Experience)
时间窗: 3 Months
Patient engagement will be defined by 60% of patients approached agreeing to participate in the study.
次要结局
- Hot Flash Severity (MyChart Feasibility)(3 Months)
- Effectiveness of Interventions for VMS - Traditional Statistical Modeling(3 Months)
- Predicting effectiveness of interventions for VMS - machine learning(3 Months)
- Effectiveness of interventions for VMS (MyChart feasibility)(3 Months)
- MyChart Feasibility in assessing effectiveness of interventions for VMS(3 months)