- ICH GCP
- 미국 임상 시험 레지스트리
- 임상시험 NCT05222464
Utilizing MyChart to Assess the Effectiveness of Interventions for Vasomotor Symptoms: A Feasibility Study
2022년 11월 5일 업데이트: Ottawa Hospital Research Institute
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.
연구 개요
상세 설명
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.
연구 유형
중재적
등록 (실제)
56
단계
- 4단계
연락처 및 위치
이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.
연구 장소
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Ontario
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Ottawa, Ontario, 캐나다
- The Ottawa Hospital Cancer Centre
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참여기준
연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.
자격 기준
공부할 수 있는 나이
18년 이상 (성인, 고령자)
건강한 자원 봉사자를 받아들입니다
아니
연구 대상 성별
모두
설명
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
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 지지 요법
- 할당: 해당 없음
- 중재 모델: 단일 그룹 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
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다른: 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.
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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.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Patient Engagement (MyChart Accessibility and User Experience)
기간: 3 Months
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Patient engagement will be defined by 60% of patients approached agreeing to participate in the study.
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3 Months
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Physician Engagement (MyChart Accessibility and User Experience)
기간: 3 Months
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Physician engagement will be defined by 60% of those completing the study log to approach patients for participation in study.
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3 Months
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Patient Accrual (MyChart Accessibility and User Experience)
기간: 3 Months
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Patient accrual will be defined by accruing 50 participants within 3 months.
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3 Months
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MyChart Utilization
기간: Baseline and 6 weeks
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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.
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Baseline and 6 weeks
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Hot Flash Severity (MyChart Feasibility)
기간: 3 Months
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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.
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3 Months
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MyChart Feasibility in assessing effectiveness of interventions for VMS
기간: 3 months
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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.
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3 months
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Effectiveness of Interventions for VMS - Traditional Statistical Modeling
기간: 3 Months
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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).
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3 Months
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Effectiveness of interventions for VMS (MyChart feasibility)
기간: 3 Months
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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.
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3 Months
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Predicting effectiveness of interventions for VMS - machine learning
기간: 3 Months
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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.
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3 Months
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공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
수사관
- 수석 연구원: Sharon McGee, MD, The Ottawa Hospital Cancer Centre
연구 기록 날짜
이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.
연구 주요 날짜
연구 시작 (실제)
2022년 2월 25일
기본 완료 (실제)
2022년 7월 22일
연구 완료 (실제)
2022년 9월 22일
연구 등록 날짜
최초 제출
2021년 12월 15일
QC 기준을 충족하는 최초 제출
2022년 1월 24일
처음 게시됨 (실제)
2022년 2월 3일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
2022년 11월 8일
QC 기준을 충족하는 마지막 업데이트 제출
2022년 11월 5일
마지막으로 확인됨
2022년 11월 1일
추가 정보
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .
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