Deep Clinical Trajectory Modeling to Optimize Accrual to Cancer Clinical Trials
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 20,707
- 试验地点
- 1
- 主要终点
- Percentage of Patients Enrolling in Any Dana-Farber Cancer Institute Therapeutic Clinical Trial of Anti-Cancer Systemic Therapy
研究概览
简要总结
This study aims to evaluate the effectiveness of proactive notifications to treating oncologist to optimize participant accrual to clinical trials by utilizing the MatchMiner AI platform. This study compares the standard MatchMinder AI access method to two enhanced recruitment methods.
详细描述
The goal of this medical record data analysis and health system implementation study is to evaluate the effectiveness of proactive notifications to treating oncologist to optimize participant accrual to clinical trials by utilizing the MatchMiner platform. This study compares the standard MatchMinder access method to two enhanced recruitment methods.
In the first phase, investigators will provide qualitative feedback to improve AI algorithm impact on clinical trial accrual and the delivery of information from the MatchMiner platform that is utilized by treating oncologists and investigators.
In the second phase, medical records identified by the MatchMiner platform as available or a "match" for clinical trial enrollment will be randomized into three cohorts with the randomization occurring at the participant level. In Group 1, treating oncologists can use MatchMiner in its traditional form to identify potential clinical trial candidates based on structured genomic data and cancer type. In Group 2, treating oncologists will automatically receive emails with lists of potential genomically matched clinical trials identified by MarchMiner for patients in whom our AI algorithm detects an elevated probability of changing treatment based on imaging reports; oncologists can also still use traditional MatchMiner workflows. In Group 3, treating oncologists will receive email lists of genomically matched clinical trials identified by Matchminer for patients with AI-detected elevated probability of treatment change, after additional manual review to confirm that patients had progressive diseased based on their imaging reports and did not meet one of the common exclusion criteria for most cancer trials (including uncontrolled brain metastases, multiple primary cancers, poor performance status, lack of measurable disease, already having changed treatment, and hospice enrollment).
Of note, this study was not itself considered a clinical trial during the initial NCI grant application process or on subsequent discussion with NIH staff, since the outcomes were research processes (whether patients enrolled in other therapeutic clinical trials), not health-related patient outcomes as per the NIH definition of a clinical trial. However, for publication, a medical journal determined that the study met ICMJE criteria for a clinical trial and requested that it be registered.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 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
- •≥ 18 years of age
- •adults with any type of cancer whose tumors underwent OncoPanel genomic sequencing from 2013-2022
排除标准
- •≤ 18 years of age.
研究组 & 干预措施
Group 1: MatchMiner
Treating oncologists and investigators can use the standard method of accessing the MatchMiner tool to identify potential clinical trials for eligible participants based on structured genomic criteria.
Group 2: MatchMiner Proactive Notification based on AI-detected progression
treating oncologists will automatically receive emails with lists of potential genomically matched clinical trials identified by MarchMiner for patients in whom our AI algorithm detects an elevated probability of changing treatment based on imaging reports; oncologists can also still use traditional MatchMiner workflows.
干预措施: AI-assisted MatchMiner Platform (Other)
MatchMiner AI with Proactive Notification Based on AI-detected progression + Study Team Confirmation
treating oncologists will receive email lists of genomically matched clinical trials identified by MatchMiner for patients with AI-detected elevated probability of treatment change, after additional manual review to confirm that patients had progressive diseased based on their imaging reports and did not meet one of the common exclusion criteria for most cancer trials (including uncontrolled brain metastases, multiple primary cancers, poor performance status, lack of measurable disease, already having changed treatment, and hospice enrollment)
干预措施: AI-assisted MatchMiner Platform (Other)
结局指标
主要结局
Percentage of Patients Enrolling in Any Dana-Farber Cancer Institute Therapeutic Clinical Trial of Anti-Cancer Systemic Therapy
时间窗: Up to 18 months
This measure assesses the proportion of patients in each study arm who enroll in any Dana-Farber Cancer Institute (DFCI) therapeutic clinical trial involving anti-cancer systemic therapy during the intervention period. Trial enrollment data will be pulled from the institutional OnCore database.
次要结局
- Number of Patients Having Consultations with the Center for Cancer Therapeutic Innovation (CCTI)(Up to 18 months)
- Percentage of Patients Predicted to Change Treatment Who Enroll in Any Therapeutic Clinical Trial(Up to 18 months)
- Percentage of Patients Consenting to Any Clinical Trial of an Anti-Cancer Systemic Therpay(Up to 18 months)
- Percentage of New Systemic Therapy Initiations That Are Clinical Trials of Anti-Cancer Systemic Therapies(Up to 18 months)
- Clinician Opt-Out Rate from Ongoing Email Notifications(Up to 18 months)
- Comparison of Anti-Cancer Systemic Therapy Clinical Trial Enrollment Proportions Between AI-Assisted Intervention Arms (Groups 2 and 3)(Up to 18 months)
研究者
Kenneth Kehl
Principal Investigator
Dana-Farber Cancer Institute
