Personalized Risk-AdaptIve Surveillance strategies in cancEr -- PRAISE

癌症的个性化风险适应性监测策略——PRAISE

• 批准号: 9767745
• 负责人: Aasthaa Bansal
• 金额: $ 44.91万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9767745
• 关键词: Address; Biological Markers; Cancer Survivor; Cancer Survivorship; Caring; Chronic Myeloid Leukemia; Clinic Visits; Clinical; Clinical Management; Cohort Studies; Collection; Colorectal Cancer; Complex; Coupling; Data; Decision Making; Decision Theory; Disease; Disease Progression; Disease remission; Economics; Electronic Health Record; Face; Flowers; Frequencies; Future; Goals; Guidelines; Health Care Costs; Heterogeneity; Individual; Information Theory; Intuition; Life Expectancy; Long-Term Survivors; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Malignant neoplasm of prostate; Measurement; Methods; Modeling; Monitor; Monitoring for Recurrence; Morbidity - disease rate; Outcome; Patient-Focused Outcomes; Patients; Performance; Policies; Recurrence; Research; Risk; Schedule; Statistical Methods; Technology; Testing; Time; Uncertainty; Update; Work; base; biomarker development; cancer biomarkers; cancer recurrence; clinical decision-making; colorectal cancer screening; comparative effectiveness; cost; cost outcomes; decision making algorithm; follow-up; high risk; holistic approach; improved; individual patient; innovation; men; mortality; novel strategies; personalized decision; precision medicine; predictive modeling; simulation; surveillance strategy; unnecessary treatment

Cancer biomarkers are at the leading edge of Precision Medicine, and offer both tremendous opportunities and challenges. In particular, biomarker development to detect recurrence in cancer survivors is blossoming, as surveillance testing with serial biomarker measurements offers an opportunity to detect recurrence at a point when treatment may be curative. However, frequent biomarker testing may cause more harm than benefit for low-risk individuals, due to the costs and complications of unnecessary testing and increased likelihood of false positives leading to unnecessary treatment. Unfortunately, tailoring surveillance to individual patients is a complex decision-making problem that requires understanding the heterogeneity in biomarker measurements across patients and across time within patients. As a result, surveillance testing guidelines using one-size-fits- all strategies continue to be common in most cancers, despite their uncertain clinical utility. The overarching goal of the proposed research is to develop a decision-making framework to identify optimal surveillance strategies among cancer survivors. The specific aims are: Aim 1. Develop and evaluate a sequential decision- making framework by merging statistical methods for prediction modeling with economics concepts for value of information (VOI) analysis to guide individualized decisions about testing and treatment for recurrence using serial biomarker testing, with the goal of optimizing long-term patient outcomes. This aim will build on preliminary work and develop a dynamic decision-making algorithm that uses accumulated information at a given time to update predictions and guide decisions. The broad applicability of the framework will be demonstrated by considering three distinct cancer surveillance settings: colorectal cancer (CRC), prostate cancer (PrCA), and chronic myeloid leukemia (CML), which capture a range of decision-making problems in cancer surveillance. Aim 2. Apply this framework to existing electronic health record (EHR) and cohort study data to identify a risk-adaptive surveillance strategy for detecting CRC recurrence that targets high-risk patients for frequent follow-up and treatment, and recommends less frequent follow-up for low-risk patients. Aim 3. Assess the comparative effectiveness of the proposed risk-adaptive surveillance strategy versus guideline- based surveillance in CRC. Aim 4. Use existing data to evaluate the generalizability of the framework by addressing the optimal frequency of follow-up among (a) low-risk men with recurrent PrCA, for whom treatment may be safely delayed for a prolonged period, and (b) long-term survivors of CML, who achieve long-term remission but currently continue to be monitored frequently. We address a significant problem in cancer survivorship care using approaches to help resolve the uncertainty that clinicians and patients face when confronted with using new and evolving biomarker technologies to monitor for recurrence after patients have survived their primary cancer.

癌症生物标记物处于精密医学的前沿，提供了巨大的机会和 挑战。特别是，用于检测癌症幸存者复发的生物标记物的开发正在蓬勃发展，因为 具有连续生物标志物测量的监测测试提供了在某个时间点检测复发的机会 当治疗可能治愈的时候。然而，频繁的生物标志物检测可能弊大于利。 低风险个人，由于不必要的测试的成本和并发症，以及错误的可能性增加 阳性导致不必要的治疗。不幸的是，针对个别患者量身定做监测是一种 需要了解生物标志物测量的异质性的复杂决策问题 跨越病人，跨越病人内部的时间。因此，使用一刀切的监督检测指南- 所有的治疗策略在大多数癌症中仍然是常见的，尽管它们的临床应用尚不确定。最重要的是 拟议研究的目标是开发一个决策框架，以确定最佳监视 癌症幸存者的策略。具体目标是：目标1.制定和评估序贯决策- 通过将预测建模的统计方法与经济概念相结合来构建框架 信息(VOI)分析，以指导有关检测和治疗复发的个性化决策 系列生物标记物测试，目标是优化患者的长期结果。这一目标将建立在 初步工作并开发一种动态决策算法，该算法使用一次积累的信息 给与时间来更新预测和指导决策。该框架的广泛适用性将是 通过考虑三种不同的癌症监测环境进行演示：结直肠癌(CRC)、前列腺癌 癌症(PRCA)和慢性粒细胞白血病(CML)，这些疾病捕捉到了一系列决策问题 癌症监测。目的2.将该框架应用于现有的电子健康记录(EHR)和队列研究 确定针对高危患者的检测结直肠癌复发的风险自适应监测策略的数据 对于频繁的随访和治疗，建议对低风险患者减少随访频率。目标3. 评估拟议的风险自适应监测战略与指南的比较有效性- 以儿童权利委员会为基础的监测。目标4.使用现有数据评估框架的概括性，方法是 解决(A)复发性PRCA的低风险男性的最佳随访频率，这些患者接受治疗 可以安全地拖延很长一段时间，以及(B)慢性粒细胞白血病长期幸存者，他们实现了长期 虽然病情有所缓解，但目前仍经常受到监测。我们解决了癌症中的一个重要问题 使用方法帮助解决临床医生和患者在以下情况下面临的不确定性的生存护理 面临使用新的和不断发展的生物标记物技术来监测患者术后复发的问题 从他们的原发癌症中幸存下来。