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PROMISS - Prostate Modeling to Identify Surveillance Strategies

PROMISS - Prostate Modeling to Identify Surveillance Strategies
PROMISS - 前列腺建模以确定监测策略
批准号:
8907971
负责人:
RUTH D ETZIONI
金额:
$57.61万
依托单位国家:
美国
项目类别:
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-30 至 2016-08-31

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中文摘要
翻译
描述(由申请人提供):本申请解决了前列腺癌管理中最关键的难题,前列腺癌是美国男性皮肤癌之后最常见的癌症。20世纪90年代,PSA筛查在美国的广泛采用导致了低风险前列腺癌在该国的流行。绝大多数低风险病例不会死于疾病,但他们仍在寻求治愈性治疗。积极监测(AS)已被权威专家组认可为管理低风险前列腺癌的可行选择,但如何实施它并没有基于证据的标准。在AS中,病例被密切监测,如果有任何疾病进展的证据,则打算治疗。几项AS研究正在进行中,但它们构成了不同方法的松散集合,其结果无法进行比较或整合。本申请的首要目标是确定一种最佳的方法,以适应患者的特点和偏好。认识到许多潜在的AS治疗方法无法在临床试验中进行比较,我们将利用我们在前列腺癌进展和预后建模方面的丰富经验来预测候选AS策略的短期和长期结果。该模型将建立在5项最高质量和最大AS研究的疾病进展荟萃分析基础上。这将通过在缺乏治疗的情况下的疾病进展的独立模型来补充,该模型将使现有的基于人群的前列腺癌自然史模型适应AS环境。我们将根据已发表的关于复发、发病率和主要治疗后效用的研究,筛选候选AS策略,并将进展和治疗事件与治疗后生存率和生活质量联系起来。由此产生的模型将预测一系列结果,包括无治疗间隔期、疾病特异性生存期和质量调整生命年。结果将被打包在一个界面中,患者可以使用该界面来比较根据其个人特征和偏好定制的不同策略的结果。我们的具体目标是:目标1:在作为AS候选者的低风险前列腺癌病例中,在未接受治疗的情况下建立前列腺癌进展模型。作为这一目标的一部分,我们将对正在进行的AS研究进行首次荟萃分析。目标二:使用目标1沿着中开发的模型,结合既往AS研究和局部疾病治愈性治疗的生存率和生活质量信息,预测并比较各种AS方案的短期和长期结局。目标3:使用这些模型开发、测试和发布一个界面,帮助患者根据诊断时的临床和病理特征、健康状况和个人偏好确定个性化的AS方法。该应用程序将推进AS的证据基础,并将是第一次在连贯的医疗决策框架内使用相同的指标来整合AS策略。这项工作的主要影响在于它有可能为患者决策提供信息,使AS变得更被接受,并提高美国每年诊断为低风险前列腺癌的许多男性的质量调整预期寿命。
英文摘要
DESCRIPTION (provided by applicant): This application addresses the most critical conundrum in the management of prostate cancer, the most common cancer after skin cancer in US men. The widespread adoption of PSA screening in the US during the 1990s has created an epidemic of low-risk prostate cancer in this country. The vast majority of low-risk cases will not die of their disease yet they continue to seek curative treatment. Active surveillance (AS) has been endorsed by an authoritative panel as a viable option for managing low-risk prostate cancer, but no evidence- based standard exists for how to implement it. In AS, cases are closely monitored and intent to treat if there is any evidence of disease progression. Several AS studies are ongoing but they constitute a loose collection of different approaches and their results cannot be readily compared or integrated. The overarching objective of this application is to determine an optimal approach to AS given patient characteristics and preferences. Recognizing that the many potential approaches to AS cannot be compared in clinical trials, we will draw on our extensive experience modeling prostate cancer progression and prognosis to project the short- and long-term outcomes of candidate AS strategies. The model will build on a meta-analysis of disease progression across 5 of the highest quality and largest AS studies. This will be complemented by an independent model of disease progression in the absence of treatment that will adapt an existing, population-based model of prostate cancer natural history to the AS setting. We will superimpose candidate AS strategies and link the progression and treatment events with post-treatment survival and quality of life based on published studies of relapse, morbidity, and utilities following primary treatment. The resulting model will project a range of outcomes, including the treatment-free interval, disease-specific survival, and quality-adjusted life years. The outcomes will be pack- aged in an interface that patients can use to compare outcomes of different strategies tailored to their individual characteristics and preferences. Our Specific Aims are: Aim 1: Model prostate cancer progression in the absence of treatment among low-risk prostate cancer cases who are candidates for AS. As part of this aim we will conduct the first meta-analysis of ongoing AS studies. Aim 2: Project and compare short- and long-term outcomes on various AS protocols, using the models developed in Aim 1 along with survival and quality of life information from prior studies of AS and curative treatments for localized disease. Aim 3: Develop, test and release an interface using these models to help patients identify a personalized AS approach based on their clinical and pathologic characteristics at the time of diagnosis, their health status, and personal preferences. This application will advance the evidence base for AS and will be the first time AS strategies have been com- pared using the same metrics within a coherent medical decision-making framework. The main impact of the work lies in its potential to inform patient decision-making so that AS becomes more accepted and improves quality-adjusted life expectancy for the many men diagnosed low-risk prostate cancer each year in the US.
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Modeling Precision Interventions for Prostate Cancer Control
  • 批准号:
    10683180
  • 项目类别:
  • 资助金额:
    $123.52万
  • 财政年份:
    2020
  • 负责人:
    RUTH D ETZIONI
  • 依托单位:
Modeling Precision Interventions for Prostate Cancer Control
  • 批准号:
    10461832
  • 项目类别:
  • 资助金额:
    $127.05万
  • 财政年份:
    2020
  • 负责人:
    RUTH D ETZIONI
  • 依托单位:
Modeling Precision Interventions for Prostate Cancer Control
  • 批准号:
    10601453
  • 项目类别:
  • 资助金额:
    $65.41万
  • 财政年份:
    2020
  • 负责人:
    RUTH D ETZIONI
  • 依托单位:
Modeling Precision Interventions for Prostate Cancer Control
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