Value of Personalized Risk Information

个性化风险信息的价值

• 批准号: 8628511
• 负责人: DAVID M KENT
• 金额: $ 46.6万
• 依托单位: TUFTS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8628511
• 关键词: Acute; Address; Base Ratios; Biological Markers; Calibration; Caring; Chronic; Clinical; Clinical Trials; Cost Effectiveness Analysis; Costs and Benefits; Decision Making; Diagnostic; Discrimination; Future; Genomics; Health; Health Benefit; Healthcare; Heterogeneity; Image; Incentives; Individual; Intervention; Laboratories; Literature; Measures; Medical; Medicine; Methods; Modeling; Outcome; Patients; Population; Population Study; Prevalence; Probability; Relative (related person); Research; Resources; Risk; Risk Estimate; Risk Factors; Risk Marker; Risk Reduction; Simulate; Stratification; Testing; Therapeutic; Translations; Work; base; clinical care; clinical decision-making; clinical practice; clinical risk; cost; cost effective; cost effectiveness; economic impact; health economics; heuristics; improved; indexing; novel; novel marker; policy implication; preference; programs; public health relevance; research clinical testing; screening; tool

Better information about the benefits of interventions in individuals has enormous potential to improve clinical decision making. Yet cost effectiveness analyses (CEA) are almost always based on average incremental cost and average incremental benefits found in groups. Since health care resources are allocated by decisions made by and for individual patients, use of average cost effectiveness (CE) ratios can be inappropriate and misleading. Interventions that are cost effective on average may not be cost effective for many (even for most) patients with the index condition and-conversely-interventions that are nominally cost-ineffective may be highly worthwhile in some. Concerns about the inappropriateness of applying average population CE ratios parallel concerns about what treatment is best for an individual patient based on summary results of clinical trials. Our prior work using risk models has shown that substantial differences in baseline risk are ubiquitous across individuals with the same index condition. This risk heterogeneity gives rise to substantial, and often clinically meaningful, differences in therapeutic benefits--particularly when benefits are considered on the absolute scale, the most relevant measure for clinical decision making and CEA. Clinical Prediction Models (CPMs) can be used across research and practice domains to address this risk heterogeneity and are abundant in the literature. Despite the important concerns about the use of average effects and average CE ratios and the availability of CPMs, the potential health and economic impact of better individualization of risk information on clinical decisions remains largely unexamined. Further, just as CEAs typically ignore the potential for population risk stratification, traditional measures used to evaluate CPM and novel risk biomarkers typically ignore the decisional context in which the predictions are applied, and focus instead on "utility-free" measures of statistical accuracy. Not surprisingly, these measures often poorly anticipate the ultimate clinical usefulness of the predictive information. Thus, our specific aims are: Aim 1: To examine the expected value of a risk-based approach to individualizing care and cost effectiveness across a broad range of medical interventions; Aim 2: To develop and test appropriate methods to assess prediction models, and incremental improvements in risk prediction, based on a decision analytic framework that estimates the health and economic impact of improved individualized medical decision-making; Aim 3: To explore the policy implications of using a risk-based approach to individualize care by: (a) simulating the impact of incentive-based programs, and (b) engaging stakeholders on real-world implementation. This project will: 1) elucidate the overall value of targeting therapy using a risk-based approach; 2) help us understand the circumstances in which such an approach might be especially useful; 3) provide heuristics and tools to expedite the evaluation of CPMs and novel risk biomarkers; and 4) help us understand how best to incentivize their translation into clinical practice.

关于个体干预益处的更好信息具有巨大的潜力，可以改善临床 决策。然而，成本效益分析（CEA）几乎总是基于平均增量成本 和群体中的平均增量效益。由于卫生保健资源是由决定分配的， 由个体患者或为个体患者制定，使用平均成本效益（CE）比率可能不合适， 误导平均而言具有成本效益的干预措施对许多人（甚至对大多数人）来说可能不具有成本效益 患有指标疾病的患者，以及相反，名义上成本效益不高的干预措施可能会 在某些方面非常有价值。对采用平均人口CE比率不适当的关切 基于临床总结结果，对个体患者最佳治疗的平行关注 审判我们先前使用风险模型的工作表明，基线风险的实质性差异是普遍存在的 在具有相同指数条件的个体之间。这种风险异质性导致了大量的，而且往往是 临床上有意义的，治疗益处的差异-特别是当益处被认为是在 绝对量表是临床决策和CEA最相关的指标。临床预测模型 （CPM）可以用于跨研究和实践领域，以解决这种风险异质性， 丰富的文学。尽管对平均效应和平均CE的使用有重要的关注， 比率和CPM的可用性，更好地个性化风险的潜在健康和经济影响 有关临床决策的信息基本上仍未得到审查。此外，正如CEA通常忽略 人群风险分层的可能性，用于评价CPM和新型风险生物标志物的传统指标 通常忽略应用预测的决策背景，而专注于“无效用” 统计准确性的衡量标准。毫不奇怪，这些措施往往很难预测最终的临床 预测信息的有用性。 因此，我们的具体目标是：目标1：研究基于风险的方法的预期价值， 在广泛的医疗干预措施中实现个性化护理和成本效益;目标2： 并测试适当的方法来评估预测模型，以及风险预测的增量改进， 基于一个决策分析框架，该框架估计改善的健康和经济影响， 个性化医疗决策;目标3： 探讨使用基于风险的 个性化护理的方法：（a）模拟基于激励的计划的影响，（B）参与 在现实世界中实施。 本课题将：1）阐明靶向治疗的整体价值 使用基于风险的方法; 2）帮助我们了解这种方法可能适用的情况 特别有用; 3）提供化学和工具以加速CPM和新风险生物标志物的评估; 以及4）帮助我们理解如何最好地激励它们转化为临床实践。