An Online Searchable Field Synopsis of Clinical Prediction Models in Cardiovascular Disease

心血管疾病临床预测模型的在线可搜索领域概要

• 批准号: 9072292
• 负责人: DAVID M KENT
• 金额: $ 16.47万
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9072292
• 关键词: Acute; Address; Base Ratios; Biological Markers; Calibration; Cardiovascular Diseases; Caring; Chronic; Clinical; Clinical Trials; Cost Effectiveness Analysis; Costs and Benefits; Decision Making; Diagnostic; Discrimination; Future; Genomics; Health; Health Benefit; Healthcare; Heterogeneity; Incentives; Individual; Intervention; Laboratories; Literature; Measures; Medical; Methods; Modeling; Outcome; Patient risk; Patients; Population; Prevalence; Probability; Research; Resources; Risk; Risk Estimate; Risk Factors; Risk Marker; Risk Reduction; 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; imaging biomarker; improved; indexing; individual patient; novel; novel marker; personalized care; personalized medicine; personalized screening; policy implication; preference; programs; research clinical testing; study population; targeted treatment; tool

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：有关个体干预措施益处的更好信息对于改善临床决策具有巨大潜力。然而，成本效益分析（CEA）几乎总是基于群体中的平均增量成本和平均增量收益。由于医疗保健资源是根据患者个人的决定进行分配的，因此平均成本效益 (CE) 比率的使用可能不恰当且具有误导性。平均而言，具有成本效益的干预措施对于许多（甚至对于大多数）患有指标病症的患者来说可能并不具有成本效益，相反，名义上成本效益较低的干预措施对于某些患者来说可能非常值得。对应用平均群体 CE 比率不恰当的担忧与基于临床试验总结结果的对个体患者最佳治疗的担忧并行。我们之前使用风险模型的工作表明，具有相同指数条件的个体之间普遍存在基线风险的显着差异。这种风险异质性导致治疗益处存在显着且通常具有临床意义的差异，尤其是在绝对范围（临床决策和 CEA 最相关的衡量标准）上考虑​​益处时。临床预测模型（CPM）可以跨研究和实践领域使用，以解决这种风险异质性，并且文献中有丰富的内容。尽管对平均效应和平均 CE 比率的使用以及 CPM 的可用性存在重要担忧，但临床决策中风险信息更好的个体化所带来的潜在健康和经济影响在很大程度上仍未得到检验。此外，正如 CEA 通常忽略人群风险分层的可能性一样，用于评估 CPM 的传统措施和新型风险生物标志物通常忽略应用预测的决策背景，而是关注统计准确性的“无效用”措施。毫不奇怪，这些措施通常很难预测预测信息的最终临床用途。 因此，我们的具体目标是： 目标 1 检查基于风险的个体化护理方法的预期价值以及广泛的医疗干预措施的成本效益；目标 2：开发和测试适当的方法来评估预测模型，并基于决策分析框架来逐步改进风险预测，该框架估计改进的个体化医疗决策对健康和经济的影响；目标 3：通过以下方式探索使用基于风险的方法进行个性化护理的政策影响：(a) 模拟基于激励的计划的影响，以及 (b) 让利益相关者参与现实世界的实施。该项目将：1）利用基于风险的方法阐明靶向治疗的总体价值； 2）帮助我们了解这种方法在什么情况下可能特别有用； 3）提供启发法和工具来加快CPM和新型风险生物标志物的评估； 4）帮助我们了解如何最好地激励他们将其转化为临床实践。