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

描述（由申请人提供）：关于个体干预益处的更好信息具有改善临床决策的巨大潜力。然而，成本效益分析（CEA）几乎总是基于群体的平均增量成本和平均增量收益。由于医护资源的分配是由个别病人自行决定，并为个别病人而作出决定，因此使用平均成本效益比率可能不恰当，并会引起误导。平均而言，具有成本效益的干预措施可能对许多（甚至对大多数）患有指数疾病的患者不具有成本效益，相反，名义上成本效益不高的干预措施在某些情况下可能非常值得。基于临床试验的总结结果，对应用平均群体CE比率的不适当性的担忧与对个体患者最佳治疗的担忧平行。我们先前使用风险模型的工作表明，在具有相同指数条件的个体中，基线风险的实质性差异是普遍存在的。这种风险异质性引起了实质性的，通常具有临床意义的，治疗获益的差异-特别是当在绝对尺度上考虑获益时，这是临床决策和CEA最相关的指标。临床预测模型（CPM）可用于跨研究和实践领域，以解决这种风险异质性，并在文献中丰富。尽管对使用平均效应和平均CE比率以及CPM的可用性存在重要关注，但风险信息更好地个性化对临床决策的潜在健康和经济影响在很大程度上仍未得到研究。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和新型风险生物标志物的传统措施通常忽略了应用预测的决策背景，而是专注于统计准确性的“无效用”措施。毫不奇怪，这些措施往往预测预测信息的最终临床有用性很差。 因此，我们的具体目标是：目标1：研究基于风险的方法在广泛的医疗干预措施中的个性化护理和成本效益的预期价值;目标2：开发和测试适当的方法来评估预测模型，并在风险预测中进行增量改进，基于决策分析框架，估计改进的个性化医疗决策对健康和经济的影响;目标3：探索使用基于风险的方法来个性化护理的政策含义：（a）模拟基于激励的计划的影响，（B）让利益相关者参与现实世界的实施。该项目将：1）阐明使用基于风险的方法进行靶向治疗的总体价值; 2）帮助我们了解这种方法可能特别有用的情况; 3）提供药物学和工具，以加快对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其转化为临床实践。

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.