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

描述(由申请人提供)：关于个体干预的益处的更好的信息对改善临床决策具有巨大的潜力。然而，成本效益分析(CEA)几乎总是基于群体中发现的平均增量成本和平均增量收益。由于卫生保健资源是根据患者个人的决定进行分配的，因此使用平均成本效益(CE)比率可能是不适当的和具有误导性的。平均而言具有成本效益的干预措施对许多(甚至对大多数)患有指标性疾病的患者来说可能并不具有成本效益，相反，在某些情况下，名义上没有成本效益的干预措施可能非常有价值。对应用平均人群CE比率的不适当的担忧与对基于临床试验总结结果的对单个患者最好的治疗的担忧同时存在。我们之前使用风险模型所做的工作表明，在具有相同指数条件的个体中，基线风险的显著差异是普遍存在的。这种风险的异质性导致了治疗效益的显著差异，而且往往具有临床意义--特别是当绝对规模的效益被考虑时，这是临床决策和CEA最相关的衡量标准。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，并在文献中大量存在。尽管对平均效应和平均CE比率的使用以及CPM的可用性有重要的关注，但风险信息更好地个体化对临床决策的潜在健康和经济影响仍在很大程度上未被研究。此外，正如CEA通常忽略人口风险分层的可能性一样，用于评估CPM和新的风险生物标记物的传统衡量标准通常忽略应用预测的决策背景，转而侧重于统计准确性的“无效用”衡量标准。不足为奇的是，这些测量方法往往很难预测预测性信息的最终临床用途。因此，我们的具体目标是：目标1：检查基于风险的方法在各种医疗干预措施中的预期价值和成本效益；目标2：开发和测试适当的方法来评估预测模型，并基于评估改进的个性化医疗决策的健康和经济影响的决策分析框架，逐步改进风险预测；目标3：通过以下方式探索使用基于风险的方法来实现个性化护理的政策含义：(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。这个项目将：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)提供启发式方法和工具，以加快对CPM和新的风险生物标志物的评估；以及4)帮助我们了解如何最好地激励它们转化为临床实践。

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.