From Pre-test and Post-test Probabilities to Medical Decision Making.
From Pre-test and Post-test Probabilities to Medical Decision Making.
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从测试前和测试后概率到医疗决策。
DOI：
10.1101/2024.02.14.24302820
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Silverman,JustinD
中科院分区：
文献类型：
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作者：
Nixon,MichellePistner;Momotaz,Farhani;Smith,Claire;Smith,JeffreyS;Sendak,Mark;Polage,Christopher;Silverman,JustinD
现代循证医学的中心目标是开发简单易用的工具，帮助临床医生将定量信息整合到医疗决策中。贝叶斯测试前/测试后概率（BPP）框架可以说是此类工具中最著名的，它提供了一种正式的方法来量化给定医学测试结果或临床症状的诊断不确定性。然而，临床决策不仅仅是量化诊断的不确定性，还需要将这种不确定性与每个可能的决策相关的各种成本和收益相平衡。尽管近年来越来越多的关注，简单和灵活的方法定量临床决策仍然难以捉摸。方法利用贝叶斯决策理论的概念对BPP框架进行扩展。通过整合成本，我们可以扩展BPP框架以允许临床决策。我们开发了一个简单的定量框架，用于二元临床决策（例如，行动/不行动，治疗/不治疗，测试/不测试）。请写出病人在测试前或测试后患病的概率。我们展示了它代表一个称为决策边界的临界值。就行动不足和行动过度的相对成本而言，表示行动和不行动同样最优的临界值。我们通过案例研究展示了这个决策边界如何在床边使用，并通过对最近一项研究的重新分析来作为一种研究工具，该研究发现临床医生对测试前和测试后概率的普遍错误估计。我们的方法是如此简单，它应该被认为是一个核心，但以前被忽视的BPP框架的一部分。与之前的定量临床决策方法不同，我们的方法只需要一个手持计算器，几乎适用于任何可以使用BPP框架的环境，并且在与特定决策相关的成本和收益是针对特定患者且难以量化的情况下表现出色。
BackgroundA central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive.MethodsWe extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making.ResultsWe develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Letpbe the pre-test or post-test probability that a patient has disease. We show thatrepresents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting,represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians.ConclusionsOur approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify.