Michelle Pistner Nixon, Farhani Momotaz, Claire Smith, Jeffrey S Smith, Mark Sendak, Christopher Polage, Justin D Silverman
{"title":"从测试前和测试后的概率到医疗决策。","authors":"Michelle Pistner Nixon, Farhani Momotaz, Claire Smith, Jeffrey S Smith, Mark Sendak, Christopher Polage, Justin D Silverman","doi":"10.1186/s12911-024-02610-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>A 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.</p><p><strong>Methods: </strong>We 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.</p><p><strong>Results: </strong>We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that <math> <mrow><mmultiscripts><mi>r</mi> <mrow></mrow> <mrow><mrow></mrow> <mo>∗</mo></mrow> </mmultiscripts> <mo>=</mo> <mrow><mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>p</mi> <mo>)</mo></mrow> <mo>/</mo> <mi>p</mi></mrow> </math> represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, <math><mmultiscripts><mi>r</mi> <mrow></mrow> <mrow><mrow></mrow> <mo>∗</mo></mrow> </mmultiscripts> </math> 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.</p><p><strong>Conclusions: </strong>Our 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.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11285418/pdf/","citationCount":"0","resultStr":"{\"title\":\"From pre-test and post-test probabilities to medical decision making.\",\"authors\":\"Michelle Pistner Nixon, Farhani Momotaz, Claire Smith, Jeffrey S Smith, Mark Sendak, Christopher Polage, Justin D Silverman\",\"doi\":\"10.1186/s12911-024-02610-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>A 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.</p><p><strong>Methods: </strong>We 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.</p><p><strong>Results: </strong>We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that <math> <mrow><mmultiscripts><mi>r</mi> <mrow></mrow> <mrow><mrow></mrow> <mo>∗</mo></mrow> </mmultiscripts> <mo>=</mo> <mrow><mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>p</mi> <mo>)</mo></mrow> <mo>/</mo> <mi>p</mi></mrow> </math> represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, <math><mmultiscripts><mi>r</mi> <mrow></mrow> <mrow><mrow></mrow> <mo>∗</mo></mrow> </mmultiscripts> </math> represents the critical value at which action and inaction are equally optimal. 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From pre-test and post-test probabilities to medical decision making.
Background: A 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.
Methods: We 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.
Results: We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that represents 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.
Conclusions: Our 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.
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.