使用最小和最大的预测概率,在获得诊断测试结果后,以自信地得出结论
Loic Desquilbet1, Maxime Kurtz2, Morgane Canonne-Guibert1
1Department of Biostatistics and Clinical Epidemiology, Ecole nationale vétérinaire d'Alfort, Univ Paris Est Créteil, INSERM, IMRB, Maisons-Alfort, France.
Journal of clinical epidemiology
|January 11, 2026
概括
解释诊断测试结果需要考虑个人的预测试概率. 新的值,PTP+conf和PTP-conf,帮助临床医生根据前测试概率和所需的确定性来确定对测试结果的信心.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 临床决策 - 临床决策
- 生物统计学 生物统计学
背景情况:
- 诊断测试的正负预测值 (PPV/NPV) 对临床决策至关重要.
- PPV和NPV取决于测试前的概率,这在个人之间有很大的差异.
- 将PPV/NPV作为单个值呈现可能对临床应用具有误导性.
研究的目的:
- 为解释诊断测试提出一种方法,考虑预测试概率的个体变异性.
- 引入临床决策的最低和最高前试验概率值 (PTP+conf和PTP-conf).
- 为临床医生提供一个工具,以评估对个体患者的诊断测试结果的信心.
主要方法:
- 根据测试灵敏度,特异性和预定义的临床医生信心水平,定义了PTP+conf和PTP-conf.
- 这些值代表了测试前的概率 (PTP+conf) 以上或以下 (PTP-conf),测试结果允许达到所需的信心.
- 拟议的临床医生将临床前测试概率估计与这些值进行比较.
主要成果:
- PTP+conf和PTP-conf是给定可信度值的诊断测试的内在特征.
- 这些值通过提供明确的决策点来简化诊断测试结果的解释.
- 临床医生可以很容易地使用这些值来确定测试后是否达到足够的信心.
结论:
- 拟议的PTP+conf和PTP-conf值为解释诊断测试提供了一种更细致,更适用于临床的方法.
- 这种方法通过直接整合个体预测概率和所需的信心水平,提高了临床决策.
- 该框架有助于从诊断测试中得出更可靠的结论,减少潜在的误解.
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