Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
NIHR Diagnostic Evidence Co-operative Newcastle, Newcastle upon Tyne Hospitals Foundation Trust, Newcastle upon Tyne, UK.
BMJ Evid Based Med. 2018 Feb;23(1):13-16. doi: 10.1136/ebmed-2017-110862.
Information about the performance of diagnostic tests is typically presented in the form of measures of test accuracy such as sensitivity and specificity. These measures may be difficult to translate directly into decisions about patient treatment, for which information presented in the form of probabilities of disease after a positive or a negative test result may be more useful. These probabilities depend on the prevalence of the disease, which is likely to vary between populations. This article aims to clarify the relationship between pre-test (prevalence) and post-test probabilities of disease, and presents two free, online interactive tools to illustrate this relationship. These tools allow probabilities of disease to be compared with decision thresholds above and below which different treatment decisions may be indicated. They are intended to help those involved in communicating information about diagnostic test performance and are likely to be of benefit when teaching these concepts. A substantive example is presented using C reactive protein as a diagnostic marker for bacterial infection in the older adult population. The tools may also be useful for manufacturers of clinical tests in planning product development, for authors of test evaluation studies to improve reporting and for users of test evaluations to facilitate interpretation and application of the results.
有关诊断测试性能的信息通常以测试准确性的度量形式呈现,例如敏感性和特异性。这些措施可能难以直接转化为患者治疗的决策,因为阳性或阴性测试结果后的疾病概率信息可能更有用。这些概率取决于疾病的流行率,而疾病的流行率在不同人群中可能有所不同。本文旨在阐明检测前(患病率)和检测后疾病概率之间的关系,并提供两个免费的在线交互式工具来说明这种关系。这些工具可以将疾病概率与指示不同治疗决策的上下决策阈值进行比较。它们旨在帮助那些参与交流诊断测试性能信息的人,并且在教授这些概念时可能会很有帮助。本文使用 C 反应蛋白作为老年人群体细菌感染的诊断标志物来呈现一个实质性的例子。这些工具对于临床测试制造商规划产品开发、测试评估研究的作者改进报告以及测试评估的使用者促进结果的解释和应用也可能非常有用。