Abstract：
Analytical performance specifications (APS) based on outcomes refer to how \'good\' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs. As testing is indirectly linked to health outcomes through clinical decisions on patient management, often simulation-based studies are used to assess the impact of analytical performance on the probability of clinical outcomes which is then translated to Model 1b APS according to the Milan consensus. This paper discusses the related key definitions, concepts and considerations that should assist in finding the most appropriate methods for deriving Model 1b APS. We review the advantages and limitations of published methods and discuss the criteria for transferability of Model 1b APS to different settings. We consider that the definition of the clinically acceptable misclassification rate is central to Model 1b APS. We provide some examples and guidance on a more systematic approach for first defining the clinical performance requirements for tests and we also highlight a few ideas to tackle the future challenges associated with providing outcome-based APS for laboratory testing.
摘要：
基于结果的分析性能规范（APS）是指测试的分析性能如何“好”需要对患者做的比伤害更多的事情。被测量的分析性能会影响其临床性能。没有首先设定临床性能要求，很难定义测试在分析上需要多好才能满足医疗需求。由于测试通过对患者管理的临床决策与健康结果间接相关，通常使用基于模拟的研究来评估分析性能对临床结局概率的影响，然后根据米兰共识将其转换为1bAPS模型。本文讨论了相关的关键定义，应有助于找到推导1b型APS的最合适方法的概念和考虑因素。我们回顾了已发布方法的优点和局限性，并讨论了1b型APS可转移到不同设置的标准。我们认为，临床上可接受的误分类率的定义是1b型APS的核心。我们提供了一些更系统的方法的例子和指导，以首先定义测试的临床性能要求，我们还强调了一些想法，以解决与为实验室测试提供基于结果的APS相关的未来挑战。
