Delta checks are a post-analytical verification tool that compare the difference in sequential laboratory results belonging to the same patient against a predefined limit. This unique quality tool highlights a potential error at the individual patient level. A difference in sequential laboratory results that exceeds the predefined limit is considered likely to contain an error that requires further investigation that can be time and resource intensive. This may cause a delay in the provision of the result to the healthcare provider or entail recollection of the patient sample. Delta checks have been used primarily to detect sample misidentification (sample mix-up, wrong blood in tube), and recent advancements in laboratory medicine, including the adoption of protocolized procedures, information technology and automation in the total testing process, have significantly reduced the prevalence of such errors. As such, delta check rules need to be selected carefully to balance the clinical risk of these errors and the need to maintain operational efficiency. Historically, delta check rules have been set by professional opinion based on reference change values (biological variation) or the published literature. Delta check rules implemented in this manner may not inform laboratory practitioners of their real-world performance. This review discusses several evidence-based approaches to the optimal setting of delta check rules that directly inform the laboratory practitioner of the error detection capabilities of the selected rules. Subsequent verification of workflow for the selected delta check rules is also discussed. This review is intended to provide practical assistance to laboratories in setting evidence-based delta check rules that best suits their local operational and clinical needs.

增量检查是一种分析后验证工具，可以将属于同一患者的连续实验室结果的差异与预定义的限制进行比较。这种独特的质量工具可突出显示各个患者级别的潜在错误。连续实验室结果中的差异超过预定义的限制可能被认为是包含需要进一步调查的错误，可能需要大量时间和资源。这可能会导致无法将结果提供给医疗保健提供者，或者需要重新收集患者样品。Delta检查主要用于检测样品的错误标识（样品混合，管内血液不正确）以及实验室医学的最新进展，包括采用协议规程，信息技术和自动化在整个测试过程中，已大大降低了此类错误的发生率。因此，需要仔细选择增量检查规则，以平衡这些错误的临床风险和维持运营效率的需要。历史上，增量检查规则是由专业人士根据参考变化值（生物变异）或已发表的文献设置的。以这种方式实施的增量检查规则可能不会将其实际性能告知实验室从业人员。这篇评论讨论了最佳设置增量检查规则的几种基于证据的方法，这些方法直接告知实验室从业人员所选规则的错误检测能力。还讨论了所选增量检查规则的工作流的后续验证。