This study describes an objective approach to deriving the clinical performance of autoverification rules to inform laboratory practice when implementing them. Anonymized historical laboratory data for 12 biochemistry measurands were collected and Box-Cox-transformed to approximate a Gaussian distribution. The historical laboratory data were assumed to be error-free. Using the probability theory, the clinical specificity of a set of autoverification limits can be derived by calculating the percentile values of the overall distribution of a measurand. The 5th and 95th percentile values of the laboratory data were calculated to achieve a 90% clinical specificity. Next, a predefined tolerable total error adopted from the Royal College of Pathologists of Australasia Quality Assurance Program was applied to the extracted data before subjecting to Box-Cox transformation. Using a standard normal distribution, the clinical sensitivity can be derived from the probability of the Z-value to the right of the autoverification limit for a one-tailed probability and multiplied by two for a two-tailed probability. The clinical sensitivity showed an inverse relationship with between-subject biological variation. The laboratory can set and assess the clinical performance of its autoverification rules that conforms to its desired risk profile.

中文翻译：

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一种推导自动验证限值临床性能的客观方法。

本研究描述了一种客观的方法来推导自动验证规则的临床性能，以便在实施它们时为实验室实践提供信息。收集了 12 个生化被测量的匿名历史实验室数据，并进行 Box-Cox 变换以近似高斯分布。假设历史实验室数据没有错误。使用概率论，一组自动验证限制的临床特异性可以通过计算被测量的整体分布的百分位值得出。计算实验室数据的第 5 和第 95 个百分位值以实现 90% 的临床特异性。下一个，在进行 Box-Cox 转换之前，将澳大利亚皇家病理学家质量保证计划采用的预定义的可容忍总误差应用于提取的数据。使用标准正态分布，对于单尾概率，临床敏感性可以从自动验证限制右侧的 Z 值的概率得出，并且对于双尾概率乘以 2。临床敏感性与受试者之间的生物学变异呈反比关系。实验室可以设置和评估符合其期望风险概况的自动验证规则的临床表现。对于单尾概率，临床敏感性可以从自动验证限制右侧的 Z 值的概率得出，对于双尾概率，可以乘以 2。临床敏感性与受试者之间的生物学变异呈反比关系。实验室可以设置和评估符合其期望风险概况的自动验证规则的临床表现。对于单尾概率，临床敏感性可以从自动验证限制右侧的 Z 值的概率得出，对于双尾概率，可以乘以 2。临床敏感性与受试者之间的生物学变异呈反比关系。实验室可以设置和评估符合其期望风险概况的自动验证规则的临床表现。