Zayed Ahmed M, Saegeman Veroniek, Delvaux Nicolas
Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Laboratory Medicine Department, Menoufia University National Liver Institute, Shebin El-Kom, Egypt.
J Appl Lab Med. 2024 Jul 1;9(4):776-788. doi: 10.1093/jalm/jfae021.
This paper presents a data-driven strategy for establishing the reportable interval in clinical laboratory testing. The reportable interval defines the range of laboratory result values beyond which reporting should be withheld. The lack of clear guidelines and methodology for determining the reportable interval has led to potential errors in reporting and patient risk.
To address this gap, the study developed an integrated strategy that combines statistical analysis, expert review, and hypothetical outlier calculations. A large data set from an accredited clinical laboratory was utilized, analyzing over 124 million laboratory test records from 916 distinct tests. The Dixon test was applied to identify outliers and establish the highest and lowest non-outlier result values for each test, which were validated by clinical pathology experts. The methodology also included matching the reportable intervals with relevant Logical Observation Identifiers Names and Codes (LOINC) and Unified Code for Units of Measure (UCUM)-valid units for broader applicability.
Upon establishing the reportable interval for 135 routine laboratory tests (493 LOINC codes), we applied these to a primary care laboratory data set of 23 million records, demonstrating their efficacy with over 1% of result records identified as implausible.
We developed and tested a data-driven strategy for establishing reportable intervals utilizing large electronic medical record (EMR) data sets. Implementing the established interval in clinical laboratory settings can improve autoverification systems, enhance data reliability, and reduce errors in patient care. Ongoing refinement and reporting of cases exceeding the reportable limits will contribute to continuous improvement in laboratory result management and patient safety.
本文提出了一种数据驱动的策略,用于确定临床实验室检测中的可报告区间。可报告区间定义了实验室结果值的范围,超出此范围的结果不应报告。缺乏明确的指南和方法来确定可报告区间已导致报告中的潜在错误和患者风险。
为填补这一空白,该研究开发了一种综合策略,将统计分析、专家评审和假设异常值计算相结合。利用了一家经认可的临床实验室的大型数据集,分析了来自916项不同检测的超过1.24亿条实验室检测记录。应用狄克逊检验来识别异常值,并为每项检测确定最高和最低非异常结果值,这些值由临床病理专家进行了验证。该方法还包括将可报告区间与相关的逻辑观察标识符名称和代码(LOINC)以及统一计量单位代码(UCUM)有效单位进行匹配,以实现更广泛的适用性。
在为135项常规实验室检测(493个LOINC代码)确定可报告区间后,我们将其应用于一个包含2300万条记录的初级保健实验室数据集,结果显示超过1%的结果记录被确定为不可信,证明了其有效性。
我们开发并测试了一种利用大型电子病历(EMR)数据集确定可报告区间的数据驱动策略。在临床实验室环境中实施既定区间可以改进自动验证系统,提高数据可靠性,并减少患者护理中的错误。对超过可报告限值的病例进行持续细化和报告将有助于不断改进实验室结果管理和患者安全。