A narrative review about regulatory acceptability standards for clinical assays

IF 1.4
J. Krouwer
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引用次数: 0

Abstract

Objective: To review acceptance standards, particularly those used by regulatory agencies to approve products. A hierarchy of standards is discussed ranging from regulatory (FDA), quasi-regulatory (CLSI, ISO 15189, ISO 15197), to academic standards (Milan conference). Background: Many clinical chemistry assays produce results that can be compared to reference. This allows regulatory bodies such as FDA to have acceptance protocols that require evaluation results between the candidate and reference assay to meet certain acceptability limits. Methods: This paper analyzes the problems arising from acceptability standards including: (I) a generic problem with the standards; (II) protocols used to evaluate the standards; (III) how the data are analyzed; (IV) how often results are observed that potentially can cause serious patient harm, and (V) why people do not pay more attention to dangerous results. Conclusions: Suggested recommendations include: (I) specifications should better reflect the harm when the magnitude of error increases; (II) results should be provided with and without pre- and post-analytical error; (III) more focus is needed on tools that prevent large errors, especially if pre- and post-analytical errors are detected. These include improved user training, Failure Mode Effects Analysis (FMEA), fault trees and Failure Reporting And Corrective Action System (FRACAS); (IV) for assays on the market, the MAUDE database should be examined for adverse events.
关于临床检测的监管可接受性标准的叙述性回顾
目的:审查验收标准,特别是监管机构用于批准产品的标准。讨论了从监管标准(FDA)、准监管标准(CLSI、ISO 15189、ISO 15197)到学术标准(米兰会议)的层次结构。背景:许多临床化学分析产生的结果可以与参考比较。这允许监管机构(如FDA)制定验收方案,要求候选分析和参考分析之间的评估结果满足某些可接受限度。方法:分析可接受性标准存在的问题,包括:(1)标准的通用性问题;(二)评价标准的方案;(三)如何分析数据;(IV)观察到可能对患者造成严重伤害的结果的频率,以及(V)为什么人们没有更多地关注危险的结果。结论:建议包括:(1)当误差幅度增大时,规范应更好地反映危害;(二)提供的结果是否存在分析前和分析后误差;(III)需要更多地关注防止大错误的工具,特别是在检测到分析前和分析后错误的情况下。这些措施包括改进用户培训、故障模式影响分析(FMEA)、故障树和故障报告和纠正措施系统(FRACAS);(IV)对于市场上的检测,应检查MAUDE数据库中的不良事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.70
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0.00%
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