Real world feasibility of patient-based real time quality control (PBRTQC) using five analytes in a South African laboratory

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2024-10-20 DOI:10.1016/j.cca.2024.120006

Sheromna Sewpersad , Bettina Chale-Matsau , Tahir S. Pillay

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引用次数: 0

Abstract

Background

Patient-based real-time quality control (PBRTQC) identifies possible bias in methods by utilising shifts in trend of statistical measures in laboratory results. In this study we aimed to compare and optimize various PBRTQC procedures for serum alanine aminotransferase, albumin, calcium, ferritin and sodium.

Methods

In a bias simulation study, we added artificial bias to intervals of patient data and then evaluated the efficiency with which various PBRTQC procedures were able to detect this bias. PBRTQC procedures used included block size, moving statistic calculation, control limits as well as truncation limits. The number of patients till error detection, the false alarm rate as well as validation charts were utilised to select the optimal PBRTQC procedure for each analyte.

Results

The optimal PBRTQC procedures identified for each analyte were: ALT − MA T0 50 MaxMin; Albumin − MA T5 100 Perc; Calcium − MM T5 50 Perc; Ferritin − MA T0 100 MaxMin; and Sodium − MA T5 75 MaxMin. (T, Truncation limits; Control limits- MA, Moving Average, MM, Moving median; Perc, percentile)

Conclusions

The use of large, real patient datasets allows for the reliable determination of laboratory-specific PBRTQC procedures. This study demonstrates that the moving average calculation excels in both normal and transformed analyte distributions. Whilst, the benefits of PBRTQC procedures are proven, the complex and time-consuming optimisation process may be a barrier to the rapid implementation in under-resourced countries.

Abstract Image

查看原文本刊更多论文

在南非实验室使用五种分析物进行基于患者的实时质量控制 (PBRTQC) 的现实可行性。

背景：基于患者的实时质量控制（PBRTQC）通过利用实验室结果中统计指标趋势的变化来识别方法中可能存在的偏差。本研究旨在比较和优化血清丙氨酸氨基转移酶、白蛋白、钙、铁蛋白和钠的各种 PBRTQC 程序：在偏差模拟研究中，我们在患者数据区间中添加了人工偏差，然后评估了各种 PBRTQC 程序检测这种偏差的效率。使用的 PBRTQC 程序包括区块大小、移动统计量计算、控制限值以及截断限值。利用检测到误差的患者人数、误报率以及验证图表，为每种分析物选择最佳的 PBRTQC 程序：结果：为每种分析物确定的最佳 PBRTQC 程序是ALT--MA T0 50 MaxMin；白蛋白--MA T5 100 Perc；钙--MM T5 50 Perc；铁蛋白--MA T0 100 MaxMin；钠--MA T5 75 MaxMin（T，截断限值；控制限值--MA，移动平均值；MM，移动中值；Perc，百分位数）：使用大型真实患者数据集可以可靠地确定实验室特定的 PBRTQC 程序。这项研究表明，移动平均值计算在正态分布和转化后的分析物分布中都表现出色。虽然 PBRTQC 程序的优点已得到证实，但复杂耗时的优化过程可能会阻碍在资源不足的国家快速实施。

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

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.