Icteria interference for 34 clinical chemistry analytes on different analytical platforms: Method or analyzer dependent?

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2024-10-09 DOI:10.1016/j.cca.2024.119993

Alen Vrtaric , Marijana Miler , Nora Nikolac Gabaj , Valentina Vidranski , Marina Bocan , Petra Filipi , Andrea Snagic , Marija Kocijancic

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引用次数: 0

Abstract

Objectives

In this study, we aimed to investigate the effect of increasing bilirubin concentration on 34 commonly measured clinical chemistry analytes on four different analytical platforms. We hypothesized that differences in icteria interference are not only method dependent, but also analyzer dependent.

Methods

Serum pool was prepared using leftover samples after routine laboratory blood testing. Serum pool was then spiked with dissolved bilirubin stock. Measurements were performed on all four locations at the same time. All measurements were done in duplicate. Mean value was calculated as: (value₁ + value₂)/2. Those values were multiplied by corresponding dilution factors obtained during the preparation of icteric samples. For each icteric sample (I_x), bias against native (I₀) sample was calculated as ((value I_x– valueI₀)/ valueI₀) × 100 %. Bias was calculated with actual average values. Obtained bias values were compared against acceptance criteria according to External quality assurance (EQA) providers. Difference in bilirubin concentration across platforms was tested using Friedman ANOVA. P values < 0.05 were considered statistically significant. Data are collected and analyzed in MS Excel 2016 (Microsoft, Redmond, Washington) and MedCalc® Statistical Software version 20.015 (MedCalc Software Ltd, Ostend, Belgium).

Results

Many of the tested parameters demonstrated low sensitivity to icterus interference. The highest sensitivity to icterus was observed for triglycerides, cholesterol, and urate.

Conclusions

Our results indicate that while some common icteric interferences were consistent across all tested platforms, others were specific to the analyzer used, even when employing the same analytical methods.

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不同分析平台上 34 种临床化学分析物的异体干扰：取决于方法还是分析仪？

研究目的本研究旨在调查胆红素浓度增加对四种不同分析平台上 34 种常用临床化学分析物的影响。我们假设teria干扰的差异不仅与方法有关，还与分析仪有关：方法：使用实验室常规血液检测后的剩余样本制备血清池。方法：使用实验室常规血液检测后的剩余样本制备血清池，然后在血清池中添加溶解胆红素原液。在所有四个地点同时进行测量。所有测量均重复进行。平均值计算公式为(value1 + value2)/2。这些值乘以制备黄疸样本时获得的相应稀释因子。每个黄疸样本（Ix）与原生样本（I0）的偏差计算公式为（（值 Ix- 值 I0）/值 I0）×100%。偏差以实际平均值计算。根据外部质量保证（EQA）提供商的验收标准，将获得的偏差值与验收标准进行比较。使用弗里德曼方差分析检验了不同平台胆红素浓度的差异。P 值结果：许多测试参数对黄疸干扰的敏感度较低。甘油三酯、胆固醇和尿酸盐对黄疸的敏感性最高：我们的研究结果表明，虽然一些常见的黄疸干扰在所有测试平台上都是一致的，但其他干扰则是所用分析仪所特有的，即使采用相同的分析方法也是如此。

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.