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Jones GRD. Using analytical performance specifications in a medical laboratory. Clin Chem Lab Med 2024; 62:1512-1519. [PMID: 38624006 DOI: 10.1515/cclm-2024-0102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/03/2024] [Indexed: 04/17/2024]
Abstract
Analytical performance specifications (APS) are used for the quantitative assessment of assay analytical performance, with the aim of providing information appropriate for clinical care of patients. One of the major locations where APS are used is in the routine clinical laboratory. These may be used to assess and monitor assays in a range of settings including method selection, method verification or validation, external quality assurance, internal quality control and assessment of measurement uncertainty. The aspects of assays that may be assessed include imprecision, bias, selectivity, sample type, analyte stability and interferences. This paper reviews the practical use of APS in a routine clinical laboratory, using the laboratory I supervise as an example.
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Affiliation(s)
- Graham Ross Dallas Jones
- Department of Chemical pathology, SydPath, St Vincent's Hospital, Darlinghurst, NSW, Australia
- Facult of Medicine, University of NSW, Kensington, Australia
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Wang W, Zhang Z, Zhang C, Zhao H, Yuan S, Liu J, Dong N, Wang Z, Kang F. Evaluation of Coefficients of Variation for Clinical Chemistry Tests Based on Internal Quality Control Data Across 5,425 Laboratories in China From 2013 to 2022. Ann Lab Med 2024; 44:245-252. [PMID: 38014482 PMCID: PMC10813826 DOI: 10.3343/alm.2023.0236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/25/2023] [Accepted: 11/07/2023] [Indexed: 11/29/2023] Open
Abstract
Background Clinical chemistry tests are most widely used in clinical laboratories, and diverse measurement systems for these analyses are available in China. We evaluated the imprecision of clinical chemistry measurement systems based on internal QC (IQC) data. Methods IQC data for 27 general chemistry analytes were collected in February each year from 2013 to 2022. Four performance specifications were used to calculate pass rates for CVs of IQC data in 2022. Boxplots were drawn to analyze trends of CVs, and differences in CVs among different groups were assessed using the Mann-Whitney U-test or Kruskal-Wallis test. Results The number of participating laboratories increased significantly from 1,777 in 2013 to 5,425 in 2022. CVs significantly decreased for all 27 analytes, except creatine kinase and lipase. Triglycerides, total bilirubin, direct bilirubin, iron, and γ-glutamyl transferase achieved pass rates >80% for all goals. Nine analytes with pass rates <80% based on 1/3 allowable total error were further analyzed; the results indicated that closed systems exhibited lower CVs than open systems for all analytes, except total protein. For all nine analytes, differences were significant between tertiary hospitals and non-tertiary hospitals and between accredited and non-accredited laboratories. Conclusions The CVs of IQC data for clinical chemistry have seen a continuous overall improvement in China. However, there is ample room for imprecision improvement for several analytes, with stricter performance specifications.
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Affiliation(s)
- Wei Wang
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Zhixin Zhang
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Chuanbao Zhang
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Haijian Zhao
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Shuai Yuan
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Jiali Liu
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Na Dong
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Zhiguo Wang
- National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Fengfeng Kang
- Laboratory Medicine Center, Zhejiang Center for Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, China
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Coskun A. Bias in Laboratory Medicine: The Dark Side of the Moon. Ann Lab Med 2024; 44:6-20. [PMID: 37665281 PMCID: PMC10485854 DOI: 10.3343/alm.2024.44.1.6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 04/15/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.
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Affiliation(s)
- Abdurrahman Coskun
- Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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Uçar KT, Çat A. A comparative analysis of Sigma metrics using conventional and alternative formulas. Clin Chim Acta 2023; 549:117536. [PMID: 37696426 DOI: 10.1016/j.cca.2023.117536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/31/2023] [Accepted: 09/02/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND AND AIM The Six Sigma approach, employing Sigma Metrics (SM), is commonly used to evaluate analytical performance in clinical laboratories. However, there is ongoing debate regarding the suitability of the conventional SM formula, which incorporates total allowable error (TEa) and bias. To address this, an alternative formula based on within-subject biological variation (CVI) as the tolerance range (TR) has been proposed. The study aimed to calculate and compare SM values using both formulas. MATERIAL AND METHODS Twenty clinical chemistry parameters were evaluated, and SM values were calculated using conventional formula with two TEa goals and the alternative formula. Intermediate precision (CVA%) values were obtained from internal quality control data, while bias values were derived from external quality assessment reports. RESULTS The results showed that using the conventional formula, 11 SM values based on CLIA TEa goals and 21 SM values based on BV TEa goals were deemed unacceptable (SM < 3). Additionally, 22 SM values calculated using the alternative formula were below 3. CONCLUSION The choice of TR had a substantial impact on the assessed analytical performance. Laboratories should carefully consider the appropriateness of each approach based on their specific quality objectives, analyte characteristics, and laboratory operations.
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Affiliation(s)
- Kamil Taha Uçar
- Health Science University, Istanbul Basaksehir Cam and Sakura City Hospital, Department of Medical Biochemistry, Istanbul, Turkey.
| | - Abdulkadir Çat
- Health Science University, Istanbul Gaziosmanpasa Training and Research Hospital, Medical Biochemistry, Istanbul, Turkey
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Application of a six sigma model to evaluate the analytical performance of cerebrospinal fluid biochemical analytes and the design of quality control strategies for these assays: A single-centre study. Clin Biochem 2023; 114:73-78. [PMID: 36796711 DOI: 10.1016/j.clinbiochem.2023.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/05/2023] [Accepted: 02/11/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND In this study, we applied a six sigma model to examine cerebrospinal fluid (CSF) biochemical analytes for the first time. Our goal was to evaluate the analytical performance of various CSF biochemical analytes, design an optimized internal quality control (IQC) strategy, and formulate scientific and reasonable improvement plans. METHODS The sigma values of CSF total protein (CSF-TP), albumin (CSF-ALB), chloride (CSF-Cl), and glucose (CSF-GLU) were calculated using the following formula: sigma = [TEa(%)-|bias(%)|]/CV(%). The analytical performance of each analyte was shown using a normalized sigma method decision chart. Individualized IQC schemes and improvement protocols for CSF biochemical analytes were formulated using the Westgard sigma rule flow chart with batch size and quality goal index (QGI). RESULTS The distribution of sigma values for CSF biochemical analytes ranged from 5.0 to 9.9, and the sigma values varied for different concentrations of the same analyte. The analytical performance of the CSF assays at the two QC levels is displayed visually in normalized sigma method decision charts. Individualized IQC strategies for CSF biochemical analytes were as follows: for CSF-ALB, CSF-TP and CSF-Cl, use 13s with N = 2 and R = 1000; for CSF-GLU, use 13s/22s/R4s with N = 2 and R = 450. In addition, priority improvement measures for analytes with sigma values less than 6 (CSF-GLU) were formulated based on the QGI, and their analytical performance was improved after the corresponding improvement measures were taken. CONCLUSIONS The six sigma model has significant advantages in practical applications involving CSF biochemical analytes and is highly useful for quality assurance and quality improvement.
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Coşkun A. Bias, the unfinished symphony. Biochem Med (Zagreb) 2022; 32:030402. [PMID: 36277430 PMCID: PMC9562803 DOI: 10.11613/bm.2022.030402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 11/24/2022] Open
Abstract
In laboratory medicine, mathematical equations are frequently used to calculate various parameters including bias, imprecision, measurement uncertainty, sigma metric (SM), creatinine clearance, LDL-cholesterol concentration, etc. Mathematical equations have strict limitations and cannot be used in all situations and are not open to manipulations. Recently, a paper “Bias estimation for Sigma metric calculation: Arithmetic mean versus quadratic mean” was published in Biochemia Medica. In the paper, the author criticized the approach of taking the arithmetic mean of the multiple biases to obtain a single bias and proposed a quadratic method to estimate the overall bias using external quality assurance services (EQAS) data for SM calculation. This approach does not fit the purpose and it should be noted that using the correct equation in calculations is as important as using the correct reagent in the measurement of the analytes, therefore before using an equation, its suitability should be checked and confirmed.
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