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Yadav D, Rathore M, Banerjee M, Tomo S, Sharma P. Beyond the basics: Sigma scores in laboratory medicine with variable total allowable errors (TEa). Clin Chim Acta 2024; 565:119971. [PMID: 39326693 DOI: 10.1016/j.cca.2024.119971] [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: 05/22/2024] [Revised: 08/21/2024] [Accepted: 09/13/2024] [Indexed: 09/28/2024]
Abstract
BACKGROUND To diagnose diseases, track the effectiveness of treatments and make well-informed clinical decisions, doctors rely on results from laboratories. Accurate and precise results minimize the necessity for additional testing, saving time and money while enhancing patient satisfaction.. Internal quality control and an external quality assurance scheme(EQAS) are metrics used to evaluate a clinical laboratory's performance. One of the numerous quality indicators that can be used to gauge the amount of errors is sigma metrics. To calculate the sigma scores bias%, CV%, and Total Error Allowable (TEa) are needed. Total Error allowable(TEa) is a crucial benchmark that establishes allowed limits on the degree of deviation from the target value for a certain analyte. Nevertheless, a proper consensus for establishing a TEa goal has not been reached and the impact of this limiting factor in standard laboratory practice and sigma calculation has not been sufficiently established. Choosing the right Total Error allowable(TEa) goal is one of the greatest challenges when employing sigma metrics as depending on the source, several measurands of TEa values may exhibit alteration. MATERIAL AND METHODS Our study aims to determine the sigma scores of 20 routine chemistry parameters using six different TEa sources: Clinical Laboratory Improvement Amendment (CLIA 88'), CLIA(Clinical Laboratory Improvement Amendment) 24, BDV (Biological Variation Desirable), RCPA(Royal College of Pathologists of Australasia), RiliBak(Guideline of the German Medical Association for Quality Assurance of Laboratory Medical Examinations), and EMC/Spain(Measurement and Control Scheme) over a 12-month period using the bias percent from the External Quality Assessment Scheme (EQAS) and coefficient of variation (CV) from the Internal Quality Control (IQC). Detection system was automated, multi-channel, selective analyzer, the Beckman Coulter AU680 which works on the principle of spectrophotometry. To compute the Sigma metrics, formula used was Sigma = (TEa - Bias%) / CV%. By comparing the sigma values from the different TEa sources, TEa variance on the evaluation of the sigma metric was ascertained after which an internal quality control plan and QGI(Quality Goal Index) for underperforming parameters were devised. RESULTS The study discovered that the sigma values of common chemical parameters varied significantly based on the TEa sources used. Maximum parameters in the above three-sigma zone were TBil, HDL, CK, ALP, amylase and uric acid in CLIA'88 while RCPA and Biological variation were determined to be the most severe, with the highest performing parameters falling below three sigma zones. Rilibaek was the most liberal, with only sodium in the lower three sigma zones along with CLIA'88. The findings indicate that there is the substantial influence of various Total Error Allowable (TEa) sources on the sigma metric evaluation. A quality control plan was devised depending on different sigma scores of the analytes using biorad unity 2.0 software(westgard sigma multirules). The origins of errors that resulted in low sigma ratings liked enhanced cleaning of electrodes, electrode replacement, ageing of reagents, instrument maintainence were pinpointed and addressed. CONCLUSION The study highlights the necessity of harmonizing and standardizing sigma metrics, stressing the significance of choosing suitable total error allowable goals (TEa). The creation of worldwide standards and recommendations for total error allowable (TEa) can lead to its harmonization. Establishing a consensus on the acceptable levels of error for various laboratory tests would necessitate the cooperation of specialists from many nations and organizations in order to set such guidelines and standards.
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Affiliation(s)
- Dharmveer Yadav
- Department of Biochemistry, All India Institute of Medical Sciences All India Institute of Medical Sciences, Jodhpur, India.
| | - Mohini Rathore
- Department of Biochemistry, All India Institute of Medical Sciences All India Institute of Medical Sciences, Jodhpur, India.
| | - Mithu Banerjee
- Department of Biochemistry, All India Institute of Medical Sciences All India Institute of Medical Sciences, Jodhpur, India.
| | - Sojit Tomo
- Department of Biochemistry, All India Institute of Medical Sciences All India Institute of Medical Sciences, Jodhpur, India.
| | - Praveen Sharma
- Department of Biochemistry, All India Institute of Medical Sciences All India Institute of Medical Sciences, Jodhpur, India.
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van Heerden M, George JA, Khoza S. The application of sigma metrics in the laboratory to assess quality control processes in South Africa. Afr J Lab Med 2022; 11:1344. [PMID: 35811754 PMCID: PMC9257767 DOI: 10.4102/ajlm.v11i1.1344] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 03/30/2022] [Indexed: 11/29/2022] Open
Abstract
Background Laboratories use quality control processes to monitor and evaluate analytical performance in terms of precision and bias. Sigma metrics provide an objective assessment of laboratory quality using the total allowable error as an additional parameter. Objective This study aimed to determine the sigma metrics of analytes when using different total allowable error guidelines. Methods A retrospective analysis was performed on 19 general chemistry analytes at Charlotte Maxeke Johannesburg Academic Hospital in South Africa between January 2017 and December 2017. Sigma metrics were calculated on two identical analysers, using internal quality control data and total allowable error guidelines from the Ricos biological variation database and three alternative sources (the Royal College of Pathologists of Australasia, the Clinical Laboratory Improvements Amendment, and the European Federation of Clinical Chemistry and Laboratory Medicine). Results The sigma performance was similar on both analysers but varied based on the guideline used, with the Clinical Laboratory Improvements Amendment guidelines resulting in the best sigma metrics (53% of analytes on one analyser and 46% on the other had acceptable sigma metrics) and the Royal College of Pathologists of Australia guidelines being the most stringent (21% and 23%). Sodium and chloride performed poorly across all guidelines (sigma < 3). There were also month-to-month variations that may result in acceptable sigma despite poor performance during certain months. Conclusion The sigma varies greatly depending on the total allowable error, but could be a valuable tool to save time and decrease costs in high-volume laboratories. Sigma metrics calculations need to be standardised.
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Affiliation(s)
- Marli van Heerden
- National Health Laboratory Service, Johannesburg, South Africa
- Faculty of Health Sciences, Charlotte Maxeke Johannesburg Academic Hospital, University of the Witwatersrand, Johannesburg, South Africa
| | - Jaya A. George
- National Health Laboratory Service, Johannesburg, South Africa
- Faculty of Health Sciences, Charlotte Maxeke Johannesburg Academic Hospital, University of the Witwatersrand, Johannesburg, South Africa
| | - Siyabonga Khoza
- National Health Laboratory Service, Johannesburg, South Africa
- Faculty of Health Sciences, Charlotte Maxeke Johannesburg Academic Hospital, University of the Witwatersrand, Johannesburg, South Africa
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Wauthier L, Di Chiaro L, Favresse J. Sigma Metrics in Laboratory Medicine: A Call for Harmonization. Clin Chim Acta 2022; 532:13-20. [PMID: 35594921 DOI: 10.1016/j.cca.2022.05.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/27/2022] [Accepted: 05/13/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND AIM Sigma metrics are applied in clinical laboratories to assess the quality of analytical processes. A parameter associated to a Sigma >6 is considered "world class" whereas a Sigma <3 is "poor" or "unacceptable". The aim of this retrospective study was to quantify the impact of different approaches for Sigma metrics calculation. MATERIAL AND METHODS Two IQC levels of 20 different parameters were evaluated for a 12-month period. Sigma metrics were calculated using the formula: (allowable total error (TEa) (%) - bias (%))/(coefficient of variation (CV) (%)). Method precision was calculated monthly or annually. The bias was obtained from peer comparison program (PCP) or external quality assessment program (EQAP), and 9 different TEa sources were included. RESULTS There was a substantial monthly variation of Sigma metrics for all combinations, with a median variation of 32% (IQR, 25.6-41.3%). Variation across multiple analyzers and IQC levels were also observed. Furthermore, TEa source had the highest impact on Sigma calculation with proportions of Sigma >6 ranging from 17.5% to 84.4%. The nature of bias was less decisive. CONCLUSION In absence of a clear consensus, we recommend that laboratories calculate Sigma metrics on a sufficiently long period of time (>6 months) and carefully evaluate the choice of TEa source.
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Affiliation(s)
- Loris Wauthier
- Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium
| | - Laura Di Chiaro
- Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium
| | - Julien Favresse
- Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium; Department of Pharmacy, Namur Research Institute for LIfe Sciences, University of Namur, Namur, Belgium.
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Zeng Y, Peng S, Meng L, Huang H. Establishment of risk quality control charts based on risk management strategies. Ann Clin Biochem 2022; 59:288-295. [PMID: 35196900 DOI: 10.1177/00045632221086468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Risk management strategies have been proposed for applications in clinical laboratories to reduce patient risks; however, effective and visual risk-monitoring tools are currently lacking in medical laboratories. In this study, we constructed a risk quality control (QC) chart based on risk management strategies. METHODS We calculated the risk levels of QC materials based on Bayes' theorem by combining the total allowable error, QC results, and the maximum number of unacceptable errors in the laboratory. Then, we constructed a risk QC chart by presenting the Z values and corresponding risk levels of QC materials simultaneously. Finally, we evaluated the risk-monitoring capabilities of the risk QC charts by simulating different long-term errors in the laboratory. RESULTS The risk levels of QC materials increased as the QC results moved further away from the set mean. Larger sigma values led to fewer risks obtained for the same QC results. The constructed risk QC charts intuitively showed specific risk levels and could warn lab staff out-of-control, without the need for QC rules to make judgments. The risk levels of erroneous results differed for items with different sigma performance. CONCLUSIONS Risk-based QC charts allowed visualization of the QC results and specific risk levels simultaneously, providing more intuitive results than those obtained from traditional QC charts.
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Affiliation(s)
- Yuping Zeng
- Department of Laboratory Medicine, West China Hospital, 34753Sichuan University, China
| | - Shiyun Peng
- Department of Laboratory Medicine, West China Hospital, 34753Sichuan University, China
| | - Liye Meng
- Department of Laboratory Medicine, West China Hospital, 34753Sichuan University, China
| | - Hengjian Huang
- Department of Laboratory Medicine, West China Hospital, 34753Sichuan University, China
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Luo Y, Yan X, Xiao Q, Long Y, Pu J, Li Q, Cai Y, Chen Y, Zhang H, Chen C, Ou S. Application of Sigma metrics in the quality control strategies of immunology and protein analytes. J Clin Lab Anal 2021; 35:e24041. [PMID: 34606652 PMCID: PMC8605144 DOI: 10.1002/jcla.24041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/19/2022] Open
Abstract
Background Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma. Methods Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C‐reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values. Results While using the TEaNCCL, 90% analytes had a world‐class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σNCCL that is calculated from TEaNCCL, Sigma Method Decision Charts were constructed. For Cys C, five multi‐rules (13s/22s/R4s/41s/6X, N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (13s, N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12). Conclusions The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool.
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Affiliation(s)
- Yanfen Luo
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Xingxing Yan
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Qian Xiao
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yifei Long
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Jieying Pu
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Qiwei Li
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yimei Cai
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yushun Chen
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Hongyuan Zhang
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Cha Chen
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Songbang Ou
- Reproductive center, Department of Obstetrics and Gynecology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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Park H, Ko Y. Internal Quality Control Data of Urine Reagent Strip Tests and Derivation of Control Rules Based on Sigma Metrics. Ann Lab Med 2021; 41:447-454. [PMID: 33824232 PMCID: PMC8041599 DOI: 10.3343/alm.2021.41.5.447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 10/12/2020] [Accepted: 03/17/2021] [Indexed: 11/19/2022] Open
Abstract
Background Urine reagent strip test (URST) results are semi-quantitative; therefore, the precision of URSTs is evaluated as the proportion of categorical results from repeated measurements of a sample that are concordant with an expected result. However, URSTs have quantitative readout values before ordinal results challenging statistical monitoring for internal quality control (IQC) with control rules. This study aimed to determine the sigma metric of URSTs and derive appropriate control rules for IQC. Methods The URiSCAN Super Plus fully automated urine analyzer (YD Diagnostics, Yongin, Korea) was used for URSTs. Change in reflectance rate (change %R) data from IQC for URSTs performed between November 2018 and May 2020 were analyzed. Red blood cells, bilirubin, urobilinogen, ketones, protein, glucose, leukocytes, and pH were measured from 2-3 levels of control materials. The total allowable error (TEa) for a grade was the difference in midpoints of a predefined change %R range between two adjacent grades. The sigma metric was calculated as TEa/SD. Sigma metric-based control rules were determined with Westgard EZ Rules 3 software (Westgard QC, Madison, WI, USA). Results Seven out of the eight analytes had a sigma metric >4 in the control materials with a negative grade (-), which were closer to the cut-offs. Corresponding control rules ranged from 12.5s to 13.5s. Conclusions Although the URST is a semi-quantitative test, statistical IQC can be performed using the readout values. According to the sigma metric, control rules recommended for URST IQC in routine clinical practice are 12.5s to 13.5s.
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Affiliation(s)
- Haeil Park
- Department of Laboratory Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Younsuk Ko
- Department of Laboratory Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Kanani FZ, Haider Kazmi A, Kaleem B. Sigma metrics of Alinity ci system - a study on thirty-nine clinical chemistry and immunoassay parameters. ADVANCES IN LABORATORY MEDICINE 2021; 2:267-285. [PMID: 37363324 PMCID: PMC10197361 DOI: 10.1515/almed-2021-0001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 02/22/2021] [Indexed: 06/28/2023]
Abstract
Objectives Sigma metrics in an invaluable and inexpensive tool used in laboratories to monitor analytical quality of the assays. Alinity ci platform is a relatively recent analytical system launched by Abbott Diagnostics, and as such performance studies on it are few. We have calculated sigma metrics of 39 clinical chemistry and immunoassay analytes on two Alinity ci systems. Methods Sigma metrics were calculated using results of method validation studies. Coefficient of variation (CV) was calculated according to CLSI EP 15 guidelines. Bias was calculated using three different methods i.e., proficiency testing material, alternate method comparison with existent analyzers and linearity experiment. Total allowable error limits were kept similar to or less than the ones used in reference studies. Results All analytes except blood urea nitrogen (BUN) demonstrated greater than six sigma value across one or more levels and methods. No analyte amongst clinical chemistry and immunoassays was at below three sigma class. Amongst electrolytes, sodium was below three sigma class at two levels by proficiency testing method, although it was above four sigma class by other two methods. Sigma levels obtained were comparable to those reported in previously published studies. Conclusions Acceptable sigma metrics were achieved for all clinical chemistry, immunoassays and electrolytes on Alinity ci. Sigma metrics is an objective and well established cost effective tool to tailor internal quality control practices. This study determines sigma metrics for a wide range of high throughput assays. Long term assay performance needs to be monitored.
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Affiliation(s)
- Fatima Zehra Kanani
- Department of Pathology, Section of Chemical Pathology, The Indus Hospital, Karachi, Pakistan
| | - Adnan Haider Kazmi
- Department of Pathology, Section of Chemical Pathology, The Indus Hospital, Karachi, Pakistan
| | - Bushra Kaleem
- Indus Hospital Research Centre, The Indus Hospital, Karachi, Pakistan
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Xia Y, Li M, Li B, Xue H, Lin Y, Li J, Ji L. Sigma metrics application for validated and non-validated detecting systems performance assessment. J Clin Lab Anal 2020; 35:e23676. [PMID: 33314338 PMCID: PMC7957966 DOI: 10.1002/jcla.23676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 10/27/2020] [Accepted: 11/22/2020] [Indexed: 12/14/2022] Open
Abstract
Background Sigma metrics provide an objective and quantitative methodology for analytical quality evaluation of clinical laboratory. This study investigated the testing performance of validated systems and non‐validated systems based on sigma metrics, and explored the major parameters affecting the system performance. Methods Sigma metrics were evaluated by six biochemistry assays based on Beckman and Mindray validated and non‐validated systems through crossing the reagents and analyzers. Imprecision and bias were assessed for all assays based on trueness programs organized by National Centre for Clinical Laboratory. Total error allowance obtained from the Chinese Ministry of Health Clinical Laboratory Centre Industry Standard (WS/T403‐2012). Results The imprecision for all systems meets the quality specifications except TP assay (2.19%) detected by Mindray non‐validated system, and the bias for four assays measured by non‐validated systems cannot fulfill the criterion, including lactate dehydrogenase (LDH), total protein (TP), triglycerides (TG), and glucose (GLU). Higher biases were detected in six assays at different levels among non‐validated and validated systems. Systems performed poorly or unacceptably for TP assay with sigma metrics lower than 3 except Mindray non‐validated system. The sigma metrics for other assays with four systems were greater than 3 except the LDH evaluated on Mindray non‐validated systems. Conclusion Non‐validated systems may introduce performance uncertainty compared with validated systems based on sigma metrics evaluation, and lower bias was provided by validated systems. The performance of non‐validated systems should be evaluated thoroughly in the clinical laboratory before they were adopted for routine use.
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Affiliation(s)
- Yong Xia
- Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China
| | - Mingyang Li
- Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China
| | - Bowen Li
- Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China
| | - Hao Xue
- Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China
| | - Yu Lin
- Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jie Li
- Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China
| | - Ling Ji
- Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China
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Liu Y, Cao Y, Liu X, Wu L, Cai W. Evaluation of the analytical performance of endocrine analytes using sigma metrics. J Clin Lab Anal 2020; 35:e23581. [PMID: 32951270 PMCID: PMC7843286 DOI: 10.1002/jcla.23581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/24/2020] [Accepted: 08/29/2020] [Indexed: 11/11/2022] Open
Abstract
Background (a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (σ) model. (b) To redesign quality control strategies for performance improvement. Methods The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re‐evaluated in September 2018 after a series of targeted corrective actions. Results Based on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (13S) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with σ < 4 at one QC material level or both needed multiple rules (13S/22S/R4S/41S/10X) with N6 and R10‐500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re‐evaluated sigma metrics of all the analytes increased significantly. Conclusions It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.
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Affiliation(s)
- Yanming Liu
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China.,Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Institute of Aging Research, Guangdong Medical University, Dongguan, China
| | - Yue Cao
- Department of Medical Technology, Medical College of Shaoguan University, Shaoguan, China
| | - Xijun Liu
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China
| | - Liangyin Wu
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China
| | - Wencan Cai
- Department of Laboratory Medicine, YueBei People's Hospital, Shaoguan, China
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Li R, Wang T, Gong L, Peng P, Yang S, Zhao H, Xiong P. Comparative analysis of calculating sigma metrics by a trueness verification proficiency testing-based approach and an internal quality control data inter-laboratory comparison-based approach. J Clin Lab Anal 2019; 33:e22989. [PMID: 31386228 PMCID: PMC6868403 DOI: 10.1002/jcla.22989] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 07/02/2019] [Accepted: 07/11/2019] [Indexed: 11/29/2022] Open
Abstract
Introduction Two methods were compared for evaluating the sigma metrics of clinical biochemistry tests using two different allowable total error (TEa) specifications. Materials and methods The imprecision (CV%) and bias (bias%) of 19 clinical biochemistry analytes were calculated using a trueness verification proficiency testing (TPT)‐based approach and an internal quality control data inter‐laboratory comparison (IQC)‐based approach, respectively. Two sources of total allowable error (TEa), the Clinical Laboratory Improvement Amendments of 1988 (CLIA '88) and the People's Republic of China Health Industry Standard (WS/T 403‐2012), were used to calculate the sigma metrics (σCLIA, σWS/T). Sigma metrics were calculated to provide a single value for assessing the quality of each test based on a single concentration level. Results For both approaches, σCLIA > σWS/T in 18 out of 19 assays. For the TPT‐based approach, 16 assays showed σCLIA > 3, and 12 assays showed σWS/T > 3. For the IQC‐based approach, 19 and 16 assays showed σCLIA > 3 and σWS/T > 3, respectively. Conclusions Both methods can be used as references for calculating sigma metrics and designing QC schedules in clinical laboratories. Sigma metrics should be evaluated comprehensively by different approaches.
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Affiliation(s)
- Runqing Li
- Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Tengjiao Wang
- Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Lijun Gong
- Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Peng Peng
- Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Song Yang
- Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Haibin Zhao
- Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Pan Xiong
- Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
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