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Panda CR, Kumari S, Mangaraj M, Nayak S. The Evaluation of the Quality Performance of Biochemical Analytes in Clinical Biochemistry Laboratory Using Six Sigma Matrices. Cureus 2023; 15:e51386. [PMID: 38292960 PMCID: PMC10826247 DOI: 10.7759/cureus.51386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/31/2023] [Indexed: 02/01/2024] Open
Abstract
Introduction This study was conducted to assess the analytical performance of biochemical tests using Six Sigma methodology and to assess the underlying causes of unsatisfied performance of analytes with a sigma value of less than 4 using quality goal index (QGI) and root cause analysis (RCA). Methodology Daily data for internal quality control (IQC) for both level 1 (L1) and level 2 (L2) and monthly data for external quality assessment for a period of six months were recorded. The coefficient of variation (CV), bias, and total allowable error (TEa) were calculated to analyze the sigma (σ) values for 19 biochemical analytes. Quality goal index (QGI) analysis was done to analyze impressions and inaccuracies in analyte performance having a sigma value of less than 4. Root cause analysis (RCA) was done to evaluate the possible causes that can improve quality performance. Results Creatinine and high-density lipoprotein (HDL) had sigma metrics of ≤2.0, and chloride, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) had sigma values between 2 and 3. Glucose, total protein (TP), phosphate (Phos), and potassium had sigma values between 4 and 5 in level 1 QC. Sigma grading for level 2 quality control (QC) also gave similar results. For analytes with σ < 4, QGI analysis exposed inaccuracy or imprecision issues and identified errors such as the reconstitution of IQC, storage temperature, and air bubbles while processing the QC, being common causes of poor performance. Conclusion Six Sigma approach is helpful for quality assurance and identifying areas for improvement. Assessing Six Sigma metrics should be a routine practice to decide the frequency of QC run and to detect errors in analysis.
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Affiliation(s)
- Chhabi R Panda
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
| | - Suchitra Kumari
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
| | | | - Saurav Nayak
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
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Pašić A, Šeherčehajić E. "Six Sigma" standard as a level of quality of biochemical laboratories. SANAMED 2022. [DOI: 10.5937/sanamed0-40408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The principal role of biochemical laboratories is responsibility for reliable, reproducible, accurate, timely, and accurately interpreted analysis results that help in making clinical decisions, while ensuring the desired clinical outcomes. To achieve this goal, the laboratory should introduce and maintain quality control in all phases of work. The importance of applying the Six SIGMA quality model has been analyzed in a large number of scientific studies. The purpose of this review is to highlight the importance of using six SIGMA metrics in biochemical laboratories and the current application of six SIGMA metrics in all laboratory work procedures. It has been shown that the six SIGMA model can be very useful in improving all phases of laboratory work, as well as that a detailed assessment of all procedures of the phases of work and improvement of the laboratory's quality control system is crucial for the laboratory to have the highest level of six SIGMA. Clinical laboratories should use SIGMA metrics to monitor their performance, as it makes it easier to identify gaps in their performance, thereby improving their efficiency and patient safety. Medical laboratory quality managers should provide a systematic methodology for analyzing and correcting quality assurance systems to achieve Six SIGMA quality-level standards.
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Moodley N, Gounden V. Assessment of Sigma Metrics for Routine Chemistry Testing in 4 Laboratories in Kwa-Zulu Natal, South Africa. J Appl Lab Med 2021; 7:689-697. [PMID: 34636901 DOI: 10.1093/jalm/jfab117] [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: 06/15/2021] [Accepted: 08/16/2021] [Indexed: 11/12/2022]
Abstract
BACKGROUND Sigma metrics is a quantitative management tool. This study assessed the Six Sigma score for 26 chemistry analytes, compared scores with different total allowable errors (TEa) and use of scores for internal quality control (IQC) rules in 4 Laboratories in Kwa-Zulu Natal, South Africa. METHODS Utilizing 6 months of IQC SD, CV, and bias data on albumin, alkaline phosphatase, alanine aminotransferase, amylase, aspartate aminotransferase, bicarbonate, calcium, total cholesterol, creatine kinase, chloride, creatinine, gamma glutamyl transferase, glucose, HDL-cholesterol, potassium, lactate dehydrogenase, magnesium, sodium, inorganic phosphate, direct bilirubin, total bilirubin, triglycerides, total protein, urea nitrogen, uric acid, and C-reactive protein (CRP) Six Sigma scores were calculated using Microsoft Excel 2016 and ideal IQC rules were determined. Six Sigma scores using Ricos et al. 2014, Royal College of Pathologists Australasia, and Clinical Laboratory Improvement Amendments TEas were compared. RESULTS For levels 1, 2, and 3 respectively, analytes scoring >3 sigma was 9 (35%), 12 (46%), and 14 (54%) in Laboratory A; Laboratory B had 15 (58%), 19 (73%), and 17 (65%); Laboratory C had 12 (46%), 13 (50%), and 15 (58%); and Laboratory D had 13 (50%), 18 (69%), and 18 (69%). Albumin, calcium, sodium, magnesium, bicarbonate, and chloride scored <3; CRP scored >6 for all. In Laboratories A, B, C, and D, 7 (27%), 7 (27%), 6 (23%), and 8 (31%) analytes, respectively, required only 1 IQC rule. One of 21 analytes for Laboratories C and D, 3 for Laboratory A, and 0 for Laboratory B had the same sigma score with all 3 databases. CONCLUSION Despite South Africa being a developing nation, many analytes are able to achieve >3 sigma.
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Affiliation(s)
- Nareshni Moodley
- Department of Chemical Pathology, Inkosi Albert Luthuli Central Hospital, National Health Laboratory Services and University of Kwa-Zulu Natal, Durban, South Africa
| | - Verena Gounden
- Department of Chemical Pathology, Inkosi Albert Luthuli Central Hospital, National Health Laboratory Services and University of Kwa-Zulu Natal, Durban, South Africa
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Ahmed El-Neanaey W, Mahmoud AbdEllatif N, Abdel Haleem Abo Elwafa R. Evaluation of Sigma metric approach for monitoring the performance of automated analyzers in hematology unit of Alexandria Main University Hospital. Int J Lab Hematol 2021; 43:1388-1393. [PMID: 34275191 DOI: 10.1111/ijlh.13660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/03/2021] [Accepted: 06/29/2021] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Sigma metric offers a quantitative framework for evaluating process performance in clinical laboratories. This study aimed to evaluate the analytical performance of automated analyzers in hematology unit of Alexandria Main University Hospital using the sigma metric approach. MATERIALS AND METHODS Quality control data were collected for 6 months, and sigma value was calculated from hematology analyzers SYSMEX (XN 1000, XT 1800i), ADVIA (2120i, 2120), and coagulation analyzers SYSMEX CA 1500 (3610, 6336). RESULTS For the normal control level, satisfactory mean sigma value ≥3 was observed for all of the studied parameters by all analyzers. For the high control level, red blood cell count by ADVIA 2120, and hematocrit by ADVIA (2120i and 2120) performed poorly with a mean sigma value <3. For the low control level, red blood cell count by ADVIA (2120i and 2120), hemoglobin by ADVIA 2120, hematocrit by ADVIA (2120i and 2120) and SYSMEX XN 1000, platelet count by the SYSMEX XT 1800i also performed poorly with a mean sigma value <3. Satisfactory mean sigma value of ≥3 was observed for prothrombin time and activated partial thromboplastin time for both normal and pathological control levels and analyzers. CONCLUSION Sigma metrics can be used as a guide to make QC strategy and plan QC frequency and can facilitate the comparison of the same assay performance across multiple systems. Harmonization for TEa source is recommended to standardize sigma value calculation.
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Affiliation(s)
- Wafaa Ahmed El-Neanaey
- Department of Clinical and Chemical pathology, Faculty of Medicine, University of Alexandria, Azarita Medical Campus, Alexandria, Egypt
| | - Nihal Mahmoud AbdEllatif
- Department of Clinical and Chemical pathology, Faculty of Medicine, University of Alexandria, Azarita Medical Campus, Alexandria, Egypt
| | - Reham Abdel Haleem Abo Elwafa
- Department of Clinical and Chemical pathology, Faculty of Medicine, University of Alexandria, Azarita Medical Campus, Alexandria, Egypt
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Goel P, Malik G, Prasad S, Rani I, Manhas S, Goel K. Analysis of performance of clinical biochemistry laboratory using Sigma metrics and Quality Goal Index. Pract Lab Med 2021; 23:e00195. [PMID: 33392370 PMCID: PMC7773579 DOI: 10.1016/j.plabm.2020.e00195] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 12/11/2020] [Indexed: 02/07/2023] Open
Abstract
Background Unreliable and ingenuine results issued by clinical laboratories have serious consequences for the patients. Sigma metrics is a standardized tool for Quality assessment for test performance in a laboratory. Objective To evaluate the performance of routine biochemistry laboratory at MMIMSR, Mullana in terms of Sigma metrics and Quality Goal Index. Material and methods This cross sectional study evaluated performance of 14 routine chemistry parameters using retrospective Internal Quality Control data of two levels on Siemens Dimension Rxl from Feb to Jul 2019 for CV% and EQAS reports from CMC, Vellore for Bias%. Sigma metrics was calculated using total allowable error targets as per CLIA and Biological Variability database guidelines. Results For level-2 IQC; TG, Chol, ALP showed excellent performance with σ > 6 while σ < 3 was observed for AST, Total Protein, Glucose, BUN and ALT using CLIA guidelines while in IQC Level-3 poor performers were only BUN and ALT with Ca, TG and Chol showing σ > 6. Further by using Biological Variability data guidelines; 10 parameters of IQC Level-2 and 5 of IQC level-3 were poor performers with σ < 3. Conclusion Sigma metrics is an excellent tool for performance analysis of tests performed in a clinical laboratory. Lack of precision in terms of CV% was seen for majority of the poor performers. Total allowable error targets using Biological Variability data revealed σ < 3 for 10 parameters while using CLIA guidelines σ < 3 was seen for only 5 parameters of IQC level-2.
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Affiliation(s)
- Parul Goel
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
- Corresponding author.
| | - Gagandeep Malik
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Suvarna Prasad
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Isha Rani
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Sunita Manhas
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Kapil Goel
- Department of Community Medicine & School of Public Health, Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh, 160012, India
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Mayne ALH, Mayne ES, Louw S. Proposed application of six sigma metrics using a gamma distribution to monitor turnaround time for a high‐volume coagulation test before and after introduction of a total laboratory automation platform in an academic laboratory in South Africa. Int J Lab Hematol 2020; 42:e124-e127. [DOI: 10.1111/ijlh.13163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/09/2020] [Accepted: 01/24/2020] [Indexed: 11/28/2022]
Affiliation(s)
- Anthony Leland Hamilton Mayne
- Department of Molecular Medicine and Haematology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa
| | - Elizabeth Sarah Mayne
- Department of Immunology National Health Laboratory Service and Faculty of Health Sciences University of Witwatersrand Johannesburg South Africa
| | - Susan Louw
- Department of Molecular Medicine and Haematology National Health Laboratory Service and Faculty of Health Sciences University of Witwatersrand Johannesburg South Africa
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TARGET SCORE OF RIQAS AND SIGMA METRICS FOR EVALUATING THE ANALYTICAL PERFORMANCE OF THYROID FUNCTION TESTING ON ADVIA CENTAUR XPT IMMUNOASSAY ANALYSER. ACTA ACUST UNITED AC 2019. [DOI: 10.14260/jemds/2019/98] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Verma M, Dahiya K, Ghalaut VS, Dhupper V. Assessment of quality control system by sigma metrics and quality goal index ratio: A roadmap towards preparation for NABL. World J Methodol 2018; 8:44-50. [PMID: 30519539 PMCID: PMC6275555 DOI: 10.5662/wjm.v8.i3.44] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/04/2018] [Accepted: 10/17/2018] [Indexed: 02/07/2023] Open
Abstract
AIM To study sigma metrics and quality goal index ratio (QGI).
METHODS The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board for Testing and Calibration of Laboratories accredited lab as per the International Organization for Standardization 15189:2012 and provides service to a > 1700-bed tertiary care hospital. Data of 16 analytes was extracted over a period of one year from January 2017 to December 2017 for calculation of precision, accuracy, sigma metrics, total error, and QGI.
RESULTS The average coefficient of variation ranged from 2.12% (albumin) to 5.42% (creatinine) for level 2 internal quality control and 2% (albumin) to 3.62% (high density lipoprotein-cholesterol) for level 3 internal quality control. Average coefficient of variation of all the parameters was below 5%, reflecting very good precision. The sigma metrics for level 2 indicated that 11 (68.5%) of the 16 parameters fall short of meeting Six Sigma quality performance. Of these, five failed to meet minimum sigma quality performance with metrics less than 3, and another six just met minimal acceptable performance with sigma metrics between 3 and 6. For level 3, the data collected indicated eight (50%) of the parameters did not achieve Six Sigma quality performance, out of which three had metrics less than 3, and five had metrics between 3 and 6. QGI ratio indicated that the main problem was inaccuracy in the case of total cholesterol, aspartate transaminase, and alanine transaminase (QGI > 1.2), imprecision in the case of urea (QGI < 0.8), and both imprecision and inaccuracy for glucose.
CONCLUSION On the basis of sigma metrics and QGI, it may be concluded that the Clinical Biochemistry Laboratory, PGIMS, Rohtak was able to achieve satisfactory results with world class performance for many analytes one year preceding the accreditation by the National Accreditation Board for Testing and Calibration of Laboratories. Aspartate transaminase and alanine transaminase required strict external quality assurance scheme monitoring and modification in quality control procedure as their QGI ratio showed inaccuracy.
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Affiliation(s)
- Monica Verma
- Department of Biochemistry, Pt. B.D. Sharma, University of Health Sciences, Rohtak 124001, Haryana, India
| | - Kiran Dahiya
- Department of Biochemistry, Pt. B.D. Sharma, University of Health Sciences, Rohtak 124001, Haryana, India
| | - Veena Singh Ghalaut
- Department of Biochemistry, Pt. B.D. Sharma, University of Health Sciences, Rohtak 124001, Haryana, India
| | - Vasudha Dhupper
- Department of Biochemistry, Pt. B.D. Sharma, University of Health Sciences, Rohtak 124001, Haryana, India
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Liu Q, Fu M, Yang F, Liang W, Yang C, Zhu W, Ma L, Zhao C. Application of Six Sigma for evaluating the analytical quality of tumor marker assays. J Clin Lab Anal 2018; 33:e22682. [PMID: 30280434 PMCID: PMC6585744 DOI: 10.1002/jcla.22682] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 08/29/2018] [Accepted: 08/31/2018] [Indexed: 11/07/2022] Open
Abstract
CONTEXT The results of detection assays for the same specimen are usually quite different in different laboratories or when tested with different detection systems. OBJECTIVE This study was designed to investigate the value of applying sigma metrics derived from different standards for allowable total error (TEa) in evaluating the analytical quality of tumor marker assays. METHODS Assays were evaluated for these six tumor markers: total prostate-specific antigen (tPSA), carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), carbohydrate antigen 199 (CA199), carbohydrate antigen 125 (CA125), and carbohydrate antigen 153 (CA153). Sigma values were calculated for two concentrations of quality control products to assess differences in quality of tumor marker assays. Improvement measures were recommended according to the quality goal index, and appropriate quality control rules were selected according to the sigma value. RESULTS The sigma value was highest using the higher biological variation-derived "appropriate" TEa standard: it was sigma ≥6 or higher in 16.7% of tumor markers. Sigma was below 6 for all tumor markers using the other three TEa. CEA, AFP, CA199, CA125, and CA153 required improved precision. The marker tPSA required improve precision and accuracy. According to sigma values by using China's external quality assessment standards, CEA, AFP, CA125, and CA153 require 13s /22s /R4s /41s multirules for internal quality control, CA199 requires use of 13s /22s /R4s /41s /8x multirules, and tPSA requires maximum quality control rules. CONCLUSION Six Sigma is useful for evaluating performance of tumor markers assays and has important application value in the quality control of these assays.
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Affiliation(s)
- Qian Liu
- Department of Medicine LaboratoryThe Second People's Hospital of LianyungangLianyungangChina
| | - Mei Fu
- Department of Medicine LaboratoryThe Second People's Hospital of LianyungangLianyungangChina
| | - Fumeng Yang
- Department of Medicine LaboratoryThe Second People's Hospital of LianyungangLianyungangChina
| | - Wei Liang
- Department of Medicine LaboratoryThe Second People's Hospital of LianyungangLianyungangChina
| | - Chuanxi Yang
- Department of CardiologyJiangsu Province Hospital, Medical School of Southeast UniversityNanjingChina
| | - Wenjun Zhu
- Department of Medicine LaboratoryThe Second People's Hospital of LianyungangLianyungangChina
| | - Liming Ma
- Department of Medicine LaboratoryThe Second People's Hospital of LianyungangLianyungangChina
| | - Changxin Zhao
- Department of Medicine LaboratoryThe Second People's Hospital of LianyungangLianyungangChina
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Cao S, Qin X. Application of Sigma metrics in assessing the clinical performance of verified versus non-verified reagents for routine biochemical analytes. Biochem Med (Zagreb) 2018; 28:020709. [PMID: 30022884 PMCID: PMC6039166 DOI: 10.11613/bm.2018.020709] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Sigma metrics analysis is considered an objective method to evaluate the performance of a new measurement system. This study was designed to assess the analytical performance of verified versus non-verified reagents for routine biochemical analytes in terms of Sigma metrics. MATERIALS AND METHODS The coefficient of variation (CV) was calculated according to the mean and standard deviation (SD) derived from the internal quality control for 20 consecutive days. The data were measured on an Architect c16000 analyser with reagents from four manufacturers. Commercial reference materials were used to estimate the bias. Total allowable error (TEa) was based on the CLIA 1988 guidelines. Sigma metrics were calculated in terms of CV, percent bias and TEa. Normalized method decisions charts were built by plotting the normalized bias (biasa: bias%/TEa) on the Y-axis and the normalized imprecision (CVa: mean CV%/TEa) on the X-axis. RESULTS The reagents were compared between different manufacturers in terms of the Sigma metrics for relevant analytes. Abbott and Leadman's verified reagents provided better Sigma metrics for the alanine aminotransferase assay than non-verified reagents (Mindray and Zybio). All reagents performed well for the aspartate aminotransferase and uric acid assays with a sigma of 5 or higher. Abbott achieved the best performance for the urea assay, evidenced by the sigma of 2.83 higher than all reagents, which were below 1-sigma. CONCLUSION Sigma metrics analysis system is helpful for clarifying the performance of candidate non-verified reagents in clinical laboratory. Our study suggests that the quality of non-verified reagents should be assessed strictly.
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Affiliation(s)
- Shuang Cao
- Department of Medical Laboratory, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaosong Qin
- Department of Medical Laboratory, Shengjing Hospital of China Medical University, Shenyang, China
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