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Sulthana P.K. S, U. R, Yassir S, Prasad V. G, Ansar M. A comparative evaluation of six sigma metrics and quality goal index ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during lockdown in a NABL accredited central laboratory. Biomedicine (Taipei) 2022. [DOI: 10.51248/.v42i5.2038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Introduction and Aim: Sigma represents Standard Deviation (SD) which indicates the degree of variation in a process, where the higher sigma value implies that less likely the laboratory reports false test results. Using a newer parameter called Quality Goal Index (QGI) we can find the reason behind the lower sigma value. Our study aimed to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown.
Methodology: A retrospective study was used to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown for the selected ten analytes from 1st of January 2020 to 30th of June 2020 from the clinical biochemistry section of Yenepoya Medical College Hospital, Deralakatte, Mangalore.
Results: The sigma metrics from January to March (level 1) indicated that urea, TSH, beta-HCG fell short of meeting Six Sigma quality performance and from April to June, glucose, creatinine, urea and ALT had metrics less than 3 at both the Internal Quality Control levels. QGI ratio indicated that from January to March, the problem was imprecision for urea, TSH and beta-HCG (QGI < 0.8). From April to June, urea and creatinine showed imprecision, glucose and ALT showed inaccuracy, urea and ALT showed both imprecision and inaccuracy.
Conclusion: This study highlights the necessity for stringent Internal Quality Control and External Quality Assurance monitoring even during the lockdown period of the pandemic. By implementing six sigma and finding QGI ratio, quality of laboratory services can be improved immensely.
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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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Kashyap A, Sampath S, Tripathi P, Sen A. Sigma Metrics: A Valuable Tool for Evaluating the Performance of Internal Quality Control in Laboratory. J Lab Physicians 2021; 13:328-331. [PMID: 34975251 PMCID: PMC8714305 DOI: 10.1055/s-0041-1731145] [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] [Indexed: 11/24/2022] Open
Abstract
Background Six Sigma is a widely accepted quality management system that provides an objective assessment of analytical methods and instrumentation. Six Sigma scale typically runs from 0 to 6, with sigma value above 6 being considered adequate and 3 sigma being considered the minimal acceptable performance for a process. Methodology Sigma metrics of 10 biochemistry parameters, namely glucose, triglycerides, high-density lipoprotein (HDL), albumin, direct bilirubin, alanine transaminase, aspartate transaminase, urea nitrogen, creatinine and uric acid, and hematology parameters such as hemoglobin (Hb), total leucocyte count (TLC), packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and platelet were calculated by analyzing internal quality control (IQC) data of 3 months (June-August 2019). Results Sigma value was found to be > 6 for triglyceride, HDL, Hb, TLC, and MCH, signifying excellent results and no further modification with respect to IQC. Sigma value was between 3 and 6 for glucose, albumin, creatinine, uric acid, PCV, and MCHC, implying the requirement of improvement in quality control (QC) processes. Sigma value of < 3 was seen in AST, ALT, direct bilirubin, urea nitrogen, platelet, and MCV, signifying suboptimal performance. Discussion Six Sigma provides a more quantitative framework for evaluating process performance with evidence for process improvement and describes how many sigmas fit within the tolerance limits. Thus, for parameters with sigma value < 3, duplicate testing of the sample along with three QCs three times a day may be used along with stringent Westgard rules for rejecting a run. Conclusion Sigma metrics help assess analytical methodologies and augment laboratory performance.
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Affiliation(s)
- Akriti Kashyap
- Department of Laboratory Medicine, Air Force Command Hospital, Bengaluru, Karnataka, India.
| | - Sangeetha Sampath
- Department of Laboratory Medicine, Air Force Command Hospital, Bengaluru, Karnataka, India.
| | - Preeti Tripathi
- Department of Laboratory Medicine, Air Force Command Hospital, Bengaluru, Karnataka, India.
| | - Arijit Sen
- Department of Laboratory Medicine, Air Force Command Hospital, Bengaluru, Karnataka, India.
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Geto Z, Getahun T, Lejisa T, Tolcha Y, Bikila D, Bashea C, Meles M, Habtu W, Ashebir G, Negasa B, Sileshi M, Daniel Y, Gashu A, Challa F. Evaluation of Sigma Metrics and Westgard Rule Selection and Implementation of Internal Quality Control in Clinical Chemistry Reference Laboratory, Ethiopian Public Health Institute. Indian J Clin Biochem 2021; 37:285-293. [DOI: 10.1007/s12291-021-00994-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 07/02/2021] [Indexed: 10/20/2022]
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Peng S, Zhang J, Zhou W, Mao W, Han Z. Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings. J Clin Lab Anal 2021; 35:e23665. [PMID: 33270940 PMCID: PMC7957980 DOI: 10.1002/jcla.23665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 11/07/2020] [Accepted: 11/10/2020] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The performance of 18 routine chemical detection methods was evaluated by the sigma (σ) metric, and Westgard Sigma rules with run size were used to establish internal quality control (IQC) standards to reduce patient risks. MATERIALS AND METHODS External quality assessment (EQA) and internal quality control data from 18 assays in a biochemical laboratory were collected from January to June 2020. The sigma values of each assay were calculated, based on the bias, total error allowable, and coefficient of variation, appropriate quality control rules were selected. According to the quality goal index, the main causes of poor performance were determined to guide quality improvement. RESULTS At IQC material level 1, seven of the 18 assays achieved five sigma (excellent), and five assays (UA, Crea, AMY, TC and Na) showed world-class performance. At IQC material level 2, 14 of the 18 assays achieved 5 sigma (excellent), and thirteen assays (UA, ALT, CK, Crea, AMY, K, AST, ALP, Na, LDH, Mg, TC and GGT) showed world-class performance. The quality goal index (QGI) was calculated for items with analysis performance <5 sigma, and the main causes of poor performance were determined to guide quality improvement. CONCLUSIONS Westgard sigma rules with run size are an effective tool for evaluating the performance of biochemical assays. These rules can be used to more simply and intuitively select the quality control strategy of related items and reduce the risk to patients.
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Affiliation(s)
- SongQing Peng
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
| | - JinFei Zhang
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
| | - WuQiong Zhou
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
| | - WeiLin Mao
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
- Key laboratory of digestive system diseases of ShengzhouShengzhou People’s HospitalShengzhouChina
| | - Zhong Han
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
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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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Kumar BV, Mohan T. Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory. J Lab Physicians 2020; 10:194-199. [PMID: 29692587 PMCID: PMC5896188 DOI: 10.4103/jlp.jlp_102_17] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE: Six Sigma is one of the most popular quality management system tools employed for process improvement. The Six Sigma methods are usually applied when the outcome of the process can be measured. This study was done to assess the performance of individual biochemical parameters on a Sigma Scale by calculating the sigma metrics for individual parameters and to follow the Westgard guidelines for appropriate Westgard rules and levels of internal quality control (IQC) that needs to be processed to improve target analyte performance based on the sigma metrics. MATERIALS AND METHODS: This is a retrospective study, and data required for the study were extracted between July 2015 and June 2016 from a Secondary Care Government Hospital, Chennai. The data obtained for the study are IQC - coefficient of variation percentage and External Quality Assurance Scheme (EQAS) - Bias% for 16 biochemical parameters. RESULTS: For the level 1 IQC, four analytes (alkaline phosphatase, magnesium, triglyceride, and high-density lipoprotein-cholesterol) showed an ideal performance of ≥6 sigma level, five analytes (urea, total bilirubin, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level and for level 2 IQCs, same four analytes of level 1 showed a performance of ≥6 sigma level, and four analytes (urea, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level. For all analytes <6 sigma level, the quality goal index (QGI) was <0.8 indicating the area requiring improvement to be imprecision except cholesterol whose QGI >1.2 indicated inaccuracy. CONCLUSION: This study shows that sigma metrics is a good quality tool to assess the analytical performance of a clinical chemistry laboratory. Thus, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of existing laboratory processes.
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Affiliation(s)
- B Vinodh Kumar
- Department of Biochemistry, ESIC Medical College Hospital and PGIMSR, Chennai, Tamil Nadu, India
| | - Thuthi Mohan
- Department of Biochemistry, ESIC Medical College Hospital and PGIMSR, Chennai, Tamil Nadu, India
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Zhou B, Wu Y, He H, Li C, Tan L, Cao Y. Practical application of Six Sigma management in analytical biochemistry processes in clinical settings. J Clin Lab Anal 2019; 34:e23126. [PMID: 31774217 PMCID: PMC6977137 DOI: 10.1002/jcla.23126] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 10/31/2019] [Accepted: 11/08/2019] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Six Sigma methodology with a zero-defect goal has long been applied in commercial settings and was utilized in this study to assure/improve the quality of various analytes. METHODS Daily internal quality control (QC) and external quality assessment data were collected and analyzed by calculating the sigma (σ) values for 19 analytes based on the coefficient of variation, bias, and total error allowable. Standardized QC sigma charts were established with these parameters. Quality goal index (QGI) analysis and root cause analysis (RCA) were used to discover potential problems for the analytes. RESULTS Five analytes with σ ≥ 6 achieved world-class performance, and only the Westgard rule (13s ) with one control measurement at two QC material levels (N2) per QC event and a run size of 1000 patient samples between QC events (R1000) was needed for QC. In contrast, more control rules (22s /R4s /41s ) along with high N values and low R values were needed for quality assurance for five analytes with 4 ≤ σ < 6. However, the sigma levels of nine analytes were σ < 4 at one or more QC levels, and a more rigorous QC procedure (13s /22s /R4s /41s /8x with N4 and R45) was implemented. The combination of QGI analysis and RCA further revealed inaccuracy or imprecision problems for these analytes with σ < 4 and discovered five aspects of potential causes considered for quality improvement. CONCLUSIONS Six Sigma methodology is an effective tool for evaluating the performance of biochemical analytes and is conducive to quality assurance and improvement.
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Affiliation(s)
- Bingfei Zhou
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Yi Wu
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Hanlin He
- Department of Medical laboratory of Hunan Normal University School of Medicine, Changsha, China
| | - Cunyan Li
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Liming Tan
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Youde Cao
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
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Guo X, Zhang T, Gao X, Li P, You T, Wu Q, Wu J, Zhao F, Xia L, Xu E, Qiu L, Cheng X. Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches: Electronic supplementary material available online for this article. Biochem Med (Zagreb) 2019; 28:020708. [PMID: 30022883 PMCID: PMC6039159 DOI: 10.11613/bm.2018.020708] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 03/13/2018] [Indexed: 11/01/2022] Open
Abstract
Introduction Two approaches were compared for the calculation of coefficient of variation (CV) and bias, and their effect on sigma calculation, when different allowable total error (TEa) values were used to determine the optimal method for Six Sigma quality management in the clinical laboratory. Materials and methods Sigma metrics for routine clinical chemistry tests using three systems (Beckman AU5800, Roche C8000, Siemens Dimension) were determined in June 2017 in the laboratory of Peking Union Medical College Hospital. Imprecision (CV%) and bias (bias%) were calculated for ten routine clinical chemistry tests using a proficiency testing (PT)- or an internal quality control (IQC)-based approach. Allowable total error from the Clinical Laboratory Improvement Amendments of 1988 and the Chinese Ministry of Health Clinical Laboratory Center Industry Standard (WS/T403-2012) were used with the formula: Sigma = (TEa - bias) / CV to calculate the Sigma metrics (σCLIA, σWS/T) for each assay for comparative analysis. Results For the PT-based approach, eight assays on the Beckman AU5800 system, seven assays on the Roche C8000 system and six assays on the Siemens Dimension system showed σCLIA > 3. For the IQC-based approach, ten, nine and seven assays, respectively, showed σCLIA > 3. Some differences in σ were therefore observed between the two calculation methods and the different TEa values. Conclusions Both methods of calculating σ can be used for Six Sigma quality management. In practice, laboratories should evaluate Sigma multiple times when optimizing a quality control schedule.
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Affiliation(s)
- Xiuzhi Guo
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Tianjiao Zhang
- National Center for Clinical Laboratories, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China
| | - Xuehui Gao
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Pengchang Li
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Tingting You
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Qiong Wu
- Clinical Laboratory, Affiliated Hospital of Chifeng University, Inner Mongolia, P.R. China
| | - Jie Wu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Fang Zhao
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Liangyu Xia
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Ermu Xu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Ling Qiu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
| | - Xinqi Cheng
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academic Medical Science and Peking Union Medical College, Beijing, P.R. China
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Mao X, Shao J, Zhang B, Wang Y. Evaluating analytical quality in clinical biochemistry laboratory using Six Sigma. Biochem Med (Zagreb) 2019; 28:020904. [PMID: 30022890 PMCID: PMC6039163 DOI: 10.11613/bm.2018.020904] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 04/10/2018] [Indexed: 11/01/2022] Open
Abstract
Introduction In recent years, Six Sigma metrics has became the hotspot in all trades and professions, which contributes a general procedure to explain the performance on sigma scale. Nowadays, many large companies, such as General Healthcare, Siemens, etc., have applied Six Sigma to clinical medicine and achieved satisfactory results. In this paper, we aim to evaluate the process performance of our laboratory by using Sigma metrics, thereby choosing the correct analytical quality control approach for each parameter. Materials and methods This study was conducted in the clinical chemistry laboratory of Shandong Provincial Hospital. The five-months data of internal quality control were harvested for the parameters: amylase (AMY), lactate dehydrogenase (LD), potassium, total bilirubin (TBIL), triglyceride, aspartate aminotransferase (AST), uric acid, high density lipoprotein-cholesterol (HDL-C), alanine aminotransferase (ALT), urea, sodium, chlorine, magnesium, alkaline phosphatase (ALP), creatinine (CRE), total protein, creatine kinase (CK), total cholesterol, glucose (GLU), albumin (ALB). Sigma metrics were calculated using total allowable error, precision and percent bias for the above-mentioned parameters. Results Sigma values of urea and sodium were below 3. Sigma values of total protein, CK, total cholesterol, GLU and ALB were in the range of 3 to 6. Sigma values of AMY, uric acid, HDL-C, TBIL, ALT, triglyceride, AST, ALP and CRE were more than 6. Conclusion Amylase was the best performer with a Sigma metrics value of 19.93, while sodium had the least average sigma values of 2.23. Actions should be taken to improve method performance for these parameters with sigma below 3.
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Affiliation(s)
- Xuehui Mao
- Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People's Republic of China
| | - Jing Shao
- Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People's Republic of China
| | - Bingchang Zhang
- Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People's Republic of China
| | - Yong Wang
- Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People's Republic of China
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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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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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Nar R, Emekli DI. The Evaluation of Analytical Performance of Immunoassay Tests by using Six-sigma Method. J Med Biochem 2017; 36:301-308. [PMID: 30581326 PMCID: PMC6294088 DOI: 10.1515/jomb-2017-0026] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 05/05/2017] [Indexed: 11/16/2022] Open
Abstract
Background The Six-Sigma Methodology is a quality measurement method in order to evaluate the performance of the laboratory. In the present study, it is aimed to evaluate the analytical performance of our laboratory by using the internal quality control data of immunoassay tests and by calculating process sigma values. Methods Biological variation database (BVD) are used for Total Allowable Error (TEa). Sigma values were determined from coefficient of variation (CV) and bias resulting from Internal Quality Control (IQC) results for 3 subsequent months. If the sigma values are ≥6, between 3 and 6, and <3, they are classified as »world-class«, »good« or »un - acceptable«, respectively. Results A sigma value >6 was found for TPSA and TSH for the both levels of IQC for 3 months. When the sigma values were analyzed by calculating the mean of 3 months, folate, LH, PRL, TPSA, TSH and vitamin B12 were found >6. The mean sigma values of CA125, CA15-3, CA19-9, CEA, cortisol, ferritin, FSH, FT3, PTH and testosteron were >3 for 3-months. However, AFP, CA125 and FT4 produced sigma values <3 for varied months. Conclusion When the analytical performance was evaluated according to Six-Sigma levels, it was generally found as good. It is possible to determine the test with high error probability by evaluating the fine sigma levels and the tests that must be quarded by a stringent quality control regime. In clinical chemistry laboratories, an appropriate quality control scheduling should be done for each test by using Six-Sigma Methodology.
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Affiliation(s)
- Rukiye Nar
- Faculty of Medicine, Department of Medical Biochemistry, Ahi Evran University, Kirsehir, Turkey
| | - Dilek Iren Emekli
- Department of Medical Biochemistry, Special Ege City Hospital, Izmir, Turkey
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Ustundag-Budak Y, Huysal K. Application of Sigma Metrics and Performance Comparison Between Two Biochemistry Analyser and a Blood Gas Analyser for the Determination of Electrolytes. J Clin Diagn Res 2017; 11:BC06-BC09. [PMID: 28384850 DOI: 10.7860/jcdr/2017/23486.9259] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/14/2016] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Electrolytes have a narrow range of biological variation and small changes are clinically significant. It is important to select the best method for clinical decision making and patient monitoring in the emergency room. The sigma metrics model provides an objective method to evaluate the performance of a method. AIM To calculate sigma metrics for electrolytes measured with one arterial blood gas analyser including two auto-analysers that use different technologies. To identify the best approach for electrolyte monitoring in an emergency setting and the context of routine emergency room workflow. MATERIALS AND METHODS The Coefficient of Variation (CV) was determined from Internal Quality Control (IQC). Data was measured from July 2015 to January 2016 for all three analysers. The records of KBUD external quality data (Association of Clinical Biochemists, Istanbul, Turkey) for both Mindray BS-2000M analyser (Mindray, Shenzhen, China) and Architect C16000 (Abbott Diagnostics, Abbott Park, IL) and MLE clinical laboratory evaluation program (Washington, DC, USA) for Radiometer ABL 700 (Radiometer Trading, Copenhagen, Denmark) during the study period were used to determine the bias. RESULTS The calculated average sigma values for sodium (-1.1), potassium (3.3), and chloride (0.06) were with the Radiometer ABL700. All calculated sigma values were better than the auto-analysers. CONCLUSION The sigma values obtained from all analysers suggest that running more controls and increasing the calibration frequency for electrolytes is necessary for quality assurance.
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Affiliation(s)
- Yasemin Ustundag-Budak
- Department of Clinical Biochemistry, Saglik Bilimleri University, Bursa Yuksek Ihtisas Training and Research Hospital , Bursa, Turkey
| | - Kagan Huysal
- Department of Clinical Biochemistry, Saglik Bilimleri University , Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey
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