Assessment of sigma metrics results of serum glucose and lipid profile tested by automated chemistry analyzer in medical city hospitals in Iraq

Background: A major target of quality assurance is the minimization of error rates in order to enhance patient safety, six sigma or sigma metrics were used to assess the analytical quality of automated clinical chemistry, six sigma metrics is used in combination with total allowable error, method imprecision and bias. The goal is to attain the highest possible sigma scale within the acceptable limits of total allowable error. For assessment of sigma metrics results of serum glucose and lipid profile and verification of reference values for these analytes tested by automated chemistry analyzer in Medical City hospitals.Methods: In the present study, internal quality control (EQA) and external quality assessment (EQA) data were analyzed for the period from May to July 2017 using chemistry autoanalyzer (Siemens Dimension RxL Max) at the Teaching Laboratories of the Medical City. Mean, standard deviation, coefficient of variation, bias, total error and sigma metrics were calculated for glucose, cholesterol, triglycerides and HDL.Results: Excellent sigma values (≥6) were elicited for triglycerides (10.9), Satisfactory sigma values (≥3) were elicited for cholesterol (3.4) and HDL (3.4), while glucose performed poorly (2.3) on the sigma scale.Conclusions: Sigma metrics helps to assess analytical methodologies and augment laboratory performance. It acts as a guide for planning quality control strategy. It can be a self-assessment tool regarding the functioning of clinical laboratory. Triglycerides was the best performer when it was gauzed on the sigma scale, with a sigma metrics value of 10.9 and glucose had the least sigma metrics value of 2.5 so there is need for improvement and the method should be controlled with greater attention to ensure quality.

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Applying The Best Practice For Internal Quality Control (IQC) Management, Consolidation And Review Process In Sidra Medicine Clinical Chemistry Division

American Journal of Clinical Pathology ◽

10.1093/ajcp/aqaa161.199 ◽

2020 ◽

Vol 154 (Supplement_1) ◽

pp. S91-S91

Author(s):

J M Asinas

Keyword(s):

Quality Control ◽

Real Time ◽

Best Practice ◽

Clinical Chemistry ◽

Internal Quality Control ◽

Internal Quality ◽

Allowable Error ◽

Chemistry Division ◽

Set Up ◽

High Level

Abstract Introduction/Objective The management of internal quality control (IQC) in Sidra Medicine Clinical Chemistry Division has been evaluated in order to promote a more consolidated and efficient process of IQC management. The statistical data produced from Cerner QC Module are transferred to IQC review templates consisting of formulas to auto- calculate parameters such as multiple of expected QC failure frequency and desirable comparison limit between analyzers. The IQC review and documentation process using the in-house excel template requires several hours to complete, hence a faster and more efficient IQC management module is required. The main objective of this study is to improve the initial IQC management set up, work flow and review procedures and to implement Biorad Unity Real Time (URT) program to develop a more efficient IQC management system. Methods The URT software has been recently configured and implemented to consolidate and streamline IQC management. URT is built through Sidra Medicine IT Enterprise level which allows multiple users to login. IQC data are downloaded using scripts from Cerner which are filtered through Biorad Unity Connect (UC) software. Additional quality tools are also explored such as various user defined statistical reports, IQC analysis using peer reviewed total allowable error (TeA) and assignment of the most appropriate Westgard rules. Determination of sigma metrics and uncertainty of measurement is also performed using the URT application. Results The generation of any IQC report is less cumbersome and time consuming as compared with the previous process. However, some user defined formulas in the IQC templates are not found on the URT reports. The URT Levey Jennings chart are also more user friendly and directly compares the daily IQC data with Unity inter-laboratory peers enabling the production of instant and monthly reports through QCNet site when assay investigation is required and for IQC report documentation. Conclusion The combination of Cerner IQC, Unity Real-time, QCNet Inter-laboratory reports and in house IQC templates produce a high level and very detailed IQC review which effectively evaluate assay performance to assist on IQC troubleshooting and root cause analysis to be able to apply the most appropriate corrective actions.

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The Evaluation of Analytical Performance of Immunoassay Tests by Using Six-Sigma Method

Journal of Medical Biochemistry ◽

10.1515/jomb-2017-0026 ◽

2017 ◽

Vol 36 (4) ◽

pp. 301-308 ◽

Cited By ~ 5

Author(s):

Rukiye Nar ◽

Dilek Iren Emekli

Keyword(s):

Quality Control ◽

Six Sigma ◽

Clinical Chemistry ◽

Quality Measurement ◽

Analytical Performance ◽

Internal Quality Control ◽

Internal Quality ◽

Variation Database ◽

The Mean ◽

Six Sigma Methodology

SummaryBackground: 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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Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory

Journal of Laboratory Physicians ◽

10.4103/jlp.jlp_102_17 ◽

2018 ◽

Vol 10 (02) ◽

pp. 194-199 ◽

Cited By ~ 6

Author(s):

B. Vinodh Kumar ◽

Thuthi Mohan

Keyword(s):

Quality Control ◽

Six Sigma ◽

Biochemical Parameters ◽

Clinical Chemistry ◽

Chemistry Laboratory ◽

Internal Quality ◽

Average Performance ◽

Sigma Level ◽

Clinical Chemistry Laboratory ◽

Level 1

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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Sigma Metric Evaluation of Drugs in a Clinical Laboratory: Importance of Choosing Appropriate Total Allowable Error and a Troubleshooting Roadmap

Journal of Laboratory Physicians ◽

10.1055/s-0041-1726572 ◽

2021 ◽

Author(s):

Lokesh Kumar Sharma ◽

Rashmi Rasi Datta ◽

Neera Sharma

Keyword(s):

Quality Control ◽

Peer Group ◽

Internal Quality ◽

Care Hospital ◽

External Quality ◽

Total Allowable Error ◽

Significant Difference ◽

Allowable Error ◽

Study Laboratory ◽

The Impact

Abstract Objectives Stringent quality control is an essential requisite of diagnostic laboratories to deliver consistent results. Measures used to assess the performance of a clinical chemistry laboratory are internal quality control and external quality assurance scheme (EQAS). However, the number of errors cannot be measured by the above but can be quantified by sigma metrics. The sigma scale varies from 0 to 6 with “6” being the ideal goal, which is calculated by using total allowable error (TEa), bias, and precision. However, there is no proper consensus for setting a TEa goal, and influence of this limiting factor during routine laboratory practice and sigma calculation has not been adequately determined. The study evaluates the impact of the choice of TEa value on sigma score derivation and also describes a detailed structured approach (followed by the study laboratory) to determine the potential causes of errors causing poor sigma score. Materials and Methods The study was conducted at a clinical biochemistry laboratory of a central government tertiary care hospital. Internal and external quality control data were evaluated for a period of 5 months from October 2019 to February 2020. Three drugs (carbamazepine, phenytoin, and valproate) were evaluated on the sigma scale using two different TEa values to determine significant difference, if any. Statistical Analysis Bias was calculated using the following formula: Bias% = (laboratory EQAS result − peer group mean) × 100 / peer group mean Peer group mean sigma metric was calculated using the standard equation: Sigma value = TEa − bias / coefficient of variation (CV)%. Results Impressive sigma scores (> 3 sigma) for two out of three drugs were obtained with TEa value 25, while with TEa value 15, sigma score was distinctly dissimilar and warranted root cause analysis and corrective action plans to be implemented for both valproate and carbamazepine. Conclusions The current study evidently recognizes that distinctly different sigma values can be obtained, depending on the TEa values selected, and using the same bias and precision values in the sigma equation. The laboratories should thereby choose appropriate TEa goals and make judicious use of sigma metric as a quality improvement tool.

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Application of Sigma Metrics for Evaluating the Analytical Performance of Thyroid Profile and Cortisol in Clinical Biochemistry Laboratory

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2020/47345.14391 ◽

2020 ◽

Author(s):

Smita Natvarbhai Vasava ◽

Roshni Gokaldas Sadaria

Keyword(s):

Quality Control ◽

Six Sigma ◽

Clinical Laboratory ◽

Clinical Biochemistry ◽

Poor Performance ◽

Analytical Performance ◽

Internal Quality ◽

Six Sigma Methodology ◽

Clinical Biochemistry Laboratory ◽

Thyroid Profile

Introduction: Now-a-days quality is the key aspect of clinical laboratory services. The six sigma metrics is an important quality measurement method for evaluating the performance of the clinical laboratory. Aim: To assess the analytical performance of clinical biochemistry laboratory by utilising thyroid profile and cortisol parameters from Internal Quality Control (IQC) data and to calculate sigma values. Materials and Methods: Study was conducted at Clinical Biochemistry Laboratory, Dhiraj General Hospital, Piparia, Gujarat, India. Retrospectively, IQC data of thyroid profile and cortisol were utilised for six subsequent months (July to December 2019). Coefficient of Variation (CV%) and bias were calculated from IQC data, from that the sigma values were calculated. The sigma values <3, >3 and >6 were indicated by poor performance procedure, good performance and world class performance, respectively. Results: The sigma values were estimated by calculating mean of six months. The mean sigma value of Thyroid Stimulating Hormone (TSH) and Cortisol were >3 for six months which indicated the good performance. However, sigma value of Triiodothyronine (T3), Tetraiodothyronine (T4) were found to be <3 which indicated poor performance. Conclusion: Six sigma methodology applications for thyroid profile and cortisol was evaluated, it was generally found as good. While T3 and T4 parameters showed low sigma values which requires detailed root cause analysis of analytical process. With the help of six sigma methodology, in clinical biochemistry laboratories, an appropriate Quality Control (QC) programming should be done for each parameter. To maintain six sigma levels is challenging to quality management personnel of laboratory, but it will be helpful to improve quality level in the clinical laboratories.

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The application of Six Sigma to perform quality analyses of plasma proteins

Annals of Clinical Biochemistry International Journal of Laboratory Medicine ◽

10.1177/0004563219892023 ◽

2019 ◽

Vol 57 (2) ◽

pp. 121-127

Author(s):

Fumeng Yang ◽

Wenjun Wang ◽

Qian Liu ◽

Xizhen Wang ◽

Guangrong Bian ◽

...

Keyword(s):

Quality Control ◽

Quality Assessment ◽

Six Sigma ◽

Plasma Proteins ◽

External Quality Assessment ◽

Internal Quality ◽

C Reactive Protein ◽

External Quality ◽

Reactive Protein ◽

Assessment Standards

Background The Six Sigma theory is an important tool for laboratory quality management. It has been widely used in clinical chemistry, haematology and other disciplines. The aim of our study was to evaluate the analytical performance of plasma proteins by application of Sigma metric and to compare the differences among three different allowable total errors in evaluating the analytical performance of plasma proteins. Methods Three different allowable total error values were used as quality goals. Data from an external quality assessment were used as bias, and the cumulative coefficient of variation in internal quality control data was used to represent the amount of imprecision during the same period. Sigma metric of analytes was calculated using the above data. The quality goal index was calculated to provide corrected measures for continuous improvements in analytical quality. Results The Sigma metric was highest using the external quality assessment standards of China: it was sigma ≥6 or higher in 57.1% of plasma proteins. But Sigma metric was lower by using RiliBÄK or biological variation standards. IgG, C3 and C-reactive protein all required quality improvements in imprecision. A single-rule 13s for internal quality control was recommended for IgA, IgM, C4 and rheumatoid factor, whereas multiple rules (13s/22s/R4s) were recommended for IgG, C3 and C-reactive protein, according to the external quality assessment standards of China. Conclusions Different quality goals can lead to different Sigma metric for the same analyte. As the lowest acceptable standard in clinical practice, the external quality assessment standard of China can guide laboratories to formulate reasonable quality improvement programmes.

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Allowable limit of error in clinical chemistry quality control.

Clinical Chemistry ◽

10.1093/clinchem/35.4.630 ◽

1989 ◽

Vol 35 (4) ◽

pp. 630-631 ◽

Cited By ~ 4

Author(s):

Z C Cui

Keyword(s):

Quality Control ◽

Clinical Laboratory ◽

Clinical Chemistry ◽

External Quality Control ◽

External Quality ◽

Allowable Limit ◽

Error Limit ◽

Allowable Error

Abstract Taking the National Clinical Chemistry Quality Control of China National Center for Clinical Laboratory as an example, I present this study of some problems with using the allowable error limit in present-day clinical chemistry quality control, and propose a new allowable error limit for use in external quality control in clinical chemistry.

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Sigma Metrics: A Valuable Tool for Evaluating the Performance of Internal Quality Control in Laboratory

Journal of Laboratory Physicians ◽

10.1055/s-0041-1731145 ◽

2021 ◽

Author(s):

Akriti Kashyap ◽

Sangeetha Sampath ◽

Preeti Tripathi ◽

Arijit Sen

Keyword(s):

Quality Control ◽

Uric Acid ◽

Six Sigma ◽

Quality Management System ◽

Direct Bilirubin ◽

Internal Quality Control ◽

Internal Quality ◽

Mean Corpuscular Hemoglobin ◽

Corpuscular Hemoglobin ◽

Urea Nitrogen

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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