Internal Quality Control Data Research Articles

Practical application of European biological variation combined with Westgard Sigma Rules in internal quality control.

Westgard Sigma Rules is a statistical tool available for quality control. Biological variation (BV) can be used to set analytical performance specifications (APS). The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) regularly updates BV data. However, few studies have used robust BV data to determine quality goals and design a quality control strategy for tumor markers. The aim of this study was to derive APS for tumor markers from EFLM BV data and apply Westgard Sigma Rules to establish internal quality control (IQC) rules. Precision was calculated from IQC data, and bias was obtained from the relative deviation of the External quality assurance scheme (EQAS) group mean values and laboratory-measured values. Total allowable error (TEa) was derived using EFLM BV data. After calculating sigma metrics, the IQC strategy for each tumor marker was determined according to Westgard Sigma Rules. Sigma metrics achieved for each analyte varied with the level of TEa. Most of these tumor markers except neuron-specific enolase reached 3σ or better based on TEamin. With TEades and TEaopt set as the quality goals, almost all analytes had sigma values below 3. Set TEamin as quality goal, each analyte matched IQC muti rules and numbers of control measurements according to sigma values. Quality goals from the EFLM BV database and Westgard Sigma Rules can be used to develop IQC strategy for tumor markers.

Application of biological variation and sigma metrics to evaluate the performance of HbA1c in external quality assessment

Background It is crucial to improve the accuracy of HbA1c measurement as its essential role in diabetes diagnosis and treatment. We aimed to establish the biological variation (BV) and sigma metrics (SM) models and apply the models to evaluate the analytical performance of HbA1c in external quality assessment (EQA) program. Methods Data of HbA1c EQA (2021) and internal quality control (IQC) (March–August 2021) were collected. The group-specific bias and coefficient of variance (CV) were computed for measuring systems with laboratory number >9 in EQA program. The analytical bias and CV for individual laboratory were estimated from EQA and IQC data. The CV% and bias% were plotted in the BV-SM models for performance evaluation of measuring system and individual laboratory. Results Totally, 380 laboratories participated in EQA program. The overall inter-laboratory CV of five EQA samples ranged from 3.02% to 3.63%. There were five measuring systems that met the minimum performance for 5/5 samples: Arkary, Primus, Roche, Mindray and Tosoh, but none of them achieved the optimum performance. Half of the 196 laboratories that reported IQC and EQA results simultaneously achieved 3σ and minimum performance limits. Further analysis indicated that 88.8%, and 31.6% of the laboratories met the minimum performance for bias and CV, respectively. Conclusions The biological variation and sigma metrics are appropriate quality management models for evaluating the performance of HbA1c in EQA program. The intra-laboratory and inter-laboratory imprecision need to be improved in order to achieve the required analytical goals for diabetes diagnosis.

The application of sigma metrics in the laboratory to assess quality control processes in South Africa.

BackgroundLaboratories 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.ObjectiveThis study aimed to determine the sigma metrics of analytes when using different total allowable error guidelines.MethodsA 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).ResultsThe 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.ConclusionThe 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.

Implementation of Six Sigma in Glucose POCT Quality Control at Dr. Soetomo General Academic Hospital

Point of Care Testing (POCT) is important for the examination of critically ill patients in the emergency room and intensive care unit. The evaluation of the analytic quality of POCT is needed to ensure the quality of patient care at Dr. Soetomo General Academic Hospital. The purpose of this study is to evaluate the analytical quality of POCT at Dr. Soetomo General Academic Hospital. This was an observational analytical study that was done at Dr. Soetomo General Academic Hospital Surabaya in July 2017. Point of care testing quality analysis was based on 20 within run Internal Quality Control (IQC). Internal quality control data was used to calculate the mean, SD, and CV%. CV% was calculated with the following equation "CV%=(SD í— 100)/mean". Bias % = [ control mean – control true value]/control true value x 100. The sigma value was obtained from Total Error Allowance (TEa) – Bias/CV. TEa in this study was based on ISO 15197:2003. The results of the glucose examination (58 samples) from Dimension EXL 1, 2, 3 (hexokinase method) compared with 7 glucose POCT (glucose oxidase:i-STAT Nova Biomedical method). The correlation was calculated with Spearman statistical analysis using the SPSS version 23.0. the mean CV of seven POCTs=3.5% (1.7%-5%), mean bias of seven POCT=7.75% (4.8%-12.5%), the mean six sigma of 7 POCT=6.77 (4.0–11.6). The best POCT with a six sigma value of &gt; 6 was glucometer K14_2, Palem 1 and GRIU. The glucometer with the lowest six sigma values but still had good quality control were K14_1 and ROI. All glucometers had a good correlation with r value &gt; 0.8 (p=0.000). Glucose POCT in Dr. Soetomo General Academic Hospital all had good quality and met world-class standards. Further study using IQC 2 levels is recommended for a better POCT quality evaluation.

Application of sigma metrics in haematology laboratory

Objectives: Sigma metrics analysis indicates; the level quality control has achieved and how far a given process deviates from perfection. Though the concept is not new to quality and utility, its implementation in the haematology laboratory is still in its nascent stages. The study aimed to compare the utility of sigma metrics as a comprehensive quality control tool in Haematology laboratory against the current quality tools of Internal Quality Control (IQC) and External quality assurance scheme for haematology analyser.Methods: In the present study, IQC data was analysed from June 2021 to September 2021. Control material included 3 batches of second party controls (SPC). Sigma were calculated for five key haematological parameters run in Sysmex XN 350. OPSpecs charts were prepared. Sigma metrics were calculated using the formula Sigma = (TEa – Bias) / CV, Bias was taken from IQC kit insert. Quality goal index was also calculated when needed.Results: Average sigma metrics achieved for selected parameters was, RBC: 8.1, HB: 7.6, HCT 5.6, PLT: 7.0, and WBC: 6.7. The laboratory has achieved excellent to world class performance in all analytes. Sigma QC rules were applied and modifications proposed to the existing internal QC protocols to reduce the number of rejections.Conclusion: Labs must preferentially use the sigma metrics and sigma QC rules to design its IQC protocols, for individual parameters, rather than follow the ‘one fit for all’ recommendation. This would help save precious time, effort, unnecessary control runs and improve efficiency with better focus on quality control, where required.

Multirule procedures vs moving average algorithms for IQC: An appropriate comparison reveals how best to combine their strengths

BackgroundMoving Average Algorithms (MAA) have been widely recommended for use in Patient Based Real Time Quality Control applications (PBRTQC) to supplement or replace traditional Internal Quality Control (IQC) techniques. A recent “proof of concept” study recommends applying MAAs to IQC data to replace traditional IQC procedures because they “outperform Westgard Rules,” which is a current standard of practice for IQC. MethodsWe generated power curves for multi-rule procedures with 2 and 4 control measurements per QC event, as well as a Simple Moving Average having block sizes of 5, 10, and 20 control measurements. We also assessed time to detection in terms of the Average Number of QC Events required to detect different sizes of systematic errors. ResultsAs expected, the more control measurements included in the control technique, the better the error detection. However, when QC performance is considered on the Sigma Scale, high Sigma methods require only 1 or 2 control measurements to detect medically important systematic errors. MAAs have very low ability to detect error at the first few QC events following shift, so they suffer a lag phase in detecting medically important errors. MAAs are most useful for methods having 4.0 Sigma performance or less. Even then, large systematic shifts are more quickly detected by simple single and multirule procedures. ConclusionsChoice of control techniques (rules, means, ranges, etc.) should consider the Sigma-metric of the method. For methods having Sigmas of 4 or greater, traditional single rule and multirule procedures with Ns up to 4 are most effective; below 4 Sigma, a multirule coupled with a Simple Moving Average (SMA) rule with Ns of 4 to 8 can improve error detection.

Effects of a mobile game on students' learning achievements and motivations in a clinical chemistry course: learning style differences

Clinical chemistry has become an essential topic for medical technologist students to perform an authentic clinical laboratory. Therefore, it has been suggested to situate students to experience various clinical chemistry situations. Recently, it has been a common recognition that educational games can motivate students' learning and help them acquire knowledge by participating in gaming scenarios. Since there are only a few digital games in clinical chemistry, this study proposes a mobile game to simulate students in a clinical chemistry laboratory and implement the game with 155 university students on the clinical chemistry course. The results revealed that the mobile game could trigger knowledge acquisition and enable students to analyse internal quality control data, leading to less anxiety for performing clinical chemistry assessments. Moreover, the impacts of the mobile game on the learning achievements and motivations of the students with different learning styles were investigated and found that visual and verbal learning styles yielded positive results during participating in the mobile game.

Evaluation of clinical biochemical laboratory analytical process performance using sigma metrics and quality goal index

Background: Application of sigma matrics across clinical laboratory diagnostic process enables identification of errors in methods together with quality goal index can help point out the cause of unacceptable process performance. We believe the analytical performance of the clinical biochemistry laboratory standards do not meet the required quality according to six sigma standards. Aim and Objective: The study aimed at performance evaluation of clinical biochemical laboratory analytical process using sigma metrics and quality goal index. Methods: Sigma (σ) for the 20 analytes performed on HITACHI 7600 - 110 was calculated based on absolute bias, total allowable error (TEa) and coefficient of variation (CV). Two level internal quality control data obtained retrospectively from December 2020 to May 2021 was used to calculate the coefficient of variation. Bias was obtained from six month external quality control data. Total allowable error was obtained from three different sources. Quality goal index (QGI) was calculated to identify the cause of unwanted analyte performance. Results: Different sigma scores were obtained for analytes on different total allowable error standard, only triglyceride showed world class performance along all the three total allowable error standards. However, creatine kinase &amp; total bilirubin showed world class on clinical laboratory improvement amendment (CLIA) and biological variable database (BVD). On the other hand, glucose, and aspertate amino transferase had poor or unacceptable performance on all the total allowable error standards. Conclusion: Six sigma is an excellent tool in evaluating the quality performance of clinical biochemistry laboratory analytical process, quality goal index can help identify the problem and westgard rules can help design individual quality improvement strategy.

Evaluation of the clinical chemistry tests analytical performance with Sigma Metric by using different quality specifications - Comparison of analyser actual performance with manufacturer data.

IntroductionThe interest in quality management tools/methodologies is gradually increasing to ensure quality and accurate results in line with international standards in clinical laboratories. Six Sigma stands apart from other methodologies with its total quality management system approach. However, the lack of standardization in tolerance limits restricts the advantages for the process. Our study aimed both to evaluate the applicability of analytical quality goals with Roche Cobas c 702 analyser and to determine achievable goals specific to the analyser used.Materials and methodsThe study examined under two main headings as Sigmalaboratory and Sigmaanalyser. Sigmalaboratory was calculated using internal and external quality control data by using Roche Cobas c 702 analyser for 21 routine biochemistry parameters and, Sigmaanalyser calculation was based on the manufacturer data presented in the package inserts of the reagents used in our laboratory during the study. Sigma values were calculated with the six sigma formula.ResultsConsidering the total number of targets achieved, Sigmaanalyser performed best by meeting all CLIA goals, while Sigmalaboratory showed the lowest performance relative to biological variation (BV) desirable goals.ConclusionsThe balance between the applicability and analytical assurance of “goal-setting models” should be well established. Even if the package insert data provided by the manufacturer were used in our study, it was observed that almost a quarter of the evaluated analytes failed to achieve even “acceptable” level performance according to BV-based goals. Therefore, “state-of-the-art” goals for the Six Sigma methodology are considered to be more reasonable, achievable, and compatible with today’s technologies.

Evaluation of Quality Control Data of Clinical Chemistry Parameters using Six Sigma Metrics Tool in Clinical Laboratory

Background: Physicians rely on laboratory results for treating patients. So it is the duty of laboratories to assure quality of the results released. So laboratory performance should be validated to maintain the quality. Six sigma has now gained recent interest in monitoring the laboratory quality.This study was designed to gauge the clinical chemistry parameters based on six sigma metrics.&#x0D; Materials and Methods: In this retrospective study, both the internal and external quality control data of 26 clinical chemistry parameters were collected for a period of 6 months from June 2020 to November 2020 and the six sigma analysis was done at the Central clinical biochemistry laboratory of Chettinad Hospital and research institute.&#x0D; Results: AST, amylase, lipase, triglyceride, HDL, iron, magnesium, creatine kinase showed sigma values more than 6.Uric acid, total protein, ALT, direct bilirubin, GGT,cholesterol, cholesterol, calcium, TIBC and phosphorus shows sigma values between 3.5 to 6. Glucose, BUN, creatinine, albumin, Na, K, Chloride, showed sigma values less than 3.5.&#x0D; Conclusion: Six sigma metrics can help in improving the quality of laboratory performance and also helps to standardisethe actual amount of QC that is required by the laboratory for maintaining quality.

Evaluation of Quality Control Data of Hormones Using Six Sigma Metrics Tool in Clinical Laboratory

Background: In health care system it is necessary to provide high quality and reliable test results to the patients. Many clinical laboratories are using six sigma as a tool to improve the quality control in health care system. Keeping this in mind, the present study was conducted using the quality control data of hormones under NABL(National Accreditation Board for Testing and Calibration Laboratories) which were assayed in our clinical laboratory.&#x0D; Materials and Methods: In this retrospective study, both the internal and external quality control data of 11 hormones were collected for a period of 6 months from April 2020 to September 2020 and the six sigma analysis was done.&#x0D; Results: Testosterone level 1(6.8), level 2(6.5) and Folate level1(6.9), level 2(6.6) showed sigma level more than 6 and hence excellent performance. The hormones, FT3 level 1(3.7), level 2(4.8), HCG level 2(3.6), TSH level 1(4.8), level 2(4.7) and Vitamin B12 level 1(4.4), level 2(4.5) showed average performance with sigma level between 3.5 and 6. The hormones, FT4 level 1(1.7), level 2(2), HCG level 1(2.2), Prolactin level 1(3), level 2(3.3), FSH level 1(1.9), level 2(2.0), LH level 1(2), level 2(1.9) and Progesterone level 1(3.4), level 2(3.3) showed poor performance with sigma level less than 3.5.&#x0D; Conclusion: Stringent rules need not be applied for hormones with sigma&gt;6. Moreover, control limits can be relaxed to 3S so that false rejections can be minimized. For hormones with sigma&lt; 6, internal QC rules have to be strictly applied and the root cause analysis has to be done. To conclude, six sigma metrics is a powerful quality control tool which helps to improve the performance of the clinical laboratory and hence the efficiency of the health cares system.

A novel practical approach to calculate measurement uncertainty in clinical pathology laboratories using quality control data with the use of biological variation where applicable

Background: Measurement Uncertainty (MU) can assist the interpretation and comparison of the laboratory results against international diagnostic protocols, facilitate a reduction in health care costs and also help protect laboratories against legal challenges. Determination of MU for quantitative testing in clinical pathology laboratories is also a requirement for ISO 15189. Methods: A practical and simple to use statistical model has been designed to make use of data readily available in a clinical laboratory to assess and establish MU for quantitative assays based on internal quality control data to calculate Random Error and external quality assurance scheme results to calculate Systematic Error. The model explained in this article has also been compared and verified against quality specifications based on Biological Variation. Results: Examples that explain and detail MU calculations for the proposed model are given where different components of MU are calculated with tabulated results. Conclusions: The designed model is cost-effective because it utilises readily available data in a clinical pathology laboratory. Data obtained from internal quality control programs and external quality assurance schemes are used to calculate the MU using a practical and convenient approach that will not require resources beyond what is available. Such information can additionally be useful not only in establishing limits for MU to satisfy ISO 15189 but also in selecting and/or improving methods and instruments in use. MU can as well play an important role in reducing health care costs as shown by examples in the article.

Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk‐based statistical quality control strategy for these assays: A multicenter study

BackgroundThe six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed.MethodsInternal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI).ResultsSigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation.ConclusionsIn multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation.

Analytical Process Evaluation of Biochemistry Laboratory by Using Six Sigma Method

Objectives: The primary purpose of medical laboratories is to provide the most accurate results appropriate to the patient's medical condition. Therefore, the reliability of each laboratory must be scientifically tested. Approximately 10% of laboratory errors occur in the analytical phase. In this study, we aimed to evaluate analytical process performances of 14 routinely assayed parameters according to six sigma methodology. Materials and Methods: Mean, standard deviation and coefficient of variation were calculated from internal quality control data for three months from 14 routinely assayed parameters (albumin, alanine aminotransferase, aspartate aminotransferase, chloride, creatinine, glucose, HDL cholesterol, lactate dehydrogenase, potassium, total cholesterol, total protein, sodium, triglyceride and urea) in the laboratory (Roche Cobas c501). Bias was calculated using external quality control values for same months. Total error was also calculated. Acceptable total error was determined according to the Clinical Laboratory Improvement Amendments and Turkey criteria. Sigma values were calculated and divided into four groups; as &lt;3 unacceptable; 3-4 suited for purpose; 4-6 acceptable; &gt;6 world-class performance. Results: According to the Clinical Laboratory Improvement Amendments and Turkey sigma assessment, first levels of chloride, total cholesterol, glucose and urea performance were unacceptable. Moreover according to the Clinical Laboratory Improvement Amendments &nbsp;sigma assessment, first levels of albumin, creatinine, total protein; and both levels of sodium, chloride and urea were unacceptable. Other tests were found to be suited for purpose, acceptable or world-class performance. Conclusions: Sigma measurements should be routinely performed in laboratories for evaluating the analytical period performance of the laboratory. That will increase its quality via regulatory preventive actions. Our study allowed us to see and improve our measurement quality by determining the three-month periodic performance of our laboratory tests.

Internal Quality Control Data of Urine Reagent Strip Tests and Derivation of Control Rules Based on Sigma Metrics.

BackgroundUrine 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.MethodsThe 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).ResultsSeven 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.ConclusionsAlthough 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.

Evaluation of 20 clinical chemistry and 12 immunoassay analytes in terms of total analytical error and measurement uncertainty

In analytical quality management, target setting models that are selected by the purpose together with the error models that are applied correctly have critical importance. In our study, we aimed to compare the analytical performance characteristics of routine clinical chemistry and immunoassay tests with different target-setting models proposed by various organizations. Our study was performed with internal and external quality control data obtained using Beckman Coulter AU680 for clinical chemistry analytes and Roche Cobas 8000 autoanalyzer for immunoassay analytes. The total analytical error (TAE) was calculated by the formula TAH%=1.65×(CV%)+Bias%. Measurement uncertainty (MU) has been calculated adhering to the Nordtest guideline. Results were compared with BVEFLM, CLIA, RCPA, PRDEQA%, pUEQAS%, and permissible MU (pU%) data to investigate analytical performance qualities. When we compare the results of TAE and MU, MU was found to be higher than TAE for all analytes. ALT, AST, glucose, K, and triglycerides met all target values, showing the best performance. Taken together, our results show that CLIA for total analytical error and PRDEQA% and pUEQAS% for measurement uncertainty can match better than BVEFLM, RCPA and pU%. These test results should be evaluated with measurement uncertainty to avoid misdiagnosis. Appropriate specification limits should be defined for the examination of test methods that meet the objectives for fitness for clinical purposes.

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

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.

Quality improvement in clinical biochemistry laboratory using six sigma metrics and quality goal index

Introduction: One of the most popular quality management system tackle employed for process perfection is six sigma. When the process outcome is measurable, six sigma can be used to assess the quality. Aim: Present study was conducted with the objective to apply six sigma matrices and quality goal index for the assessment of quality assurance in a clinical biochemistry laboratory. Materials and methods: Present study is a retrospective study. Internal and external quality control data were analyzed retrospectively during July 2020 to December 2020. Descriptive statistics like laboratory mean ± standard deviation; bias and coefficient of variation (CV) were calculated for the parameters glucose, urea, creatinine, ALT (SGPT), AST (SGOT), cholesterol, triglyceride and HDL. Sigma value was calculated for both level I &amp; level II of internal quality control (IQC). Results: Satisfactory sigma values (≥3) were elicited for blood glucose, cholesterol, triglyceride, HDL, urea and creatinine, while ALT (SGPT) and AST (SGOT) performed poorly (&lt;3) on the sigma scale. The quality goal index (QGI) ratio was found (&gt; 1.2) for only 2 parameters SGPT and SGOT (with sigma value &lt;3) for both levels 1 and 2, indicating inaccuracy. Conclusion: Results of present study focuses on meticulous appraisal and execution of quality measures to improve sigma standards of all the analytical processes. Even though six sigma provides benefits over former approaches to quality assurance, it also opens newer challenges for laboratory practitioners. Therefore, sigma metric analysis provides a point of reference to design a protocol for IQC for the laboratory; address poor assess performance, and assess the existing laboratory process efficiency.

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

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.

Klinik Laboratuvarımızın Toplam Kalite Performansının Altı-Sigma Yöntemiyle Değerlendirilmesi

Aim: In order to evaluate the performance of the clinical laboratories, a quality measurement method, The Six-Sigma Methodology, is used. We aimed to evaluate the analytical performance of our clinical laboratory by using the internal and external quality control data of 28 analytes, and by calculating their process sigma values. Methods: Sigma values of 28 analytes of our laboratory were determined from coefficient of variation (CV %) and bias for 3 subsequent months from December 2018 to January 2019. If the sigma values are &amp;gt;6, between 3 and 6, and &amp;lt;3, they are classified as »world-class«, »good« or »un-acceptable«, respectively. Results: 6 (21%) analytes; T Protein, Creatinin, Ca, Na, Cl, HbA1c; provided &amp;lt;3 sigma value, 1 (04%) analyte; lipase; provided between 3-6 value and 11 (39%) analytes; amylase, GGT, ALT, LDH, CRP, Lactate, BUN, Direct Bil., T Bil., CK and CK-MB; provided &amp;gt; 6 sigma value for both IQC1 and IQC2 levels for three consecutive months, respectively. Conclusions: In terms of Six-Sigma Metrics; our results were generally found as world-class or good. An appropriate quality control scheduling should be carried out for each test by using Six-Sigma Methodology in all clinical chemistry laboratories.