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The sigma metric offers a quantitative framework for evaluating process performance in clinical laboratories. This study aimed to evaluate the analytical performance of automated analysers in haematology laboratories, using the sigma metric to choose the best analyser as an internal reference analyser. internal quality control (IQC) data were collected for 6 months from SNCS, and the sigma value was calculated for 9 haematology analysers in the laboratory. For the normal control level, a satisfactory mean sigma value ≥3 was observed for all of the studied parameters of all automated analysers. For the low control level, platelet (PLT) count by Instrument (Inst.) G performed poorly, with a mean sigma value <3. Inst. H, with all parameters' sigma values >4, performed best and was chosen as the internal reference analyser. The sigma metric can be used as a guide to choose the QC strategy and plan QC frequency. It can facilitate the comparison of the same assay performed by multiple systems. [ABSTRACT FROM AUTHOR]

Sigma metric can be calculated using a simple equation. However, there are multiple sources for the elements in the equation that may produce different Sigma values. This study aimed to investigate the importance of different bias estimation approaches for Sigma metric calculation.

ABSTRACTIntroduction The sigma metric offers a quantitative framework for evaluating process performance in clinical laboratories. This study aimed to evaluate the analytical performance of automated analysers in haematology laboratories, using the sigma metric to choose the best analyser as an internal reference analyser.Materials and Methods internal quality control (IQC) data were collected for 6 months from SNCS, and the sigma value was calculated for 9 haematology analysers in the laboratory.Results For the normal control level, a satisfactory mean sigma value ≥3 was observed for all of the studied parameters of all automated analysers. For the low control level, platelet (PLT) count by Instrument (Inst.) G performed poorly, with a mean sigma value 4, performed best and was chosen as the internal reference analyser.Conclusion The sigma metric can be used as a guide to choose the QC strategy and plan QC frequency. It can facilitate the comparison of the same assay performed by multiple systems.

INTRODUCTION: The aim of this study was to evaluate the analytical performances of various clinical biochemistry analytes by the sigma metrics method according to different total allowable error (TEa) targets and to determine the causes of errors that lead to low sigma score by using Quality Goal Index (QGI). METHODS: The study was carried out in the Central Laboratory of Bursa Karacabey State Hospital. Twelve analytes that were studied on the Roche Cobas c 501 autoanalyzer were included in the study. Internal (level 1 and 2) and external quality control data for the period March August 2020 were obtained retrospectively. The TEa targets were obtained from the Clinical Laboratory Improvement of 2019 (CLIA 2019), biological variation database (BVD), Rili-BAEK, and Turkish data. QGI was calculated for analytes with sigma score 6 were 7, 10, 6, and 18 according to TEa targets of CLIA, BVD, Rili-BAEK, and Turkey, respectively. When QGI was calculated, it was found that there was inaccuracy problem for albumin and chlorine L1 and imprecision for chlorine L2. DISCUSSION AND CONCLUSION: Laboratories should determine the appropriate TEa targets and use the sigma metrics method and QGI as a quality improvement tool. In the light of the obtained data, necessary quality improvements should be made, and the reliability of the results should be increased.

The application of Sigma metrics can be used for assessing the performance of diagnostic laboratories. Clinical laboratories are confronted with the trouble of having to select the best and suitable quality specifications that are required for quality planning. In this regard, our study aims at evaluating the performance of Sysmex XN-1000 hematology analyzer by using Sigma metrics based on different total allowable error (TEa) source and to determine the effects of these variations in Sigma metric evaluation.

INTRODUCTION: Sigma metric model is one of the most popular quality management system tools used for Six Sigma process improvement. This model provides three features that improve the results: low inaccuracy, less deviation, and correct performance of the analytical method. Internal quality control and external quality assessment programs are routinely carried out in all clinical laboratories to evaluate and continually improve the analytical quality. The Six Sigma model is a global quality management system that can also be applied in the determination of glycated hemoglobin (HbA1c). In recent years, this model has been supported by Quality Goal Index (QGI). In this study, we aimed to evaluate the analytical performances of Arkray HA8180V HbA1c analyzer according to internal and external quality Sigma metrics and QGI. METHODS: The data have been evaluated according to two internal control materials (Bio-DPC and KBUDEK External Quality Program) to calculate Sigma levels [Sigma=(TEa%-bias%)/CV%] and quality target indexes (QGI=bias/1.5×CV), where TEa is the total analytical error and CV is the coefficient of variation. QGI is a metric that can distinguish between techniques to deal with sensitivity and accuracy issues as well as calibrator lot changes. RESULTS: The mean Sigma levels for low and high quality control materials were found to be 5.17 and 2.51, respectively. QGI was found to be 0.8 1.2 for both levels. DISCUSSION AND CONCLUSION: The performance of HA8180V HbA1c analyzers was found to be acceptable compared to Sigma metrics. The values of OGI between 0.8 and 1.2 indicate inaccurate and inconsistent result. However, when evaluated as a whole with Sigma values, the results of the devices were found reliable.

Objectives: The aim of this study was to evaluate the analytical performances of various clinical biochemistry analytes by the sigma metrics method according to different total allowable error (TEa) targets and to determine the causes of errors that lead to low sigma score by using Quality Goal Index (QGI). Methods: The study was carried out in the Central Laboratory of Bursa Karacabey State Hospital. Twelve analytes that were studied on the Roche Cobas c 501 autoanalyzer were included in the study. Internal (level 1 and 2) and external quality control data for the period March–August 2020 were obtained retrospectively. The TEa targets were obtained from the Clinical Laboratory Improvement of 2019 (CLIA 2019), biological variation database (BVD), Rili-BAEK, and Turkish data. QGI was calculated for analytes with sigma score <3 according to CLIA. Results: According to the TEa goals of four different guides, different sigma scores were obtained. Three parameters with sigma scores <3 were determined according to TEa targets of CLIA, 8 according to BVD, and 6 according to RiliBAEK, while there were no parameters with sigma score <3 according to the TEa targets of Turkey. Number of parameters with sigma scores >6 were 7, 10, 6, and 18 according to TEa targets of CLIA, BVD, Rili-BAEK, and Turkey, respectively. When QGI was calculated, it was found that there was inaccuracy problem for albumin and chlorine L1 and imprecision for chlorine L2. Conclusion: Laboratories should determine the appropriate TEa targets and use the sigma metrics method and QGI as a quality improvement tool. In the light of the obtained data, necessary quality improvements should be made, and the reliability of the results should be increased. [ABSTRACT FROM AUTHOR]

Objectives: Sigma metric model is one of the most popular quality management system tools used for Six Sigma process improvement. This model provides three features that improve the results: low inaccuracy, less deviation, and correct performance of the analytical method. Internal quality control and external quality assessment programs are routinely carried out in all clinical laboratories to evaluate and continually improve the analytical quality. The Six Sigma model is a global quality management system that can also be applied in the determination of glycated hemoglobin (HbA1c). In recent years, this model has been supported by Quality Goal Index (QGI). In this study, we aimed to evaluate the analytical performances of Arkray HA8180V HbA1c analyzer according to internal and external quality Sigma metrics and QGI. Methods: The data have been evaluated according to two internal control materials (Bio-DPC and KBUDEK External Quality Program) to calculate Sigma levels [Sigma=(TEa%-bias%)/CV%] and quality target indexes (QGI=bias/1.5×CV), where TEa is the total analytical error and CV is the coefficient of variation. QGI is a metric that can distinguish between techniques to deal with sensitivity and accuracy issues as well as calibrator lot changes. Results: The mean Sigma levels for low and high quality control materials were found to be 5.17 and 2.51, respectively. QGI was found to be 0.8-1.2 for both levels. Conclusion: The performance of HA8180V HbA1c analyzers was found to be acceptable compared to Sigma metrics. The values of OGI between 0.8 and 1.2 indicate inaccurate and inconsistent result. However, when evaluated as a whole with Sigma values, the results of the devices were found reliable. [ABSTRACT FROM AUTHOR]

Background: Urine reagent strip test (URST) results are semi-quantitative; therefore, the precision of URSTs is evaluated as the proportion of categorical results from repeated measurements of a sample that are concordant with an expected result. However, URSTs have quantitative readout values before ordinal results challenging statistical monitoring for internal quality control (IQC) with control rules. This study aimed to determine the sigma metric of URSTs and derive appropriate control rules for IQC. Methods: The URiSCAN Super Plus fully automated urine analyzer (YD Diagnostics, Yongin, Korea) was used for URSTs. Change in reflectance rate (change %R) data from IQC for URSTs performed between November 2018 and May 2020 were analyzed. Red blood cells, bilirubin, urobilinogen, ketones, protein, glucose, leukocytes, and pH were measured from 2-3 levels of control materials. The total allowable error (TEa) for a grade was the difference in midpoints of a predefined change %R range between two adjacent grades. The sigma metric was calculated as TEa/SD. Sigma metric-based control rules were determined with Westgard EZ Rules 3 software (Westgard QC, Madison, WI, USA). Results: Seven out of the eight analytes had a sigma metric >4 in the control materials with a negative grade (-), which were closer to the cut-offs. Corresponding control rules ranged from 12.5s to 13.5s. Conclusions: Although the URST is a semi-quantitative test, statistical IQC can be performed using the readout values. According to the sigma metric, control rules recommended for URST IQC in routine clinical practice are 12.5s to 13.5s. [ABSTRACT FROM AUTHOR]

In laboratory medicine, mathematical equations are frequently used to calculate various parameters including bias, imprecision, measurement uncertainty, sigma metric (SM), creatinine clearance, LDL-cholesterol concentration, etc. Mathematical equations have strict limitations and cannot be used in all situations and are not open to manipulations. Recently, a paper "Bias estimation for Sigma metric calculation: Arithmetic mean versus quadratic mean" was published in Biochemia Medica. In the paper, the author criticized the approach of taking the arithmetic mean of the multiple biases to obtain a single bias and proposed a quadratic method to estimate the overall bias using external quality assurance services (EQAS) data for SM calculation. This approach does not fit the purpose and it should be noted that using the correct equation in calculations is as important as using the correct reagent in the measurement of the analytes, therefore before using an equation, its suitability should be checked and confirmed. [ABSTRACT FROM AUTHOR]

Variability in analytical performance of some analyte indicated the need of evaluation of quality plan of our laboratory. We tried to put the same degree of effort into our quality metrics as we put into the laboratory processes themselves. Application of six sigma methodologies improve the quality by focusing on the root causes of the problems in performance and analyzing by flowcharts, fishbone diagrams and other quality tools. Sigma metric was calculated for laboratory parameters for a period of 8 months during 2018–19. The analytes with poor sigma metric were free Thyroxine (FT3, FT4), Sodium, Calcium and Magnesium. Sigma metric of free Thyroxine (FT3, FT4), Sodium, Calcium and Magnesium were below 3. A road map for process improvement was designed with DMAIC (Define-Measure-Analyze-Improve-Control) model to solve the issue. Possible causes for low analytical performance of the particular analytes were depicted in Fishbone diagram. The Fishbone analysis identified the water quality issues with electrolyte analysis while high ambient temperature was culprit for poor assay performance of free Thyroxine. Sigma metric of the analytical performance was assessed once again after root cause analysis. Sigmametric showed marked improvement in control phase. Identification of problems led to reduction in non value added work leading to adequate resource utilization by addressing the priority issue. Therefore, DMAIC tool with Fish bone model analysis can be recommended as a well suited method for troubleshooting in poor performance of laboratory parameter. [ABSTRACT FROM AUTHOR]

Introduction: Laboratories minimize risks through quality control but analytical errors still occur. Risk management can improve the quality of processes and increase patient safety. This study aims to use the failure mode and effect analysis (FMEA) to assess the analytical performance and measure the effectiveness of the risk mitigation actions implemented. Materials and methods: The measurands to be included in the study were selected based on the measurement errors obtained by participating in an External Quality Assessment (EQA) Scheme. These EQA results were used to perform an FMEA of the year 2017, providing a risk priority number that was converted into a Sigma value (sFMEA). A root-cause analysis was done when sFMEA was lower than 3. Once the causes were determined, corrective measures were implemented. An FMEA of 2018 was carried out to verify the effectiveness of the actions taken. Results: The FMEA of 2017 showed that alkaline phosphatase (ALP) and sodium (Na) presented a sFMEA of less than 3. The FMEA of 2018 revealed that none of the measurands presented a sFMEA below 3 and that sFMEA for ALP and Na had increased. Conclusions: Failure mode and effect analysis is a useful tool to assess the analytical performance, solve problems and evaluate the effectiveness of the actions taken. Moreover, the proposed methodology allows to standardize the scoring of the scales, as well as the evaluation and prioritization of risks. [ABSTRACT FROM AUTHOR]

The global demand for effective utilization of both humans and machinery is increasing due to wastage incurred during product manufacturing. Excessive waste generation has made entrepreneurs find it difficult to breakeven. The development of dynamic error-proof Overall Equipment Effectiveness (OEE) model for optimizing a complex production process is targeted at minimizing/eradicating operational wastes/losses. In this study, the error-proof sigma metric was integrated into the extended traditional OEE factors (availability, performance, quality) to include losses due to waste and man-machine relationships. Error-proof sigma statistics enabled continuous corrective measures on unsatisfactory or low-level OEE resulted from process output variations (quantity delivered or expected), which were mapped into sigma statistical standards (one- to sixsigma). Application of the model in a processing company showed that errors of the process were reduced by 78% and 42% respectively for traditional OEE and the new Error-Proof OEE (OEE-EP). The results revealed that the OEE-EP model is better than the other existing schemes in terms of losses elimination in the production process. [ABSTRACT FROM AUTHOR]

Introduction: Laboratories minimize risks through quality control but analytical errors still occur. Risk management can improve the quality of processes and increase patient safety. This study aims to use the failure mode and effect analysis (FMEA) to assess the analytical performance and measure the effectiveness of the risk mitigation actions implemented. Materials and methods: The measurands to be included in the study were selected based on the measurement errors obtained by participating in an External Quality Assessment (EQA) Scheme. These EQA results were used to perform an FMEA of the year 2017, providing a risk priority number that was converted into a Sigma value (σFMEA). A root-cause analysis was done when σFMEA was lower than 3. Once the causes were determined, corrective measures were implemented. An FMEA of 2018 was carried out to verify the effectiveness of the actions taken. Results: The FMEA of 2017 showed that alkaline phosphatase (ALP) and sodium (Na) presented a σFMEA of less than 3. The FMEA of 2018 revealed that none of the measurands presented a σFMEA below 3 and that σFMEA for ALP and Na had increased. Conclusions: Failure mode and effect analysis is a useful tool to assess the analytical performance, solve problems and evaluate the effectiveness of the actions taken. Moreover, the proposed methodology allows to standardize the scoring of the scales, as well as the evaluation and prioritization of risks. [ABSTRACT FROM AUTHOR]

The Six Sigma methodology has been widely implemented in industry, healthcare, and laboratory medicine since the mid-1980s. The performance of a process is evaluated by the sigma metric (SM), and 6 sigma represents world class performance, which implies that only 3.4 or less defects (or errors) per million opportunities (DPMO) are expected to occur. However, statistically, 6 sigma corresponds to 0.002 DPMO rather than 3.4 DPMO. The reason for this difference is the introduction of a 1.5 standard deviation (SD) shift to account for the random variation of the process around its target. In contrast, a 1.5 SD shift should be taken into account for normally distributed data, such as the analytical phase of the total testing process; in practice, this shift has been included in all type of calculations related to SM including non-normally distributed data. This causes great deviation of the SM from the actual level. To ensure that the SM value accurately reflects process performance, we concluded that a 1.5 SD shift should be used where it is necessary and formally appropriate. Additionally, 1.5 SD shift should not be considered as a constant parameter automatically included in all calculations related to SM. [ABSTRACT FROM AUTHOR]

Background: A pilot external quality assurance (EQA) survey for the free light chain (FLC) assay was developed and implemented in Korea. Methods: Survey data over 6 years (2010–2015) were collected retrospectively and Sigma metrics were calculated for method-specific peer groups. Results: Nineteen to 29 laboratories participated in the EQA survey, and nephelometric (20%) and turbidimetric (80%) methods were used. Using a previously published clinically relevant reference change value (RCV) of 54.5% as the tolerance limit, the method-specific median Sigma metrics of kappa (κ) and lambda (λ) FLC achieved greater than Three-Sigma for 86–97% of all EQA distributions, and Five-Sigma for 48–72% of all distributions. Conclusions: This EQA analysis of FLC assay applied clinically relevant quality specifications using Sigma metrics. During the 6-year EQA survey, we found that most of the results from participating laboratories meet clinically relevant quality specifications. In addition, method-specific differences were noted for λ FLC, at FLC concentrations above the initial measuring range that require a sample dilution. [ABSTRACT FROM AUTHOR]

Six Sigma methodology has been used successfully in industry since the mid-1980s. Unfortunately, the same success has not been achieved in laboratory medicine. In this case, although the multidisciplinary structure of laboratory medicine is an important factor, the concept and statistical principles of Six Sigma have not been transferred correctly from industry to laboratory medicine. Furthermore, the performance of instruments and methods used in laboratory medicine is calculated by a modified equation that produces a value lower than the actual level. This causes unnecessary, increasing pressure on manufacturers in the market. We concluded that accurate implementation of the sigma metric in laboratory medicine is essential to protect both manufacturers by calculating the actual performance level of instruments, and patients by calculating the actual error rates. [ABSTRACT FROM AUTHOR]

A zero defects goal was implemented in the clinical laboratory settings using a six-sigma model. Daily Internal Quality Control (IQC) and external quality control data from April-September 2023 was extracted to calculate the sigma metrics of 21 biochemical analytes based on Total Error Allowable (TEa), % bias and co-efficient of variation percent (CV%). A retrospective comparative study was conducted in the department of Clinical Biochemistry at Kanva Diagnostic Services Pvt. Ltd, Bengaluru, India. The analytical performance of the 21 biochemical analytes was tested on Cobas 6000 and C311 analyzers. Quality Goal Index (QGI) and root cause analysis was calculated to infer the reason for the deviation of six sigma. Method decision charts were plotted to show the comparison of the problem analytes on both the analyzers. On Cobas 6000 at level 1 IQC, out of 21 analytes, 10 analytes showed σ>6 and 10 analytes showed σ 3-6 and on C311, 15 analytes which showed σ>6 and 6 analytes that showed σ 3-6. On Cobas 6000 at level 2 IQC, out of 21 analytes, 12 analytes showed σ>6 and 8 analytes showed σ 3-6 and on C311 17 analytes showed σ>6 and 4 analytes showed σ 3-6. Creatinine failed to meet minimal sigma performance at both levels of IQC on Cobas 6000., Competing Interests: There is no conflict of interest between the authors., (© 2023 Biomedical Informatics.)

Background: Urine reagent strip test (URST) results are semi-quantitative; therefore, the precision of URSTs is evaluated as the proportion of categorical results from repeated measurements of a sample that are concordant with an expected result. However, URSTs have quantitative readout values before ordinal results challenging statistical monitoring for internal quality control (IQC) with control rules. This study aimed to determine the sigma metric of URSTs and derive appropriate control rules for IQC., Methods: The URiSCAN Super Plus fully automated urine analyzer (YD Diagnostics, Yongin, Korea) was used for URSTs. Change in reflectance rate (change %R) data from IQC for URSTs performed between November 2018 and May 2020 were analyzed. Red blood cells, bilirubin, urobilinogen, ketones, protein, glucose, leukocytes, and pH were measured from 2-3 levels of control materials. The total allowable error (TE a ) for a grade was the difference in midpoints of a predefined change %R range between two adjacent grades. The sigma metric was calculated as TE a /SD. Sigma metric-based control rules were determined with Westgard EZ Rules 3 software (Westgard QC, Madison, WI, USA)., Results: Seven out of the eight analytes had a sigma metric >4 in the control materials with a negative grade (-), which were closer to the cut-offs. Corresponding control rules ranged from 1 2.5s to 1 3.5s ., Conclusions: Although the URST is a semi-quantitative test, statistical IQC can be performed using the readout values. According to the sigma metric, control rules recommended for URST IQC in routine clinical practice are 1 2.5s to 1 3.5s .