Sigma Metrics Research Articles

A comparative analysis of Sigma metrics using conventional and alternative formulas

Background and AimThe Six Sigma approach, employing Sigma Metrics (SM), is commonly used to evaluate analytical performance in clinical laboratories. However, there is ongoing debate regarding the suitability of the conventional SM formula, which incorporates total allowable error (TEa) and bias. To address this, an alternative formula based on within-subject biological variation (CVI) as the tolerance range (TR) has been proposed. The study aimed to calculate and compare SM values using both formulas. Material and MethodsTwenty clinical chemistry parameters were evaluated, and SM values were calculated using conventional formula with two TEa goals and the alternative formula. Intermediate precision (CVA%) values were obtained from internal quality control data, while bias values were derived from external quality assessment reports. ResultsThe results showed that using the conventional formula, 11 SM values based on CLIA TEa goals and 21 SM values based on BV TEa goals were deemed unacceptable (SM < 3). Additionally, 22 SM values calculated using the alternative formula were below 3. ConclusionThe choice of TR had a substantial impact on the assessed analytical performance. Laboratories should carefully consider the appropriateness of each approach based on their specific quality objectives, analyte characteristics, and laboratory operations.

Analytical Performance Evaluation of Hematology Analyzer Using Various TEa Sources and Sigma Metrics

Analytical Performance Evaluation of Hematology Analyzer Using Various TEa Sources and Sigma Metrics

Comparison of Sigma metrics computed by three bias estimation approaches for 33 chemistry and 26 immunoassay analytes.

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. Sigma metrics were computed for 33 chemistry and 26 immunoassay analytes on the Roche Cobas 6000 analyzer. Bias was estimated by three approaches: (1) averaging the monthly bias values obtained from the external quality assurance (EQA) studies; (2) calculating the bias values from the regression equation derived from the EQA data; and (3) averaging the monthly bias values from the internal quality control (IQC) events. Sigma metrics were separately calculated for the two levels of the IQC samples using three bias estimation approaches. The resulting Sigma values were classified into five categories considering Westgard Sigma Rules as≥6, <6 and≥5, <5 and≥4, <4 and≥3, and <3. When classifying Sigma metrics estimated by three bias estimation approaches for each assay, 16 chemistry assays at the IQC level 1 and 2 were observed to fall into different Sigma categories under at least one bias estimation approach. Similarly, for 12 immunoassays at the IQC level 1 and 2, Sigma category was different depending on bias estimation approach. Sigma metrics may differ depending on bias estimation approaches. This should be considered when using Six Sigma for assessing analytical performance or scheduling the IQC events.

Evaluation of analytical performance of tests worked on the same brand devices with six sigma metrics

Amaç: Altı sigma ile analitik testlerin istenilen kalitede olup olmadığı ve kalitenin sayısal değeri görülebilir. Laboratuvarlara sunulan testlerin yöntem kalitesini değerlendirmek, cihazlar arasında yöntem performansını karşılaştırmak, kalite kontrol prosedürlerini yeniden gözden geçirmek konusunda altı sigma metriklerinden faydalanılmaktadır.&#x0D; Gereç ve Yöntemler: Bu çalışmanın amacı; laboratuvarda aktif çalışılan aynı marka cihazlar arasındaki altı sigma metrikleri ile analitik performansın değerlendirilmesi ve laboratuvar test çalışma hızına göre bu cihazlarda hangi testlerin çalışılıp çalışılmayacağı sorusuna yanıt bulmaktır. Yapılan araştırmada bütün testler ABBOTT marka Architeck c 8000 ve Architeck ci 4000 cihazlarında 6 ay süre ile çalışılmıştır. Glukoz (Glc), üre (BUN), kreatinin (CREA), aspartat aminotransferaz (AST), total kolesterol (CHOL), trigliseritler (Tg), Sodyum (Na), potasyum (K), klor (Cl) parametreleri değerlendirilmiş testlerin sigma değerleri performans yaklaşımına göre hesaplanmış ve cihazlar arasında karşılaştırma yapılmıştır. Toplam kabul edilebilir hata ( TEa), Klinik Laboratuvarları İyileştirme Yasası (CLIA) klavuzlarından alınmıştır. Bias, yeterlilik test verilerine göre belirlenmiştir. Biyokimyasal analitler için varyasyon katsayısı (CV) laboratuvarımızın IQC kayıtlarından elde edilmiştir. Sigma metrikleri (SM) = ( TEa-%Bias )/ %CV formülüne göre hesaplanmıştır&#x0D; Bulgular: 6 ay boyunca yapılan karşılaştırılmalı takipte ay bazında hangi cihazda çalışılması gereken parametrelerin belirlenmesi değişkenlik göstermiştir. Laboratuvarımızda en fazla çalışılan glucose, urea ve creatinine testlerinin Architeck ci 4000 cihazında sigma değerleri Architeck c8000 cihazından daha düşük olduğu için bu testlerin sadece c8000 cihazında çalışılmasına karar verildi. Elde edilen metriklere göre, 2019 Ekim ayından itibaren başlattığımız çalışmada altı ay sonra elektrolitlerin tek cihazda çalışılmaya başlanması ile sigma değerinde artış tespit edildi.&#x0D; Sonuç: Altı sigma metrikleri, binlerce test yapılan aynı marka cihazların yöntem performansını değerlendirmede aylık olarak özellikle düşük biyolojik varyasyona sahip testleri takip etmek için kullanılmalıdır.

Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data.

Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal quality control (IQC) and external quality assurance (EQA) data in the calculation of sigma metrics. This retrospective study was conducted in Marmara University Pendik E&R Hospital Biochemistry Laboratory. Sigma metrics calculation was performed as (TEa-bias)/CV). CV was calculated from IQC data from June 2018 - February 2019. EQA bias was calculated as the mean of % deviation from the peer group means in the last seven surveys, and IQC bias was calculated as (laboratory control result mean-manufacturer control mean)/ manufacturer control mean) x100. In parameters where sigma metrics were <5; QGI=bias/1.5 CV) score of <0.8 indicated imprecision, >1.2 pointed inaccuracy, and 0.8-1.2 showed both imprecision and inaccuracy. Creatine kinase (both levels), iron and magnesium (pathologic levels) showed an ideal performance with ≥6 sigma level for both bias determinations. Eight of the 17 parameters had different sigma levels when we compared sigma values calculated from EQA and IQC derived bias% while the rest were grouped at the same levels. Sigma metrics is a good quality tool to assess a laboratory's analytical performance and facilitate the comparison of the assay performances in the same manner across multiple systems. However, we might need to design a tight internal quality control protocol for analytes showing poor assay performance.

Application of Sigma Metrics for Quality Improvement in OGH Laboratory's Analytical Chemistry

Application of Sigma Metrics for Quality Improvement in OGH Laboratory's Analytical Chemistry

Repeat patient testing-quality control with canine samples shows promise as an alternative to commercial quality control material for a network of four Sysmex XT-2000iV hematology analyzers.

Repeat patient testing-quality control (RPT-QC) uses retained patient samples as an alternative to commercial quality control material (QCM). We elected to calculate and validate RPT-QC limits for red blood cell count (RBC), hemoglobin (HBG), hematocrit (HCT), and white blood cell count (WBC). (1) To validate RPT-QC across a network of four harmonized Sysmex XT-2000iV hematology analyzers and determine the total error that can be controlled with RPT-QC. (2) To generate quality control (QC) limits using the standard deviation (SD) of the duplicate measurement differences and determine a suitable simple QC rule with a probability of error detection >0.85 and probability of false rejection <0.05. (3) Monitor RPT-QC using sigma metrics as a performance indicator and (4) to challenge RPT-QC to ensure acceptable sensitivity. Fresh adult canine EDTA samples with results within reference intervals were selected and run again on days 2, 3, and 4. QC limits were generated from the SD of the duplicate measurement differences. The QC limits were challenged using interventions designed to promote unstable system performance. The total error detectable by RPT-QC was determined using EZRULES 3 software. In all, 20-40 data points were needed for RPT-QC calculations and validated using 20 additional data points. The calculated limits differed among the network of analyzers. The total error that could be controlled was the same or better than that of the manufacturer's commercially available quality control material using the same analyzer for all measurands except hematocrit, which required a higher total error goal than that proposed by ASVCP guidelines to achieve an acceptable probability of error detection. The challenges designed to mimic unstable system performance were successfully detected as out-of-control QC. The challenges for RPT-QC resulted in acceptable detection of potential unstable system performance. This initial study demonstrates that RPT-QC limits differ among the network of Sysmex XT-2000iV analyzers, indicating a requirement to customize for the individual analyzer and laboratory conditions. RPT-QC could achieve ASVCP total allowable error goals for RBC, HGB, and WBC, but not for HCT. Sigma metrics were consistently >5.5 for RBC, HGB, and WBC, but not for HCT.

Neurolinguistics: A scientometric review

This scientometric study aimed to map the knowledge domains of neurolinguistics, an interdisciplinary field of linguistics, to provide a comprehensive understanding of the development process and structural relationships of scientific knowledge in neuroscience. A total of 5,104 neurolinguistics documents published between 1913 and 2022 were analysed and visualized from three databases, Scopus, WOS, and Lens, using CiteSpace 5.8.R3 and VOSviewer 1.6.18. Knowledge production was mapped by country, university, journal, publisher, research field, and author, and examined using scientometric indicators, including co-citation networks, citation networks, sigma metrics, and clusters of topics. The identified clusters include discourse processing of flexible word order language language-ready brain, and language talent in cultural neurolinguistics; object knowledge of agrammatic aphasia; early experience of cerebral localization; language in autism spectrum disorder; and language evolution in relation to cognitive adequacy in language-ready brain. This study provides valuable insights into the field of neurolinguistics and offers a visual representation of the development process of scientific knowledge in neuroscience.

Use of Sigma Metrics in the Evaluation of Analytical Performance of Apo lipoproteins A1 and B Analytes

Use of Sigma Metrics in the Evaluation of Analytical Performance of Apo lipoproteins A1 and B Analytes

Analytical evaluation and Sigma metrics of 6 next generation chemistry assays on the Abbott Architect system.

We evaluated analytical and Sigma performance for 6 next generation chemistry assays on the Abbott Architect c8000 system. Albumin with bromocresol purple or green, amylase, cholesterol, total protein, and urea nitrogen were analyzed using photometric technology. Analytical performance goals were defined based on Accreditation Canada Diagnostics (ACD) and Clinical Laboratory Improvement Amendments (CLIA). Precision study consisted of testing 2 quality control concentrations and 3 patient serum sample pools, twice a day in quintuplicate over 5days. Linearity testing consisted of 5-6 concentrations of commercial linearity materials. We tested a minimum of 120 serum/plasma specimens on the new and current Architect methods for comparison. We assessed accuracy with reference materials for 5 assays, and a calibration standard for cholesterol. Bias from the reference standard target value was used for Sigma metric analysis. Observed total imprecision of the assays ranged from 0.5 to 4%, meeting pre-defined goals. Linearity was acceptable over the tested range. Measurements on the new and current Architect methods were comparable. Accuracy ranged from 0 to 2.0% absolute mean difference from target value. All 6 next generation clinical chemistry assays demonstrated Six Sigma quality, using CLIA standards. Applying ACD recommendations, 5 assays showed Six Sigma, while cholesterol showed Five Sigma performance.

Evaluation of Measurement Performance of Routine Chemistry Analytes by Application of Sigma Metrics, Method Evaluation Decision (MEDx) Charts and Quality Goal Index (QGI)

Objective: To evaluate performance of routine chemistry analytes in a tertiary care hospital laboratory by the application of sigma metrics. Introduction: Six sigma (6σ) is a popular Quality Management System (QMS) tool. Laboratories are increasingly using the six sigma method for the objective assessment and comparison of the analytical methods and instrument performance. Six sigma is about measuring or counting the number of defects. It quantifies the performance of a process as a rate of Defects-Per-Million-Opportunities (DPMO or DPM). The aim is to assess the performance and to eliminate or reduce the variation in a process. Materials and Methods: This prospective study was conducted over a period of six months duration. Sigma metrics were calculated using coefficient of variation (%CV), %Bias and total allowable error (%TEa). For %CV we used Internal Quality Control (IQC) samples, at two levels L1 and L2. Daily IQC results of L1 and L2 for 25 routine chemistry analytes were recorded in an excel sheet and %CV was calculated for each analyte for the period of study. For each analyte %Bias was calculated based on values obtained from monthly RIQAS-EQA program data. The total allowable error (%TEa) values for each analyte were extracted from various sources like Clinical Laboratories Improvement Amendment act (CLIA), Canadian Fixed Limits from the College of Physicians and Surgeons of Saskatchewan (CFX) and Spanish Society of Clinical Chemistry and Molecular Pathology (SEQC) table of Desirable Quality Specifications based on Biological Variation (BV) criteria for acceptable performance. Sigma values were calculated. The minimal acceptable performance criteria was considered as 3 sigma. Normalized MEDx charts were used to plot sigma metrics to visually present the performance. Quality Goal Index (QGI) analysis was carried out as a part of Root Cause Analysis (RCA). Results: Highest sigma value of 16.7 was noted for HDL-C and the lowest of 2.08 for chloride at level L1. Many analytes like ALP, Amylase, AST, CK, GGT, HDL-C, Magnesium and Uric Acid attained world class quality performance at both levels L1 and L2 with sigma levels of &gt;6. Many other analytes showed satisfactory performance with sigma levels of &gt;3. Sodium, potassium, chloride and urea did not show a satisfactory performance. Conclusion: Sigma metrics evaluation of analytical performance of our laboratory showed an acceptable performance for a wide range of analytes in patient samples.

Evaluation of analytical performance of homocysteine LC-MS/MS assay and design of internal quality control strategy.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become a common technique in clinical laboratories in recent years. Because most methods are laboratory-developed tests (LDTs), their reproducibility and quality control (QC) have been controversial. In this study, Westgard Sigma Rules were used to evaluate the analytical performance and establish an individualised internal QC (IQC) strategy for these LDTs. Taking the LC-MS/MS LDT method for homocysteine (Hcy) as an example, the 'desirable specifications' from the Biological Variation Database were used as quality goals. Based on the external quality assessment (EQA) samples, bias was calculated and the coefficient of variation (CV) was also calculated by IQC measurements for six consecutive months. The analytical performance was evaluated by calculated sigma metrics and an IQC strategy was designed using the Westgard Sigma Rules with run size. Over 116days within 6months, a total of 850 data points were collected for each of IQC 1 and IQC 2. The monthly coefficient of variation CV% was 2.57-4.01%, which was non-significant (p-value: 0.75). The absolute bias% for IQC1 and IQC2 was 1.23 and 1.87%, respectively. The allowable total error (TEa) was selected as 15.5%, Sigma metrics were 4.02 and 4.30, and the analytical performance was 'Good'. The 13s/22s/R4s/41s multi rules (n=4, r=1) with a run size of 200 samples were suggested for the Hcy IQC scheme. The quality goal index (QGI) values were over 1.2, indicating that trueness needed to be improved. The analytical performance of the Hcy LC-MS/MS LDT conformed to the Six Sigma rating level, achieving 'good' (four Sigma). Clinical practice indicated that calibration bias was the primary factor affecting trueness.

Assessment of Analytical Quality through Sigma Metrics & its Application for Selection of Westgard Rule

Assessment of Analytical Quality through Sigma Metrics & its Application for Selection of Westgard Rule

Performance specifications for sodium should not be based on biological variation

When increasing the quality in clinical laboratories by decreasing measurement uncertainty, reliable methods are needed not only to quantify the performance of measuring systems, but also to set goals for the performance. Sigma metrics used in medical laboratories for documenting and expressing levels of performance, are evidently totally dependent on the “total permissible error” used in the formulas.Although the conventional biological variation (BV) based model for calculation of the permissible (or allowable) total error is commonly used, it has been shown to be flawed. Alternative methods are proposed, mainly also based on the within-subject BV. Measurement uncertainty models might offer an alternative to total error models. Defining the limits for analytical quality still poses a challenge in both models.The aim of the present paper is to critically discuss current methods for establishing performance specifications by using the measurement of sodium concentrations in plasma or serum. Sodium can be measured with high accuracy but fails by far to meet conventional performance specifications based on BV. Since the use of sodium concentrations is well established for supporting clinical care, we question the concept that quality criteria for sodium and similar analytes that are under strict homeostatic control are best set by biology.

Effect of Matrix and Source of Quality Specification Data on the Sigma Metrics of Common Chemistry Analytes in Clinical Laboratory

Effect of Matrix and Source of Quality Specification Data on the Sigma Metrics of Common Chemistry Analytes in Clinical Laboratory

Six Sigma metrics as an indicator for Randox International Quality Assessment Scheme in clinical biochemistry laboratory

Context: Six Sigma methodology represents an evolution in quality assessment and management that has been implemented widely in industry and health sector since the mid-1980s. It consists of five steps: define, measure, analyze, improve, and control (DMAIC). Randox International Quality Assessment Scheme (RIQAS) program Target Score (TS) is a performance indicator, and Sigma metrics is considered as a gold standard for overall performance. Aim: The aim of this study is to present the TS and Sigma metrics observed in clinical chemistry laboratory in SSK Hospital associated with Lady Hardinge Medical College to evaluate the analytical performance of T3, T4, and thyroid-stimulating hormone (TSH) parameters. The study period is from June to August 2022. Materials and Methods: The Sigma metrics for the various analytes was calculated by the following equation: Σ = TEa − bias/CV (TEa—total allowable error, CV—coefficient of variation). Acceptable performance criteria: standard deviation index (SDI) &lt;2; TS &gt;50; the closer a value is to 120, the better the performance, &lt;40 is unacceptable; and %deviation should be less than the defined acceptable limits which is unique to each analyte, the closer the value is to zero, the better is the performance. Results: Our RIQAS results lie in acceptable range except for T3 in the month of August in which TS was unacceptable. Our laboratory Sigma metrics were below three sigmas. The findings of our exercise emphasize the need for a detailed evaluation, and we promote the use of Sigma metrics in quality assurance in the best interest of the patient. Conclusion: Quality check strategies were applied according to the TS and sigma values.

Sigma Metrics as a Valuable Tool for Effective Analytical Performance and Quality Control Planning in Clinical Laboratory: A Retrospective Study

Introduction: For the release of precise and accurate reports of routine tests, its necessary to follow a proper quality management system in the clinical laboratory. One of the most popular quality management system tools, for process improvement, six sigma has been accepted widely in the laboratory testing process. It gives an objective assessment of analytical methods and instrumentation. Six sigma measures the outcome of a process, on a scale of 0 to 6. The poor outcomes are measured in terms of defects per million opportunities (DPMO). Aim: To do the performance assessment of each analyte by six sigma analysis and to plan and chart out a better, customised, quality control plan for each analyte, according to its own sigma value. Materials and Methods: This was a retrospective observational study, conducted from January 2022 to June 2022, in the Department of Central Laboratory, KMCT Medical College, Kozhikode, Kerala, India. The precision and accuracy of 26 parameters in both haematology and biochemistry were assessed via Internal Quality Control (IQC) and External Quality Assurance (EQAS) Programme, analysis, and their performance was assessed by sigma analysis. Results: Clinical chemistry parameters showed an average percentage of Coefficient of Variation (CV%) of 2.65% and 2.3% for all the parameters in L2 (normal level) and L3 (abnormal levels) respectively. In haematology, the average CV% came out as, 1.3% (high level),1.82% (low level), and 1.35% (normal level). These values indicate excellent precision for all parameters in both clinical chemistry and haematology; with CV% below 3%. It was observed in the month of May, due to reconstitution errors, bias% showed a setback in a few chemistry parameters, due to which the sigma was lowered. Parameters with &lt;3 sigma metrics (poor performance) occupy 37%, 3-6 sigma metrics (good performance) occupy 29% and &gt;6 sigma metrics (world-class performance) occupy 34% of all the 26 parameters of clinical chemistry and haematology. Mean, Standard Deviation (SD) for biochemistry parameters were calculated using the daily IQC data. Conclusion: With the present study, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of the existing laboratory processes. It is on the basis of strict quality control measures and sigma analysis, the present Institute, was able to achieve world-class performance in many analytes of clinical chemistry and haematology disciplines. However, a few analytes like alkaline phosphatase, alanine transaminase, aspartate transaminase, and total protein needed more stringent external quality assurance monitoring and modified quality control measures.

Comparing the Performance of Serial and Parallel Production Systems through Six Sigma Metrics

Comparing the Performance of Serial and Parallel Production Systems through Six Sigma Metrics

A Six Sigma and DEA approach for learning outcomes assessment at industrial engineering programs

This research assesses the performance of 89 industrial engineering degrees in Colombia. The proposed approach articulates the Six Sigma concept of quality with the efficiency assessment from data envelopment analysis. The data used correspond to the standardised test taken by university students in Colombia in their last year of training (SABER_PRO). The input variables used for the Six Sigma metrics are quantitative reasoning, critical reading, citizen competencies, English and written communication. The output variable is the learning outcome, 'formulation and evaluation of projects'. The study's findings show that universities with institutional quality accreditation have a higher level of compliance than non-accredited universities. Also, private universities have a higher level of compliance than public universities. Regarding the data envelopment analysis model results, the average level of efficiency of the universities determined by the CRS, VRS, and performance to scale models is 93%, 90%, and 92%, respectively.

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

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 &lt; 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.