Analytical Sigma metrics: A review of Six Sigma implementation tools for medical laboratories

The diagnostic accuracy of quality control rules

Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfr and only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules.

A Novel Approach for Routinely Assessing Laboratory Sigma Metrics for a Broad Range of Automated Assays

BACKGROUND

Sigma metrics have been adapted for the clinical laboratory to incorporate observed accuracy, precision, and total error allowed. The higher the Sigma level for a process, the better performance that process has. A limitation of studies assessing Sigma metrics is that they are performed on a small number of well-controlled systems.

METHODS

An algorithm was developed to extract QC data and derive the Sigma metric for 115 analytes from sites connected to the QuidelOrtho E-Connectivity® database. The median of these results was then used to derive the Sigma metric for each assay.

RESULTS

In this analysis, 79 out of 115 (68.7%) of the assays assessed achieved 6 Sigma or better and 98 out of 115 (85.2%) achieved 5 Sigma or better.

CONCLUSIONS

This study has demonstrated a methodology that can be used to condense Sigma metrics from hundreds of analyzers into a single metric of assay quality. Because these analyzers are running in working laboratories from around the world, this analysis can serve as a baseline for understanding the assay performance achieved in the presence of variabilities such as lab-to-lab, instrument-to-instrument, material handling, environmental conditions, and reagent lot. The significant number of assays demonstrating high Sigma levels did so despite this variation. The ability of the methods reported here to include hundreds of analyzers represents a novel approach for assessing Sigma metrics in clinical laboratories.

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

The Evaluation of the Quality Performance of Biochemical Analytes in Clinical Biochemistry Laboratory Using Six Sigma Matrices

Introduction This study was conducted to assess the analytical performance of biochemical tests using Six Sigma methodology and to assess the underlying causes of unsatisfied performance of analytes with a sigma value of less than 4 using quality goal index (QGI) and root cause analysis (RCA). Methodology Daily data for internal quality control (IQC) for both level 1 (L1) and level 2 (L2) and monthly data for external quality assessment for a period of six months were recorded. The coefficient of variation (CV), bias, and total allowable error (TEa) were calculated to analyze the sigma (σ) values for 19 biochemical analytes. Quality goal index (QGI) analysis was done to analyze impressions and inaccuracies in analyte performance having a sigma value of less than 4. Root cause analysis (RCA) was done to evaluate the possible causes that can improve quality performance. Results Creatinine and high-density lipoprotein (HDL) had sigma metrics of ≤2.0, and chloride, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) had sigma values between 2 and 3. Glucose, total protein (TP), phosphate (Phos), and potassium had sigma values between 4 and 5 in level 1 QC. Sigma grading for level 2 quality control (QC) also gave similar results. For analytes with σ < 4, QGI analysis exposed inaccuracy or imprecision issues and identified errors such as the reconstitution of IQC, storage temperature, and air bubbles while processing the QC, being common causes of poor performance. Conclusion Six Sigma approach is helpful for quality assurance and identifying areas for improvement. Assessing Six Sigma metrics should be a routine practice to decide the frequency of QC run and to detect errors in analysis.

Advances in clinical chemistry patient-based real-time quality control (PBRTQC)

Patient-Based Real-Time Quality Control involves monitoring an assay using patient samples rather than external material. If the patient population does not change, then a shift in the long-term assay population results represents the introduction of a change in the assay. The advantages of this approach are that the sample(s) are commutable, it is inexpensive, the rules are simple to interpret and there is virtually continuous monitoring of the assay. The disadvantages are that the laboratory needs to understand their patient population and how they may change during the day, week or year and the initial change of mindset required to adopt the system. The concept is not new, having been used since the 1960s and widely adopted on hematology analyzers in the mid-1970s. It was not widely used in clinical chemistry as there were other stable quality control materials available. However, the limitations of conventional quality control approaches have become more evident. There is a greater understanding of how to collect and use patient data in real time and a range of powerful algorithms which can identify changes in assays. There are more assays on more samples being run. There is also a greater interest in providing a theoretical basis for the validation and integration of these techniques into routine practice.

A comparative analysis of Sigma metrics using conventional and alternative formulas

BACKGROUND AND AIM

The 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 METHODS

Twenty 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.

RESULTS

The 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.

CONCLUSION

The 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.

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

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

Comparación de la métrica Sigma calculada con tres métodos de estimación del sesgo en 33 magnitudes químicas y 26 de inmunoensayo

Objetivos

Aunque la métrica Sigma se puede calcular mediante una sencilla ecuación, la diversidad de fuentes de las que se extraen los elementos de la ecuación pueden arrojar diferentes valores Sigma. El objetivo de este estudio era investigar la importancia de las distintas estrategias de estimación del sesgo para el cálculo de la métrica Sigma.

Métodos

Se calculó la métrica Sigma de 33 magnitudes químicas y 26 magnitudes de inmunoensayo en un analizador Roche Cobas 6,000. El sesgo se calculó mediante tres métodos: a) calculando la media del sesgo mensual obtenida en los estudios de control de calidad externo (EQA, por sus siglas en inglés); 2) calculando los valores de sesgo mediante una ecuación de regresión a partir de datos obtenidos del EQA; y 3) calculando la media de los valores de sesgo mensual de los eventos de control de calidad internos (IQC, por sus siglas en inglés). Se realizó una métrica Sigma para cada uno de los dos niveles de muestras de IQC empleando tres métodos para calcular el sesgo. Los valores Sigma obtenidos se clasificaron en cinco categorías, en función de las reglas Sigma de Westgard, siendo ≥6, <6 y ≥5, <5 y ≥4, <4 y ≥3, y <3.

Resultados

Al clasificar la métrica Sigma, calculada aplicando tres métodos de estimación del sesgo para cada magnitud, se observó que 16 magnitudes químicas en los niveles 1 y 2 de IQC fueron clasificadas en categorías Sigma diferentes por al menos uno de los métodos de estimación de la desviación. Del mismo modo, dependiendo del método de estimación del sesgo empleado, se clasificaba en diferentes categorías a 12 magnitudes de inmunoensayo con niveles 1 y 2 de IQC.

Conclusiones

La métrica Sigma puede variar dependiendo del método empleado para calcular el sesgo, lo cual debe ser tenido en cuenta a la hora de evaluar el rendimiento analítico o programar eventos de IQC aplicando el método Seis Sigma.

LC-MS/MS in Clinical Chemistry: did it live up to its promise?: Consideration from the Dutch EQAS organisation

BACKGROUND

Over the past decade the use of LC-MS/MS has increased significantly in the hospital laboratories. Clinical laboratories have switched from immunoassays to LC-MS/MS methods due to the promise of improvements in sensitivity and specificity, better standardization with often non-commutable international standards, and better between-laboratory comparison. However, it remains unclear whether routine performance of the LC-MS/MS methods have met these expectations.

METHOD

This study examined the EQAS results, from the Dutch SKML, of serum cortisol, testosterone, 25OH-vitaminD and cortisol in urine and saliva over 9 surveys (2020 to first half of 2021).

RESULTS

The study found a significant increase in the number of compounds and in the number of results measured in the different matrices, with LC-MS/MS over a period of eleven years. In 2021, approximately 4000 LC-MS/MS results were submitted (serum: urine: saliva = 58:31:11%) compared to only 34 in 2010. When compared to the individual immunoassays, the LC-MS/MS based methods for serum cortisol, testosterone and 25OH-vitaminD showed comparable but also higher between-laboratory CVs in different samples of the surveys. For cortisol, testosterone and 25OH-vitaminD the median CV was 6.8%, 6.1% and 4.7% respectively for the LC-MS/MS compared to 3.9-8.0%,4.5-6.7%, and 7.5-18.3% for immunoassays. However, the bias and imprecision of the LC-MS/MS was better than that of the immunoassays.

CONCLUSION

Despite the expectation that LC-MS/MS methods would result in smaller between-laboratory differences, as they are relatively matrix independent and better to standardize, the results of the SKML round robins do not reflect this for some analytes and may be in part explained by the fact that in most cases laboratory developed tests were used.

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

BACKGROUND

We evaluated analytical and Sigma performance for 6 next generation chemistry assays on the Abbott Architect c8000 system.

METHODS

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 5 days. 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.

RESULTS

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.

CONCLUSION

Applying ACD recommendations, 5 assays showed Six Sigma, while cholesterol showed Five Sigma performance.

Technical quality assurance and quality control for medical laboratories: a review and proposal of a new concept to obtain integrated and validated QA/QC plans

Technical quality assurance (QA) and quality control (QA/QC) are important activities within medical laboratories to ensure the adequate quality of obtained test results. QA/QC tools available at medical laboratories include external QC and internal QC, patient-based real-time quality control (PBRTQC) tools such as moving average quality control (MAQC), limit checks, delta checks, and multivariate checks, and finally, analyzer flagging. Recently, for PBRTQC tools, new optimization and validation methods based on error detection simulation have been developed to obtain laboratory-specific insights into PBRTQC error detection. These developments have enabled implementation and application of these individual tools in routine clinical practice. As a next step, they also enable performance comparison of the individual QA/QC tools and integration of all the individual QA/QC tools in order to obtain the most powerful and efficient QA/QC plans. In this review, a brief overview of the individual QA/QC tools and their characteristics is provided and the error detection simulation approaches are explained. Finally, a new concept entitled integrated quality assurance and control (IQAC) is presented. To enable IQAC, a conceptual framework is suggested and demonstrated for sodium, based on available published data. The proposed IQAC framework provides ways and tools by which the performance of different QA/QC tools can be compared in a so-called QA/QC error detection table to enable optimization and validation of the overall QA/QC plan in terms of alarm rate as well as pre-analytical, analytical, and post-analytical error detection performance.

Evaluation of preanalytical and postanalytical phases in clinical biochemistry laboratory according to IFCC laboratory errors and patient safety specifications

Introduction

The aim of the study was to determine the current state of laboratory's extra-analytical phase performance by calculating preanalytical and postanalytical phase quality indicators (QIs) and sigma values and to compare obtained data according to desired quality specifications and sigma values reported by The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group - Laboratory errors and Patient Safety.

Materials and methods

Preanalytical and postanalytical phase data were obtained through laboratory information system. Rejected samples in preanalytical phase were grouped according to reasons for rejection and frequencies were calculated both monthly and for 2019. Sigma values were calculated according to "short term sigma" table.

Results

The number of rejected samples in laboratory was 643 out of 191,831 in 2019. Total preanalytical phase rejection frequency was 0.22%. According to the reasons for rejection, QIs and sigma values were: "Samples with excessive transportation time": 0.0036 and 5.47; "Samples collected in wrong container" 0.02 and 5.11. In December, QIs and sigma values were: "Samples with excessive transportation time": 0.01 and 5.34; "Samples collected in wrong container": 0.03 and 4.98. The postanalytical QIs and sigma values were: "Reports delivered outside the specified time": 0.34 and 4.21; "Turn around time of potassium": 56 minute and 3.84, respectively. There were no errors in "Critical values of inpatients and outpatients notified after a consensually agreed time".

Conclusions

Extra-analytical phase was evaluated by comparing it with the latest quality specifications and sigma values which will contribute to improving the quality of laboratory medicine.

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

OBJECTIVES

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.

METHODS

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.

RESULTS

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 TEa_min. With TEa_des and TEa_opt set as the quality goals, almost all analytes had sigma values below 3. Set TEa_min as quality goal, each analyte matched IQC muti rules and numbers of control measurements according to sigma values.

CONCLUSIONS

Quality goals from the EFLM BV database and Westgard Sigma Rules can be used to develop IQC strategy for tumor markers.

Verification of examination procedures for 72 biochemical parameters on the atellica® clinical chemistry and immunoassay analyzers

The verification of examination procedures is a responsibility for clinical laboratories in order to guarantee that their performance characteristics comply with the specifications obtained during the validation process and are congruent with the intended scope of the assay. The aim was to perform an evaluation of precision, bias, linearity, linear drift, sample carry-over, and comparability of 73 assays from Siemens Healthineers, by following the CLSI EP10-A3 guidelines. The verification was performed by measuring 72 biochemical parameters in quality control (QC) materials from Bio-Rad (except for IL6) with 73 assays installed on eight measuring systems (five Atellica^® CH 930 and three IM 1600 analyzers from Siemens Healthcare Diagnostics). The following information was collected: validation data from manufacturer, biological variation data from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) database, and specifications for fβhCG and PAPP-A assays to meet the Fetal Medicine Foundation standards. A total of 17550 results were obtained during EP10 verification process. Out of the 73 methods, only Cl-S, Mg-S, and Na-S failed the criteria for adequate precision, trueness, and comparability. The assays did not show significant loss of linearity, linear drift, or sample carry-over. This study allowed the initial training and familiarization with the instruments and the identification of operational issues. It also represented an opportunity to evaluate the QCs and to obtain analytical performance information for application of sigma six metrics for quality assurance. Professionals are advised to adequately standardize and protocolize their verification processes to ensure laboratory competence and patient safety.

Integrating moving average control procedures into the risk-based quality control plan in small-volume medical laboratories

The modern approach to quality control (QC) in medical laboratories implies the development of a risk-based control plan. This paper aims to develop a risk-based QC plan for a laboratory with a small daily testing volume and to integrate the already optimized moving average (MA) control procedures into this plan. A multistage bracketed QC plan for ten clinical chemistry analytes was made using a Westgard QC frequency calculator. Previously, MA procedures were optimized by the bias detection simulation method. Aspartate aminotransferase, HDL-cholesterol and potassium had patient-risk sigma metrics greater than 6, albumin and cholesterol greater than 5, creatinine, chlorides, calcium and total proteins between 4 and 5, and sodium less than 4. Based on the calculated run sizes and characteristics of optimized MA procedures, for 6 tests, it was possible to replace the monitoring QC procedure with an MA procedure. For the remaining 4 tests, it was necessary to keep the monitoring QC procedure and introduce MA control for added security. This study showed that even in a laboratory with a small volume of daily testing, it is possible to make a risk-based QC plan and integrate MA control procedures into that plan.

Sigma Metrics in Laboratory Medicine: A Call for Harmonization

BACKGROUND AND AIM

Sigma metrics are applied in clinical laboratories to assess the quality of analytical processes. A parameter associated to a Sigma >6 is considered "world class" whereas a Sigma <3 is "poor" or "unacceptable". The aim of this retrospective study was to quantify the impact of different approaches for Sigma metrics calculation.

MATERIAL AND METHODS

Two IQC levels of 20 different parameters were evaluated for a 12-month period. Sigma metrics were calculated using the formula: (allowable total error (TEa) (%) - bias (%))/(coefficient of variation (CV) (%)). Method precision was calculated monthly or annually. The bias was obtained from peer comparison program (PCP) or external quality assessment program (EQAP), and 9 different TEa sources were included.

RESULTS

There was a substantial monthly variation of Sigma metrics for all combinations, with a median variation of 32% (IQR, 25.6-41.3%). Variation across multiple analyzers and IQC levels were also observed. Furthermore, TEa source had the highest impact on Sigma calculation with proportions of Sigma >6 ranging from 17.5% to 84.4%. The nature of bias was less decisive.

CONCLUSION

In absence of a clear consensus, we recommend that laboratories calculate Sigma metrics on a sufficiently long period of time (>6 months) and carefully evaluate the choice of TEa source.

Six-sigma and quality planning of TORCH tests in the Peruvian population: a single-center cross-sectional study

Objective

To ensure the health of newborns, it is necessary to perform high-quality diagnostic tests. The TORCH panel is a set of tests that identifies infectious pathogens such as Toxoplasma (Toxo) and Cytomegalovirus (CMV) that are common in low-setting populations. We performed TORCH panel quality planning using six sigma in a reference laboratory at Peru.

Results

This was a cross-sectional study. TORCH tests include Toxo, Rubella, CMV, and Herpes. We processed all samples by fourth-generation ELISA on the GEMINI XCR200 analyzer (Diatron, Budapest, Hungary). We obtained the imprecision from the annual data of the external quality assessment plan and we used the CLSI EP12-A3 guideline. In a total of 44,788 analyses, the average imprecision was 3.69 ± 1.47%, and CMV had lower imprecision (2.3 and 2.6% for IgM and IgG, respectively). Quality planning of the TORCH panel allowed estimating the sigma value that ranged from 4 to 10 (average 7 ± 2 sigma), where rubella had the highest values (10 for IgM and 8 for IgG) while HSV2 had the lowest values (4 for IgM and 5 for IgG). Our results suggest the optimal performance of half of the markers including Toxoplasma, Rubella, and CMV in the Peruvian population.

Sigma and Risk in the Quality Control Routine: Analysis in Chilean Clinical Laboratories

BACKGROUND

The Six Sigma methodology is focused toward improvement, based on the Total Quality Management. It has been implemented in analytical procedures for clinical laboratories in the form of Sigma Metrics. This method is used in the evaluation of analytical procedures, providing evidence for risk-based management.

METHODS

A descriptive study was carried using data from 18 Chilean clinical laboratories. The information of their performance and quality specifications used in their routine work was obtained from UNITY, an internal quality comparison program.

RESULTS

A total of 3461 sigma evaluations was gathered, mostly from biyearly controls. The general distribution shows a median of 5.5 with positive asymmetry similar to other publications. The reported quality specifications are based in CLIA for 51.2% of the cases, 30.2% from biological variation, and 10.7% from other programs for the external quality evaluation. Significant differences (P < 0.05) were found between medians against their specification source.

CONCLUSIONS

In the studied series, it would be feasible to implement a risk-based quality control system with simple rules and minimal control materials for 55.5% of the evaluated sigmas. 19.6% of the sigmas require improvement mainly in precision. The variety in specifications reveals a lack of harmonization in the specification's selections.

Atellica CH 930 chemistry analyzer versus Cobas 6000 c501 and Architect ci4100 - a multi-analyte method comparison

Large clinical laboratories often rely on multiple chemistry analyzers. However, when a new analyzer is introduced, the laboratory must establish whether the old and new methods are comparable and can be used interchangeably. In this study, we compared the newly introduced Atellica CH930 chemistry analyzer with the already established Architect ci4100 and Cobas 6000 c501 from our laboratory.

Patient samples were randomly selected from daily routine testing and a total of 22 analytes were investigated. Total error (TEobs) between test (Atellica) and comparative (Architect and Cobas) methods was calculated at relevant medical decision levels (MDL). For demonstrative purposes, the assessment of method comparability was based on three different criteria: allowable total error (TEa) derived from biological variation (BV), CLIA proficiency testing criteria for acceptable analytical performance, and CLIA-calculated Sigma metrics. These sets of analytical performance specifications were also compared, and their strengths and limitations are discussed in this paper.

Performance of Atellica CH930 against Architect ci4100 was acceptable or nearly acceptable at 82%, 95%, and 64% of the 22 investigated MDLs across 9 analytes, according to BV-TEa, CLIA-TEa, and CLIA-calculated Sigma metrics, respectively. Similarly, performance of Atellica CH930 against Cobas 6000 c501 was acceptable or nearly acceptable at 61%, 93%, and 63% of the 54 investigated MDLs across 22 analytes, according to BV-TEa, CLIATEa, and CLIA-calculated Sigma metrics, respectively. However, method comparability should not be evaluated by a “one size fits all” approach as some analytes require different criteria of acceptability, ideally based on medically allowable error and clinical outcome.

Application of Sigma metrics in the quality control strategies of immunology and protein analytes

Background

Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma.

Methods

Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C‐reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values.

Results

While using the TEa_NCCL, 90% analytes had a world‐class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σ_NCCL that is calculated from TEa_NCCL, Sigma Method Decision Charts were constructed. For Cys C, five multi‐rules (1_3s/2_2s/R_4s/4_1s/6_X, N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (1_3s, N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12).

Conclusions

The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool.

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

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.

Utility of process capability indices in assessment of quality control processes at a clinical laboratory chain

BACKGROUND

To evaluate the utility of the process capability indices Cp and Cpk for assessing the quality control processes at chain laboratory facilities.

METHODS

In April 2020, the minimum Cp and Cpk values for 33 assays of a laboratory chain with 19 facilities were collected for further analysis and a total of 627 datasets (Cp and Cpk ) were compared. In addition, standard values for Cp and Cpk , defined as the lowest of the top 20%, were obtained for comparison and the indices were used to determine whether precision or trueness improvements were required for the corresponding assay.

RESULTS

A total of 627 datasets of 33 assays from 19 laboratory facilities were collected for further analysis. Based on the Cp results, 329 (52.5%), 211 (33.7%), 65 (10.3%), and 22 (3.5%) were rated as excellent, good, marginal, and poor, respectively. While the corresponding results for Cpk were 300 (47.8%), 216 (34.4%), 79 (12.6%), and 32 (5.1%). In addition, it was noteworthy that eight (Cp criteria) and six assays (Cpk criteria) were rated as excellent or good at all 19 facilities. Comparison of the process capability indices at the Jinan KingMed Center with the standard values revealed that total protein, albumin, and urea showed trueness individual improvement, precision individual improvement, and precision common improvement, respectively, while the results of other assays were stable.

CONCLUSION

Process capability indices are useful for evaluating the quality control procedures in laboratory facilities and can help improve the precision and trueness of laboratory tests.

Evaluation of Sigma metric approach for monitoring the performance of automated analyzers in hematology unit of Alexandria Main University Hospital

INTRODUCTION

Sigma metric offers a quantitative framework for evaluating process performance in clinical laboratories. This study aimed to evaluate the analytical performance of automated analyzers in hematology unit of Alexandria Main University Hospital using the sigma metric approach.

MATERIALS AND METHODS

Quality control data were collected for 6 months, and sigma value was calculated from hematology analyzers SYSMEX (XN 1000, XT 1800i), ADVIA (2120i, 2120), and coagulation analyzers SYSMEX CA 1500 (3610, 6336).

RESULTS

For the normal control level, satisfactory mean sigma value ≥3 was observed for all of the studied parameters by all analyzers. For the high control level, red blood cell count by ADVIA 2120, and hematocrit by ADVIA (2120i and 2120) performed poorly with a mean sigma value <3. For the low control level, red blood cell count by ADVIA (2120i and 2120), hemoglobin by ADVIA 2120, hematocrit by ADVIA (2120i and 2120) and SYSMEX XN 1000, platelet count by the SYSMEX XT 1800i also performed poorly with a mean sigma value <3. Satisfactory mean sigma value of ≥3 was observed for prothrombin time and activated partial thromboplastin time for both normal and pathological control levels and analyzers.

CONCLUSION

Sigma metrics can be used as a guide to make QC strategy and plan QC frequency and can facilitate the comparison of the same assay performance across multiple systems. Harmonization for TEa source is recommended to standardize sigma value calculation.

Multiplex Analysis of 230 Medications and 30 Illicit Compounds in Dried Blood Spots and Urine

Drugs of abuse and medication reconciliation testing can benefit from analysis methods capable of detecting a broader range of drug classes and analytes. Mass spectrometry analysis of a wide variety of commonly prescribed medications and over-the-counter drugs per sample also allows for application of a drug-drug interaction (DDI) algorithm to detect adverse drug reactions. In order to prevent adulteration of commonly collected clinical samples such as urine, dried blood spots (DBS) present a reliable alternative. A novel method is described for qualitative and quantitative multiplex analysis of 230 parent drugs, 30 illicit drugs and 43 confirmatory metabolites by HPLC-MS-MS This method is applicable to DBS specimens collected by volumetric absorptive microsamplers and confirmable in urine specimens. A patient cohort (n = 67) providing simultaneous urine specimens and DBS resulted in 100% positive predictive values of medications or illicits confirmed by detection of a parent drug and/or its metabolite during routine medication adherence analysis. An additional 5,508 DBS specimens screened (n = 5,575) showed 5,428 (97%) with an inconsistent positive compared to the provided medication list (including caffeine, cotinine or ethanol metabolites), 29 (0.5%) with no medication list and no unexpected positive results (consistent negative) and 22 (0.4%) showed all positive results matching the provided medication list (consistent positive). A DDI algorithm applied to all positive results revealed 17% with serious and 56% with moderate DDI warnings. Comprehensive DBS analysis proves a reliable alternative to urine drug testing for extended medication reconciliation, with the added advantage of detecting DDIs.

A Survey of Sigma Metrics across Three Academic Medical Centers

BACKGROUND

Sigma metric calculations provide laboratories an objective means to assess analytical method performance. Methods with higher sigma values are desirable because they are more reliable and may use less frequent quality control in order to maintain optimal performance. Sigma metrics can also serve as a tool when comparing method performance across assay and manufacturer platforms.

METHODS

Sigma values were calculated for 28 common chemistry and 24 immunoassay assays across 3 academic medical centers. Method imprecision and percent bias relative to peer group means was tabulated from Bio-Rad quality control (QC) data. Sigma values were calculated for each method using allowable total error (TEa) from either the CLIA evaluation limits or desirable biological variation. Average sigma values were generated for each site and graded as optimal: >6 sigma; good: 5-6 sigma; marginal: 3-5 sigma; or poor: <3 sigma. Analysis of NIST SRM1950 standards for a subset of analytes allowed an estimation of absolute bias.

RESULTS

Clinical chemistry assays displayed similar method performance across all 3 study sites. Immunoassays showed significant differences between manufacturers, and a majority of assays failed to meet an optimal level of performance. Different TEa values produced different sigma metrics with more stringent TEa limits based on biological variation, resulting in poorer performance estimates than the wider CLIA limits. Analysis of NIST standards revealed similar performance.

CONCLUSIONS

Sigma metrics are comparable for chemistry but not immunoassay platforms. The selection of total allowable error goals led to differences in sigma metrics.

Sigma metrics of Alinity ci system - a study on thirty-nine clinical chemistry and immunoassay parameters

Objectives

Sigma metrics in an invaluable and inexpensive tool used in laboratories to monitor analytical quality of the assays. Alinity ci platform is a relatively recent analytical system launched by Abbott Diagnostics, and as such performance studies on it are few. We have calculated sigma metrics of 39 clinical chemistry and immunoassay analytes on two Alinity ci systems.

Methods

Sigma metrics were calculated using results of method validation studies. Coefficient of variation (CV) was calculated according to CLSI EP 15 guidelines. Bias was calculated using three different methods i.e., proficiency testing material, alternate method comparison with existent analyzers and linearity experiment. Total allowable error limits were kept similar to or less than the ones used in reference studies.

Results

All analytes except blood urea nitrogen (BUN) demonstrated greater than six sigma value across one or more levels and methods. No analyte amongst clinical chemistry and immunoassays was at below three sigma class. Amongst electrolytes, sodium was below three sigma class at two levels by proficiency testing method, although it was above four sigma class by other two methods. Sigma levels obtained were comparable to those reported in previously published studies.

Conclusions

Acceptable sigma metrics were achieved for all clinical chemistry, immunoassays and electrolytes on Alinity ci. Sigma metrics is an objective and well established cost effective tool to tailor internal quality control practices. This study determines sigma metrics for a wide range of high throughput assays. Long term assay performance needs to be monitored.

Application of the sigma metrics to evaluate the analytical performance of cystatin C and design a quality control strategy

BACKGROUND

Sigma metrics are commonly used to evaluate laboratory management. In this study, we aimed to evaluate the analytical performance of cystatin C using sigma metrics and to develop an individualized quality control scheme for cystatin C concentrations.

METHODS

Bias was calculated based on the samples used for the external quality assessment. The coefficient of variation was calculated using six months of internal quality control measurements at two levels, and desirable specification derived from biological variation was used as the quality goal. The sigma value for cystatin C was calculated using the above data. The internal quality control scheme and improvement measures were formulated according to the Westgard sigma standards for batch size and quality goal index.

RESULTS

The sigma values for cystatin C, for quality control levels 1 and 2, were 3.04 and 4.95, respectively. The 1_3s/2_2s/R_4s/4_1s/6_x multirules (n = 6, R = 1), with a batch size of 45 patient samples, were selected as the internal quality control schemes for cystatin C. With different concentrations of cystatin C, the power function graph showed a probability for error detection of 94% and 100% and a probability for false rejection of 4% and 2%, respectively. According to the quality goal index of cystatin C, its precision needs to be improved.

CONCLUSIONS

With a 'desirable' biological variation of 6.50%, the Westgard rule 1_3s/2_2s/R_4s/4_1s/6_x (n = 6, R = 1, batch size of 45) with high efficacy for determining the detection error is recommended for individualized quality control schemes of cystatin C.

Sigma metrics application for validated and non-validated detecting systems performance assessment

Background

Sigma metrics provide an objective and quantitative methodology for analytical quality evaluation of clinical laboratory. This study investigated the testing performance of validated systems and non‐validated systems based on sigma metrics, and explored the major parameters affecting the system performance.

Methods

Sigma metrics were evaluated by six biochemistry assays based on Beckman and Mindray validated and non‐validated systems through crossing the reagents and analyzers. Imprecision and bias were assessed for all assays based on trueness programs organized by National Centre for Clinical Laboratory. Total error allowance obtained from the Chinese Ministry of Health Clinical Laboratory Centre Industry Standard (WS/T403‐2012).

Results

The imprecision for all systems meets the quality specifications except TP assay (2.19%) detected by Mindray non‐validated system, and the bias for four assays measured by non‐validated systems cannot fulfill the criterion, including lactate dehydrogenase (LDH), total protein (TP), triglycerides (TG), and glucose (GLU). Higher biases were detected in six assays at different levels among non‐validated and validated systems. Systems performed poorly or unacceptably for TP assay with sigma metrics lower than 3 except Mindray non‐validated system. The sigma metrics for other assays with four systems were greater than 3 except the LDH evaluated on Mindray non‐validated systems.

Conclusion

Non‐validated systems may introduce performance uncertainty compared with validated systems based on sigma metrics evaluation, and lower bias was provided by validated systems. The performance of non‐validated systems should be evaluated thoroughly in the clinical laboratory before they were adopted for routine use.

Evaluation of the analytical performance of endocrine analytes using sigma metrics

Background

(a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (σ) model. (b) To redesign quality control strategies for performance improvement.

Methods

The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re‐evaluated in September 2018 after a series of targeted corrective actions.

Results

Based on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (1_3S) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with σ < 4 at one QC material level or both needed multiple rules (1_3S/2_2S/R_4S/4_1S/10_X) with N6 and R10‐500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re‐evaluated sigma metrics of all the analytes increased significantly.

Conclusions

It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.

Analytical performance assessment and improvement by means of the Failure mode and effect analysis (FMEA)

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 (σ^FMEA). A root-cause analysis was done when σ^FMEA 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 σ^FMEA of less than 3. The FMEA of 2018 revealed that none of the measurands presented a σ^FMEA below 3 and that σ^FMEA 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.

Practical application of Six Sigma management in analytical biochemistry processes in clinical settings

BACKGROUND

Six Sigma methodology with a zero-defect goal has long been applied in commercial settings and was utilized in this study to assure/improve the quality of various analytes.

METHODS

Daily internal quality control (QC) and external quality assessment data were collected and analyzed by calculating the sigma (σ) values for 19 analytes based on the coefficient of variation, bias, and total error allowable. Standardized QC sigma charts were established with these parameters. Quality goal index (QGI) analysis and root cause analysis (RCA) were used to discover potential problems for the analytes.

RESULTS

Five analytes with σ ≥ 6 achieved world-class performance, and only the Westgard rule (1_3s ) with one control measurement at two QC material levels (N2) per QC event and a run size of 1000 patient samples between QC events (R1000) was needed for QC. In contrast, more control rules (2_2s /R_4s /4_1s ) along with high N values and low R values were needed for quality assurance for five analytes with 4 ≤ σ < 6. However, the sigma levels of nine analytes were σ < 4 at one or more QC levels, and a more rigorous QC procedure (1_3s /2_2s /R_4s /4_1s /8_x with N4 and R45) was implemented. The combination of QGI analysis and RCA further revealed inaccuracy or imprecision problems for these analytes with σ < 4 and discovered five aspects of potential causes considered for quality improvement.

CONCLUSIONS

Six Sigma methodology is an effective tool for evaluating the performance of biochemical analytes and is conducive to quality assurance and improvement.

Performance characteristics of automated clinical chemistry analyzers using commercial assay reagents contributing to quality assurance and clinical decision in a hospital laboratory

Background: Clinical laboratories provide essential diagnostic services that are essential in clinical decision making, contributing to the quality of healthcare. The performance of two Siemens ADVIA 1800 analyzers was characterized in a hospital Biochemistry laboratory in order to evaluate the analytical characteristics of such automated analyzer systems using nonoriginal assay reagents attempting to support laboratory quality service and crucial clinical decision making. Methods: We independently completed performance validation studies including trueness, precision, sensitivity as well as measurement of uncertainty and sigma metrics calculation for 25 biochemical parameters. Results: Trueness expressed as bias was less than 20% for both ADVIA 1800 analyzers. Within run and total precisions expressed as CV% were ≤9.85% on both analyzers for most parameters studied with few exceptions (Mg, TB, DB, Cl, HDL and UA) observed either in low or in high level samples and between the two analyzers. LoB, LoD and LoQ values produced by the two analyzers were comparable except Cl. Uncertainty values produced by the two analyzers were comparable with no significant differences. Quality performance of reagent assays was studied using the sigma metrics system. The sigma values were plotted on normalized method decision charts for graphical representation of assay performances for each analyzer. Conclusions: The two ADVIA systems, independently evaluated, showed consistent performance characteristics with certain discrepancies by several reagents. Sigma analysis was helpful for revealing the quality performance of non-original reagents supporting the need for strict assessment of quality assurance and in some instances optimization/improvement of assay methods.

Multi-site performance evaluation and Sigma metrics of 20 assays on the Atellica chemistry and immunoassay analyzers

Background

The Atellica Solution comprises chemistry (CH) and immunoassay (IM) analyzers. Recently, six early adopter clinical laboratories across Europe evaluated the analytical performance of 20 CH and IM assays. To measure analytical performance quality, Sigma metrics were calculated for individual-site and pooled-site results.

Methods

Precision, detection capability, linearity, and method comparison studies were performed according to Clinical Laboratory Standards Institute protocols. Global Sigma metrics across sites were calculated from pooled data at the medical decision level using total allowable error (TEa) goals from CLIA for CH assays, and TEa goals from RiliBÄK for IM assays; and, the equation:

Sigma metrics=%TEa–%bias/%CV.

A pooled %CV was calculated by combining the imprecision obtained from individual sites. Bias calculations were performed against the ADVIA Chemistry system or ADVIA Centaur system using Deming regression analysis (Passing-Bablok regression for electrolytes) on the pooled-site data. The 103 individual-site Sigma metric calculations used individual-site imprecision and pooled-bias.

Results

The limits of blank and detection results agreed with the manufacturer’s claims. Most assays were linear across the assay range tested. Pooled Sigma metrics were good or better (>4 Sigma) for 18 of 20 assays; and, acceptable for urea nitrogen (3.1) and sodium (3.9), the latter values attributable to higher imprecision at one of five sites.

Conclusions

Sigma metrics for data generated across multiple real-world sites evaluating the Atellica Solution demonstrated good or better performance of greater than 4 Sigma for 18 of 20 assays tested. Overall, results verified the manufacturer’s claims that methods were fit for use in clinical laboratories.

Design and implementation of quality control plans that integrate moving average and internal quality control: incorporating the best of both worlds

Background

New moving average quality control (MA QC) optimization methods have been developed and are available for laboratories. Having these methods will require a strategy to integrate MA QC and routine internal QC.

Methods

MA QC was considered only when the performance of the internal QC was limited. A flowchart was applied to determine, per test, whether MA QC should be considered. Next, MA QC was examined using the MA Generator (www.huvaros.com), and optimized MA QC procedures and corresponding MA validation charts were obtained. When a relevant systematic error was detectable within an average daily run, the MA QC was added to the QC plan. For further implementation of MA QC for continuous QC, MA QC management software was configured based on earlier proposed requirements. Also, protocols for the MA QC alarm work-up were designed to allow the detection of temporary assay failure based on previously described experiences.

Results

Based on the flowchart, 10 chemistry, two immunochemistry and six hematological tests were considered for MA QC. After obtaining optimal MA QC settings and the corresponding MA validation charts, the MA QC of albumin, bicarbonate, calcium, chloride, creatinine, glucose, magnesium, potassium, sodium, total protein, hematocrit, hemoglobin, MCH, MCHC, MCV and platelets were added to the QC plans.

Conclusions

The presented method allows the design and implementation of QC plans integrating MA QC for continuous QC when internal QC has limited performance.

Comparative analysis of calculating sigma metrics by a trueness verification proficiency testing-based approach and an internal quality control data inter-laboratory comparison-based approach

Introduction

Two methods were compared for evaluating the sigma metrics of clinical biochemistry tests using two different allowable total error (TEa) specifications.

Materials and methods

The imprecision (CV%) and bias (bias%) of 19 clinical biochemistry analytes were calculated using a trueness verification proficiency testing (TPT)‐based approach and an internal quality control data inter‐laboratory comparison (IQC)‐based approach, respectively. Two sources of total allowable error (TEa), the Clinical Laboratory Improvement Amendments of 1988 (CLIA '88) and the People's Republic of China Health Industry Standard (WS/T 403‐2012), were used to calculate the sigma metrics (σ_CLIA, σ_WS/T). Sigma metrics were calculated to provide a single value for assessing the quality of each test based on a single concentration level.

Results

For both approaches, σ_CLIA > σ_WS/T in 18 out of 19 assays. For the TPT‐based approach, 16 assays showed σ_CLIA > 3, and 12 assays showed σ_WS/T > 3. For the IQC‐based approach, 19 and 16 assays showed σ_CLIA > 3 and σ_WS/T > 3, respectively.

Conclusions

Both methods can be used as references for calculating sigma metrics and designing QC schedules in clinical laboratories. Sigma metrics should be evaluated comprehensively by different approaches.

Determination of sigma score based on biological variation for haemostasis assays: fit-for-purpose for daily practice?

Background

Internal quality control (QC) rules for laboratory tests can be derived from analytical performance specifications (APS) using the six-sigma method. We tested the applicability of this paradigm to routine haemostasis measurements.

Methods

Three laboratories using different instruments and reagents calculated sigma scores for their prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen and antithrombin (AT) measurements. Sigma scores were calculated using biological variation (BV) data from the literature in combination with internal and external QC data.

Results

Wide ranges in sigma scores for the PT (0.1–6.8), APTT (0.0–4.3), fibrinogen (1.5–8.3) and AT (0.1–2.4) were observed when QC data was combined with the minimum, median and maximum value of BV data, due in particular to a large variation in within-subject and between-subjects coefficients of variation. When the median BV values were applied, most sigma scores were below 3.0, for internal QC data; 75% and for external QC data; 92%.

Conclusions

Our findings demonstrate that: (1) The sigma scores for common haemostasis parameters are relatively low, and (2) The application of the six-sigma method to BV-derived APS is hampered by the large variation in published BV data. As the six-sigma concept is based on requirements for monitoring, and many haemostasis tests are only designed for diagnostic purposes, a fit-for-purpose APS is needed to achieve clinically relevant quality goals.

Evaluating the analytical quality control of urinary albumin measurements using sigma metrics

BACKGROUND

There is no worldwide recognized reference system and standard for urinary albumin measurement until now, so the analytical quality from different laboratories has always varied. In this study, we aimed to evaluate the analytical performance of a urinary albumin assay system using Sigma-metric, and thereby choose a suitable control rule to guarantee the analytical quality of the assays.

METHOD

Two levels of diluted reference material (ERM-DA47OK/IFCC) were used to calculate the biases, the coefficient of variation (CV) were calculated from six months of internal quality control measurements at two levels, and the external quality assessment standard of China for urinary albumin (30%) was used as the total allowable error(TEa).

RESULTS

The Sigma values for quality control levels 1 and 2 were 4.28 and 6.14, leading to recommended Westgard rules of 1_3s/2_2s/R_4s/4_1s (N = 2, R = 2) and 1_3s(N = 2, R = 1), respectively. Westgard rule 1_3s/2_2s/R_4s/4_1s(N = 2, R = 2) was selected for the quality control of the urinary albumin measurements, and with it, the power function graph showed a high efficacy for determining the detection errors with a probability of false rejection of 1.004% and a probability of error detection of 98.80%.

CONCLUSION

With a TEa of 30% recommended by the external quality assessment standard of China, Westgard rule 1_3s/2_2s/R_4s/4_1s(N = 2, R = 2) with a high efficacy for determining the detection error is recommended for the quality control of urinary albumin measurements.