Evaluating laboratory performance on quality indicators with the six sigma scale

Analysis of hematology quality control using six sigma metrics

INTRODUCTION

Clinical laboratories serve a critical role in increasing the efficiency of patient care. Choosing the right test, getting trustworthy results and appropriate interpretation are of utmost importance in improving the patient's well-being. Quality management strategies should be applied in routine patient care because laboratory errors have a major impact on the quality of patient care. In sigma metrics, errors identified are quantified as percentage errors or defects per million (DPM). It aims at improving the quality control (QC) process by forming an appropriate strategy.

AIM AND OBJECTIVES

To analyze the internal quality control (IQC) of hematology analytes using the sigma metrics method and to devise the frequency of IQC by the results of six sigma metric analysis.

MATERIALS AND METHODS

This study was conducted in a tertiary care center of western India. Internal quality control (IQC) data sets of five analytes- Red Blood Cell count (RBC), Hemoglobin (Hb), Hematocrit (Hct), White blood cell count (WBC), and Platelet count (PLT) were analyzed retrospectively of six months using Beckman Coulter DXH 800 hematology analyzers.

RESULTS

The observed sigma value was >6 for Hb, TLC, and PLT, indicating excellent results and requiring no modification in IQC. The Sigma value was between 3 and 4 for RBC and Hct suggested the need for improvement in quality control (QC) processes. No analytes showed a Sigma value of <3.

CONCLUSION

Sigma metrics provide a quantitative framework that helps to assess analytic methodologies and can serve as an important self-assessment tool for quality assurance in the clinical laboratory.

Assessment of hematology laboratory performance in the total testing process using quality indicators and sigma metrics in the northwest of Ethiopia: A cross-sectional study

Background and Aims

Assuring laboratory quality by minimizing the magnitude of errors is essential. Therefore, this study aimed to assess hematology laboratory performance in the total testing process using quality indicators and sigma metrics.

Methods

A cross-sectional study was conducted from April to June 2022. The study included a total of 13,546 samples. Data on included variables were collected using a checklist. Descriptive statistics were used to present the overall distribution of errors. Binary logistic regression models were applied. Furthermore, using a Sigma scale, the percentage of errors was converted to defects per million opportunities to assess laboratory performance. Finally, the defect per million opportunities was converted to a sigma value using a sigma calculator.

Results

Of the 13,546 samples and corresponding requests, the overall error rate was 123,296/474,234 (26%): 93,412/47,234 (19.7%) pre-analytical, 2364/474,234 (0.5%) analytical, and 27,520/474,234 (5.8%) post-analytical. Of the overall errors, 93,412/123,296 (75.8%), 2364/123,296 (1.9%), and 27,520/123,296 (22.3%) were pre-analytical, analytical, and post-analytical errors, respectively. The overall sigma value of the laboratory was 2.2. The sigma values of the pre-analytical, analytical, and post-analytical phases were 2.4, 4.1, and 3.1, respectively. The sample from the inpatient department and collected without adherence to the standard operating procedures (SOPs) had a significantly higher (p < 0.05) rejection rate as compared to the outpatient department and collected with adherence to SOPs, respectively. In addition, an association between prolonged turnaround times and manual recording, inpatient departments, and morning work shifts was observed.

Conclusion

The current study found that the overall performance of the laboratory was very poor (less than three sigma). Therefore, the hospital leadership should change the manual system of ordering tests and release of results to a computerized system and give need-based training for all professionals involved in hematology laboratory sample collection and processing.

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.

Application of a six sigma model to evaluate the analytical performance of cerebrospinal fluid biochemical analytes and the design of quality control strategies for these assays: A single-centre study

BACKGROUND

In this study, we applied a six sigma model to examine cerebrospinal fluid (CSF) biochemical analytes for the first time. Our goal was to evaluate the analytical performance of various CSF biochemical analytes, design an optimized internal quality control (IQC) strategy, and formulate scientific and reasonable improvement plans.

METHODS

The sigma values of CSF total protein (CSF-TP), albumin (CSF-ALB), chloride (CSF-Cl), and glucose (CSF-GLU) were calculated using the following formula: sigma = [TEa(%)-|bias(%)|]/CV(%). The analytical performance of each analyte was shown using a normalized sigma method decision chart. Individualized IQC schemes and improvement protocols for CSF biochemical analytes were formulated using the Westgard sigma rule flow chart with batch size and quality goal index (QGI).

RESULTS

The distribution of sigma values for CSF biochemical analytes ranged from 5.0 to 9.9, and the sigma values varied for different concentrations of the same analyte. The analytical performance of the CSF assays at the two QC levels is displayed visually in normalized sigma method decision charts. Individualized IQC strategies for CSF biochemical analytes were as follows: for CSF-ALB, CSF-TP and CSF-Cl, use 1_3s with N = 2 and R = 1000; for CSF-GLU, use 1_3s/2_2s/R_4s with N = 2 and R = 450. In addition, priority improvement measures for analytes with sigma values less than 6 (CSF-GLU) were formulated based on the QGI, and their analytical performance was improved after the corresponding improvement measures were taken.

CONCLUSIONS

The six sigma model has significant advantages in practical applications involving CSF biochemical analytes and is highly useful for quality assurance and quality improvement.

Quality indicators in laboratory medicine: state-of-the-art, quality specifications and future strategies

In the last few decades, quality in laboratory medicine has evolved in concert with the transformation and the changes (technological, scientific and organizational) in this sector. Laboratory professionals have faced great challenges, at times being overwhelmed, yet also involved in this progress. Worldwide, laboratory professionals and scientific societies involved in laboratory medicine have raised awareness concerning the need to identify new quality assurance tools that are effective in reducing the error rate and enhancing patient safety, in addition to Internal Quality Control (IQC) procedures and the participation in the External Quality Assessment Schemes (EQAS). The use of Quality Indicators (QIs), specifically designed for laboratory medicine are effective in assessing and monitoring all critical events occurring in the different phases of Total Testing Process (TTP), in particular, in the extra-analytical phases. The Model of Quality Indicators (MQI), proposed by the Working Group "Laboratory Errors and Patient Safety" (WG-LEPS) of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and validated by experts in consensus conferences, is an important window of opportunity for the medical laboratory to demonstrate the use of an effective quality assurance tool fit for this purpose. Aim of this paper is to provide an update of the state-of-the-art concerning the most used QIs data collected in 2021 and the Quality Specifications (QSs) proposed for their evaluation. Moreover, a strategy for the future is proposed in order to improve the MQI and encourage its use in medical laboratories throughout the world.

Analysis of Quality Indicators of the Pre-Analytical Phase on Blood Gas Analyzers, Point-Of-Care Analyzer in the Period of the COVID-19 Pandemic

Aim of the study: We evaluated and compared blood gas analysis (EGA) non-conformities (NC) considered operator-dependent performed in Point-Of-Care (POC) analyzer as quality indicators (IQ) of the pre-analytical phase. To this end, four different NC registered in the resuscitation departments of the Hospital Polyclinic Bari from the beginning of the pandemic (March 2020) until February 2022 were evaluated. The results obtained were compared with those recorded in the pre-COVID period (March 2018–February 2020) to check if there were differences in number and type. Material and methods: GEM 4000 series blood gas analyzers (Instrumentation Laboratory, Bedford, MA, United States) are installed with integrated Intelligent Quality Management (iQM®), which automatically identify and log pre-analytical errors. All blood gas analyzers are connected to the company intranet and interfaced with the GEM Web Plus (Werfen Instrumentation Laboratory, Bedford, MA, United States) data management information system, which allows the core laboratory to remotely supervise all decentralized POC stations. The operator-dependent process NC were expressed in terms of absolute and relative proportions (percentiles and percentage changes). For performance evaluation, the Mann–Whitney U test, Chi-squared test and Six-Sigma Metric calculation for performance classification were performed. Results: In the COVID period, 31,364 blood gas tests were performed vs. 16,632 tests in the pre-COVID period. The NC related to the suitability of the EGA sample and manageable by the operators were totals of 652 (3.9%) and 749 (2.4%), respectively, in the pre-COVID and COVID periods. The pre-analytical phase IQs used did not show statistically significant differences in the two periods evaluated. The Sigma evaluation did not show an increase in error rates. Conclusions: Considering the increase in the number of EGAs performed in the two periods, the training procedures performed by the core laboratory staff were effective; the clinical users of the POC complied with the indications and procedures shared with the core laboratory without increasing the operator-dependent NCs. Furthermore, the core laboratory developed monitoring activities capable of guaranteeing the maintenance of the pre-analytical quality.

Detecting Preanalytical Errors Using Quality Indicators in a Hematology Laboratory

BACKGROUND AND OBJECTIVES

Monitoring laboratory performance continuously is crucial for recognizing errors and fostering further improvements in laboratory medicine. This study aimed to review the quality indicators (QIs) and describe the laboratory errors in the preanalytical phase of hematology testing in a clinical laboratory.

METHODS

All samples received in the Hematology Laboratory of the Maternity and Pediatric Hospital in Hail for 3 years were retrospectively reviewed and evaluated for preanalytical issues using a set of QIs. The rate of each QI was compared to the quality specifications cited in the literature.

RESULTS

A total of 95002 blood samples were collected for analysis in the hematology laboratory from January 2017 through December 2019. Overall, 8852 (9.3%) were considered to show preanalytical errors. The most common were "clotted specimen" (3.6%) and "samples not received" (3.5%). Based on the quality specifications, the preanalytical QIs were classified generally as low and medium level of performance. In contrast, the sigma-based performance level indicates acceptable performance on all the key processes. Further analysis of the study showed a decreasing rate of preanalytical errors from 11.6% to 6.5%.

CONCLUSIONS

Preanalytical errors remain a challenge to hematology laboratories. The errors in this case were predominantly related to specimen collection procedures that compromised the specimen quality. Quality indicators are a valuable instrument in the preanalytical phase that allows an opportunity to improve and explore clinical laboratory process performance and progress. Continual monitoring and management of QI data are critical to ensure ongoing satisfactory performance and to enhance the quality in the preanalytical phase.

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.

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

INTRODUCTION

The interest in quality management tools/methodologies is gradually increasing to ensure quality and accurate results in line with international standards in clinical laboratories. Six Sigma stands apart from other methodologies with its total quality management system approach. However, the lack of standardization in tolerance limits restricts the advantages for the process. Our study aimed both to evaluate the applicability of analytical quality goals with Roche Cobas c 702 analyser and to determine achievable goals specific to the analyser used.

MATERIALS AND METHODS

The study examined under two main headings as Sigmalaboratory and Sigmaanalyser. Sigmalaboratory was calculated using internal and external quality control data by using Roche Cobas c 702 analyser for 21 routine biochemistry parameters and, Sigmaanalyser calculation was based on the manufacturer data presented in the package inserts of the reagents used in our laboratory during the study. Sigma values were calculated with the six sigma formula.

RESULTS

Considering the total number of targets achieved, Sigmaanalyser performed best by meeting all CLIA goals, while Sigmalaboratory showed the lowest performance relative to biological variation (BV) desirable goals.

CONCLUSIONS

The balance between the applicability and analytical assurance of "goal-setting models" should be well established. Even if the package insert data provided by the manufacturer were used in our study, it was observed that almost a quarter of the evaluated analytes failed to achieve even "acceptable" level performance according to BV-based goals. Therefore, "state-of-the-art" goals for the Six Sigma methodology are considered to be more reasonable, achievable, and compatible with today's technologies.

Analytical performance evaluation of sensitive and old generation reagent in routine practical use: estradiol experience

Evaluation of the analytical performance of tests in medical laboratories is important. Total Error (TE) and sigma analysis have been used as a quantitative indicator of quality for many years. The aim of this study is to evaluate the analytical performance of Beckman Coulter Access Estradiol (E2) and Sensitive E2 reagents. Analytical performance of two reagents were evaluated with TE, six sigma and measurement uncertainty values. Two Beckman Coulter Unicel DxI-800 autoanalyzers (A1 and A2) included in the study. Quality control data between December 2017 and December 2019 were used. CLIA-2019 values were used for total allowable error (TEa) limits. Uncertainty values were calculated with ISO/TS 20914. The median TE of the old generation and sensitive E2 reagent were 27.46% (between 13.49 and 48.88) and 11.16% (between 7.08 and 24.81), respectively (p < .005) The process sigma results were below 3 sigma in all months with the old reagent, whereas with the new reagents it was seen to be above 3 sigma in 11 of 12 months for both autoanalyzers. Uncertainty of old reagent is higher than new reagent. Imprecisions decrease as concentration increases with both reagents. The uncertainty values of low concentration levels are greater than high concentration levels. In conclusion, in both auto analyzers, Sensitive E2 reagent was found to have better performance than old reagent in terms of TE, process sigma and measurement uncertainty. We believe that the sensitive E2 reagent still needs further improvement for patients who have low E2 levels.

"Six Sigma" standard as a level of quality of biochemical laboratories

The principal role of biochemical laboratories is responsibility for reliable, reproducible, accurate, timely, and accurately interpreted analysis results that help in making clinical decisions, while ensuring the desired clinical outcomes. To achieve this goal, the laboratory should introduce and maintain quality control in all phases of work. The importance of applying the Six SIGMA quality model has been analyzed in a large number of scientific studies. The purpose of this review is to highlight the importance of using six SIGMA metrics in biochemical laboratories and the current application of six SIGMA metrics in all laboratory work procedures. It has been shown that the six SIGMA model can be very useful in improving all phases of laboratory work, as well as that a detailed assessment of all procedures of the phases of work and improvement of the laboratory's quality control system is crucial for the laboratory to have the highest level of six SIGMA. Clinical laboratories should use SIGMA metrics to monitor their performance, as it makes it easier to identify gaps in their performance, thereby improving their efficiency and patient safety. Medical laboratory quality managers should provide a systematic methodology for analyzing and correcting quality assurance systems to achieve Six SIGMA quality-level standards.

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.

Evaluation of Sysmex XT-2000iV analyzer performance across a network of five veterinary laboratories using a commercially available quality control material

BACKGROUND

Laboratory and instrument harmonization is seldom reported in the veterinary literature despite its advantages to clinical interpretation, including the use of interchangeable results and common reference intervals within a system of laboratories.

OBJECTIVES

A three-step process was employed to evaluate and optimize performance and then assess the appropriateness of common reference intervals across a network of six Sysmex XT-2000iV hematology analyzers at 5 commercial veterinary laboratory sites. The aims were to discover if harmonization was feasible in veterinary hematology and which quality parameters would best identify performance deviations to ensure a harmonized status could be maintained.

METHODS

The performance of 10 measurands of a commercially available quality control material (Level 2-Normal e-CHECK (XE)-Hematology Control) was evaluated during three 1-month time periods. Precision and bias were assessed with Six Sigma, American Society of Veterinary Clinical Pathology (ASVCP) total error quality goals and biologic variation (BV)-based quality goal approaches to performance measurement.

RESULTS

Instrument adjustments were made to 1 analyzer twice and 3 analyzers once between evaluations to improve performance and achieve harmonization. Sigma metrics improved from 37/50 > 6 to 58/60 > 6 and to all >5 over the course of the harmonization project. BV-based quality goals for desirable bias and for laboratory systems of 0.33 × CV_I (within-subject biologic variation) were more sensitive and useful for assessing performance than the ASVCP total error goals.

CONCLUSIONS

Optimization and harmonization were achieved, and because BV-derived bias goals were achieved, common reference intervals could be implemented across the network of analyzers.

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

Background Six Sigma is a widely accepted quality management system that provides an objective assessment of analytical methods and instrumentation. Six Sigma scale typically runs from 0 to 6, with sigma value above 6 being considered adequate and 3 sigma being considered the minimal acceptable performance for a process. Methodology Sigma metrics of 10 biochemistry parameters, namely glucose, triglycerides, high-density lipoprotein (HDL), albumin, direct bilirubin, alanine transaminase, aspartate transaminase, urea nitrogen, creatinine and uric acid, and hematology parameters such as hemoglobin (Hb), total leucocyte count (TLC), packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and platelet were calculated by analyzing internal quality control (IQC) data of 3 months (June-August 2019). Results Sigma value was found to be > 6 for triglyceride, HDL, Hb, TLC, and MCH, signifying excellent results and no further modification with respect to IQC. Sigma value was between 3 and 6 for glucose, albumin, creatinine, uric acid, PCV, and MCHC, implying the requirement of improvement in quality control (QC) processes. Sigma value of < 3 was seen in AST, ALT, direct bilirubin, urea nitrogen, platelet, and MCV, signifying suboptimal performance. Discussion Six Sigma provides a more quantitative framework for evaluating process performance with evidence for process improvement and describes how many sigmas fit within the tolerance limits. Thus, for parameters with sigma value < 3, duplicate testing of the sample along with three QCs three times a day may be used along with stringent Westgard rules for rejecting a run. Conclusion Sigma metrics help assess analytical methodologies and augment laboratory performance.

Management of CMV, EBV, and HIV viral load quality control data using Unity Real Time

Quality control (QC) rules (Westgard rules) are applied to viral load testing to identify runs that should be reviewed or repeated, but this requires balancing the patient safety benefits of error detection with the cost and inefficiency of false rejection. In this study, we identified the total allowable errors (TEa) from the literature and utilized a commercially available software program (Unity Real Time; Bio-Rad Laboratories) to manage QC data, assess assay performance, and provide QC decision support for both FDA-approved/cleared (Abbott cytomegalovirus [CMV] and HIV viral load) as well as laboratory-developed (Epstein-Barr virus [EBV] viral load) assays. Unity Real Time was used to calculate means, standard deviations (SDs), and coefficient of variation (CV; in percent) of negative, low-positive, and high-positive control data from 73 to 83 days of testing. Sigma values were calculated to measure the test performance relative to a TEa of 0.5 log₁₀. The sigma value of 5.06 for EBV predicts ∼230 erroneous results per million individual patient tests (0.02% frequency), whereas sigma values of >6 for CMV (11.32) and HIV (7.66) indicate <4 erroneous results per million individual patient tests. The Unity Real Time QC Design module utilized these sigma values to recommend QC rules and provided objective evidence for loosening the laboratory’s existing QC rules for run acceptability, potentially reducing false rejection rates by 10-fold for the assay with the most variation (EBV viral load). This study provides a framework for laboratories, with Unity Real Time as a tool, to evaluate assay performance relative to clinical decision points and establish optimal rules for routine monitoring of molecular viral load assay performance.

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

Background

The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed.

Methods

Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI).

Results

Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation.

Conclusions

In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation.

Six Sigma performance of quality indicators in total testing process of point-of-care glucose measurement: A two-year review

Objectives

The error rate in the total testing process (TTP) of point-of-care (POC) glucose measurement remains high although a total quality management system has been applied. Quality indicators (QIs) in the TTP of glucose meter were established via risk assessment. Their two-year Six Sigma values were reviewed for quality improvement.

Design

The TTP of POC glucose measurement was mapped to identify risks in key steps. The risks were assessed for their frequency and severity of impact on patient safety. Whenever possible, measurable data from the data management system and other sources was collected to establish QIs for risk monitoring. Average Six Sigma value of each QI in the last two years was calculated for acceptance and for determining corrective action.

Results

29 risks were identified in eight key steps of the TTP. Eight QIs were established for monitoring six risks and three QIs for two accepted risks were established for improving operator testing skill. The QIs had a good coverage to key steps. Two, five and four QIs showed Six Sigma values <3, 3-4 and >4 respectively. Six Sigma values of two QIs related to quality control (QC) testing were improved by using meters with accurate QC sample loading.

Conclusions

The establishment of QIs for glucose measurement by risk assessment with measurable data from the data management system and on Six sigma scale was effective, efficient, and manageable. Most of QIs’ Six Sigma values were between 3 and 5, which could be improved by using upgraded meters.

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The total testing process of POC glucose measurement was assessed to identify all risks that might impact patient safety.

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QIs that established from data management system monitored the risks related to all of the meters and operators.

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Six Sigma values of QIs provided a straightforward acceptance in their performance evaluation.

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Most of the Six Sigma values of QIs for glucose meters were between 3 and 5 under current total quality management system.

Application of a six sigma model to the evaluation of the analytical performance of serum enzyme assays and the design of a quality control strategy for these assays: A multicentre study

BACKGROUND

Six medical testing laboratories at six different sites in China participated in this study. We applied a six sigma model for (a) the evaluation of the analytical performance of serum enzyme assays at each of the laboratories, (b) the design of individualized quality control programs and (c) the development of improvement measures for each of the assays, as appropriate.

METHODS

Internal quality control (IQC) and external quality assessment (EQA) data for selected serum enzyme assays were collected from each of the laboratories. Sigma values for these assays were calculated using coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts were generated using these parameters. IQC design and improvement measures were defined using the Westgard sigma rules. The quality goal index (QGI) was used to assist with identification of deficiencies (bias problems, precision problems, or their combination) affecting the analytical performance of assays with sigma values <6.

RESULTS

Sigma values for the selected serum enzyme assays were significantly different at different levels of enzyme activity. Differences in assay quality in different laboratories were also seen, despite the use of identical testing instruments and reagents. Based on the six sigma data, individualized quality control programs were outlined for each assay with sigma <6 at each laboratory.

CONCLUSIONS

In multi-location laboratory systems, a six sigma model can evaluate the quality of the assays being performed, allowing management to design individualized IQC programs and strategies for continuous improvement as appropriate for each laboratory. This will improve patient care, especially for patients transferred between sites within multi-hospital systems.

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.

Applicability of commercial clinical chemistry test kits for horse serum

Objective

Validation of a test method is critical for confirming that the test can generate accurate and precise data. Although commercial biochemical test kits exist there are no specific and validated commercial clinical chemistry test kits designed for horses. The aim of this study was to validate commercial clinical chemistry test kits designed for a human serum for use in horses.

Results

Blood samples were collected from 29 apparently healthy adult male horses and pooled serum was prepared. Validation comprises replication and recovery experiments. Total observable error (TE_o), sigma (σ) metrics, and quality goal index (QGI) were used to support the validation studies. Intra- and inter-assay variability was 2.05% and 2.08%, 2.26% and 1.89%, 2.4% and 1.63%, for total cholesterol, urea and total protein, respectively; recovery was 99.46%, 97.32%, and 100.1% for total cholesterol, urea and total protein, respectively. TE_o% for the specified analytes was within the total allowable error (TE_a). All three analytes satisfied the recommended requirement (> 3σ). The QGI for urea, as it had below 6σ was 0.95 indicating imprecision and inaccuracy. The results endorse the suitability of the studied commercial test kits and illustrated the acceptance criteria for horse’s serum.

Evaluation of Sigma-Metric and Application of Quality Tools in Clinical Laboratory of a Tertiary Care Hospital

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.

Five-Year Two-Center Retrospective Comparison of Central Laboratory Glucose to GEM 4000 and ABL 800 Blood Glucose: Demonstrating the (In)adequacy of Blood Gas Glucose

PURPOSE

To evaluate the glucose assays of two blood gas analyzers (BGAs) in intensive care unit (ICU) patients by comparing ICU BGA glucoses to central laboratory (CL) glucoses of almost simultaneously drawn specimens.

METHODS

Data repositories provided five years of ICU BGA glucoses and contemporaneously drawn CL glucoses from a Calgary, Alberta ICU equipped with IL GEM 4000 and CL Roche Cobas 8000-C702, and an Edmonton, Alberta ICU equipped with Radiometer ABL 800 and CL Beckman-Coulter DxC. Blood glucose analyzer and CL glucose differences were evaluated if they were both drawn either within ±15 or ±5 minutes. Glucose differences were assessed graphically and quantitatively with simple run charts and the surveillance error grid (SEG) and quantitatively with the 2016 Food and Drug Administration guidance document, with ISO 15197 and SEG statistical summaries. As the GEM glucose exhibits diurnal variation, CL-arterial blood gas (ABG) differences were evaluated according to time of day.

RESULTS

Compared to the GEM glucoses measured between 0200 and 0800, the run charts of (GEM-CL) glucose demonstrate significant outliers between 0800 and 0200 which are identified as moderate to severe clinical outliers by SEG analysis (P < .002 and P < .0005 for 5- and 15-minute intervals). Over the entire 24-hour period, the rates of moderate to severe glucose clinical outliers are 3.5/1000 (GEM) and 0.6/1000 glucoses (ABL), respectively, using the 15-minute interval (P < .0001).

DISCUSSION

The GEM ABG glucose is associated with a higher frequency of moderate to severe glucose clinical outliers, especially between 0800 and 0200, increased CL testing and higher average patient glucoses.

Changes in error rates in the Australian key incident monitoring and management system program

Introduction

The Key incident monitoring and management system program (KIMMS) program collects data for 19 quality indicators (QIs) from Australian medical laboratories. This paper aims to review the data submitted to see whether the number of errors with a higher risk priority number (RPN) have been reduced in preference to those with a lower RPN, and to calculate the cost of these errors.

Materials and methods

Data for QIs from 60 laboratories collected through the KIMMS program from 2015 until 2018 were retrospectively reviewed. The results for each QI were averaged for the four-year average and coefficient of variation. To review the changes in QI frequency, the yearly averages for 2015 and 2018 were compared. By dividing the total RPN by 4 and multiplying that number by the cost of recollection of 30 AUD, it was possible to assign the risk cost of these errors.

Results

The analysis showed a drop in the overall frequency of incidents (6.5%), but a larger drop in risk (9.4%) over the period investigated. Recollections per year in Australia cost the healthcare industry 27 million AUD. If the RPN data is used, this cost increases to 66 million AUD per year.

Conclusions

Errors with a higher RPN have fallen more than those with lower RPN. The data shows that the errors associated with phlebotomy are the ones that have most improved. Further improvements require a better understanding of the root cause of the errors and to achieve this, work is required in the collection of the data to establish best-practice guidelines.

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

OBJECTIVE:

Six Sigma is one of the most popular quality management system tools employed for process improvement. The Six Sigma methods are usually applied when the outcome of the process can be measured. This study was done to assess the performance of individual biochemical parameters on a Sigma Scale by calculating the sigma metrics for individual parameters and to follow the Westgard guidelines for appropriate Westgard rules and levels of internal quality control (IQC) that needs to be processed to improve target analyte performance based on the sigma metrics.

MATERIALS AND METHODS:

This is a retrospective study, and data required for the study were extracted between July 2015 and June 2016 from a Secondary Care Government Hospital, Chennai. The data obtained for the study are IQC - coefficient of variation percentage and External Quality Assurance Scheme (EQAS) - Bias% for 16 biochemical parameters.

RESULTS:

For the level 1 IQC, four analytes (alkaline phosphatase, magnesium, triglyceride, and high-density lipoprotein-cholesterol) showed an ideal performance of ≥6 sigma level, five analytes (urea, total bilirubin, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level and for level 2 IQCs, same four analytes of level 1 showed a performance of ≥6 sigma level, and four analytes (urea, albumin, cholesterol, and potassium) showed an average performance of <3 sigma level. For all analytes <6 sigma level, the quality goal index (QGI) was <0.8 indicating the area requiring improvement to be imprecision except cholesterol whose QGI >1.2 indicated inaccuracy.

CONCLUSION:

This study shows that sigma metrics is a good quality tool to assess the analytical performance of a clinical chemistry laboratory. Thus, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of existing laboratory processes.

Six Sigma revisited: We need evidence to include a 1.5 SD shift in the extraanalytical phase of the total testing process

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.

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.

Establishment of reference intervals for thyroid hormones in premature infants beyond the first week of life using Beckman Coulter Unicel DxI 800

BACKGROUND

This 4-year retrospective cohort study aimed to establish reference intervals for free triiodothyronine (FT3), free thyroxine (FT4), and thyrotropin (TSH) in premature infants using the Beckman Coulter Unicel DxI 800 automated immunoassay system.

METHODS

Study subjects included 605 preterm infants with a gestational age of 26-36 weeks (corrected: 29-38 weeks). Pearson correlation was used to evaluate the association between hormone levels and gestational and corrected gestational ages. A nonparametric method was used to establish reference intervals based on corrected gestational age.

RESULTS

FT3 and FT4 levels were positively correlated with gestational and corrected gestational ages, respectively. TSH levels were slightly negatively correlated with gestational and corrected gestational ages. FT3 significantly differed according to corrected gestational age (29-33 weeks vs 34-38 weeks); however, the difference was smaller than the reference change value (RCV) for the FT3 test. Thus, we combined the FT3 reference intervals into a single reference interval: 2.65-4.93 pmol/L (29-38 weeks). The reference intervals of FT4 and TSH were 11.20-24.97 pmol/L (29-38 weeks) and 1.01-10.14 mIU/L (29-38 weeks), respectively.

CONCLUSIONS

Unlike those of full-term infants or adults, the reference intervals established in this study are applicable in premature infants. These results highlight the importance and complexity of establishing instrument-specific thyroid hormone reference intervals for preterm infants.

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.

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

Sigma metrics have become a useful tool for all parts of the quality control (QC) design process. Through the allowable total error model of laboratory testing, analytical assay performance can be judged on the Six Sigma scale. This not only allows benchmarking the performance of methods and instruments on a universal scale, it allows laboratories to easily visualize performance, optimize the QC rules and numbers of control measurements they implement, and now even schedule the frequency of running those controls.

Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches: Electronic supplementary material available online for this article

Introduction

Two approaches were compared for the calculation of coefficient of variation (CV) and bias, and their effect on sigma calculation, when different allowable total error (TEa) values were used to determine the optimal method for Six Sigma quality management in the clinical laboratory.

Materials and methods

Sigma metrics for routine clinical chemistry tests using three systems (Beckman AU5800, Roche C8000, Siemens Dimension) were determined in June 2017 in the laboratory of Peking Union Medical College Hospital. Imprecision (CV%) and bias (bias%) were calculated for ten routine clinical chemistry tests using a proficiency testing (PT)- or an internal quality control (IQC)-based approach. Allowable total error from the Clinical Laboratory Improvement Amendments of 1988 and the Chinese Ministry of Health Clinical Laboratory Center Industry Standard (WS/T403-2012) were used with the formula: Sigma = (TEa - bias) / CV to calculate the Sigma metrics (σ_CLIA, σ_WS/T) for each assay for comparative analysis.

Results

For the PT-based approach, eight assays on the Beckman AU5800 system, seven assays on the Roche C8000 system and six assays on the Siemens Dimension system showed σ_CLIA > 3. For the IQC-based approach, ten, nine and seven assays, respectively, showed σ_CLIA > 3. Some differences in σ were therefore observed between the two calculation methods and the different TEa values.

Conclusions

Both methods of calculating σ can be used for Six Sigma quality management. In practice, laboratories should evaluate Sigma multiple times when optimizing a quality control schedule.

Evaluating analytical quality in clinical biochemistry laboratory using Six Sigma

Introduction

In recent years, Six Sigma metrics has became the hotspot in all trades and professions, which contributes a general procedure to explain the performance on sigma scale. Nowadays, many large companies, such as General Healthcare, Siemens, etc., have applied Six Sigma to clinical medicine and achieved satisfactory results. In this paper, we aim to evaluate the process performance of our laboratory by using Sigma metrics, thereby choosing the correct analytical quality control approach for each parameter.

Materials and methods

This study was conducted in the clinical chemistry laboratory of Shandong Provincial Hospital. The five-months data of internal quality control were harvested for the parameters: amylase (AMY), lactate dehydrogenase (LD), potassium, total bilirubin (TBIL), triglyceride, aspartate aminotransferase (AST), uric acid, high density lipoprotein-cholesterol (HDL-C), alanine aminotransferase (ALT), urea, sodium, chlorine, magnesium, alkaline phosphatase (ALP), creatinine (CRE), total protein, creatine kinase (CK), total cholesterol, glucose (GLU), albumin (ALB). Sigma metrics were calculated using total allowable error, precision and percent bias for the above-mentioned parameters.

Results

Sigma values of urea and sodium were below 3. Sigma values of total protein, CK, total cholesterol, GLU and ALB were in the range of 3 to 6. Sigma values of AMY, uric acid, HDL-C, TBIL, ALT, triglyceride, AST, ALP and CRE were more than 6.

Conclusion

Amylase was the best performer with a Sigma metrics value of 19.93, while sodium had the least average sigma values of 2.23. Actions should be taken to improve method performance for these parameters with sigma below 3.

Retrospective study showed that blood sampling errors risked children's well-being and safety in a Swedish paediatric tertiary care

AIM

Blood analyses containing preanalytical errors (PAEs) are hazardous for patients. This study investigated the frequency of PAEs in blood analysis and the corresponding quality indicators of the sampling process in Swedish paediatric tertiary care.

METHODS

Data were retrieved from the laboratory at Astrid Lindgren Children's Hospital between 2013 and 2014. Preanalytical blood sampling performance was analysed according to the Six Sigma scale, ranging from 0 to 6 (933 137-3.4 defects per million [DPM]).

RESULTS

Of the 1 148 716 analyses, 61 656 (5.4%) were rejected due to PAEs. The PAEs ranged between hospital specialities from 1.9 to 9.4% (p < 0.001) and work shift times, from 6.0% in the day to 5.7% in the evening and 4.3% at night (p values <0.001). Clotting was the most prominent error (51.3%), affecting mostly haematology and coagulation analyses. Incorrectly filled samples represented almost 25% of all PAEs, with effects on chemistry, haematology and coagulation analyses. The sigma score for the overall preanalytical phase (3.2) corresponded to 44 565 DPM.

CONCLUSION

Samples with PAEs were frequently clotted and insufficiently filled, and the distribution of errors varied within working shifts and specific analyses. The overall quality control in paediatric blood sampling was barely acceptable.

Analytical quality assessment and method comparison of immunoassays for the measurement of serum cobalamin and folate in dogs and cats

Serum cobalamin and folate are often measured in cats and dogs as part of laboratory testing for intestinal disease, small intestinal dysbiosis, or exocrine pancreatic deficiency. We performed an analytical validation of human immunoassays for cobalamin and folate measurement (AIA-900 analyzer, Tosoh Bioscience) and compared results with those obtained using chemiluminescence assays (Immulite 2000 analyzer, Siemens Medical Solutions Diagnostics). Accuracy, precision, total observable error (TE_obs%), and σ values were calculated for the immunoassays. Correlation and agreement were evaluated with Deming regression, Passing-Bablok regression, and Bland-Altman analysis. Cobalamin intra-assay and inter-assay CVs were 1.8-9.3% and 2.6-6.8%, respectively. Folate intra-assay and inter-assay CVs were 1.5-9.1% and 3.4-8.1%, respectively. TE_obs (%) were ≤19 and ≤31 for cobalamin and folate, respectively. Sigma values were 3.60-11.50 for cobalamin and 2.90-7.50 for folate. Regression analysis demonstrated very high or high correlations for cobalamin [ r = 0.98 (dogs), 0.97 (cats)] and folate [ r = 0.88 (dogs), 0.92 (cats)] but Bland-Altman analysis revealed poor agreement for both. The immunoassays had good analytical performance for measuring cobalamin and folate in both species. Results obtained by the 2 analyzers cannot be used interchangeably and should be interpreted using instrument-specific reference intervals. Further studies are required to establish immunoassay-specific reference intervals and to evaluate the diagnostic performance and clinical utility of the analyzer for these analytes.

Mistaken assumptions drive new Six Sigma model off the road

Oosterhuis and Coskun recently proposed a new model for applying the Six Sigma concept to laboratory measurement processes. In criticizing the conventional Six Sigma model, the authors misinterpret the industrial basis for Six Sigma and mixup the Six Sigma “counting methodology” with the “variation methodology”, thus many later attributions, conclusions, and recommendations are also mistaken. Although the authors attempt to justify the new model based on industrial principles, they ignore the fundamental relationship between Six Sigma and the process capability indices. The proposed model, the Sigma Metric is calculated as the ratio CV_I/CV_A, where CV_I is individual biological variation and CV_A is the observed analytical imprecision. This new metric does not take bias into account, which is a major limitation for application to laboratory testing processes. Thus, the new model does not provide a valid assessment of method performance, nor a practical methodology for selecting or designing statistical quality control procedures.

Validation of the Six Sigma Z-score for the quality assessment of clinical laboratory timeliness

BACKGROUND

The International Federation of Clinical Chemistry and Laboratory Medicine has introduced in recent times the turnaround time (TAT) as mandatory quality indicator for the postanalytical phase. Classic TAT indicators, namely, average, median, 90th percentile and proportion of acceptable test (PAT), are in use since almost 40 years and to date represent the mainstay for gauging the laboratory timeliness. In this study, we investigated the performance of the Six Sigma Z-score, which was previously introduced as a device for the quantitative assessment of timeliness.

METHODS

A numerical simulation was obtained modeling the actual TAT data set using the log-logistic probability density function. Five thousand replicates for each size of the artificial TAT random sample (n=20, 50, 250 and 1000) were generated, and different laboratory conditions were simulated manipulating the PDF in order to generate more or less variable data. The Z-score and the classic TAT indicators were assessed for precision (%CV), robustness toward right-tailing (precision at different sample variability), sensitivity and specificity.

RESULTS

Z-score showed sensitivity and specificity comparable to PAT (≈80% with n≥250), but superior precision that ranged within 20% by moderately small sized samples (n≥50); furthermore, Z-score was less affected by the value of the cutoff used for setting the acceptable TAT, as well as by the sample variability that reflected into the magnitude of right-tailing.

CONCLUSIONS

The Z-score was a valid indicator of laboratory timeliness and a suitable device to improve as well as to maintain the achieved quality level.

Long story short: an introduction to the short-term and longterm Six Sigma quality and its importance in laboratory medicine for the management of extra-analytical processes

There is a compelling need for quality tools that enable effective control of the extra-analytical phase. In this regard, Six Sigma seems to offer a valid methodological and conceptual opportunity, and in recent times, the International Federation of Clinical Chemistry and Laboratory Medicine has adopted it for indicating the performance requirements for non-analytical laboratory processes. However, the Six Sigma implies a distinction between short-term and long-term quality that is based on the dynamics of the processes. These concepts are still not widespread and applied in the field of laboratory medicine although they are of fundamental importance to exploit the full potential of this methodology. This paper reviews the Six Sigma quality concepts and shows how they originated from Shewhart's control charts, in respect of which they are not an alternative but a completion. It also discusses the dynamic nature of process and how it arises, concerning particularly the long-term dynamic mean variation, and explains why this leads to the fundamental distinction of quality we previously mentioned.

Application of Six Sigma for evaluating the analytical quality of tumor marker assays

CONTEXT

The results of detection assays for the same specimen are usually quite different in different laboratories or when tested with different detection systems.

OBJECTIVE

This study was designed to investigate the value of applying sigma metrics derived from different standards for allowable total error (TEa) in evaluating the analytical quality of tumor marker assays.

METHODS

Assays were evaluated for these six tumor markers: total prostate-specific antigen (tPSA), carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), carbohydrate antigen 199 (CA199), carbohydrate antigen 125 (CA125), and carbohydrate antigen 153 (CA153). Sigma values were calculated for two concentrations of quality control products to assess differences in quality of tumor marker assays. Improvement measures were recommended according to the quality goal index, and appropriate quality control rules were selected according to the sigma value.

RESULTS

The sigma value was highest using the higher biological variation-derived "appropriate" TEa standard: it was sigma ≥6 or higher in 16.7% of tumor markers. Sigma was below 6 for all tumor markers using the other three TEa. CEA, AFP, CA199, CA125, and CA153 required improved precision. The marker tPSA required improve precision and accuracy. According to sigma values by using China's external quality assessment standards, CEA, AFP, CA125, and CA153 require 1_3s /2_2s /R_4s /4_1s multirules for internal quality control, CA199 requires use of 1_3s /2_2s /R_4s /4_1s /8_x multirules, and tPSA requires maximum quality control rules.

CONCLUSION

Six Sigma is useful for evaluating performance of tumor markers assays and has important application value in the quality control of these assays.

Application of Sigma metrics in assessing the clinical performance of verified versus non-verified reagents for routine biochemical analytes

INTRODUCTION

Sigma metrics analysis is considered an objective method to evaluate the performance of a new measurement system. This study was designed to assess the analytical performance of verified versus non-verified reagents for routine biochemical analytes in terms of Sigma metrics.

MATERIALS AND METHODS

The coefficient of variation (CV) was calculated according to the mean and standard deviation (SD) derived from the internal quality control for 20 consecutive days. The data were measured on an Architect c16000 analyser with reagents from four manufacturers. Commercial reference materials were used to estimate the bias. Total allowable error (TEa) was based on the CLIA 1988 guidelines. Sigma metrics were calculated in terms of CV, percent bias and TEa. Normalized method decisions charts were built by plotting the normalized bias (biasa: bias%/TEa) on the Y-axis and the normalized imprecision (CVa: mean CV%/TEa) on the X-axis.

RESULTS

The reagents were compared between different manufacturers in terms of the Sigma metrics for relevant analytes. Abbott and Leadman's verified reagents provided better Sigma metrics for the alanine aminotransferase assay than non-verified reagents (Mindray and Zybio). All reagents performed well for the aspartate aminotransferase and uric acid assays with a sigma of 5 or higher. Abbott achieved the best performance for the urea assay, evidenced by the sigma of 2.83 higher than all reagents, which were below 1-sigma.

CONCLUSION

Sigma metrics analysis system is helpful for clarifying the performance of candidate non-verified reagents in clinical laboratory. Our study suggests that the quality of non-verified reagents should be assessed strictly.

Extra-analytical quality indicators – where to now?

A large body of evidence collected in recent years demonstrates the vulnerability of the extra-analytical phases of the total testing process (TTP) and the need to promote quality and harmonization in each and every step of the testing cycle. Quality indicators (QIs), which play a key role in documenting and improving quality in TTP, are essential requirements for clinical laboratory accreditation. In the last few years, wide consensus has been achieved on the need to adopt universal QIs and common terminology and to harmonize the management procedure concerning their use by adopting a common metric and reporting system. This, in turn, has led to the definition of performance specifications for extra-analytical phases based on the state of the art as indicated by data collected on QIs, particularly by clinical laboratories attending the Model of Quality Indicators program launched by the Working Group “Laboratory Errors and Patient Safety” of the International Federation of Clinical Chemistry and Laboratory Medicine. Harmonization plays a fundamental role defining not only the list of QIs to use but also performance specifications based on the state of the art, thus providing a valuable interlaboratory benchmark and tools for continuous improvement programs.

Quality specifications of routine clinical chemistry methods based on sigma metrics in performance evaluation

INTRODUCTION

Sigma metrics were applied to evaluate the performance of 20 routine chemistry assays, and individual quality control criteria were established based on the sigma values of different assays.

METHODS

Precisions were expressed as the average coefficient variations (CVs) of long-term two-level chemistry controls. The biases of the 20 assays were obtained from the results of trueness programs organized by National Center for Clinical Laboratories (NCCL, China) in 2016. Four different allowable total error (TEa) targets were chosen from biological variation (minimum, desirable, optimal), Clinical Laboratory Improvements Amendments (CLIA, US), Analytical Quality Specification for Routine Analytes in Clinical Chemistry (WS/T 403-2012, China) and the National Cholesterol Education Program (NECP).

RESULTS

The sigma values from different TEa targets varied. The TEa targets for ALT, AMY, Ca, CHOL, CK, Crea, GGT, K, LDH, Mg, Na, TG, TP, UA and Urea were chosen from WS/T 403-2012; the targets for ALP, AST and GLU were chosen from CLIA; the target for K was chosen from desirable biological variation; and the targets for HDL and LDL were chosen from the NECP. Individual quality criteria were established based on different sigma values.

CONCLUSIONS

Sigma metrics are an optimal tool to evaluate the performance of different assays. An assay with a high value could use a simple internal quality control rule, while an assay with a low value should be monitored strictly.

Lean six sigma methodologies improve clinical laboratory efficiency and reduce turnaround times

BACKGROUND

Organizing work flow is a major task of laboratory management. Recently, clinical laboratories have started to adopt methodologies such as Lean Six Sigma and some successful implementations have been reported. This study used Lean Six Sigma to simplify the laboratory work process and decrease the turnaround time by eliminating non-value-adding steps.

METHODS

The five-stage Six Sigma system known as define, measure, analyze, improve, and control (DMAIC) is used to identify and solve problems. The laboratory turnaround time for individual tests, total delay time in the sample reception area, and percentage of steps involving risks of medical errors and biological hazards in the overall process are measured.

RESULTS

The pre-analytical process in the reception area was improved by eliminating 3 h and 22.5 min of non-value-adding work. Turnaround time also improved for stat samples from 68 to 59 min after applying Lean. Steps prone to medical errors and posing potential biological hazards to receptionists were reduced from 30% to 3%.

CONCLUSION

Successful implementation of Lean Six Sigma significantly improved all of the selected performance metrics. This quality-improvement methodology has the potential to significantly improve clinical laboratories.

Sigma Metrics Across the Total Testing Process

Laboratory quality control has been developed for several decades to ensure patients' safety, from a statistical quality control focus on the analytical phase to total laboratory processes. The sigma concept provides a convenient way to quantify the number of errors in extra-analytical and analytical phases through the defect per million and sigma metric equation. Participation in a sigma verification program can be a convenient way to monitor analytical performance continuous quality improvement. Improvement of sigma-scale performance has been shown from our data. New tools and techniques for integration are needed.

Evaluation of a handheld point-of-care analyser for measurement of creatinine in cats

Objectives The aim of the study was to evaluate whether a handheld creatinine analyser (StatSensor Xpress; SSXp), available for human patients, can be used to measure creatinine reliably in cats. Methods Analytical performance was evaluated by determining within- and between-run coefficient of variation (CV, %), total error observed (TE_obs, %) and sigma metrics. Fifty client-owned cats presenting for investigation of clinical disease had creatinine measured simultaneously, using SSXp (whole blood and plasma) and a reference instrument (Konelab, serum); 48 paired samples were included in the study. Creatinine correlation between methodologies (SSXp vs Konelab) and sample types (SSXp_{whole blood} vs SSXp_plasma) was assessed by Spearman's correlation coefficient and agreement was determined using Bland-Altman difference plots. Each creatinine value was assigned an IRIS stage (1-4); correlation and agreement between Konelab and SSXp IRIS stages were evaluated. Results Within-run CV (4.23-8.85%), between-run CV (8.95-11.72%), TE_obs (22.15-34.92%) and sigma metrics (⩽3) did not meet desired analytical requirements. Correlation between sample types was high (SSXp_{whole blood} vs SSXp_plasma; r = 0.89), and between instruments was high (SSXp_{whole blood} vs Konelab_serum; r = 0.85) to very high (SSXp_plasma vs Konelab_serum; r = 0.91). Konelab and SSXp_{whole blood} IRIS scores exhibited high correlation ( r = 0.76). Packed cell volume did not significantly affect SSXp determination of creatinine. Bland-Altman difference plots identified a positive bias for the SSXp (7.13 μmol/l SSXp_{whole blood}; 20.23 μmol/l SSXp_plasma) compared with the Konelab. Outliers (1/48 whole blood; 2/48 plasma) occurred exclusively at very high creatinine concentrations. The SSXp failed to identify 2/21 azotaemic cats. Conclusions and relevance Analytical performance of the SSXp in feline patients is not considered acceptable. The SSXp exhibited a high to very high correlation compared with the reference methodology but the two instruments cannot be used interchangeably. Improvements in the SSXp analytical performance are needed before its use can be recommended in feline clinical practice.

Timeliness “at a glance”: assessing the turnaround time through the six sigma metrics

Almost thirty years of systematic analysis have proven the turnaround time to be a fundamental dimension for the clinical laboratory. Several indicators are to date available to assess and report quality with respect to timeliness, but they sometimes lack the communicative immediacy and accuracy. The six sigma is a paradigm developed within the industrial domain for assessing quality and addressing goal and issues. The sigma level computed through the Z-score method is a simple and straightforward tool which delivers quality by a universal dimensionless scale and allows to handle non-normal data. Herein we report our preliminary experience in using the sigma level to assess the change in urgent (STAT) test turnaround time due to the implementation of total automation. We found that the Z-score method is a valuable and easy to use method for assessing and communicating the quality level of laboratory timeliness, providing a good correspondence with the actual change in efficiency which was retrospectively observed.

Application of Sigma Metrics Analysis for the Assessment and Modification of Quality Control Program in the Clinical Chemistry Laboratory of a Tertiary Care Hospital

Sigma is a metric that quantifies the performance of a process as a rate of Defects-Per-Million opportunities. In clinical laboratories, sigma metric analysis is used to assess the performance of laboratory process system. Sigma metric is also used as a quality management strategy for a laboratory process to improve the quality by addressing the errors after identification. The aim of this study is to evaluate the errors in quality control of analytical phase of laboratory system by sigma metric. For this purpose sigma metric analysis was done for analytes using the internal and external quality control as quality indicators. Results of sigma metric analysis were used to identify the gaps and need for modification in the strategy of laboratory quality control procedure. Sigma metric was calculated for quality control program of ten clinical chemistry analytes including glucose, chloride, cholesterol, triglyceride, HDL, albumin, direct bilirubin, total bilirubin, protein and creatinine, at two control levels. To calculate the sigma metric imprecision and bias was calculated with internal and external quality control data, respectively. The minimum acceptable performance was considered as 3 sigma. Westgard sigma rules were applied to customize the quality control procedure. Sigma level was found acceptable (≥3) for glucose (L2), cholesterol, triglyceride, HDL, direct bilirubin and creatinine at both levels of control. For rest of the analytes sigma metric was found <3. The lowest value for sigma was found for chloride (1.1) at L2. The highest value of sigma was found for creatinine (10.1) at L3. HDL was found with the highest sigma values at both control levels (8.8 and 8.0 at L2 and L3, respectively). We conclude that analytes with the sigma value <3 are required strict monitoring and modification in quality control procedure. In this study application of sigma rules provided us the practical solution for improved and focused design of QC procedure.

Evaluation of an in-practice wet-chemistry analyzer using canine and feline serum samples

A wet-chemistry biochemical analyzer was assessed for in-practice veterinary use. Its small size may mean a cost-effective method for low-throughput in-house biochemical analyses for first-opinion practice. The objectives of our study were to determine imprecision, total observed error, and acceptability of the analyzer for measurement of common canine and feline serum analytes, and to compare clinical sample results to those from a commercial reference analyzer. Imprecision was determined by within- and between-run repeatability for canine and feline pooled samples, and manufacturer-supplied quality control material (QCM). Total observed error (TEobs) was determined for pooled samples and QCM. Performance was assessed for canine and feline pooled samples by sigma metric determination. Agreement and errors between the in-practice and reference analyzers were determined for canine and feline clinical samples by Bland–Altman and Deming regression analyses. Within- and between-run precision was high for most analytes, and TEobs(%) was mostly lower than total allowable error. Performance based on sigma metrics was good (σ > 4) for many analytes and marginal (σ > 3) for most of the remainder. Correlation between the analyzers was very high for most canine analytes and high for most feline analytes. Between-analyzer bias was generally attributed to high constant error. The in-practice analyzer showed good overall performance, with only calcium and phosphate analyses identified as significantly problematic. Agreement for most analytes was insufficient for transposition of reference intervals, and we recommend that in-practice–specific reference intervals be established in the laboratory.