Sigma Metrics Research Articles

Sigma Metrics: A Powerful Tool for Performance Evaluation and Quality Control Planning in a Clinical Biochemistry Laboratory

Introduction: Any error in the laboratory testing processes can affect the diagnosis and patient management. Six Sigma is a data driven quality management system for identifying and reducing errors and variations in clinical laboratory processes. Aim: This study was carried out to estimate Sigma metrics of various biochemical analytes in order to evaluate performance of quality control and implement optimum quality control strategy for each analyte. Quality goal index (QGI) was also calculated to identify the problems of inaccuracy and imprecision for parameters having lower sigma values. Materials and Methods: This retrospective, observational study was conducted at the Central Clinical Biochemistry Laboratory of Seth GS Medical College and KEM hospital in Mumbai for a period of six months (July 2019 to December 2019). Sigma metrics for 20 analytes was calculated by using internal quality control and external quality control data. Further, QGI was calculated for analytes having sigma value of <4 to identify imprecision or inaccuracy. Statistical analysis was performed using Microsoft office excel 2010 software. Results: Total protein, Glucose, Urea, Triglyceride (TAG), High Density Lipoprotein (HDL), and Low Density Lipoprotein (LDL) for normal (L1) and pathological (L2) controls achieved excellent performance (>6 sigma). Westgard rule (13s) with two control measurement (N2) per QC event and run size (R1000) i.e. 1000 patient samples between consecutive QC events was adopted for these analytes. For analytes with sigma value of 4-6, more rules (sigma 4-5: Westgardrules-13s/22s/R4s/41s, N4 and R200 and for sigma value 5-6: 13S/22s/R4s, N2 and R450) were adopted. The sigma values of six analytes (Creatinine, Sodium, Potassium, Calcium, Chloride, Inorganic phosphate) were <4 at one or more QC levels. For these analytes, strict QC procedures (Westgard rules-13s/22s/R4s/41s/6x, N4 and R45) were incorporated. QGI of these analytes was <0.8 which indicated the problem of imprecision. Staff training programs and review of standard operating procedures were done for these analytes to improve method performance. Conclusion: Sigma Metrics estimation helps in designing optimum QC protocols, minimising unnecessary QC runs and reducing the cost for analytes having high sigma metrics. Focused and effective QC strategy for analytes having low sigma helps in improving the performance of those analytes.

Total Clinical Chemistry Laboratory Errors and Evaluation of the Analytical Quality Control Using Sigma Metric for Routine Clinical Chemistry Tests.

BackgroundCurrently, the use of clinical laboratory tests is growing at a promising rate and about 80% of the clinical decisions made are based on the laboratory test results. Therefore, it is a major task to achieve quality service. This study was conducted to assess the magnitude of errors in the total testing process of Clinical Chemistry Laboratory and to evaluate analytical quality control using sigma metrics.MethodsA cross-sectional study was conducted at Dessie Comprehensive Specialized Hospital Clinical Chemistry Laboratory, Northeast Ethiopia, from 10 February 2020 to 10 June 2020. All Clinical Chemistry Laboratory test requests with their respective samples, external quality control and all daily internal quality control data during the study period were included in the study. Data were collected using a prepared checklist and analyzed using SPSS version 21.ResultsA total of 4719 blood samples with their test requests were included in the study. Out of 145,383 quality indicators, an error rate of 22,301 (15.3%) was identified in the total testing process. Of the total errors, 76.3% were pre-analytical, 2.1% were analytical and 21.6% were post-analytical errors (p<0.0001). Of the total 14 analytes in the sigma metric evaluation, except ALP, all routine clinical chemistry tests were below the standard (<3). In multivariate logistic regression, the location of patients in the inpatient department was significantly associated with the specimen rejection ((AOR=1.837, 95% CI (1.288–2.618), p=0.001).ConclusionThe study found a higher frequency of errors in the total testing process in the Clinical Chemistry Laboratory and almost all test parameters had an unsatisfactory sigma metric value.

Six Sigma Metrics in Clinical Analysis and Quality in Laboratory Routine

Six Sigma Metrics in Clinical Analysis and Quality in Laboratory Routine

Evaluating the Performance of an Automated Chemistry Analyzer Using Sigma Metrics

Evaluating the Performance of an Automated Chemistry Analyzer Using Sigma Metrics

Performance verification and sigma metrics of creatinine assays

Introduction: Creatinine enzymatic method is more accurate but its own higher cost is the main reason why the use of Jaffe assays is still in practice. The verification step is performed because assay procedure confirms to the manufacturer’s claims in the end users’ setup. Desirable specification based on biological variation gives Allowable Total Error (ATE) and Sigma metrics provides summative evaluation of method performance. Objectives: Verification of manufacturer’s precision claims. Calculation of bias, Total Analytical Error (TAE) and sigma metrics. Materials and Methods: Modified Jaffe’s (No deproteinization) and enzymatic creatinine Erba XL liquid pack on Erba EM360 fully auto analyser were used. Intra assay and inter assay imprecision done on 20 replicates each. The estimates of bias uses data from precision experiment. Results & Discussion: The performance of jaffe’s and enzymatic serum creatinine is comparable to manufacturer's stated claimed value and are within desirable specification based on biological variation. The bias calculated in both assays is less than allowable bias. Total Analytical Error (TAE) is less than Allowable Total Error (ATE). Both the methods overall showed ATE ? bias + 4 SD. Within-run showed ATE ? bias + 5 SD complying Six Sigma concepts. Conclusion: Both Jaffe’s and enzymatic creatinine assays gives good performance and better sigma metrics. This makes enzymatic method a better choice due to inherent advantages such as increased accuracy and less interference if cost is neglected. Keywords: Allowable Total Error, Bias, Creatinine, Imprecision, Sigma metrics, Total Analytical Error.

Analysis of performance of clinical biochemistry laboratory using Sigma metrics and Quality Goal Index

BackgroundUnreliable and ingenuine results issued by clinical laboratories have serious consequences for the patients. Sigma metrics is a standardized tool for Quality assessment for test performance in a laboratory. ObjectiveTo evaluate the performance of routine biochemistry laboratory at MMIMSR, Mullana in terms of Sigma metrics and Quality Goal Index. Material and methodsThis cross sectional study evaluated performance of 14 routine chemistry parameters using retrospective Internal Quality Control data of two levels on Siemens Dimension Rxl from Feb to Jul 2019 for CV% and EQAS reports from CMC, Vellore for Bias%. Sigma metrics was calculated using total allowable error targets as per CLIA and Biological Variability database guidelines. ResultsFor level-2 IQC; TG, Chol, ALP showed excellent performance with σ ​> ​6 while σ ​< ​3 was observed for AST, Total Protein, Glucose, BUN and ALT using CLIA guidelines while in IQC Level-3 poor performers were only BUN and ALT with Ca, TG and Chol showing σ ​> ​6. Further by using Biological Variability data guidelines; 10 parameters of IQC Level-2 and 5 of IQC level-3 were poor performers with σ ​< ​3. ConclusionSigma metrics is an excellent tool for performance analysis of tests performed in a clinical laboratory. Lack of precision in terms of CV% was seen for majority of the poor performers. Total allowable error targets using Biological Variability data revealed σ ​< ​3 for 10 parameters while using CLIA guidelines σ ​< ​3 was seen for only 5 parameters of IQC level-2.

Evaluation of clinical biochemistry laboratory performance using sigma metrics

Producing a reliable, reproducible, accurate, timely, and correctly interpreted test results by clinical laboratory is very important, since physicians’ decisions mostly depends on it for screening, diagnosis and monitoring diseases. To provide quality report, routinely in all clinical laboratories internal quality control (IQC) and external quality assessment (EQA) programs are done which helps to evaluate and continuously improve analytical quality. Recently new quality assessment (QA) systems, six-sigma became more popular because it offers a different approach to problems. Sigma metrics allow comparison of different processes with each other and thus improving the quality of testing processes. This study was done to evaluate clinical chemistry laboratory performance using sigma metrics. Keywords: Internal quality control, External quality assessment programs, Laboratory, Sigma metrics, Six-sigma.

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

BackgroundSigma 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.MethodsSigma 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).ResultsThe 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.ConclusionNon‐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.

Applying The Best Practice For Internal Quality Control (IQC) Management, Consolidation And Review Process In Sidra Medicine Clinical Chemistry Division

Abstract Introduction/Objective The management of internal quality control (IQC) in Sidra Medicine Clinical Chemistry Division has been evaluated in order to promote a more consolidated and efficient process of IQC management. The statistical data produced from Cerner QC Module are transferred to IQC review templates consisting of formulas to auto- calculate parameters such as multiple of expected QC failure frequency and desirable comparison limit between analyzers. The IQC review and documentation process using the in-house excel template requires several hours to complete, hence a faster and more efficient IQC management module is required. The main objective of this study is to improve the initial IQC management set up, work flow and review procedures and to implement Biorad Unity Real Time (URT) program to develop a more efficient IQC management system. Methods The URT software has been recently configured and implemented to consolidate and streamline IQC management. URT is built through Sidra Medicine IT Enterprise level which allows multiple users to login. IQC data are downloaded using scripts from Cerner which are filtered through Biorad Unity Connect (UC) software. Additional quality tools are also explored such as various user defined statistical reports, IQC analysis using peer reviewed total allowable error (TeA) and assignment of the most appropriate Westgard rules. Determination of sigma metrics and uncertainty of measurement is also performed using the URT application. Results The generation of any IQC report is less cumbersome and time consuming as compared with the previous process. However, some user defined formulas in the IQC templates are not found on the URT reports. The URT Levey Jennings chart are also more user friendly and directly compares the daily IQC data with Unity inter-laboratory peers enabling the production of instant and monthly reports through QCNet site when assay investigation is required and for IQC report documentation. Conclusion The combination of Cerner IQC, Unity Real-time, QCNet Inter-laboratory reports and in house IQC templates produce a high level and very detailed IQC review which effectively evaluate assay performance to assist on IQC troubleshooting and root cause analysis to be able to apply the most appropriate corrective actions.

The comparison of analytical performances of Mindray CL-1000i and Beckman Coulter Access II Troponin I methods in the light of recent guidelines and the quality requirements.

BackgroundThe results of cardiac troponin I (cTnI) methods used in the diagnosis of acute myocardial infarction (AMI) are highly variable. In this study, it was aimed to compare the analytical performance of the Mindray CL-series TnI method with the Beckman Coulter-Access II AccuTnI+3 method.MethodsAnalytical performance and method comparison experiments for cTnI measurement with Mindray CL-1000i and Beckman Coulter-Access II instruments were with the Clinical and Laboratory Standards Institute (CLSI) documents EP15-A3 and EP9-A3. Precision studies were performed with commercially available third-party quality control (QC) materials. Method comparison experiments were performed by using patient samples. Furthermore, the limit of quantification (LoQ), total analytical error (TAE), and sigma metrics of both methods was determined.ResultsThe repeatability CV% for the three levels of Mindray CL-series TnI were 1.86, 1.75, and 1.08, while within the laboratory, CV% values were 3.36, 5.27, and 5.82. The quantification limits for Mindray CL-series and Beckman AccuTnI+3 were found to be 0.0085 and 0.0366 ng/mL with a CV level of less than 10%, respectively. The Mindray CL-series TnI results in the method comparison study were higher compared to the Beckman Coulter Access II AccuTnI+3 method.ConclusionsWith low repeatability, low bias, and low LoQ, The Mindray CL-series cTnI method shows it may be used safely in its category. The significant difference between the two methods in the method comparison study reveals cTnI methods in the market should be standardized to ensure global traceability.

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.MethodsThe 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.ResultsBased on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (13S) 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 (13S/22S/R4S/41S/10X) 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.ConclusionsIt was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.

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 8months 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.

Evaluation of quality indicators in pre-analytical phase of testing in clinical biochemistry laboratory of a tertiary care hospital in India

Preanalytical errors account for nearly 70% of the total number of laboratory errors. Hence, controlling them is a big challenge. Quality Indicators expressed as sigma metrics provide a convenient way to objectively quantify errors. Aim: To quantify performance in the preanalytical phase of the testing process in Clinical Biochemistry laboratory of a tertiary care hospital in India using quality indicators. Study period: January to September 2016. Quality Indicators (QIs) used: samples lost–not received (QI-8); samples collected in an inappropriate blood collection tube (QI-9); haemolyzed samples (QI-10); clotted samples (QI-11); samples with insufficient sample volume (QI-12); improperly labelled (QI-15); damaged in transport (QI-14). Sigma metric was calculated for the above mentioned QIs. The total number of samples received during the study period was 5,73,694 and the total number of preanalytical errors was 1,782. Among the preanalytical errors, 43.9% were samples with insufficient volume (sigma: 4.5), 33.2 % were haemolyzed samples (sigma: 4.6), 11.3% were samples collected in an inappropriate blood collection tube (sigma: 4.9), 6.7% were samples not received in the laboratory (sigma: 5.1), 4.2% were clotted samples (sigma: 5.2), 0.7% were improperly labelled (sigma: 5.6), only one sample (0.06%) was lost over 9 months period due to spill in pneumatic chute. QIs serve as a tool to monitor process performance in the laboratory. In this study, insufficient sample volume and haemolysis were the major causes of preanalytical errors. All QIs had acceptable sigma value. Regular training of phlebotomists regarding the preanalytical errors needs to be conducted to achieve six sigma performance.

Six Sigma quality evaluation of life test data based on Weibull distribution

PurposeWhile Six Sigma metrics have been studied by researchers in detail for normal distribution-based data, in this paper, we have attempted to study the Six Sigma metrics for two-parameter Weibull distribution that is useful in many life test data analyses.Design/methodology/approachIn the theory of Six Sigma, most of the processes are assumed normal and Six Sigma metrics are determined for such a process of interest. In reliability studies non-normal distributions are more appropriate for life tests. In this paper, a theoretical procedure is developed for determining Six Sigma metrics when the underlying process follows two-parameter Weibull distribution. Numerical evaluations are also considered to study the proposed method.FindingsIn this paper, by matching the probabilities under different normal process-based sigma quality levels (SQLs), we first determined the Six Sigma specification limits (Lower and Upper Six Sigma Limits- LSSL and USSL) for the two-parameter Weibull distribution by setting different values for the shape parameter and the scaling parameter. Then, the lower SQL (LSQL) and upper SQL (USQL) values are obtained for the Weibull distribution with centered and shifted cases. We presented numerical results for Six Sigma metrics of Weibull distribution with different parameter settings. We also simulated a set of 1,000 values from this Weibull distribution for both centered and shifted cases to evaluate the Six Sigma performance metrics. It is found that the SQLs under two-parameter Weibull distribution are slightly lesser than those when the process is assumed normal.Originality/valueThe theoretical approach proposed for determining Six Sigma metrics for Weibull distribution is new to the Six Sigma Quality practitioners who commonly deal with normal process or normal approximation to non-normal processes. The procedure developed here is, in fact, used to first determine LSSL and USSL followed by which LSQL and USQL are obtained. This in turn has helped to compute the Six Sigma metrics such as defects per million opportunities (DPMOs) and the parts that are extremely good per million opportunities (EGPMOs) under two-parameter Weibull distribution for lower-the-better (LTB) and higher-the-better (HTB) quality characteristics. We believe that this approach is quite new to the practitioners, and it is not only useful to the practitioners but will also serve to motivate the researchers to do more work in this field of research.

Application of Six Sigma Metrics to Improve Quality Control for Point-of-care Glucose Testing

Objectives Six sigma is a quality management system for the assessment of precision and accuracy. We aim to apply the six sigma rule to quality control (QC) of point-of-care (POC) glucose meters in a tertiary hospital. Methods Thirty POC glucose meters installed at Ewha Womans University Mokdong Hospital were monitored between January 2013 and March 2014. The QC data from the POC glucose meters at low and high levels were collected. The monthly mean, standard deviation, bias, coefficient of variation, and mean sigma metrics were calculated. The correlation between accuracy and precision was assessed based on the percentage bias and coefficient of variation. Comprehensive instructions on the QC and maintenance of the devices were provided in the departments with poor sigma scores. A follow-up assessment was performed after the intervention. Results The mean sigma values for the low and high controls were 3.29 and 3.71, respectively. At the low and high controls, 36.6% and 10% of the glucose meters showed a sigma value &lt;3. The causes of low sigma values included the use of expired control materials, prolonged air exposure of the sample strip, lack of user training, and errors in device maintenance. On follow-up monitoring for 3 months following QC intervention, 23.3% (low control) and 6.6% (high control) of the glucose meters scored a sigma value &lt;3, indicating improved QC. Conclusion Sigma metrics-based QC can successfully improve accuracy and precision of POC glucose meters in an objective and quantitative manner and can be used for follow up after QC intervention.

Sigma metrics - A guide to quality control strategy in clinical Biochemistry laboratory

Clinical laboratories are backbone of health care system &amp; the responsibility lies in the laboratory to produce reliable, reproducible &amp; timely interpreted test results. Presently Westgard quality control(QC) rules are the basis for quality control validation of laboratories. A new quality assessment system known as Six sigma is considered as world class quality &amp; is a uniquely defined scale with which one can assess the performance of a laboratory. An analytical procedure should achieve a good sigma levels for high reliability for the results. Many laboratories are not aware about the usefulness of six sigma. Hence this study was aimed to assess and to plan the quality control strategy. In this study, the internal quality control data of urea, creatinine, sodium, potassium &amp; chloride analysed for a period of 6 months &amp; used to calculate bias, coefficient of variation (CV)%, mean &amp; standard deviation(SD) for each levels(L1,L2). Sigma value calculated shown Highest for Urea &amp; Potassium (Level1 &amp; Level 2) COBAS 6000 an integrated modular analyser. Sigma of 3 is the minimum acceptable level of quality. The frequency of internal QC should be increased and corrective action should be taken for those parameters with sigma &amp;#60;3. Sigma metrics of each parameter helped us to identify the right QC strategy for that analyte using Westgard sigma rule. Hence six sigma is a more efficient way to control quality &amp; is a self-assessment tool for quality improvement.

Risk assessment of the total testing process based on quality indicators with the Sigma metrics.

Background Evidence-based evaluation of laboratory performances including pre-analytical, analytical and post-analytical stages of the total testing process (TTP) is crucial to ensure patients receiving safe, efficient and effective care. To conduct risk assessment, quality management tools such as Failure Mode and Effect Analysis (FMEA) and the Failure Reporting and Corrective Action System (FRACAS) were constantly used for proactive or reactive analysis, respectively. However, FMEA and FRACAS faced big challenges in determining the scoring scales and failure prioritization in the assessment of real-world cases. Here, we developed a novel strategy, by incorporating Sigma metrics into risk assessment based on quality indicators (QIs) data, to provide a more objective assessment of risks in TTP. Methods QI data was collected for 1 year and FRACAS was applied to produce the risk rating based on three variables: (1) Sigma metrics for the frequency of defects; (2) possible consequence; (3) detection method. The risk priority number (RPN) of each QI was calculated by a 5-point scale score, where a value of RPN > 50 was rated as high-risk. Results The RPNs of two QIs in post-analytical phase (TAT of Stat biochemistry analyte and Timely critical values notification) were above 50 which required rigorous monitoring and corrective actions to eliminate the high risks. Nine QIs (RPNs between 25 and 50) required further investigation and monitoring. After 3 months of corrective action the two identified high-risk processes were successfully reduced. Conclusions The strategy can be implemented to reduce identified risk and assuring patient safety.

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.

Performance evaluation of exponential distribution using Six Sigma-based tail probabilities

While it is customary and simple to deal with normality assumption of the data on hand, such assumption may lead to inaccurate outcomes if the underlying distribution is non-normal. Six Sigma analysis of any process is, in general, based on normality assumption irrespective of the nature of the original data. There are studies where non-normal data is transformed into normal data before performing Six Sigma analysis. In this paper, we have proposed to study the Six Sigma metrics for life test data that follow exponential distribution by matching the Six Sigma-based tail probabilities. Both centered and shifted cases of the exponential data are considered. Further, we considered higher-the-better and lower-the-better type product specifications to determine defects per million opportunities (DPMO) and extremely good units per million opportunities (EGPMO) based on the exponential distribution. Extensive numerical computations are done in addition to simulations to illustrate how DPMO and EGPMO are determined for centered or shifted exponential distribution. Some comparisons are also done with the existing approaches.

Sigma Metrics used to Evaluate the Performance of Internal Quality Control in a Clinical Biochemistry Laboratory.

The performance of 17 routine chemical detection methods was evaluated by the Sigma (σ) index, and separate quality control standards were established according to the sigma values of different detection methods. The internal quality control (IQC) and external quality assessment (EQA) data of 17 assays in the biochemical laboratory of our hospital were collected from January to June 2019. Referring to the total allowed error (TEa) standards established in the Health Industry Standards of the People's Republic of China (WS/T 403-2012), the sigma metric of each assay was calculated, the performance level for inspection was evaluated, the quality goal index (QGI) was calculated for items with analysis performance < 5 sigma, and the main causes of poor performance were determined to guide quality improvement. For level 1 internal quality control (IQC), five assays (AMY, Crea, UA, TP, and Na) showed a performance of ≥ 6 sigma levels. Five assays (GGT, LDH, ALP, K, and Ca) had a performance lower than 3 sigma. For level 2 IQC, nine assays (ALT, AST, CK, AMY, Crea, UA, TP, Na, and Mg) achieved 6 sigma, and four assays (GGT, LDH, ALP, and K) achieved less than 3 sigma. Among the 12 assays with a sigma value < 5, the precision of 1 assay should be improved first, the accuracy of 6 assays should be improved next, and both the precision and the accuracy of 5 assays should be improved. The sigma metric is the best tool for evaluating the performance of different test methods. Assays with high sigma values can be evaluated with single-rule quality control, while assays with low values should be evaluated with strict quality control rules.