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Background: Quality control (QC) is an important part of clinical laboratory management which evaluates and maintains the performance of the clinical laboratory. Six Sigma is a continuous quality improvement process which is widely applied in the health care industry mainly in clinical laboratories. Aims and Objectives: The study aimed to evaluate the performance of biochemical analytes in a clinical laboratory using Six Sigma metrics and to implement quality improvement interventions. Sigma value was analyzed using bias, coefficient of variation (CV), and total allowable error (TEa) for each analyte. The quality goal index ratio was calculated to identify specific types of errors to introduce targeted quality interventions. Materials and Methods: This quasi-experimental study was carried out at the Government Institute of Medical Sciences, Greater Noida, Uttar Pradesh. Internal QC (IQC) and external QC data were collected during the study period and analyzed. The CV, Bias, and TEa were used to calculate the sigma (s) values for 20 biochemical analytes. Results: On retrospective data analysis, triglycerides-Level 1 (L1) and Level 2 (L2), and direct bilirubin-L2 showed a world-class performance (above 6s) while 14 parameters were placed below average (<3s) on sigma metrics. Prospective data analysis was performed following quality improvement intervention. Amylase, direct bilirubin, triglycerides and alanine transferase performed at world-class sigma metric for both the control levels (L1 and L2) whereas, Albumin and aspartate transferase showed above six sigma values for L2 control only. Five analytes out of 20 performed below average (<3s) on sigma metrics, for which further continuous intervention will be carried out. Conclusion: Six Sigma metrics is a continuous improvement and monitoring program that should be implemented to achieve world-class quality performance. [ABSTRACT FROM AUTHOR]

Background: Quality control (QC) is an important part of clinical laboratory management which evaluates and maintains the performance of the clinical laboratory. Six Sigma is a continuous quality improvement process which is widely applied in the health care industry mainly in clinical laboratories. Aims and Objectives: The study aimed to evaluate the performance of biochemical analytes in a clinical laboratory using Six Sigma metrics and to implement quality improvement interventions. Sigma value was analyzed using bias, coefficient of variation (CV), and total allowable error (TEa) for each analyte. The quality goal index ratio was calculated to identify specific types of errors to introduce targeted quality interventions. Materials and Methods: This quasi-experimental study was carried out at the Government Institute of Medical Sciences, Greater Noida, Uttar Pradesh. Internal QC (IQC) and external QC data were collected during the study period and analyzed. The CV, Bias, and TEa were used to calculate the sigma (σ) values for 20 biochemical analytes. Results: On retrospective data analysis, triglycerides-Level 1 (L1) and Level 2 (L2), and direct bilirubin-L2 showed a world-class performance (above 6σ) while 14 parameters were placed below average (

Objectives: The objective of the study is to assess the performance of individual biochemical parameters on a sigma scale and also to do root cause analysis and take corrective actions for the parameters with poor performance to improve the quality of our clinical laboratory. Materials and Methods: This is a retrospective and prospective study done in the central laboratory of a tertiary care hospital from January 2023 to September 2023. The daily internal quality control (IQC) data and monthly external quality assessment service data for 10 biochemical parameters from January 2022 to December 2022 were collected retrospectively and from April 2023 to September 2023 prospectively. Parameters with poor sigma performance and root cause analysis were done, and corrective actions were taken. Data were collected prospectively for the next 4 months (April 2023–September 2023), and sigma was calculated. Statistical analysis: Data were input into Microsoft Excel and analyzed using Stata version 14. Results: Out of the ten, 7 parameters at level 1 and five at level 2 IQC showed sigma values between 3 and 6, whereas 2 parameters showed poor performance at both the quality control (QC) levels with sigma metrics values <3. With quality goal index and root cause analysis, the source of error was detected and corrected. Conclusions: Sigma metric analysis is a tool to determine the performance of QC design. This gives the laboratory a select the right QC strategy. This will help to save time, effort, unnecessary runs, calibration, and reagent waste, which affect the outcome of turnaround time. [ABSTRACT FROM AUTHOR]

Background: The application of the Six Sigma (σ) metric in biochemical laboratories is a powerful tool for reducing the occurrence of errors and prioritizing important improvements in laboratory quality control. The National Accreditation Board for Testing and Calibration Laboratories (NABL) is an accreditation body with an accreditation system established in accordance with ISO/IEC 17011, providing specialty- or scope-based certification based on the conformance of quality indices to medical laboratories. In this context, a study has been designed that considers the quality guidelines set by NABL as well as the sigma metric rule in the assessment of analytical performance. Aims and Objectives: The aims of this study were to identify the gaps and need for strategy modification for quality improvement by assessing the performance of two NABL-accredited medical testing laboratories on a Sigma metric scale. Materials and Methods: A retrospective analytical study was conducted over 6 months (January–June 2021). Internal quality control (IQC) and EQAS data were obtained from third-party QC providers (Bio-Rad, India) and analyzed by calculating sigma (σ) values based on the coefficient of variation, bias, and total error allowable in two NABL-accredited medical testing laboratories. To identify potential problems for analytes with poor sigma values, a quality goal index (QGI) analysis was performed. Results: By analyzing the sigma values obtained by both NABL-accredited laboratories, we can see that laboratory 2 performed better than laboratory 1. After calculating the QGI, there was a problem of inaccuracy and imprecision in laboratory 1, and laboratory 2 had QGI values that indicated only imprecision. Conclusion: Diagnostic laboratories should incorporate Six Sigma metrics to identify gaps in their performance to ensure better quality control and patient safety.

Background: Clinical laboratories errors can impose serious adverse effects on patient management. Clinical laboratories can evaluate their analytical performance by effective quality management tool i.e. Six Sigma metrics. Objectives: To evaluate the analytical performance of routine chemistry parameters by Six Sigma approach. Methods: Cross sectional, study carried out from July 2022 till February 2024 in the Chemical Pathology section of University of Lahore Diagnostic Lab and Research Center, Lahore. Analytical performance of nineteen routine chemistry parameters were evaluated by using the external and internal quality control material. Only those parameters were selected who enrolled in External Quality Assurance Scheme. Inclusion criteria for IQC following West gard rule +/- 2 SD while for EQAS results follow +/- 2 Z score. Sample size for 19 parameters analyzed in EQAS from July 2022 till December 2023 was 342. 2700 IQC samples were analyzed from August 2023 till February 2024. All control values were entered in Microsoft Excel 2013 calculated manually by conventional equations. Results: Most of our laboratory parameters showed satisfactory analytical performance in between 3-6 when evaluated by six sigma metrics. Conclusion: Control frequency for parameters showed score > 6 can be reduce for saving laboratory resources whereas parameters with sigma score around 3 needs more vigilant monitoring. Health care and laboratory outcome need high quality results to improve patient health. Six sigma tool allows laboratory to identify the right method, right rule, run controls at right frequency to enable the right patient outcome. [ABSTRACT FROM AUTHOR]

Background: The application of the Six Sigma (σ) metric in biochemical laboratories is a powerful tool for reducing the occurrence of errors and prioritizing important improvements in laboratory quality control. The National Accreditation Board for Testing and Calibration Laboratories (NABL) is an accreditation body with an accreditation system established in accordance with ISO/IEC 17011, providing specialty- or scope-based certification based on the conformance of quality indices to medical laboratories. In this context, a study has been designed that considers the quality guidelines set by NABL as well as the sigma metric rule in the assessment of analytical performance. Aims and Objectives: The aims of this study were to identify the gaps and need for strategy modification for quality improvement by assessing the performance of two NABL-accredited medical testing laboratories on a Sigma metric scale. Materials and Methods: A retrospective analytical study was conducted over 6 months (January-June 2021). Internal quality control (IQC) and EQAS data were obtained from third-party QC providers (Bio-Rad, India) and analyzed by calculating sigma (σ) values based on the coefficient of variation, bias, and total error allowable in two NABL-accredited medical testing laboratories. To identify potential problems for analytes with poor sigma values, a quality goal index (QGI) analysis was performed. Results: By analyzing the sigma values obtained by both NABL-accredited laboratories, we can see that laboratory 2 performed better than laboratory 1. After calculating the QGI, there was a problem of inaccuracy and imprecision in laboratory 1, and laboratory 2 had QGI values that indicated only imprecision. Conclusion: Diagnostic laboratories should incorporate Six Sigma metrics to identify gaps in their performance to ensure better quality control and patient safety. [ABSTRACT FROM AUTHOR]

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

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

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

• A six sigma model was successfully applied for the quality management of CSF biochemical analytes. • We evaluated the analytical performance of CSF biochemical analytes using the six sigma model and formulated individualized IQC procedures. • We provided appropriate improvement measures for the CSF biochemical analytes with a sigma less than 6. 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. 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). 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. The six sigma model has significant advantages in practical applications involving CSF biochemical analytes and is highly useful for quality assurance and quality improvement. [ABSTRACT FROM AUTHOR]

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

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

Background: Limitations of Internal quality control (IQC) and external quality assessment system (EQAS) like cost for repeated analysis is a challenge to most of the clinical Biochemistry laboratories. Six sigma system is a part of lean laboratory practices. The higher the sigma value, chance of false test results are less likely. Aim: To calculate sigma metrics of liver function parameters in laboratory and using it to decide on the QC strategy using Westgard Sigma rules for reliable and accurate report. Methods: IQC data for Total bilirubin, Direct bililrubin , AST, ALT, ALP, Total protein & Albumin were analysed for 6 months. Sigma value is calculated and Quality goal index used to identify the reason for low sigma value. Results: Both AST and ALP in level 1 and AST, ALT and ALP in Level 2 has sigma value above 6. Parameters which showed sigma value between 3-6 are ALT and Albumin in Level 1 and BIT and TP in level 2 QC. Parameters which showed sigma value below 3 are BIT, BID & TP in Level 1 &BID and Albumin in Level 2 QC. Conclusion: Appropriate control limits and control measurements can be adapted for each parameters with westgard sigma rule and laboratories can use it as a self-assessment tool.

Introduction: The analytical phase of the total testing process is the one in which the clinical biochemist can directly intervene to improve the quality of tests reporting. The sigma metrics and Operational Process Specification (OPSpec) chart can specify to which category the laboratory belongs. Aim: To apply sigma metrics to analytical process of testing, do the root cause analysis and apply the corrective measures according to Westgard rules to improve laboratory performance towards the quality assurances. Materials and Methods: This was a retrospective-prospective study carried out in a clinical laboratory of MKCG Medical College and Hospital, Berhampur, Odisha, India, from July 2020 to March 2021. A retrospective secondary data analysis of six months duration was carried out in a clinical chemistry laboratory with a follow-up prospective study for three months. During this period, 16 analytes were tabulated to analyse the Internal Quality Control (IQC). External Quality Control (EQC) for the same analytes were obtained on monthly basis and the sigma metrics was calculated for each analytes. For analytes with sigma value <3, appropriate measures were taken according to Westgard rules to improvise the quality of laboratory investigations. The statistical analysis of sigma metrics was performed in “R” v-3.6.3. Results: Out of total 16 analytes, three analytes at level 1 and two analytes at level 2 Quality control (QC) showed a world class performance whereas four analytes showed a poor performance at both the QC levels with sigma metrics value <3. From Quality Goal Index (QGI) and root cause analysis, the source of error was detected and corrected. Conclusion: The inaccuracy and imprecision of different parameters in the analytical phase of the testing process can be addressed by calculating the sigma metrics and do the root cause analysis. Application of corrective measures according to Westgard rule can improve the laboratory performance towards the quality assurance. [ABSTRACT FROM AUTHOR]

Introduction: The analytical phase of the total testing process is the one in which the clinical biochemist can directly intervene to improve the quality of tests reporting. The sigma metrics and Operational Process Specification (OPSpec) chart can specify to which category the laboratory belongs. Aim: To apply sigma metrics to analytical process of testing, do the root cause analysis and apply the corrective measures according to Westgard rules to improve laboratory performance towards the quality assurances. Materials and Methods: This was a retrospective-prospective study carried out in a clinical laboratory of MKCG Medical College and Hospital, Berhampur, Odisha, India, from July 2020 to March 2021. A retrospective secondary data analysis of six months duration was carried out in a clinical chemistry laboratory with a follow-up prospective study for three months. During this period, 16 analytes were tabulated to analyse the Internal Quality Control (IQC). External Quality Control (EQC) for the same analytes were obtained on monthly basis and the sigma metrics was calculated for each analytes. For analytes with sigma value

Clinical laboratories are mandated to deliver accurate, reliable, timely and correctly reported result which, used in decision making for disease screening, diagnosis and monitoring. With aid of six sigma principles and metrics it is possible to assess the quality laboratory process and the quality control that is needed to ensure that the desired quality is achieved. Thus, this study was undertaken to evaluate the performance of biochemical parameters by calculating the sigma metrics of individual parameters using internal quality control (IQC) and Proficiency Testing (PT) results. The sigma metrics of 21 clinical chemistry parameters were calculated from COBAS 6000 analyzer with internal quality control (IQC) materials and external quality assurance scheme (EQAS) performance in national clinical chemistry laboratory for the period of six months. We obtained an excellent performance (≥ 6 sigma) for test parameters amylase pancreatic, amylase total, HDL, magnesium, AST, triglyceride, total bilirubin and ALT in both levels of quality control. Urea, creatinine and chloride were failed to meet the minimal sigma performance for both level 1 and 2. Sigma values of 3–6 were observed for ALP, Direct bilirubin, total protein, albumin, glucose, potassium, and phosphate with both levels of quality control. Though, stringent IQC strategy is not mandatory for analytes that scored sigma value ≥ 6. However, continuous monitoring quality control is required for renal function tests and process improvement will be designed for those with poor sigma values. [ABSTRACT FROM AUTHOR]

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

Introduction: One of the most popular quality management system tackle employed for process perfection is six sigma. When the process outcome is measurable, six sigma can be used to assess the quality. Aim: Present study was conducted with the objective to apply six sigma matrices and quality goal index for the assessment of quality assurance in a clinical biochemistry laboratory. Materials and methods: Present study is a retrospective study. Internal and external quality control data were analyzed retrospectively during July 2020 to December 2020. Descriptive statistics like laboratory mean ± standard deviation; bias and coefficient of variation (CV) were calculated for the parameters glucose, urea, creatinine, ALT (SGPT), AST (SGOT), cholesterol, triglyceride and HDL. Sigma value was calculated for both level I & level II of internal quality control (IQC). Results: Satisfactory sigma values (≥3) were elicited for blood glucose, cholesterol, triglyceride, HDL, urea and creatinine, while ALT (SGPT) and AST (SGOT) performed poorly ( 1.2) for only 2 parameters SGPT and SGOT (with sigma value

Introduction: Sigma metrics is a quality management tool used for process improvement which usually comes into application when there is a measurable outcome in the process. It can play an important role in health care laboratory services as Quality Assurance (QA) of the same, is the need of the hour. Aim: To gauge the performance of a few biochemical parameters by calculating their sigma metrics on a sigma scale. Materials and Methods: This retrospective study was undertaken using Quality Control (QC) and External QA Scheme (EQAS) data for 17 biochemical parameters from Biochemistry section, diagnostic laboratory of an M.S. Ramaiah Medical College and Hospital, Bengaluru. Sigma values for these parameters were determined and sigma metrics was evaluated for duration of 13 months. Results: In level 1 coefficient of variation percentage (CV%), five parameters (ALP, calcium, magnesium, triglycerides and HDL-cholesterol) showed an ideal performance of ≥6 sigma level and in level 2 CV%, eight parameters (total bilirubin, urea, creatinine, albumin, AST, total cholesterol, total protein and phosphorus) showed a sigma of ≥6. Quality Goal Index (QGI) for 11 analytes in level 1 and seven analytes in level 2 was

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

Introduction Six-sigma is emerging method of choice for performance testing of clinical laboratory. This study was designed to evaluate the performance of 12 routine biochemical analytes on sigma-scale and calculate the quality goal index (QGI). Methods A cross-sectional study was conducted at Tribhuvan University Teaching Hospital (TUTH), Biochemistry Laboratory for 3 months. BT1500 and BT3500 automated biochemistry analyzers were used. Internal quality control (IQC) performed routinely for 12 clinical analytes for both control levels were recorded from both analyzers and used for calculation of coefficient of variation (CV%). Bias was estimated based on the average difference obtained for each analyte from the target values provided. Values for total allowable errors (TEa) were taken from Clinical Laboratories Improvement Act guidelines. Variables used for calculation of sigma values and QGI were CV%, percentage bias and TEa. Results Both levels of control for alanine-aminotransferase (ALT) in BT1500 and only control level L2 for aspartate-aminotransferase (AST) in both analyzers showed the sigma value greater than six. Sigmavalues between three and six were found for uric acid for both levels of control in both analyzers. Less than three sigma values were obtained for parameters urea, creatinine, albumin, triglyceride, total-cholesterol, alkaline phosphatase (ALP) and magnesium for both levels of control in both analyzers indicating the need towards improvement in these methods. Conclusion The quality of test for urea, creatinine, albumin, triglyceride, totalcholesterol, ALP and magnesium were unacceptable. Hence, appropriate actions should be taken towards measurement method in these parameters to improve accuray and report quality. [ABSTRACT FROM AUTHOR]

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 13s/22s/R4s/41s/6x 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 13s/22s/R4s/41s/6x (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. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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. Materials and Methods: This retrospective, observational study was conducted in year 2020 based on the data obtained 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 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: Westgardrules13s/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

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 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.

Background: The performance of 17 routine chemical detection methods was evaluated by the Sigma (s) index, and separate quality control standards were established according to the sigma values of different detection methods. 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

Background: The Six Sigma theory is an important tool for laboratory quality management. It has been widely used in clinical chemistry, haematology and other disciplines. The aim of our study was to evaluate the analytical performance of plasma proteins by application of Sigma metric and to compare the differences among three different allowable total errors in evaluating the analytical performance of plasma proteins. Methods: Three different allowable total error values were used as quality goals. Data from an external quality assessment were used as bias, and the cumulative coefficient of variation in internal quality control data was used to represent the amount of imprecision during the same period. Sigma metric of analytes was calculated using the above data. The quality goal index was calculated to provide corrected measures for continuous improvements in analytical quality. Results: The Sigma metric was highest using the external quality assessment standards of China: it was sigma ≥6 or higher in 57.1% of plasma proteins. But Sigma metric was lower by using RiliBÄK or biological variation standards. IgG, C3 and C-reactive protein all required quality improvements in imprecision. A single-rule 13s for internal quality control was recommended for IgA, IgM, C4 and rheumatoid factor, whereas multiple rules (13s/22s/R4s) were recommended for IgG, C3 and C-reactive protein, according to the external quality assessment standards of China. Conclusions: Different quality goals can lead to different Sigma metric for the same analyte. As the lowest acceptable standard in clinical practice, the external quality assessment standard of China can guide laboratories to formulate reasonable quality improvement programmes. [ABSTRACT FROM AUTHOR]

Introduction This study was conducted to assess the analytical performance of biochemical tests using Six Sigma methodology and to assess the underlying causes of unsatisfied performance of analytes with a sigma value of less than 4 using quality goal index (QGI) and root cause analysis (RCA). Methodology Daily data for internal quality control (IQC) for both level 1 (L1) and level 2 (L2) and monthly data for external quality assessment for a period of six months were recorded. The coefficient of variation (CV), bias, and total allowable error (TEa) were calculated to analyze the sigma (σ) values for 19 biochemical analytes. Quality goal index (QGI) analysis was done to analyze impressions and inaccuracies in analyte performance having a sigma value of less than 4. Root cause analysis (RCA) was done to evaluate the possible causes that can improve quality performance. Results Creatinine and high-density lipoprotein (HDL) had sigma metrics of ≤2.0, and chloride, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) had sigma values between 2 and 3. Glucose, total protein (TP), phosphate (Phos), and potassium had sigma values between 4 and 5 in level 1 QC. Sigma grading for level 2 quality control (QC) also gave similar results. For analytes with σ < 4, QGI analysis exposed inaccuracy or imprecision issues and identified errors such as the reconstitution of IQC, storage temperature, and air bubbles while processing the QC, being common causes of poor performance. Conclusion Six Sigma approach is helpful for quality assurance and identifying areas for improvement. Assessing Six Sigma metrics should be a routine practice to decide the frequency of QC run and to detect errors in analysis., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Panda et al.)

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

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. [ABSTRACT FROM AUTHOR]

Background Information on quality requirements and objective evaluation of performance of veterinary point-of-care analyzers (POCAs) is scarce. Objectives The study was aimed at assessing observed total errors (TEobss) for veterinary hematology POCAs via meta-analysis and comparing TEobs to allowable total error (TEa) specifications based on experts′ opinions. Methods The TEobs for POCAs (impedance and laser-based) was calculated based on data from instrument validation studies published between 2006 and 2013 as follows: TEobs = 2 × CV [%] + bias [%]. The CV was taken from published studies; the bias was estimated from the regression equation at 2 different concentration levels of measurands. To fulfill quality requirements, TEobs should be < TEa. Measurands were considered as globally acceptable if > 60% of analyzers showed TEobs < TEa. Results Six studies evaluating 11 analyzers and 5 studies evaluating 5 analyzers were included for canine and feline hematology variables, respectively. For the CBC, TEobs was < 15% for canine and < 13% for feline measurands, except for HGB and platelet counts. Measurands of the CBC, excluding differential WBC and platelet counts, and HGB concentration were considered globally acceptable. For most of the cell types in the WBC differential count, TEobs was > TEa (data from 3 analyzers). Conclusion This meta-analysis is considered a pilot study. Experts′ requirements (TEobs < TEa) were fulfilled for most measurands except HGB (due to instrument-related bias for the ADVIA 2120) and platelet counts. Available data on the WBC differential count suggest an analytic bias, so nonstatistical quality control is recommended. [ABSTRACT FROM AUTHOR]

Background Scarce information exists about quality requirements and objective evaluation of performance of large veterinary bench top hematology analyzers. Objective The study was aimed at comparing the observed total error (TEobs) derived from meta-analysis of published method validation data to the total allowable error (TEa) for veterinary hematology variables in small animals based on experts′ opinions. Ideally, TEobs should be < TEa. Methods An online survey was sent to veterinary experts in clinical pathology and small animal internal medicine for providing the maximal allowable deviation from a given result for each variable. Percent of TEa = (allowable median deviation/clinical threshold) * 100%. Second, TEobs for 3 laser-based bench top hematology analyzers (ADVIA 2120; Sysmex XT2000iV, and CellDyn 3500) was calculated based on method validation studies published between 2005 and 2013 ( n = 4). The percent TEobs = 2 * CV (%) + bias (%). The CV was derived from published studies except for the ADVIA 2120 (internal data), and bias was estimated from the regression equation. Results A total of 41 veterinary experts (19 diplomates, 8 residents, 10 postgraduate students, 4 anonymous specialists) responded. The proposed range of TEa was wide, but generally ≤ 20%. The TEobs was < TEa for all variables and analyzers except for canine and feline HGB (high bias, low CV) and platelet counts (high bias, high CV). Conclusions Overall, veterinary bench top analyzers fulfilled experts′ requirements except for HGB due to method-related bias, and platelet counts due to known preanalytic/analytic issues. [ABSTRACT FROM AUTHOR]

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., (© 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

Background: Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma., Methods: Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C-reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values., Results: While using the TEa NCCL , 90% analytes had a world-class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σ NCCL that is calculated from TEa NCCL , Sigma Method Decision Charts were constructed. For Cys C, five multi-rules (1 3s /2 2s /R 4s /4 1s /6 X , N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (1 3s , N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12)., Conclusions: The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool., (© 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

Background: (a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (σ) model. (b) To redesign quality control strategies for performance improvement., Methods: The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re-evaluated in September 2018 after a series of targeted corrective actions., Results: Based on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (1 3S ) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with σ < 4 at one QC material level or both needed multiple rules (1 3S /2 2S /R 4S /4 1S /10 X ) with N6 and R10-500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re-evaluated sigma metrics of all the analytes increased significantly., Conclusions: It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes., (© 2020 The Authors. Journal of Clinical Laboratory Analysis Published by Wiley Periodicals LLC.)

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., (© 2019 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals, Inc.)

Aim: To study sigma metrics and quality goal index ratio (QGI)., Methods: The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board for Testing and Calibration of Laboratories accredited lab as per the International Organization for Standardization 15189:2012 and provides service to a > 1700-bed tertiary care hospital. Data of 16 analytes was extracted over a period of one year from January 2017 to December 2017 for calculation of precision, accuracy, sigma metrics, total error, and QGI., Results: The average coefficient of variation ranged from 2.12% (albumin) to 5.42% (creatinine) for level 2 internal quality control and 2% (albumin) to 3.62% (high density lipoprotein-cholesterol) for level 3 internal quality control. Average coefficient of variation of all the parameters was below 5%, reflecting very good precision. The sigma metrics for level 2 indicated that 11 (68.5%) of the 16 parameters fall short of meeting Six Sigma quality performance. Of these, five failed to meet minimum sigma quality performance with metrics less than 3, and another six just met minimal acceptable performance with sigma metrics between 3 and 6. For level 3, the data collected indicated eight (50%) of the parameters did not achieve Six Sigma quality performance, out of which three had metrics less than 3, and five had metrics between 3 and 6. QGI ratio indicated that the main problem was inaccuracy in the case of total cholesterol, aspartate transaminase, and alanine transaminase (QGI > 1.2), imprecision in the case of urea (QGI < 0.8), and both imprecision and inaccuracy for glucose., Conclusion: On the basis of sigma metrics and QGI, it may be concluded that the Clinical Biochemistry Laboratory, PGIMS, Rohtak was able to achieve satisfactory results with world class performance for many analytes one year preceding the accreditation by the National Accreditation Board for Testing and Calibration of Laboratories. Aspartate transaminase and alanine transaminase required strict external quality assurance scheme monitoring and modification in quality control procedure as their QGI ratio showed inaccuracy., Competing Interests: Conflict-of-interest statement: None exists.