Evaluation of the Quality of Some Serum Biochemical Testing Using Sigma Metrics and Quality Goal Index in Veterinary Laboratory Setting

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In this regard, assessing the quality of biochemical testing in Ethiopia is limited, and researchers have not conducted any studies in the veterinary laboratory setting. Objectives: The objective of this study was to examine the testing quality for six analytes, namely glucose, urea, triglycerides, total cholesterol, albumin, and total protein via sigma metric and quality goal index. Results: Data from two-level quality control (normal and pathological) materials were used to calculate sigma metrics and the quality goal index. The results revealed that three analytes glucose, cholesterol, and triglyceride from normal quality control and three analytes triglyceride, total cholesterol, and albumin from pathological quality control had below the minimum acceptable performance of three sigma metric, indicating poor performances. The quality goal index indicated that the poor performances were attributed to imprecision. Conclusion: To address this, the laboratory must strictly adhere to quality management practices, including implementing a robust quality control strategy to identify and resolve the root cause of imprecision. TE a value for respective analytes should be revisited as it might be another factor varying sigma and QGI. Biochemical Imprecision Quality goal index Sigma metric 1. INTRODUCTION Veterinary clinical laboratories are complicated and dynamic enterprises tasked with constantly enhancing testing quality and adhering to stringent criteria, including but not limited to the quality of test methods, instruments, reagents, and personnel, while attempting to reduce costs. Today, veterinary clinical laboratories handle significant workloads encompassing a broad range of parameters, all within limited capacity, while delivering consistent quality findings within specified turnaround time cost-effectively [ 1 ]. Quality control (QC) is essential in any setting where a reliable and accurate result is necessary for determining the final diagnostic results. QC validation is used to identify the statistical methodologies suitable for discerning variances crucial for clinical interpretation [ 2 ]. The importance of QC in ensuring the precision and accuracy of laboratory results cannot be overestimated. Commonly employed QC metrics include manufacturers’ mean (target value) to determine bias, and coefficient of variation to determine precision. These criteria, however, do not provide a comprehensive assessment of laboratory test performance. These criteria focus on individual aspects of performance without integrating the broader picture of test reliability. To address this limitation, the sigma metric which hold three parameters: bias, precision and total allowable error (TE a ) was employed. This approach provides more holistic and reliable method of assessing the quality of laboratory tests, offering deeper insights in to the overall effectiveness and robustness of QC practices in in clinical chemistry laboratories [ 3 , 4 ]. Sigma metrics are a useful tool for objectively assessing the quality and performance of clinical laboratory measurement methods. The metric takes into account the variability in a method’s output, which is expresses by the SD or % CV acquired from repeated measurements of QC materials. Sigma is the number of standard deviations that meet inside a certain analyte’s pre -defined analytical performance parameters. Deviation from the target value can also obtained in terms of bias. The total allowable error (TE a ) is the analytical performance standard most commonly used for determining Sigma-metrics. By reducing the sources of random and systemic errors in a process, the variability decreases, resulting in a higher sigma value [ 5 ]. In the biochemical laboratory, mistakes are defined as values differ from the true value by more than the entire permitted error. The tolerance limit and offset specified in the sigma industrial formula are the same as the total permitted error, and total analytical bias in laboratory operations respectively. Sigma metric is calculated as: [TE a (%) – |bias (%)|] / CV (%) [ 6 ]. Based on the sigma metric scale, a sigma-metric zone of six indicates world class performance. Excellent, good, marginal poor, and unacceptable are applied for five, four, three, and two sigma regions, respectively [ 7 ]. The sigma metric is used to plan QC practice for daily laboratory activities. Methods achieving a sigma metric zone of six can be managed with one level of QC per day using the 1 3s , with two control measurements in each run one on each level of control. The notation N = 2 R = 1 indicates that two control measurements are needed in a single run. For methods in five sigma zone, QC practice require three rules, 1 3s /2 2s /R 4s , with two control measurements in each run (N = 2, R = 1). For methods in four sigma zone, a fourth rule is added, resulting 1 3s /2 2s /R 4s /4 1s multirule, preferably with four control measurements in each run (N = 4, R = 1), or alternatively, two control measurements in each of two runs (N = 2, R = 2), using the 4 1s rule to inspect the control rules across both runs. A method with a three sigma zone must use all the Westgard Rules. A method with a sigma metric of less than three indicates poor quality/performance and root cause analysis should be performed [ 8 ]. The cause of low sigma value for those with sigma scale of less than three can also be determined by using quality goal-index (QGI) whether the greater cause of defects is imprecision or inaccuracy or both [ 9 ]. Although studies have been conducted in different parts of the world, no reports of evaluation of veterinary laboratory with Sigma-metric and QGI have been performed in Ethiopia. Therefore, the current study aimed to examine the laboratory’s testing quality via sigma-metrics and QGI in order to ensure the delivery of trustworthy and consistent results of biochemical testing in selected analytes. 2. MATERIALS AND METHODS 2.1 Study Area This research was conducted at Biochemistry Laboratory of the College of Veterinary Medicine and Agriculture of Addis Ababa University. 2.2 Study Design This is a descriptive cross sectional study, which is ideal for capturing data at a single point in time to assess the laboratory’s testing quality via sigma-metric and QGI. This descries the characteristics of the laboratory under study and without implying causations. QC data from April to May 2023 were used to evaluate laboratory testing quality by applying sigma metric. Twenty data points were used (Ten days in each month). The QC materials of same lot number were utilized throughout the study. Each month, QC materials were prepared, divided in to 10 aliquots, and stored at -20 o c in 1.5ml Eppendorf tubes with screw cap to ensure stability and prevent contamination. Each tube was designated for single use analysis to maintain the integrity of QC material. The lab have standard operating procedure (SOP), it follows the procedures outlined in test reagent manuals. Additionally the laboratory benefits from experienced and well trained staff. The analytes tested include glucose, urea, triglyceride, cholesterol, albumin and total protein. 2.3 Laboratory Methods Test principle: Laboratory determination of analytes were done according to standard protocols as glucose by glucose oxidase method, urea by kinetic urease/GLDH (Glutamate dehydrogenase); total cholesterol by CHOD-PAP (Cholesterol peroxidase4-aminophenazone); triglycerides by G-PAP (Glycero-3-phosapte oxidase-4-aminophenazone); total protein by biuret; and albumin by bromocresol green [10]. Instrument: The laboratory utilizes EMP-168 semi-automated biochemical analyzer (Chengdu Empsun Medical Technology Co., Ltd., China) for purpose of teaching and diagnostic activities. The instrument is calibrated and, a two level QC is performed prior to each test. QC results are verified to ensure if they fall within the manufacturer designated values. However since the laboratory doesn’t frequently run biochemical tests, Westgard rules are not applied. 2.4 Quality Indicators Total allowable Error (TE a ) The analytical performance was assessed using total allowable error (TE a ) obtained from American Society of Veterinary Clinical pathology (ASVCP) guidelines for biochemistry [4]. Sigma Metric ( s ) Sigma metric values were calculated using the formula: Sigma metric = [TE a (%) – |bias (%)|] / CV (%). Sigma metric rated as: world class (6), excellent (5), good (4), marginal (3), poor (2), and unacceptable (less than 2) [7]. Quality Goal Index (QGI) QGI represents the extent to which both precision and bias meet their respective quality standards, where quality refers to the accepted benchmarks for precision and bias as defined in Westgard rules [11].QGI calculated as:|Bias|/(1.5 ´ CV %). QGI was used to analyse the reason for the sigma less than three, i.e., the poor performance was due to which precision, bias or both contribute to analytical quality. QGI less than 0.8 indicates imprecision, QGI between 0.8 to 1.2 indicates both imprecision and inaccuracy and QGI greater than 1.2 indicates inaccuracy [8]. [9]. 3. RESULTS QC results for six analytes at two QC levels (Normal and Pathological) were analysed and sigma metric and QGI was calculated for both QC levels using CV%, bias% and TE a . Accordingly, Triglyceride and Cholesterol testing hold acceptable sigma metric value of greater than three, while Glucose, Albumin, Urea and Total protein had sigma metric less than three. Higher CV% of all analytes was also noted indicating greater dispersion around the mean (Table 1 and 2). Table 1. Normal quality control laboratory mean, coefficient of variation, absolute bias%, sigma metric and quality goal index of selected analytes from April to May 2023 Analyte N LM SD CV (%) MM ASVCP TE a (%) | Bias | (%) Sigma metric (σ) QGI Problem Glucose (mg/dl) 20 103.23 4.19 4.06 107 20 3.52 4.06 0.58 Imprecision Triglyceride (mg/dl) 20 89.13 4.78 5.36 88.5 25 0.71 4.53 0.09 Imprecision Cholesterol (mg/dl) 20 159.57 7.56 4.74 158 20 0.99 4.01 0.14 Imprecision Albumin (g/dl) 20 4.22 0.71 16.82 4.11 15 2.68 0.73 0.11 Imprecision Urea (mg/dl) 20 43.5 5.25 12.07 44.3 12 1.81 0.84 0.10 Imprecision Total Protein (g/dl) 20 5.52 0.49 8.88 5.82 10 5.15 0.55 0.39 Imprecision Table 2. Pathological quality control mean, coefficient of variation, bias%, sigma metric and quality goal index of selected analytes from April to May 2023 Analyte N LM SD CV (%) MM ASVCP TE a (%) | Bias | (%) Sigma metric (σ) QGI Problem Glucose (mg/dl) 20 269.43 17.8 6.61 276 20 2.38 2.67 0.24 Imprecision Triglyceride (mg/dl) 20 250.9 8.93 3.56 261 25 3.87 5.94 0.72 Imprecision Cholesterol (mg/dl) 20 288.8 11.69 4.05 299 20 3.41 4.10 0.56 Imprecision Albumin (g/dl) 20 3.1 0.13 4.19 3.09 15 0.32 3.50 0.05 Imprecision Urea (mg/dl) 20 105.53 9.22 8.74 114 12 7.43 0.52 0.57 Imprecision Total Protein (g/dl) 20 4.57 0.45 9.85 4.53 10 0.88 0.93 0.06 Imprecision It has been observed that three analytes namely Albumin (g/dl), Urea (mg/dl) and Total Protein (g/dl) from normal QC scored sigma metric value of less than three. Similarly in pathological QC three analytes namely Glucose (mg/dl), Urea (mg/dl) and Total Protein (g/dl) scored sigma value of less than three. The sigma metric value less than three is indicating poor performance of the indicated anaytes. The QGI values for analytes scored in both QC levels were also less than 0.8 indicating the problem was imprecision. 4. DISCUSSION The current study described the performance of biochemical testing using sigma metric and QGI Notably to the author’s knowledge, there is no study in Ethiopia has assessed the performance of biochemical testing in veterinary laboratory setting by these quality assessment tools. The study’s findings revealed that analytes namely albumin, urea and total protein from the normal QC and glucose, urea and total protein from pathological QC had a sigma value below three which is below the minimum acceptable performance. This indicates that test results in this condition would not be in any way reportable. Several reports address the values of sigma metric and QGI with inconsistent findings. A study conducted in the clinical chemistry laboratory of Shandong Provincial Hospital, China revealed both normal and pathological QC results for cholesterol and triglyceride shown sigma value of greater than three [ 12 ]. Sigma metric value of less than three for cholesterol and triglyceride, and sigma metric values greater than three for urea and total protein for both normal and pathological QC was noted by clinical biochemistry laboratory of College of Medicine and Sagore Dutta Hospital Kolkata, West Bengal [ 13 ]. A study at tertiary care hospital in Bhubaneswar, India shown sigma metric value of less than three for total protein and triglycerides at normal and pathological QC. A study in teaching hospital at Bhubaneswar, India has also shown lower sigma values for cholesterol normal QC and Albumin pathological QC were noted [ 14 ]. The direct comparison of sigma metric values is challenging due to different instruments, reagents, QC materials, storage and handling of QC materials, maintenance and calibration requirements and schedules, water quality, environmental requirements, training and selection of TE a target. In the current study, the cause of poor performance was assessed via QGI. It was found that the cause for all anaytes testing was high imprecision. Imprecision as a cause of low poor performance was noted by different studies including but not limited to a study at Department of Clinical Laboratory, Shengzhou People's Hospital, Shengzhou Branch of the First Affiliated Hospital of Zhejiang University, Shengzhou, China for urea, glucose and total protein [ 15 ]. A study conducted in a Secondary Care Government Hospital, Chennai, India has also noted imprecision as a cause of poor performance for urea, total protein, and albumin [ 16 ]. In India Maharishi Markandeshwar Institute of Medical Sciences and Research Mullana, a study also noted imprecision as a cause of poor performance for total protein, urea, albumin, and glucose [ 17 ]. Similar reasons indicated to sigma metric as there are disparities among labs attributed to the differences in analytical conditions including selection of the TE a target and thus direct comparison of QGI is also difficult. Thus each lab must follow robust QC, including corrective actions as outlined in Westgard protocol [ 18 ]. On the other hand despite of sigma metric and QGI value, SD data and TE a value for respective analytes namely, albumin, urea and total protein from the normal QC and glucose, total cholesterol, triglycerides and urea from pathological QC shown that the respective test methods needs validation as their SD exceed 0.33 × TE a respectively. Thus the laboratory has to validate the indicated test methods before it routinely used as the quality of the test in such cases cannot be assured [ 19 ]. Overall the study highlights imprecision as a significant contributor to poor performance, which negatively impacts sigma metric values. Factors contributing to this imprecision might include the attention of different thing including but not limited to correct storage of QC material, adherence to maintenance and calibration schedules, inadequate water purity requirements, uncontrolled environmental temperature and humidity. Furthermore the laboratory lack of strict adherence to Westgard rules further exacerbates troubleshooting challenges, hindering overall performance management. Besides the TE a value for respective analytes are just recommendations and might be different for calculating sigma and QGI in the study LIMITATIONS OF THE STUDY : The study used manufacturers QC values, which is weakness to be noted, while third party QC material is considered a better assessment of performance. The study was also limited to determine six analytes due to logistic reasons. 5. CONCLUSION AND RECOMMENDATIONS Sigma metrics and QGI are an important indicators for laboratory testing quality. The findings highligts the need for quality improvement in the testing of analytes specifically for glucose, total protein, albumin and urea which scored the minimum acceptable perofrmance criteia of less than three sigma metrics. Imprecision was identified as the primary cause of poor performance, such findings indicate the presence of systemic errors in the laboratory testing procedure. To address these issues and enhance sigma metric values the laboratory must implement robust quality management practices and adopt QC rules for the acceptance and rejection of analytical run as outlined in Westgard protocol. Furthermore, conducting extensive study over a long period is recommended to asses and monitor the peformance of the laboratory.incluidng method validation before the indicated test methods routinely used and results released. Besides TE a value for respective analytes should considered. Abbreviations LM: Laboratory Mean, MM: Manufacturer mean, CV: Coefficient of Variation, QC: Quality control, SD: Standard Deviation, TE a : Total allowable error, ASVCP: American society of veterinary clinical pathology, QGI: Quality Goal Index Declarations Acknowledgements The authors would like to acknowledge Addis Ababa University for granting the research Consent for publication All authors consent the publications and permission to present the findings of this research to public was verbally obtained from farmers. Authors’ Contributions: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; validation; visualization; writing – original draft; writing – review and editing: Anwar Aminu Umer. Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; validation; visualization; writing – original draft; writing – review and editing: Yoseph Cherinet Megerssa Competing Interests: The authors declare that they have no competing interests. Funding: This research was supported by the Addis Ababa University office of vice president for research and technology transfer through Adaptive research entitled “Implementation of Sigma Metrics for evaluation of analytical quality in diagnostic and research for commonly assayed clinical chemistry tests ” Grant No RD/PY-223/2022. Availability of data and materials The data used to support this study are available from the corresponding author on request Ethics approval and consent to participate Not Applicable; however, the study protocol obtained research ethical clearance from institutional animal research ethics committee of Addis Ababa University College of Veterinary Medicine and Agriculture (Certificate reference no VM/ERC/38/02/15/2023). Author details 1 Addis Ababa University, College of Veterinary Medicine and Agriculture, Bishoftu, Ethiopia References Inal TC, Goruroglu Ozturk O, Kibar F, Cetiner S, Matyar S, Daglioglu G, Yaman A. Lean six sigma methodologies improve clinical laboratory efficiency and reduce turnaround times. J Clin Lab Anal. 2018; 32(1):e22180. doi:10.1002/jcla.22180 Westgard JO, and Barry PL. Beyond quality assurance: Committing to quality improvement. Laboratory Medicine 1989; 20:241-7. Van Heerden M, George JA, Khoza S. Corrigendum: The application of sigma metrics in the laboratory to assess quality control processes in South Africa. Afr J Lab Med. 2023; 12(1), a1996. https://doi.org/10.4102/ajlm.v12i1.1996 Harr KE, Flatland B, Nabity M, Freeman KP; ASVCP. ASVCP guidelines: allowable total error guidelines for biochemistry [published correction appears in Vet Clin Pathol. 2018 Mar; 47(1):170. doi: 10.1111/vcp.12580]. Vet Clin Pathol. 2013;42(4):424-436. doi:10.1111/vcp.12101 Westgard JO, Westgard SA. Six Sigma Quality Management System and Design of Risk-based Statistical Quality Control. Clin Lab Med. 2017;37(1):85-96. doi:10.1016/j.cll.2016.09.008 Westgard JO, Westgard SA. Assessing quality on the Sigma scale from proficiency testing and external quality assessment surveys. Clin Chem Lab Med. 2015;53(10):1531-1535. doi:10.1515/cclm-2014-1241 Charuruks N. Sigma Metrics across the Total Testing Process. Clin Lab Med. 2017;37(1):97-117. doi:10.1016/j.cll.2016.09.009 van Heerden M, George JA, Khoza S. The application of sigma metrics in the laboratory to assess quality control processes in South Africa [published correction appears in Afr J Lab Med. 2023 May 15; 12(1):1996. doi: 10.4102/ajlm.v12i1.1996]. Afr J Lab Med. 2022; 11(1):1344. Published 2022 Jun 22. doi:10.4102/ajlm.v11i1.1344 Farr AJ, Freeman KP. Quality control validation, application of sigma metrics, and performance comparison between two biochemistry analyzers in a commercial veterinary laboratory. J Vet Diagn Invest. 2008; 20(5):536-544. doi:10.1177/104063870802000502 Burtis CA, Ashwood ER, Bruns DE (2007). Tietz fundamentals of clinical chemistry. 6th ed. St. Louis: Saunders Elsevier; Westgard JO, Westgard SA. The quality of laboratory testing today: an assessment of σ metrics for analytic quality using performance data from proficiency testing surveys and the CLIA criteria for acceptable performance. Am J Clin Pathol. 2006 doi: 10.1309/V50H4FRVVWX12C79. Westgard JO. A method evaluation decision chart (MEDx chart) for judging method performance. Clin Lab Sci. 1995;8(5):277-283. Mao X, Shao J, Zhang B, Wang Y. Evaluating analytical quality in clinical biochemistry laboratory using Six Sigma. Biochem Med (Zagreb). 2018; 28(2):020904. doi:10.11613/BM.2018.020904 Choudhury J.R, Banerjee S, Chakraborty I. Implementation of sigma metrics for evaluation of analytical quality in clinical biochemistry laboratory of a tertiary care hospital. Journal of Evolution of Medical and Dental Sciences 2018; 7(21):2539-2543. Aggarwal K, Patra S, Acharya V, Agrawal M, Mahapatra SK. Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement. Int J Adv Med 2019; 6(5):1524-1530. Peng S, Zhang J, Zhou W, Mao W, Han Z. Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings. J Clin Lab Anal. 2021; 35(3):e23665. doi:10.1002/jcla.23665 Kumar BV, Mohan T. Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory. J Lab Physicians. 2018;10(2):194-199. doi:10.4103/JLP.JLP_102_17 Westgard JO. Six sigma quality design & control, 2 nd ed. Madison WI: Westgard QC Inc., 2006 Westgard JO. Method validation-the replication experiment. In: Westgard JO, editor. Basic Method Validation. 3 rd ed. Madison: Westgard QC, Inc; 2008. p. 114–22. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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INTRODUCTION","content":"\u003cp\u003eVeterinary clinical laboratories are complicated and dynamic enterprises tasked with constantly enhancing testing quality and adhering to stringent criteria, including but not limited to the quality of test methods, instruments, reagents, and personnel, while attempting to reduce costs. Today, veterinary clinical laboratories handle significant workloads encompassing a broad range of parameters, all within limited capacity, while delivering consistent quality findings within specified turnaround time cost-effectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQuality control (QC) is essential in any setting where a reliable and accurate result is necessary for determining the final diagnostic results. QC validation is used to identify the statistical methodologies suitable for discerning variances crucial for clinical interpretation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The importance of QC in ensuring the precision and accuracy of laboratory results cannot be overestimated. Commonly employed QC metrics include manufacturers\u0026rsquo; mean (target value) to determine bias, and coefficient of variation to determine precision. These criteria, however, do not provide a comprehensive assessment of laboratory test performance. These criteria focus on individual aspects of performance without integrating the broader picture of test reliability. To address this limitation, the sigma metric which hold three parameters: bias, precision and total allowable error (TE\u003csub\u003ea\u003c/sub\u003e) was employed. This approach provides more holistic and reliable method of assessing the quality of laboratory tests, offering deeper insights in to the overall effectiveness and robustness of QC practices in in clinical chemistry laboratories [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSigma metrics are a useful tool for objectively assessing the quality and performance of clinical laboratory measurement methods. The metric takes into account the variability in a method\u0026rsquo;s output, which is expresses by the SD or % CV acquired from repeated measurements of QC materials. Sigma is the number of standard deviations that meet inside a certain analyte\u0026rsquo;s pre -defined analytical performance parameters. Deviation from the target value can also obtained in terms of bias. The total allowable error (TE\u003csub\u003ea\u003c/sub\u003e) is the analytical performance standard most commonly used for determining Sigma-metrics. By reducing the sources of random and systemic errors in a process, the variability decreases, resulting in a higher sigma value [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the biochemical laboratory, mistakes are defined as values differ from the true value by more than the entire permitted error. The tolerance limit and offset specified in the sigma industrial formula are the same as the total permitted error, and total analytical bias in laboratory operations respectively. Sigma metric is calculated as: [TE\u003csub\u003ea\u003c/sub\u003e (%) \u0026ndash; |bias (%)|] / CV (%) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the sigma metric scale, a sigma-metric zone of six indicates world class performance. Excellent, good, marginal poor, and unacceptable are applied for five, four, three, and two sigma regions, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The sigma metric is used to plan QC practice for daily laboratory activities. Methods achieving a sigma metric zone of six can be managed with one level of QC per day using the 1\u003csub\u003e3s\u003c/sub\u003e, with two control measurements in each run one on each level of control. The notation N\u0026thinsp;=\u0026thinsp;2 R\u0026thinsp;=\u0026thinsp;1 indicates that two control measurements are needed in a single run. For methods in five sigma zone, QC practice require three rules, 1\u003csub\u003e3s\u003c/sub\u003e/2\u003csub\u003e2s\u003c/sub\u003e/R\u003csub\u003e4s\u003c/sub\u003e, with two control measurements in each run (N\u0026thinsp;=\u0026thinsp;2, R\u0026thinsp;=\u0026thinsp;1). For methods in four sigma zone, a fourth rule is added, resulting 1\u003csub\u003e3s\u003c/sub\u003e/2\u003csub\u003e2s\u003c/sub\u003e/R\u003csub\u003e4s\u003c/sub\u003e/4\u003csub\u003e1s\u003c/sub\u003e multirule, preferably with four control measurements in each run (N\u0026thinsp;=\u0026thinsp;4, R\u0026thinsp;=\u0026thinsp;1), or alternatively, two control measurements in each of two runs (N\u0026thinsp;=\u0026thinsp;2, R\u0026thinsp;=\u0026thinsp;2), using the 4\u003csub\u003e1s\u003c/sub\u003e rule to inspect the control rules across both runs. A method with a three sigma zone must use all the Westgard Rules. A method with a sigma metric of less than three indicates poor quality/performance and root cause analysis should be performed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The cause of low sigma value for those with sigma scale of less than three can also be determined by using quality goal-index (QGI) whether the greater cause of defects is imprecision or inaccuracy or both [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough studies have been conducted in different parts of the world, no reports of evaluation of veterinary laboratory with Sigma-metric and QGI have been performed in Ethiopia. Therefore, the current study aimed to examine the laboratory\u0026rsquo;s testing quality via sigma-metrics and QGI in order to ensure the delivery of trustworthy and consistent results of biochemical testing in selected analytes.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study \u0026nbsp;Area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted at Biochemistry Laboratory of the College of Veterinary Medicine and Agriculture of Addis Ababa University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Study Design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a descriptive cross sectional study, which is ideal for capturing data at a single point in time to assess the laboratory\u0026rsquo;s testing quality via sigma-metric and QGI. This descries the characteristics of the laboratory under study and without implying causations. QC data from April to May 2023 were used to evaluate laboratory testing quality by applying sigma metric. Twenty data points were used (Ten days in each month). The QC materials of same lot number were utilized throughout the study.\u0026nbsp;Each month, QC materials were prepared, divided in to 10 aliquots, and stored at -20\u003csup\u003eo\u003c/sup\u003ec \u0026nbsp;in 1.5ml Eppendorf tubes with screw cap to ensure stability and prevent contamination. Each tube was designated for single use analysis to maintain the integrity of QC material. \u0026nbsp;The lab have standard operating procedure (SOP), it follows the procedures outlined in test reagent manuals. Additionally the laboratory benefits from experienced and well trained staff. The analytes tested include glucose, urea, triglyceride, cholesterol, albumin and total protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Laboratory Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTest principle:\u0026nbsp;\u003c/strong\u003eLaboratory determination of analytes were done according to standard protocols as glucose by glucose oxidase method, urea by kinetic urease/GLDH (Glutamate dehydrogenase); total cholesterol by CHOD-PAP (Cholesterol peroxidase4-aminophenazone); triglycerides by G-PAP (Glycero-3-phosapte oxidase-4-aminophenazone); total protein by biuret; and albumin by bromocresol green [10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstrument:\u0026nbsp;\u003c/strong\u003eThe laboratory utilizes EMP-168 semi-automated biochemical analyzer (Chengdu Empsun Medical Technology Co., Ltd., China) for purpose of teaching and diagnostic activities. The instrument is calibrated and, a two level QC is performed prior to each test. QC results are verified to ensure if they fall within the manufacturer designated values. However since the laboratory doesn\u0026rsquo;t frequently run biochemical tests, Westgard rules are not applied. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Quality Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal allowable Error (TE\u003csub\u003ea\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical performance was assessed using total allowable error (TE\u003csub\u003ea\u003c/sub\u003e) obtained from American Society of Veterinary Clinical pathology (ASVCP) guidelines for biochemistry\u0026nbsp;[4].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSigma Metric (\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSigma metric values were calculated using the formula: Sigma metric = [TE\u003csub\u003ea\u0026nbsp;\u003c/sub\u003e(%) \u0026ndash; |bias (%)|] / CV (%). Sigma metric rated as: world class (6), excellent (5), good (4), marginal (3), poor (2), and unacceptable (less than 2) [7]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Goal Index (QGI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQGI represents the extent to which both precision and bias meet their respective quality standards, where quality refers to the accepted benchmarks for precision and bias as defined in Westgard rules [11].QGI calculated as:|Bias|/(1.5 \u0026acute; CV %). QGI was used to analyse the reason for the sigma less than three, i.e., the poor performance was due to which precision, bias or both contribute to analytical quality. QGI less than 0.8 indicates imprecision, QGI between 0.8 to 1.2 indicates both imprecision and inaccuracy and QGI greater than 1.2 indicates inaccuracy [8]. [9].\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eQC results for six analytes at two QC levels (Normal and Pathological) were analysed and sigma metric and QGI was calculated for both QC levels using CV%, bias% and TE\u003csub\u003ea\u003c/sub\u003e. Accordingly, Triglyceride and Cholesterol testing hold acceptable sigma metric value of greater than three, while Glucose, Albumin, Urea and Total protein had sigma metric less than three. Higher CV% of all analytes was also noted indicating greater dispersion around the mean (Table 1 and 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1.\u0026nbsp;\u003c/strong\u003eNormal quality control laboratory mean, coefficient of variation, absolute bias%, sigma metric and quality goal index of selected analytes from April to May 2023\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.7207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.35435%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.30631%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.45946%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASVCP\u003c/strong\u003e \u0026nbsp;\u003cstrong\u003eTE\u003csub\u003ea\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e|\u003cstrong\u003eBias\u003c/strong\u003e|\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.60961%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSigma\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emetric\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026sigma;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQGI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProblem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.7207%;\"\u003e\n \u003cp\u003eGlucose (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.35435%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e103.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.30631%;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.45946%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.60961%;\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.7207%;\"\u003e\n \u003cp\u003eTriglyceride (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.35435%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e89.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.30631%;\"\u003e\n \u003cp\u003e88.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.45946%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.60961%;\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.7207%;\"\u003e\n \u003cp\u003eCholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.35435%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e159.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.30631%;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.45946%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.60961%;\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.7207%;\"\u003e\n \u003cp\u003eAlbumin (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.35435%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e16.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.30631%;\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.45946%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.60961%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.7207%;\"\u003e\n \u003cp\u003eUrea (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.35435%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.30631%;\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.45946%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.60961%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.7207%;\"\u003e\n \u003cp\u003eTotal Protein (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.35435%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.05706%;\"\u003e\n \u003cp\u003e8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.30631%;\"\u003e\n \u003cp\u003e5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.45946%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.25826%;\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.60961%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePathological quality control mean, coefficient of variation, bias%, sigma metric and quality goal index of selected analytes from April to May 2023\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.5357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.46429%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.82143%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASVCP\u0026nbsp;TE\u003csub\u003ea\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.25%;\"\u003e\n \u003cp\u003e|\u003cstrong\u003eBias\u003c/strong\u003e|\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.92857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSigma\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emetric\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026sigma;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQGI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProblem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.5357%;\"\u003e\n \u003cp\u003eGlucose (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.46429%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e269.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.82143%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.25%;\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.92857%;\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.5357%;\"\u003e\n \u003cp\u003eTriglyceride (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.46429%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e250.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.82143%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.25%;\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.92857%;\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.5357%;\"\u003e\n \u003cp\u003eCholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.46429%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e288.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e11.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.82143%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.25%;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.92857%;\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.5357%;\"\u003e\n \u003cp\u003eAlbumin (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.46429%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.82143%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.25%;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.92857%;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.5357%;\"\u003e\n \u003cp\u003eUrea (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.46429%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e105.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.82143%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.25%;\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.92857%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.5357%;\"\u003e\n \u003cp\u003eTotal Protein (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.46429%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.82143%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.25%;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.92857%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIt has been observed that three analytes namely Albumin (g/dl), Urea (mg/dl) and Total Protein (g/dl) from normal QC scored sigma metric value of less than three. Similarly in pathological QC three analytes namely Glucose (mg/dl), Urea (mg/dl) and Total Protein (g/dl) scored sigma value of less than three. The sigma metric value less than three is indicating poor performance of the indicated anaytes. The QGI values for analytes scored in both QC levels were also less than 0.8 indicating the problem was imprecision.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe current study described the performance of biochemical testing using sigma metric and QGI Notably to the author\u0026rsquo;s knowledge, there is no study in Ethiopia has assessed the performance of biochemical testing in veterinary laboratory setting by these quality assessment tools. The study\u0026rsquo;s findings revealed that analytes namely albumin, urea and total protein from the normal QC and glucose, urea and total protein from pathological QC had a sigma value below three which is below the minimum acceptable performance. This indicates that test results in this condition would not be in any way reportable. Several reports address the values of sigma metric and QGI with inconsistent findings. A study conducted in the clinical chemistry laboratory of Shandong Provincial Hospital, China revealed both normal and pathological QC results for cholesterol and triglyceride shown sigma value of greater than three [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sigma metric value of less than three for cholesterol and triglyceride, and sigma metric values greater than three for urea and total protein for both normal and pathological QC was noted by clinical biochemistry laboratory of College of Medicine and Sagore Dutta Hospital Kolkata, West Bengal [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A study at tertiary care hospital in Bhubaneswar, India shown sigma metric value of less than three for total protein and triglycerides at normal and pathological QC. A study in teaching hospital at Bhubaneswar, India has also shown lower sigma values for cholesterol normal QC and Albumin pathological QC were noted [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The direct comparison of sigma metric values is challenging due to different instruments, reagents, QC materials, storage and handling of QC materials, maintenance and calibration requirements and schedules, water quality, environmental requirements, training and selection of TE\u003csub\u003ea\u003c/sub\u003e target.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the current study, the cause of poor performance was assessed via QGI. It was found that the cause for all anaytes testing was high imprecision. Imprecision as a cause of low poor performance was noted by different studies including but not limited to a study at Department of Clinical Laboratory, Shengzhou People's Hospital, Shengzhou Branch of the First Affiliated Hospital of Zhejiang University, Shengzhou, China for urea, glucose and total protein [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A study conducted in a Secondary Care Government Hospital, Chennai, India has also noted imprecision as a cause of poor performance for urea, total protein, and albumin [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In India Maharishi Markandeshwar Institute of Medical Sciences and Research Mullana, a study also noted imprecision as a cause of poor performance for total protein, urea, albumin, and glucose [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similar reasons indicated to sigma metric as there are disparities among labs attributed to the differences in analytical conditions including selection of the TE\u003csub\u003ea\u003c/sub\u003e target and thus direct comparison of QGI is also difficult. Thus each lab must follow robust QC, including corrective actions as outlined in Westgard protocol [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand despite of sigma metric and QGI value, SD data and TE\u003csub\u003ea\u003c/sub\u003e value for respective analytes namely, albumin, urea and total protein from the normal QC and glucose, total cholesterol, triglycerides and urea from pathological QC shown that the respective test methods needs validation as their SD exceed 0.33 \u0026times; TE\u003csub\u003ea\u003c/sub\u003e respectively. Thus the laboratory has to validate the indicated test methods before it routinely used as the quality of the test in such cases cannot be assured [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall the study highlights imprecision as a significant contributor to poor performance, which negatively impacts sigma metric values. Factors contributing to this imprecision might include the attention of different thing including but not limited to correct storage of QC material, adherence to maintenance and calibration schedules, inadequate water purity requirements, uncontrolled environmental temperature and humidity. Furthermore the laboratory lack of strict adherence to Westgard rules further exacerbates troubleshooting challenges, hindering overall performance management. Besides the TE\u003csub\u003ea\u003c/sub\u003e value for respective analytes are just recommendations and might be different for calculating sigma and QGI in the study\u003c/p\u003e \u003cp\u003e \u003cb\u003eLIMITATIONS OF THE STUDY\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe study used manufacturers QC values, which is weakness to be noted, while third party QC material is considered a better assessment of performance. The study was also limited to determine six analytes due to logistic reasons.\u003c/p\u003e"},{"header":"5. CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003eSigma metrics and QGI are an important indicators for laboratory testing quality. The findings highligts the need for quality improvement in the testing of analytes specifically for glucose, total protein, albumin and urea which scored the minimum acceptable perofrmance criteia of less than three sigma metrics. Imprecision was identified as the primary cause of poor performance, such findings indicate the presence of systemic errors in the laboratory testing procedure. To address these issues and enhance sigma metric values the laboratory must implement robust quality management practices and adopt QC rules for the acceptance and rejection of analytical run as outlined in Westgard protocol. Furthermore, conducting extensive study over a long period is recommended to asses and monitor the peformance of the laboratory.incluidng method validation before the indicated test methods routinely used and results released. Besides TE\u003csub\u003ea\u003c/sub\u003e value for respective analytes should considered.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLM: Laboratory Mean, MM: Manufacturer mean, CV: Coefficient of Variation, QC: Quality control, SD: Standard Deviation, TE\u003csub\u003ea \u0026nbsp;\u003c/sub\u003e: Total allowable error, ASVCP: American society of veterinary clinical pathology, QGI: Quality Goal Index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Addis Ababa University for granting the research \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent the publications and permission to present the findings of this research to public was verbally obtained from farmers. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; validation; visualization; writing \u0026ndash; original draft; writing \u0026ndash; review and editing: Anwar Aminu Umer. Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; validation; visualization; writing \u0026ndash; original draft; writing \u0026ndash; review and editing: Yoseph Cherinet Megerssa \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Addis Ababa University office of vice president for research and technology transfer through Adaptive research entitled \u0026ldquo;Implementation of Sigma Metrics for evaluation of analytical quality in diagnostic and research for commonly assayed clinical chemistry tests\u003ccite\u003e\u0026rdquo;\u0026nbsp;\u003c/cite\u003eGrant No RD/PY-223/2022.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support this study are available from the corresponding author on request\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable; however, the study protocol obtained research ethical clearance from institutional animal research ethics committee of Addis Ababa University College of Veterinary Medicine and Agriculture (Certificate reference no VM/ERC/38/02/15/2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eAddis Ababa University, College of Veterinary Medicine and Agriculture, Bishoftu, Ethiopia\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eInal TC, Goruroglu Ozturk O, Kibar F, Cetiner S, Matyar S, Daglioglu G, Yaman A. Lean six sigma methodologies improve clinical laboratory efficiency and reduce turnaround times. J Clin Lab Anal. 2018; 32(1):e22180. doi:10.1002/jcla.22180\u003c/li\u003e\n \u003cli\u003eWestgard JO, and Barry PL. Beyond quality assurance: Committing to quality improvement. Laboratory Medicine 1989; 20:241-7.\u003c/li\u003e\n \u003cli\u003eVan Heerden M, George JA, Khoza S. Corrigendum: The application of sigma metrics in the laboratory to assess quality control processes in South Africa. Afr J Lab Med. 2023; 12(1), a1996. https://doi.org/10.4102/ajlm.v12i1.1996\u003c/li\u003e\n \u003cli\u003eHarr KE, Flatland B, Nabity M, Freeman KP; ASVCP. ASVCP guidelines: allowable total error guidelines for biochemistry [published correction appears in Vet Clin Pathol. 2018 Mar; 47(1):170. doi: 10.1111/vcp.12580].\u0026nbsp;Vet Clin Pathol. 2013;42(4):424-436. doi:10.1111/vcp.12101\u003c/li\u003e\n \u003cli\u003eWestgard JO, Westgard SA. Six Sigma Quality Management System and Design of Risk-based Statistical Quality Control.\u0026nbsp;Clin Lab Med. 2017;37(1):85-96. doi:10.1016/j.cll.2016.09.008\u003c/li\u003e\n \u003cli\u003eWestgard JO, Westgard SA. Assessing quality on the Sigma scale from proficiency testing and external quality assessment surveys.\u0026nbsp;Clin Chem Lab Med. 2015;53(10):1531-1535. doi:10.1515/cclm-2014-1241\u003c/li\u003e\n \u003cli\u003eCharuruks N. Sigma Metrics across the Total Testing Process.\u0026nbsp;Clin Lab Med. 2017;37(1):97-117. doi:10.1016/j.cll.2016.09.009\u003c/li\u003e\n \u003cli\u003evan Heerden M, George JA, Khoza S. The application of sigma metrics in the laboratory to assess quality control processes in South Africa [published correction appears in Afr J Lab Med. 2023 May 15; 12(1):1996. doi: 10.4102/ajlm.v12i1.1996].\u0026nbsp;Afr J Lab Med. 2022; 11(1):1344. Published 2022 Jun 22. doi:10.4102/ajlm.v11i1.1344\u003c/li\u003e\n \u003cli\u003eFarr AJ, Freeman KP. Quality control validation, application of sigma metrics, and performance comparison between two biochemistry analyzers in a commercial veterinary laboratory.\u0026nbsp;J Vet Diagn Invest. 2008; 20(5):536-544. doi:10.1177/104063870802000502\u003c/li\u003e\n \u003cli\u003eBurtis CA, Ashwood ER, Bruns DE (2007). Tietz fundamentals of clinical chemistry. 6th ed. St. Louis: Saunders Elsevier;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWestgard JO, Westgard SA. The quality of laboratory testing today: an assessment of \u0026sigma; metrics for analytic quality using performance data from proficiency testing surveys and the CLIA criteria for acceptable performance. Am J Clin Pathol. 2006 doi: 10.1309/V50H4FRVVWX12C79.\u003c/li\u003e\n \u003cli\u003eWestgard JO. A method evaluation decision chart (MEDx chart) for judging method performance.\u0026nbsp;Clin Lab Sci. 1995;8(5):277-283.\u003c/li\u003e\n \u003cli\u003eMao X, Shao J, Zhang B, Wang Y. Evaluating analytical quality in clinical biochemistry laboratory using Six Sigma.\u0026nbsp;Biochem Med (Zagreb). 2018; 28(2):020904. doi:10.11613/BM.2018.020904\u003c/li\u003e\n \u003cli\u003eChoudhury J.R, Banerjee S, Chakraborty I. Implementation of sigma metrics for evaluation of analytical quality in clinical biochemistry laboratory of a tertiary care hospital.\u0026nbsp;Journal of Evolution of Medical and Dental Sciences 2018;\u0026nbsp;7(21):2539-2543.\u003c/li\u003e\n \u003cli\u003eAggarwal K, Patra S, Acharya V, Agrawal M, Mahapatra SK. Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement.\u0026nbsp;Int J Adv Med 2019;\u0026nbsp;6(5):1524-1530.\u003c/li\u003e\n \u003cli\u003ePeng S, Zhang J, Zhou W, Mao W, Han Z. Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings.\u0026nbsp;J Clin Lab Anal. 2021; 35(3):e23665. doi:10.1002/jcla.23665\u003c/li\u003e\n \u003cli\u003eKumar BV, Mohan T. Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory.\u0026nbsp;J Lab Physicians. 2018;10(2):194-199. doi:10.4103/JLP.JLP_102_17\u003c/li\u003e\n \u003cli\u003eWestgard JO. Six sigma quality design \u0026amp; control, 2\u003csup\u003end\u003c/sup\u003e ed. Madison WI: Westgard QC Inc., 2006\u003c/li\u003e\n \u003cli\u003eWestgard JO. Method validation-the replication experiment. In: Westgard JO, editor. Basic Method Validation. 3\u003csup\u003erd\u003c/sup\u003e ed. Madison: Westgard QC, Inc; 2008. p. 114\u0026ndash;22.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biochemical, Imprecision, Quality goal index, Sigma metric","lastPublishedDoi":"10.21203/rs.3.rs-6206250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6206250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Sigma metric and quality goal index are tools for evaluating the performance of clinical laboratory measurements. In this regard, assessing the quality of biochemical testing in Ethiopia is limited, and researchers have not conducted any studies in the veterinary laboratory setting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives: \u0026nbsp;\u003c/strong\u003eThe objective of this study was to examine the testing quality for six analytes, namely glucose, urea, triglycerides, total cholesterol, albumin, and total protein via sigma metric and quality goal index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eData from two-level quality control (normal and pathological) materials were used to calculate sigma metrics and the quality goal index. The results revealed that three analytes glucose, cholesterol, and triglyceride from normal quality control and three analytes triglyceride, total cholesterol, and albumin from pathological quality control had below the minimum acceptable performance of three sigma metric, indicating poor performances. The quality goal index indicated that the poor performances were attributed to imprecision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e To address this, the laboratory must strictly adhere to quality management practices, including implementing a robust quality control strategy to identify and resolve the root cause of imprecision. TE\u003csub\u003ea \u003c/sub\u003evalue for respective analytes should be revisited as it might be another factor varying sigma and QGI.\u003c/p\u003e","manuscriptTitle":"Evaluation of the Quality of Some Serum Biochemical Testing Using Sigma Metrics and Quality Goal Index in Veterinary Laboratory Setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-13 05:11:40","doi":"10.21203/rs.3.rs-6206250/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c71d6f4d-f3a2-4a0e-bb24-c82f5c2b8738","owner":[],"postedDate":"March 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T04:25:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-13 05:11:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6206250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6206250","identity":"rs-6206250","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}