Evaluation of inter-batch variability in the establishing and quality control of glucose

Moya‐Salazar, Jeel; Pio-Dávila, Liz

doi:10.1016/j.rmu.2016.03.004

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Six sigma has demonstrated efficiency in the assessment of clinical laboratory assays using software or manually with multiple institutional benefits [ 15 – 17 ]. In low-income settings laboratories, the implementation of efficient quality programs is so necessary because in Peru [ 18 ], several public and private laboratories work daily without establishing their quality limits, putting at risk medical decisions based on clinical analysis.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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Objective To ensure the health of newborns, it is necessary to perform high-quality diagnostic tests. The TORCH panel is a set of tests that identifies infectious pathogens such as Toxoplasma (Toxo) and Cytomegalovirus (CMV) that are common in low-setting populations. We performed TORCH panel quality planning using six sigma in a reference laboratory at Peru. Results This was a cross-sectional study. TORCH tests include Toxo, Rubella, CMV, and Herpes. We processed all samples by fourth-generation ELISA on the GEMINI XCR200 analyzer (Diatron, Budapest, Hungary). We obtained the imprecision from the annual data of the external quality assessment plan and we used the CLSI EP12-A3 guideline. In a total of 44,788 analyses, the average imprecision was 3.69 ± 1.47%, and CMV had lower imprecision (2.3 and 2.6% for IgM and IgG, respectively). Quality planning of the TORCH panel allowed estimating the sigma value that ranged from 4 to 10 (average 7 ± 2 sigma), where rubella had the highest values (10 for IgM and 8 for IgG) while HSV2 had the lowest values (4 for IgM and 5 for IgG). Our results suggest the optimal performance of half of the markers including Toxoplasma, Rubella, and CMV in the Peruvian population.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…A search for new and improved calibration methods may improve the precision and, subsequently, the sigma metrics. 33 When imprecision is poor relative to analytical goals, good error detection is hard to attain, regardless of the QC rules used. 30…”

Section: Discussionmentioning

confidence: 99%

The application of sigma metrics in the laboratory to assess quality control processes in South Africa

2022

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Background: Laboratories use quality control processes to monitor and evaluate analytical performance in terms of precision and bias. Sigma metrics provide an objective assessment of laboratory quality using the total allowable error as an additional parameter.Objective: This study aimed to determine the sigma metrics of analytes when using different total allowable error guidelines.Methods: A retrospective analysis was performed on 19 general chemistry analytes at Charlotte Maxeke Johannesburg Academic Hospital in South Africa between January 2017 and December 2017. Sigma metrics were calculated on two identical analysers, using internal quality control data and total allowable error guidelines from the Ricos biological variation database and three alternative sources (the Royal College of Pathologists of Australasia, the Clinical Laboratory Improvements Amendment, and the European Federation of Clinical Chemistry and Laboratory Medicine).Results: The sigma performance was similar on both analysers but varied based on the guideline used, with the Clinical Laboratory Improvements Amendment guidelines resulting in the best sigma metrics (53% of analytes on one analyser and 46% on the other had acceptable sigma metrics) and the Royal College of Pathologists of Australia guidelines being the most stringent (21% and 23%). Sodium and chloride performed poorly across all guidelines (sigma 3). There were also month-to-month variations that may result in acceptable sigma despite poor performance during certain months.Conclusion: The sigma varies greatly depending on the total allowable error, but could be a valuable tool to save time and decrease costs in high-volume laboratories. Sigma metrics calculations need to be standardised.

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“…These control charts also evaluate the performance of the test under stable conditions and warn of changes that may affect the report. Their objective is to obtain ≤ 5% probability of false rejection and ≥ 90% Ped in a single analytical run 10,17,34 .…”

Section: Discussionmentioning

confidence: 99%

“…If the institution had done quality planning, it would have established the rules and/or multi-rules necessary for the control of the analyses. Some of these infractions would have been omitted because they were considered part of the analytical run, and others that indicate systematic or random errors, which affect the results, would be corrected 10,17,20 .…”

Section: Discussionmentioning

confidence: 99%

“…In 1963, Dorsey described the use of a daily mean of "mean corpuscular hemoglobin (MCH)," this fact almost went unnoticed up until the development of the Bull algorithm for erythrocyte indexes (red blood cell [RBC]) in 1974, representing the first quality control system for hematologic counters 16 . The benefits of quality management systems in different countries and health centers are unknown 17,18 .…”

Section: Introductionmentioning

confidence: 99%

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Critical Systematic Errors in the Implementation and Follow-up of Performance in the Hematology Area: A Prospective Study

Moya‐Salazar¹,

Pio-Dávila²

2021

RMU

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Due to the low use of the quality assessment of the hematological laboratories under the critical systematic error (ΔSE), and with the imperative to explore the implications of the use of the average of standard deviations and the control charts of each hematological parameter, we developed a prospective research at the University Clinic in Lima, Peru. We analyzed eight hematological parameters under the CLSI EP-15 A2 and CLSI H26-A2 guidelines for 19 days in the Landwind LW D3600 hematology autoanalyzer. In total, 1368 assays were performed (171 runs for each hematological parameter and 57 for each control level), which generated 19 averages in each level and for each parameter where we found 90 violations of the Westgard rules. Performance characteristics were optimal for leukocyte count (white blood cell) and platelets (ΔSE = ≥ 5.8) and were deficient for all erythrocyte parameters (ΔSE = 0.17-3.27). We identify significant differences between the determined automatic mean (by the autoanalyzer) and the calculated mean, having in many cases marked the difference. The quality evaluation of the ΔSE analysis showed a heterogeneous performance and intra-parameter control levels, as well as violations of the Westgard rules and mismatches in the determination of means (calculated and automatic).

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Evaluation of inter-batch variability in the establishing and quality control of glucose

Cited by 4 publications

References 9 publications

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

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

The application of sigma metrics in the laboratory to assess quality control processes in South Africa

Critical Systematic Errors in the Implementation and Follow-up of Performance in the Hematology Area: A Prospective Study

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