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Cited by 18Clinical Laboratory Practices and Quality ControlHealthcare Quality and ManagementScientific Measurement and Uncertainty Evaluation

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Evaluation of the analytical performance of endocrine analytes using sigma metrics.

Open access
Liu, Yanming, Cao, Yue, Liu, Xijun, Wu, Liangyin, Cai, Wencan·2021·Journal of Clinical Laboratory Analysis
DOI 10.1002/jcla.23581PMC PMC7843286
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Abstract

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.

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Crossref · 10.1002/jcla.23581

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Figure 1 The research contents and roadmap of this present study
Figure 1 The research contents and roadmap of this present studyCC BY · PubMed Central
Figure 2 Normalized QC performance decision chart for 13 endocrine analytes (Levels 1 and 2) based on the first EQA plan of March 2018. (A) chart for QC material Level 1 (TEa‐NCCL). (B) chart for QC material Level 2 (TEa‐NCCL). (C) chart for QC material Level 1 (TEa‐EFLM). (D) chart for QC material Level 2 (TEa‐EFLM). The normalized performance decision diagram was drawn with CV/TEa as abscissa (imprecision) and Bias/TEa as ordinate (inaccuracy), and the chart is divided into six areas by five performance lines. Different colored circles represent different sigma grades
Figure 2 Normalized QC performance decision chart for 13 endocrine analytes (Levels 1 and 2) based on the first EQA plan of March 2018. (A) chart for QC material Level 1 (TEa‐NCCL). (B) chart for QC material Level 2 (TEa‐NCCL). (C) chart for QC material Level 1 (TEa‐EFLM). (D) chart for QC material Level 2 (TEa‐EFLM). The normalized performance decision diagram was drawn with CV/TEa as abscissa (imprecision) and Bias/TEa as ordinate (inaccuracy), and the chart is divided into six areas by five performance lines. Different colored circles represent different sigma gradesCC BY · PubMed Central
Figure 3 The effects of analytical temperature and humidity on the performance of analytes evaluated with sigma metrics (Level 1 and Level 2) by the same staff. The sigma value of analytes was calculated using the TEa‐NCCL. (A) temperature VS QC material level 1 of FT3; (B) temperature VS QC material level 2 of FT3; (C) humidity VS QC material level 1 of FT3; (D) humidity VS QC material level 2 of FT3; (E) temperature VS QC material level 1 of INS; (F) temperature VS QC material level 2 of INS; (G) humidity VS QC material level 1 of INS; (H) humidity VS QC material level 2 of INS. The temperature and humidity were automatically measured and acquired every half hour by temperature and humidity sensor per day. The mean temperature and humidity of the third hours after Roche E602 starting was represented as the temperature and humidity result today
Figure 3 The effects of analytical temperature and humidity on the performance of analytes evaluated with sigma metrics (Level 1 and Level 2) by the same staff. The sigma value of analytes was calculated using the TEa‐NCCL. (A) temperature VS QC material level 1 of FT3; (B) temperature VS QC material level 2 of FT3; (C) humidity VS QC material level 1 of FT3; (D) humidity VS QC material level 2 of FT3; (E) temperature VS QC material level 1 of INS; (F) temperature VS QC material level 2 of INS; (G) humidity VS QC material level 1 of INS; (H) humidity VS QC material level 2 of INS. The temperature and humidity were automatically measured and acquired every half hour by temperature and humidity sensor per day. The mean temperature and humidity of the third hours after Roche E602 starting was represented as the temperature and humidity result todayCC BY · PubMed Central
Figure 4 Significantly improved analytical performance of the analytes with the initial σNCCL < 4. The sigma value of analytes was calculated using the TEa‐NCCL. (A) Chart for QC material Level 1. (B) Chart for QC material Level 2. a: the initial sigma value evaluated in March 2018; b: the second sigma value re‐evaluated in September 2018
Figure 4 Significantly improved analytical performance of the analytes with the initial σNCCL < 4. The sigma value of analytes was calculated using the TEa‐NCCL. (A) Chart for QC material Level 1. (B) Chart for QC material Level 2. a: the initial sigma value evaluated in March 2018; b: the second sigma value re‐evaluated in September 2018CC BY · PubMed Central
Figure 5 The workflow for performance improvement of endocrine analytes based on sigma metrics. The sigma value of analytes was calculated using the TEa‐NCCL. R, Run size of patient samples between QC events, R500 represents a run size of 500 patient samples between QC events; R200‐500 represents the run size interval from 200 to 500 depending on the sigma value of analytes, and a similar definition applied to R10‐500. When the two levels of QC procedures of the same analyte were different, the more strict QC procedure (more rules, larger N, and smaller R) was selected for analyte's performance improvement
Figure 5 The workflow for performance improvement of endocrine analytes based on sigma metrics. The sigma value of analytes was calculated using the TEa‐NCCL. R, Run size of patient samples between QC events, R500 represents a run size of 500 patient samples between QC events; R200‐500 represents the run size interval from 200 to 500 depending on the sigma value of analytes, and a similar definition applied to R10‐500. When the two levels of QC procedures of the same analyte were different, the more strict QC procedure (more rules, larger N, and smaller R) was selected for analyte's performance improvementCC BY · PubMed Central

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Metadata

Authors
Liu, Y., Cao, Y., Liu, X., Wu, L., & Cai, W.
Affiliations
  • Department of Laboratory Medicine YueBei People’s Hospital Shaoguan China
  • Shaoguan University (CN)
  • Yue Bei People's Hospital (CN)
ORCID
Venue
Journal of Clinical Laboratory Analysis
Publisher
Wiley
Published
2020-09-20
Volume
35
Issue
1
Pages
e23581-e23581
ISSN
  • 0887-8013 (ISSN-L)
  • 1098-2825
DOI
10.1002/jcla.23581
PMC
PMC7843286
OpenAlex
W3087185205
Cited by
18
References
31
Citations by year
2026: 3 · 2025: 2 · 2024: 5 · 2023: 1 · 2022: 2 · 2021: 5
FWCI
1.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Clinical Laboratory Analysis (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Quality and Management
  • Physical Sciences › Physics and Astronomy › Instrumentation › Scientific Measurement and Uncertainty Evaluation
Keywords
sigma metrics, total allowable error, quality control rules, quality goal index, root cause analysis, corrective actions, analytical performance evaluation, thyroid function tests, sex hormones, prolactin
MeSH terms
Adult, Diagnostic Techniques, Endocrine, Female, Humans, Male, Metabolic Diseases, Quality Control, Biomarkers, Total Quality Management, Clinical Chemistry Tests, Young Adult

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