ArticleCureus2024
Achieving High Standards in Clinical Biochemistry: Integrating Six Sigma, Quality Goal Index (QGI), and Operating Specifications (OPSpecs) for Targeted Quality Enhancement.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Longitudinal Sigma-Metric Profiling of Two Clinical Chemistry Platforms Using Peer-Group Internal Quality Control.Diagnostics (Basel, Switzerland) · 2026Article
- The Application of Six Sigma to Assess the Analytical Performance of Plasma Proteins and Design a Risk-Based Statistical Quality Control Strategy: A Multicenter Study.Journal of clinical laboratory analysis · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Sigma metrics, a cornerstone of quality control (QC) in manufacturing, have been increasingly adopted in analytical processes. In clinical biochemistry labs, Sigma analysis provides insights into the level of QC achieved and identifies deviations from perfection. Methods A prospective-retrospective observational study was conducted at the Central Diagnostic Laboratory of Shree Krishna Hospital between August 2021 and July 2022. Sigma metrics were calculated using the formula: Sigma = (TEa - Bias%) / CV%. Total allowable error (TEa) values were derived from guidelines including the Clinical Laboratory Improvement Amendments (CLIA), Royal College of Pathologists of Australasia (RCPA), and Rilibäk. Bias% was assessed using data from External Quality Assurance Services (EQAS), while the coefficient of variation (CV%) was calculated based on internal QC data. For parameters with Sigma values less than 3, Quality Goal Index (QGI) ratios were computed to assess whether errors were primarily due to precision or accuracy issues. Operating Specifications (OPSpecs) charts were generated to guide adjustments to internal QC procedures for underperforming tests. Results The analysis revealed that high-density lipoprotein (HDL) cholesterol exhibited excellent performance with a Sigma value of ≥ 6 based on newer CLIA guidelines. Parameters such as alkaline phosphatase (ALP), low-density lipoprotein (LDL) cholesterol, creatine kinase, iron, and sodium achieved Sigma values between 4 and 6, indicating satisfactory QC. However, certain parameters, including urea, creatinine, thyroid-stimulating hormone (TSH), free thyroxine (FT4), vitamin B12, and chloride, demonstrated Sigma values less than 3, signaling the need for immediate quality improvement. QGI analysis highlighted precision issues in some tests, while others showed accuracy deficits. The OPSpecs charts provided a comprehensive framework for customizing QC protocols to address specific parameter deficiencies. Conclusions This study highlights the value of Sigma metrics in refining QC protocols in clinical biochemistry laboratories. Shifting from generalized to parameter-specific QC plans allows for better resource allocation, improved efficiency, and higher test accuracy. Tools like QGI and OPSpecs charts help identify underperforming tests, supporting continuous quality improvement in diagnostics.
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Registered trials
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