Analysis of biochemical analytes using six sigma metrics with two analyzers at an Indian lab setting

Beyond the basics: Sigma scores in laboratory medicine with variable total allowable errors (TEa)

BACKGROUND

To diagnose diseases, track the effectiveness of treatments and make well-informed clinical decisions, doctors rely on results from laboratories. Accurate and precise results minimize the necessity for additional testing, saving time and money while enhancing patient satisfaction.. Internal quality control and an external quality assurance scheme(EQAS) are metrics used to evaluate a clinical laboratory's performance. One of the numerous quality indicators that can be used to gauge the amount of errors is sigma metrics. To calculate the sigma scores bias%, CV%, and Total Error Allowable (TEa) are needed. Total Error allowable(TEa) is a crucial benchmark that establishes allowed limits on the degree of deviation from the target value for a certain analyte. Nevertheless, a proper consensus for establishing a TEa goal has not been reached and the impact of this limiting factor in standard laboratory practice and sigma calculation has not been sufficiently established. Choosing the right Total Error allowable(TEa) goal is one of the greatest challenges when employing sigma metrics as depending on the source, several measurands of TEa values may exhibit alteration.

MATERIAL AND METHODS

Our study aims to determine the sigma scores of 20 routine chemistry parameters using six different TEa sources: Clinical Laboratory Improvement Amendment (CLIA 88'), CLIA(Clinical Laboratory Improvement Amendment) 24, BDV (Biological Variation Desirable), RCPA(Royal College of Pathologists of Australasia), RiliBak(Guideline of the German Medical Association for Quality Assurance of Laboratory Medical Examinations), and EMC/Spain(Measurement and Control Scheme) over a 12-month period using the bias percent from the External Quality Assessment Scheme (EQAS) and coefficient of variation (CV) from the Internal Quality Control (IQC). Detection system was automated, multi-channel, selective analyzer, the Beckman Coulter AU680 which works on the principle of spectrophotometry. To compute the Sigma metrics, formula used was Sigma = (TEa - Bias%) / CV%. By comparing the sigma values from the different TEa sources, TEa variance on the evaluation of the sigma metric was ascertained after which an internal quality control plan and QGI(Quality Goal Index) for underperforming parameters were devised.

RESULTS

The study discovered that the sigma values of common chemical parameters varied significantly based on the TEa sources used. Maximum parameters in the above three-sigma zone were TBil, HDL, CK, ALP, amylase and uric acid in CLIA'88 while RCPA and Biological variation were determined to be the most severe, with the highest performing parameters falling below three sigma zones. Rilibaek was the most liberal, with only sodium in the lower three sigma zones along with CLIA'88. The findings indicate that there is the substantial influence of various Total Error Allowable (TEa) sources on the sigma metric evaluation. A quality control plan was devised depending on different sigma scores of the analytes using biorad unity 2.0 software(westgard sigma multirules). The origins of errors that resulted in low sigma ratings liked enhanced cleaning of electrodes, electrode replacement, ageing of reagents, instrument maintainence were pinpointed and addressed.

CONCLUSION

The study highlights the necessity of harmonizing and standardizing sigma metrics, stressing the significance of choosing suitable total error allowable goals (TEa). The creation of worldwide standards and recommendations for total error allowable (TEa) can lead to its harmonization. Establishing a consensus on the acceptable levels of error for various laboratory tests would necessitate the cooperation of specialists from many nations and organizations in order to set such guidelines and standards.

Clinical performance and application of novel serum collection "Ser-Col" device in the practice of laboratory diagnosis of infection diseases and several other immunochemical tests

BACKGROUND

Dried blood collection devices might be beneficial for diagnosing infectious diseases in areas far from the medical facilities and in lockdown situations. There are several reports on the efficacy of such applications for qualitative tests. Here we demonstrated the feasibility of a novel Ser-Col blood collection device as a standardized approach for qualitative and quantitative detection of infectious markers and several over immunochemical tests.

METHODS

In the current study, we included 395 adult participants, 191 men and 204 women, with a median age of 41 years, as well as 75 children with a median age of 3 years. Serological status was determined by testing serum samples for three groups of infection diseases: hepatitis A and C, SARS-CoV-2, and herpes family viruses, as well as for thyroid peroxidase (TPO), prolactin, vitamin B12, and folate. Blood collected on the Ser-Col device (Labonovum) was eluted using an automated system (SCAUT Ser-Col automation, Blok System Supply) and manually. Ser-Col results were compared with serum sampled via standard venipuncture considered as the reference.

RESULTS

High correlation coefficients (r = 0.95-0.99) were observed between serum samples collected with Ser-Col and via standard venipuncture for the following tests: anti-HCV, anti-SARS-CoV-2 IgG, anti-HSV-2 IgG, and anti-CMV IgM. Correlation coefficients between Ser-Col and standard venipuncture serum for anti-HSV-1 IgG, anti-CMV IgG, and anti-EBV tests were relatively low (r = 0.73-0.77). Correlation coefficients for anti-TPO, prolactin, vitamin B12, and folate were also characterized with high values (r = 0.97-0.99).

CONCLUSIONS

High accuracy and quantitative correlation were demonstrated between Ser-Col and samples collected by standard venipuncture. Hence, the Ser-Col blood collection device should be considered as a promising alternative for blood collection, storage, and transportation in both adult and pediatric populations.

Revolutionising Quality Management in the Oral Pathology Laboratory: A Deep Dive Into the Six Sigma Methodology

Six Sigma Foundations is a statistical standard that indicates an exceptionally high level of quality, along with a customer satisfaction management approach that intends to lower error rates and boost process efficiency. The Define, Measure, Analyse, Improve, and Control (DMAIC) approach is a fundamental component of Six Sigma and provides an organised framework for process improvement. In contrast to conventional techniques that are more manual-based, Six Sigma emphasises and focuses on making decisions based on facts and evidence. The key to the success of Six Sigma is its reliance on statistical methods. Advanced tools like Pareto charts, histograms, regression analysis, and fishbone diagrams are used ardently for the benefit of customers and to reduce the overall error rate. To support clinical decision-making, a clinical laboratory's primary responsibility is to generate test results that are accurate, repeatable, fast, and appropriately interpreted. Ensuring desired clinical outcomes must be the ultimate objective. To accomplish this goal, laboratories must prioritise cost-effectiveness while establishing and maintaining quality in all laboratory procedures. The concept of the Lean Six Sigma (LSS) methodology, which mainly centres on efficiency by discerning and eradicating actions or operations that do not provide any benefit to the organisation, is combined with the proposition of Six Sigma, which emphasises data-driven analyses and optimization. The integration of these powerful concepts aids in the overall improvement of the organisations adopting these techniques. This review provides a brief overview of the benefits of the LSS methodology and its implementation in the oral pathology laboratory.