An assessment of analytical performance using the six sigma scale in second-trimester maternal prenatal screening practices in China

Objectives

We aimed to evaluate the analytical performance of second-trimester maternal serum screening in China, and to compare if there are differences in sigma levels across different methods and months.

Methods

A retrospective study was conducted to assess the analytical quality levels of laboratories by calculating the Sigma metrics with prenatal screening biomarkers: AFP, Total β-hCG, free β-hCG, uE3. Data from 591 laboratories were selected. Sigma metrics were computed using the formula: Sigma metrics(σ) = (%TEa - |%Bias|)/%CV. The Friedman test and Mann-Whitney test were used to compare differences across various methods and different months. The Hodges-Lehmann was used for determining 95 % confidence intervals of pseudo-medians.

Results

Only uE3 showed significant monthly variations in sigma calculations. However, around 8 % of laboratories across all four analytes demonstrated sigma levels both above 6 and below 3 in different months. Laboratories utilizing time-resolved fluorescence methods significantly outperformed those using chemiluminescence in sigma level. For AFP, the pseudo-median difference between these methods lies within a 95 % confidence interval of (-3.22, -1.93), while for uE3, it is at (-2.30, -1.40). Notably, the median sigma levels for all analytes reached the 4-sigma threshold, with free β-hCG even attaining the 6-sigma level.

Conclusion

With current standards, China's second-trimester maternal serum screening is of relatively high analytical quality, and variations in sigma levels exist across different months and methods.

Establishing metrological traceability for small molecule measurands in laboratory medicine

For molecules that can be well described metrologically in the sense of the definition of measurands, and which can also be recorded analytically as individual substances, reference measurement service traceability to a metrologically sound foundation is a necessity. The establishment of traceability chains must be initiated by National Metrology Institutes (NMIs) according to applicable standards; they are at the top and leading position in this concept. If NMIs are not in the position to take up this task, alternative approaches must be sought. Traceability initiatives established by in vitro device industry or academia must meet the quality standards of NMIs. Adherence to International Organization for Standardization (ISO) procedure 15193 must be a matter of course for the establishment of reference measurement procedures (RMPs). Certified reference material (CRM) characterization must be thorough, e.g., by the application of quantitative nuclear magnetic resonance measurements and by adherence to ISO 15194. Both for RMPs and CRMs Joint Committee for Traceability in Laboratory Medicine (JCTLM) listing must be the ultimate goal. Results must be shared in a transparent manner to allow other stakeholders including NMIs to reproduce and disseminate the reference measurement procedures.

A comparative analysis of Sigma metrics using conventional and alternative formulas

BACKGROUND AND AIM

The Six Sigma approach, employing Sigma Metrics (SM), is commonly used to evaluate analytical performance in clinical laboratories. However, there is ongoing debate regarding the suitability of the conventional SM formula, which incorporates total allowable error (TEa) and bias. To address this, an alternative formula based on within-subject biological variation (CVI) as the tolerance range (TR) has been proposed. The study aimed to calculate and compare SM values using both formulas.

MATERIAL AND METHODS

Twenty clinical chemistry parameters were evaluated, and SM values were calculated using conventional formula with two TEa goals and the alternative formula. Intermediate precision (CVA%) values were obtained from internal quality control data, while bias values were derived from external quality assessment reports.

RESULTS

The results showed that using the conventional formula, 11 SM values based on CLIA TEa goals and 21 SM values based on BV TEa goals were deemed unacceptable (SM < 3). Additionally, 22 SM values calculated using the alternative formula were below 3.

CONCLUSION

The choice of TR had a substantial impact on the assessed analytical performance. Laboratories should carefully consider the appropriateness of each approach based on their specific quality objectives, analyte characteristics, and laboratory operations.

Comparison of Sigma metrics computed by three bias estimation approaches for 33 chemistry and 26 immunoassay analytes

Objectives

Sigma metric can be calculated using a simple equation. However, there are multiple sources for the elements in the equation that may produce different Sigma values. This study aimed to investigate the importance of different bias estimation approaches for Sigma metric calculation.

Methods

Sigma metrics were computed for 33 chemistry and 26 immunoassay analytes on the Roche Cobas 6000 analyzer. Bias was estimated by three approaches: (1) averaging the monthly bias values obtained from the external quality assurance (EQA) studies; (2) calculating the bias values from the regression equation derived from the EQA data; and (3) averaging the monthly bias values from the internal quality control (IQC) events. Sigma metrics were separately calculated for the two levels of the IQC samples using three bias estimation approaches. The resulting Sigma values were classified into five categories considering Westgard Sigma Rules as ≥6, <6 and ≥5, <5 and ≥4, <4 and ≥3, and <3.

Results

When classifying Sigma metrics estimated by three bias estimation approaches for each assay, 16 chemistry assays at the IQC level 1 and 2 were observed to fall into different Sigma categories under at least one bias estimation approach. Similarly, for 12 immunoassays at the IQC level 1 and 2, Sigma category was different depending on bias estimation approach.

Conclusions

Sigma metrics may differ depending on bias estimation approaches. This should be considered when using Six Sigma for assessing analytical performance or scheduling the IQC events.

Comparación de la métrica Sigma calculada con tres métodos de estimación del sesgo en 33 magnitudes químicas y 26 de inmunoensayo

Objetivos

Aunque la métrica Sigma se puede calcular mediante una sencilla ecuación, la diversidad de fuentes de las que se extraen los elementos de la ecuación pueden arrojar diferentes valores Sigma. El objetivo de este estudio era investigar la importancia de las distintas estrategias de estimación del sesgo para el cálculo de la métrica Sigma.

Métodos

Se calculó la métrica Sigma de 33 magnitudes químicas y 26 magnitudes de inmunoensayo en un analizador Roche Cobas 6,000. El sesgo se calculó mediante tres métodos: a) calculando la media del sesgo mensual obtenida en los estudios de control de calidad externo (EQA, por sus siglas en inglés); 2) calculando los valores de sesgo mediante una ecuación de regresión a partir de datos obtenidos del EQA; y 3) calculando la media de los valores de sesgo mensual de los eventos de control de calidad internos (IQC, por sus siglas en inglés). Se realizó una métrica Sigma para cada uno de los dos niveles de muestras de IQC empleando tres métodos para calcular el sesgo. Los valores Sigma obtenidos se clasificaron en cinco categorías, en función de las reglas Sigma de Westgard, siendo ≥6, <6 y ≥5, <5 y ≥4, <4 y ≥3, y <3.

Resultados

Al clasificar la métrica Sigma, calculada aplicando tres métodos de estimación del sesgo para cada magnitud, se observó que 16 magnitudes químicas en los niveles 1 y 2 de IQC fueron clasificadas en categorías Sigma diferentes por al menos uno de los métodos de estimación de la desviación. Del mismo modo, dependiendo del método de estimación del sesgo empleado, se clasificaba en diferentes categorías a 12 magnitudes de inmunoensayo con niveles 1 y 2 de IQC.

Conclusiones

La métrica Sigma puede variar dependiendo del método empleado para calcular el sesgo, lo cual debe ser tenido en cuenta a la hora de evaluar el rendimiento analítico o programar eventos de IQC aplicando el método Seis Sigma.

Verification of examination procedures for 72 biochemical parameters on the atellica® clinical chemistry and immunoassay analyzers

The verification of examination procedures is a responsibility for clinical laboratories in order to guarantee that their performance characteristics comply with the specifications obtained during the validation process and are congruent with the intended scope of the assay. The aim was to perform an evaluation of precision, bias, linearity, linear drift, sample carry-over, and comparability of 73 assays from Siemens Healthineers, by following the CLSI EP10-A3 guidelines. The verification was performed by measuring 72 biochemical parameters in quality control (QC) materials from Bio-Rad (except for IL6) with 73 assays installed on eight measuring systems (five Atellica^® CH 930 and three IM 1600 analyzers from Siemens Healthcare Diagnostics). The following information was collected: validation data from manufacturer, biological variation data from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) database, and specifications for fβhCG and PAPP-A assays to meet the Fetal Medicine Foundation standards. A total of 17550 results were obtained during EP10 verification process. Out of the 73 methods, only Cl-S, Mg-S, and Na-S failed the criteria for adequate precision, trueness, and comparability. The assays did not show significant loss of linearity, linear drift, or sample carry-over. This study allowed the initial training and familiarization with the instruments and the identification of operational issues. It also represented an opportunity to evaluate the QCs and to obtain analytical performance information for application of sigma six metrics for quality assurance. Professionals are advised to adequately standardize and protocolize their verification processes to ensure laboratory competence and patient safety.

Associations of urinary and dietary cadmium with urinary 8-oxo-7,8-dihydro-2'-deoxyguanosine and blood biochemical parameters

Cadmium is a heavy metal with established adverse effects on human health, namely on bone, liver and kidney function and the cardiovascular system. We assessed cadmium exposure and its correlation with biomarkers of toxicity. We recruited 137 non-smoking blood donors without a history of chronic disease or cancer who resided in the Northern Italy province of Reggio Emilia (mean age 47 years, range 30-60 years) in the 2017-2019 period. We used a semi-quantitative food frequency questionnaire to estimate dietary cadmium intake and urine samples to assess concentrations of urinary cadmium and 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG). Median urinary cadmium and 8-oxodG concentrations were 0.21 μg/L (interquartile range (IQR): 0.11-0.34 μg/L) and 3.21 μg/g creatinine (IQR: 2.21-4.80 μg/g creatinine), respectively, while median dietary cadmium intake was 6.16 μg/day (IQR: 5.22-7.93 μg/day). We used multivariable linear and spline regression models to estimate mean differences exposure concentrations. Dietary and urinary cadmium were positively correlated, and both were positively and linearly correlated with 8-oxodG. We found a positive association of urinary cadmium with blood alanine aminotransferase (ALT), total cholesterol, low-density lipoprotein (LDL)-cholesterol and thyroid-stimulating hormone (TSH) concentrations. We also observed a positive association with triglycerides, in both linear (beta regression coefficient = 77.03, 95% confidence interval 32.27-121.78) and non-linear spline regression analyses. Despite the positive correlation between dietary and urinary cadmium estimates, dietary cadmium intake showed inconsistent results with the study endpoints and generally weaker associations, suggesting a decreased capacity to reflect actual cadmium exposure. Overall, these findings suggest that even low levels of cadmium exposure may adversely alter hematological and biochemical variables and induce oxidative stress.

Sigma Metrics in Laboratory Medicine: A Call for Harmonization

BACKGROUND AND AIM

Sigma metrics are applied in clinical laboratories to assess the quality of analytical processes. A parameter associated to a Sigma >6 is considered "world class" whereas a Sigma <3 is "poor" or "unacceptable". The aim of this retrospective study was to quantify the impact of different approaches for Sigma metrics calculation.

MATERIAL AND METHODS

Two IQC levels of 20 different parameters were evaluated for a 12-month period. Sigma metrics were calculated using the formula: (allowable total error (TEa) (%) - bias (%))/(coefficient of variation (CV) (%)). Method precision was calculated monthly or annually. The bias was obtained from peer comparison program (PCP) or external quality assessment program (EQAP), and 9 different TEa sources were included.

RESULTS

There was a substantial monthly variation of Sigma metrics for all combinations, with a median variation of 32% (IQR, 25.6-41.3%). Variation across multiple analyzers and IQC levels were also observed. Furthermore, TEa source had the highest impact on Sigma calculation with proportions of Sigma >6 ranging from 17.5% to 84.4%. The nature of bias was less decisive.

CONCLUSION

In absence of a clear consensus, we recommend that laboratories calculate Sigma metrics on a sufficiently long period of time (>6 months) and carefully evaluate the choice of TEa source.

Atellica CH 930 chemistry analyzer versus Cobas 6000 c501 and Architect ci4100 - a multi-analyte method comparison

Large clinical laboratories often rely on multiple chemistry analyzers. However, when a new analyzer is introduced, the laboratory must establish whether the old and new methods are comparable and can be used interchangeably. In this study, we compared the newly introduced Atellica CH930 chemistry analyzer with the already established Architect ci4100 and Cobas 6000 c501 from our laboratory.

Patient samples were randomly selected from daily routine testing and a total of 22 analytes were investigated. Total error (TEobs) between test (Atellica) and comparative (Architect and Cobas) methods was calculated at relevant medical decision levels (MDL). For demonstrative purposes, the assessment of method comparability was based on three different criteria: allowable total error (TEa) derived from biological variation (BV), CLIA proficiency testing criteria for acceptable analytical performance, and CLIA-calculated Sigma metrics. These sets of analytical performance specifications were also compared, and their strengths and limitations are discussed in this paper.

Performance of Atellica CH930 against Architect ci4100 was acceptable or nearly acceptable at 82%, 95%, and 64% of the 22 investigated MDLs across 9 analytes, according to BV-TEa, CLIA-TEa, and CLIA-calculated Sigma metrics, respectively. Similarly, performance of Atellica CH930 against Cobas 6000 c501 was acceptable or nearly acceptable at 61%, 93%, and 63% of the 54 investigated MDLs across 22 analytes, according to BV-TEa, CLIATEa, and CLIA-calculated Sigma metrics, respectively. However, method comparability should not be evaluated by a “one size fits all” approach as some analytes require different criteria of acceptability, ideally based on medically allowable error and clinical outcome.

Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk-based statistical quality control strategy for these assays: A multicenter study

Background

The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed.

Methods

Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI).

Results

Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation.

Conclusions

In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation.

Is lifelong endurance training associated with maintaining levels of testosterone, interleukin-10, and body fat in middle-aged males?

Background:

Aging is associated with a gradual physiological decline, including an imbalance in hormone profile, increased adiposity, and reduced anti-inflammatory cytokines. However, lifelong physical exercise mitigates aging, as observed in endurance-trained middle-aged athletes (EMA).

Aim:

We compared and associated testosterone, interleukin 10 (IL-10), and body fat in EMA and untrained age-matched individuals (UAM).

Methods:

Participants were EMA (n=25; 51.48±9.49 years) and UAM (n=23; 46.0±9.37 years). Both groups underwent body composition measurements (evaluated by a skinfold protocol) and blood sampling for IL-10 (assessed through ELISA^® kit) and testosterone (assessed with Roche Diagnostics^® kit, Mannheim, Germany, by chemiluminescence technique in a third-party laboratory).

Results:

EMA had lower body fat (14.15±3.82% vs. 23.42±4.95%; P<0.05), higher testosterone (751.68±191.45 ng/dL vs. 493.04±175.15 ng/dL; P<0.05), and higher IL-10 (8.00±1.21 pg/mL vs. 5.89±1.16 pg/mL; P<0.05) compared to UAM. A significant linear correlation was found between testosterone and IL-10 (r=0.56; P=0.001), whereas significant inverse correlations were observed between body fat and testosterone (r=–0.52; P=0.001) and body fat and IL-10 (r=–0.69; P=0.001).

Conclusions:

EMA had higher levels of IL-10 and testosterone and lower body fat in comparison with UAM. In addition, higher IL-10 was associated with increased levels of circulating testosterone and lower body fat.

Relevance for Patients:

The adoption of endurance training as part of a healthy lifestyle may contribute to decreasing age-related testosterone reduction, besides reducing markers of inflammaging, preventing the occurrence of chronic age-related diseases, and thus contributing to healthy aging. For people who already have chronic diseases, physical exercise can shift the immune system toward a more anti-inflammatory profile and, thus, improve their pathological condition. In both cases, physical exercise can help attenuate the decline in testosterone, decrease body fat, and increase anti-inflammatory levels.

Relationship between inflammatory biomarkers and testosterone levels in male master athletes and non-athletes

BACKGROUND

Aging is often associated with low-grade systemic inflammation and reduced anabolic hormone levels. To investigate whether lifelong exercise training can decrease the age-related low-grade inflammation and anabolic hormone levels, we examined hormonal and inflammatory parameters among highly-trained male masters athletes and age-matched non-athletes.

METHODS

From 70 elite power and endurance master athletes - EMA (51.3 ± 8.0 yr), 32 young controls - YC (23.7 ± 3.9 yr) and 24 untrained age-matched controls - MAC (47.2 ± 8.0 yr) venous blood was drawn to measure inflammatory parameters (interleukin-6 [IL-6], tumor necrosis factor-α [TNF-α] and interleukin-10 [IL-10]) and circulating hormones (luteinizing hormone [LH], total testosterone, estradiol, sex hormone-binding globulin [SHBG] and free androgen index [FAI]).

RESULTS

EMA showed a better anti-inflammatory status than MAC (higher IL-10 and IL-10/IL-6 ratio and lower IL-6), but a lower anti-inflammatory status than YC (higher TNF-α) (p < 0.05). The MAC group had lower testosterone levels compared to the YC and EMA group (p < 0.05), and lower estradiol levels and testosterone/LH ratio compared to YC (p < 0.05). In the control groups (MAC and YC), testosterone correlated negatively with age and proinflammatory parameters, and positively with anti-inflammatory parameters.

CONCLUSION

Elite master athletics elevated levels of anti-inflammatory cytokines above that seen in non-athlete peers and mitigated the age-related reduction in testosterone levels.

Application of a six sigma model to the evaluation of the analytical performance of serum enzyme assays and the design of a quality control strategy for these assays: A multicentre study

BACKGROUND

Six medical testing laboratories at six different sites in China participated in this study. We applied a six sigma model for (a) the evaluation of the analytical performance of serum enzyme assays at each of the laboratories, (b) the design of individualized quality control programs and (c) the development of improvement measures for each of the assays, as appropriate.

METHODS

Internal quality control (IQC) and external quality assessment (EQA) data for selected serum enzyme assays were collected from each of the laboratories. Sigma values for these assays were calculated using coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts were generated using these parameters. IQC design and improvement measures were defined using the Westgard sigma rules. The quality goal index (QGI) was used to assist with identification of deficiencies (bias problems, precision problems, or their combination) affecting the analytical performance of assays with sigma values <6.

RESULTS

Sigma values for the selected serum enzyme assays were significantly different at different levels of enzyme activity. Differences in assay quality in different laboratories were also seen, despite the use of identical testing instruments and reagents. Based on the six sigma data, individualized quality control programs were outlined for each assay with sigma <6 at each laboratory.

CONCLUSIONS

In multi-location laboratory systems, a six sigma model can evaluate the quality of the assays being performed, allowing management to design individualized IQC programs and strategies for continuous improvement as appropriate for each laboratory. This will improve patient care, especially for patients transferred between sites within multi-hospital systems.