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.

Application of Sigma metrics in the quality control strategies of immunology and protein analytes

Background: Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma.
Methods: Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C-reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values.
Results: While using the TEaNCCL , 90% analytes had a world-class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σNCCL that is calculated from TEaNCCL , Sigma Method Decision Charts were constructed. For Cys C, five multi-rules (13s /22s /R4s /41s /6X , N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (13s , N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12).
Conclusions: The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool.

Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation

Background: Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systematic error in skewed distributions. Our study combines delta check and MA to develop an average of deltas (AoD) strategy that monitors the mean delta of consecutive, intrapatient results.
Methods: Arrays of the differences (delta) between paired patient results collected within 20-28 h of each other were generated from historical data. AoD protocols were developed using a simulated annealing algorithm in MatLab (Mathworks) to select the number of patient delta values to average and truncation limits to eliminate large deltas. We simulated systematic error by adding bias to arrays for plasma albumin, alanine aminotransferase, alkaline phosphatase, amylase, aspartate aminotransferase, bicarbonate, bilirubin (total and direct), calcium, chloride, creatinine, lipase, sodium, phosphorus, potassium, total protein, and magnesium. The average number of deltas to detection (ANDED) was then calculated in response to induced systematic error.
Results: ANDED varied by combination of assay and AoD protocol. Errors in albumin, lipase, and total protein were detected with a mean of 6 delta pairs. The highest ANDED was calcium, with a positive 0.6-mg/dL shift detected with an ANDED of 75. However, a negative 0.6-mg/dL calcium shift was detected with an ANDED of 25.
Conclusions: AoD detects systematic error with relatively few paired patient samples and is a patient-based QC technique that will enhance error detection.

A Multi-Pump Magnetohydrodynamics Lab-On-A-Chip Device for Automated Flow Control and Analyte Delivery

This article shows the development of a computer-controlled lab-on-a-chip device with three magnetohydrodynamic (MHD) pumps and a pneumatic valve. The chip was made of a stack of layers of polymethylmethacrylate (PMMA), cut using a laser engraver and thermally bonded. The MHD pumps were built using permanent magnets (neodymium) and platinum electrodes, all of them controlled by an Arduino board and a set of relays. The implemented pumps were able to drive solutions in the open channels with a flow rate that increased proportionally with the channel width and applied voltage. To address the characteristic low pressures generated by this kind of pump, all channels were interconnected.
Because the electrodes were immersed in the electrolyte, causing electrolysis and pH variations, the composition and ionic strength of the electrolyte solution were controlled. Additionally, side structures for releasing bubbles were integrated. With this multi-pump and valve solution, the device was used to demonstrate the possibility of performing an injection sequence in a system that resembles a traditional flow injection analysis system. Ultimately, the results demonstrate the possibility of performing injection sequences using an array of MHD pumps that can perform fluid handling in the 0-5 µL s-1 range.

Analyte Mix

608-A Scientific Laboratory Supplies 1ML 63.87 EUR

Analyte Mix

8140-A Scientific Laboratory Supplies 1ML 157.53 EUR

Analyte Mix

8141-AB Scientific Laboratory Supplies 1ML 175.35 EUR

Analyte Mix

5491-A Scientific Laboratory Supplies 1ML 38.3 EUR

Analyte Mix A

5311-A10 Scientific Laboratory Supplies 1ML 71.07 EUR

Analyte Mix A

5081-A Scientific Laboratory Supplies 1ML 76.35 EUR

Analyte Mix B

5081-B Scientific Laboratory Supplies 1ML 117.54 EUR

Analyte Mix A

5521-A Scientific Laboratory Supplies 1ML 70.31 EUR

Analyte Mix - 1ML

5031-A Scientific Laboratory Supplies 1ML 211.95 EUR

Analyte Mix A - 1ML

507-A Scientific Laboratory Supplies 1ML 264.6 EUR

Analyte Mix B - 1ML

507-B Scientific Laboratory Supplies 1ML 264.6 EUR

PAH Analyte Mix - 1ML

550-A Scientific Laboratory Supplies 1ML 99.9 EUR

Analyte Mix (High Level)

8140-AH Scientific Laboratory Supplies 1ML 268.97 EUR

Analyte Mix (High Level) - 1ML

506-AH Scientific Laboratory Supplies 1ML 76.95 EUR

Free Acids Analyte Mix - 1ML

5522-A Scientific Laboratory Supplies 1ML 106.65 EUR

Volatile Analyte Mix A - 1ML

CLPV-A Scientific Laboratory Supplies 1ML 159.3 EUR

Analyte Mix C (High Level) - 1ML

5242-CH Scientific Laboratory Supplies 1ML 116.1 EUR

Analyte Mix D (High Level) - 1ML

5242-DH Scientific Laboratory Supplies 1ML 222.75 EUR

Volatile Analyte Mix A (High Level) - 1ML

CLPV-AH Scientific Laboratory Supplies 1ML 47.39 EUR

Free Acids Analyte Mix with Surrogate - 1ML

5522-AS Scientific Laboratory Supplies 1ML 193.05 EUR

Polynuclear Aromatic Hydrocarbons Analyte Mix - 1ML

CLPS-B Scientific Laboratory Supplies 1ML 116.1 EUR

Volatile Organics Combination Mix (Analyte Mixes A C and D)

HICAL-VOC Scientific Laboratory Supplies 1ML 120.9 EUR

Silicon Nanofluidic Membrane for Electrostatic Control of Drugs and Analytes Elution

  • Individualized long-term management of chronic pathologies remains an elusive goal despite recent progress in drug formulation and implantable devices. The lack of advanced systems for therapeutic administration that can be controlled and tailored based on patient needs precludes optimal management of pathologies, such as diabetes, hypertension, rheumatoid arthritis. Several triggered systems for drug delivery have been demonstrated. However, they mostly rely on continuous external stimuli, which hinder their application for long-term treatments. In this work, we investigated a silicon nanofluidic technology that incorporates a gate electrode and examined its ability to achieve reproducible control of drug release.
  • Silicon carbide (SiC) was used to coat the membrane surface, including nanochannels, ensuring biocompatibility and chemical inertness for long-term stability for in vivo deployment. With the application of a small voltage (≤ 3 V DC) to the buried polysilicon electrode, we showed in vitro repeatable modulation of membrane permeability of two model analytes-methotrexate and quantum dots.
  • Methotrexate is a first-line therapeutic approach for rheumatoid arthritis; quantum dots represent multi-functional nanoparticles with broad applicability from bio-labeling to targeted drug delivery. Importantly, SiC coating demonstrated optimal properties as a gate dielectric, which rendered our membrane relevant for multiple applications beyond drug delivery, such as lab on a chip and micro total analysis systems (µTAS).

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