Madison, WI, United States
Madison, WI, United States

Time filter

Source Type

Agrawal Y.,New York Medical College | Cid M.,New York Medical College | Westgard S.,Westgard QC Inc. | Parker T.,New York Medical College | And 4 more authors.
Therapeutic Drug Monitoring | Year: 2014

BACKGROUND:: A global tacrolimus proficiency study recently showed clinically significant variability between laboratories, the inability of a common calibrator to harmonize methods, and differences in patient classification depending on the test method. The authors evaluated (1) the effect of a change in methodology on patient classification based on tacrolimus blood concentration and (2) the ability of 2 methods to position the concentration in a given specimen within the correct range. METHODS:: A total of 839 consecutive samples were analyzed at The Rogosin Institute and New York Presbyterian Hospital for routine tacrolimus monitoring over 30 days. Concordance analysis between the methods was performed covering dosage target ranges of 8-10, 6-8, 4-6 ng/mL currently used at our center. Six Sigma Metrics were applied to statistically evaluate the discordance rate. RESULTS:: Deming regression comparing liquid chromatography-tandem mass spectrometry and immunoassay yielded y = 0.927x - 0.24; 95% confidence interval, 0.903-0.951; R = 0.875; n = 839. There were 310 pairs (37%) discordant by 1, 21 (2.5%) discordant by 2, and 4 (0.5%) discordant by 3 therapeutic ranges. Surprisingly, 40% of patient samples were discordant when therapeutic ranges were 2 ng/mL wide. This discordant rate is equivalent to 1.7 Sigma and falls far below the minimum acceptable threshold of 3 Sigma. CONCLUSIONS:: Both methods are capable of measuring tacrolimus in the clinically relevant range between 1 and 10 ng/mL, yet 40% of the samples were discordant with an unacceptable Sigma level. Standardization of tacrolimus assays will mitigate this issue. © 2014 by Lippincott Williams & Wilkins.


Westgard J.O.,Westgard QC Inc | Westgard S.A.,Westgard QC Inc
Annals of Clinical Biochemistry | Year: 2016

This review focuses on statistical quality control in the context of a quality management system. It describes the use of a ‘Sigma-metric’ for validating the performance of a new examination procedure, developing a total quality control strategy, selecting a statistical quality control procedure and monitoring ongoing quality on the sigma scale. Acceptable method performance is a prerequisite to the design and implementation of statistical quality control procedures. Statistical quality control can only monitor performance, and when properly designed, alert analysts to the presence of additional errors that occur because of unstable performance. A new statistical quality control planning tool, called ‘Westgard Sigma Rules,’ provides a simple and quick way for selecting control rules and the number of control measurements needed to detect medically important errors. The concept of a quality control plan is described, along with alternative adaptations of a total quality control plan and a risk-based individualized quality control plan. Finally, the ongoing monitoring of analytic performance and test quality are discussed, including determination of measurement uncertainty from statistical quality control data collected under intermediate precision conditions and bias determined from proficiency testing/external quality assessment surveys. A new graphical tool, called the Sigma Quality Assessment Chart, is recommended for demonstrating the quality of current examination procedures on the sigma scale. © 2015, The Author(s) 2015.


Westgard J.O.,University of Wisconsin - Madison | Westgard J.O.,Westgard QC Inc
Clinics in Laboratory Medicine | Year: 2013

The right quality control (QC) should ensure the detection of important errors. Statistical QC (SQC) should be included in all QC plans. The Clinical and Laboratory Standards Institute (CLSI) C24A3 provides guidance for the application of SQC in medical laboratories. It describes a QC planning process and provides an SQC selection tool that relates the sigma-metric of a testing process to the medically important systematic error and the rejection characteristics of different SQC procedures. Once the right SQC has been selected, the laboratory must implement SQC right. CLSI C24A3 also provides guidance for establishing run length and control limits. © 2013 Elsevier Inc.


Westgard J.O.,University of Wisconsin - Madison | Westgard J.O.,Westgard QC Inc.
Clinics in Laboratory Medicine | Year: 2013

Quality control (QC) practices are changing in US laboratories as Centers for Medicare and Medicaid Services adopts individualized QC plans as a new option for compliance with the Clinical Laboratory Improvement Amendments regulations. The Joint Commission provides general guidance for applying risk management in health care organizations. The EP23A (Evaluation Protocol 23A) document from the Clinical and Laboratory Standards Institute provides specific guidance on the use of risk management for developing analytical QC plans. Medical laboratories should integrate risk management tools with existing quality management techniques and activities to provide an overall plan for analytical quality management. © 2013 Elsevier Inc.


Westgard J.O.,University of Wisconsin - Madison | Westgard J.O.,Westgard QC Inc.
Clinical Chemistry and Laboratory Medicine | Year: 2016

The 2014 Milan Conference "Defining analytical performance goals 15 years after the Stockholm Conference" initiated a new discussion of issues concerning goals for precision, trueness or bias, total analytical error (TAE), and measurement uncertainty (MU). Goal-setting models are critical for analytical quality management, along with error models, quality-assessment models, quality-planning models, as well as comprehensive models for quality management systems. There are also critical underlying issues, such as an emphasis on MU to the possible exclusion of TAE and a corresponding preference for separate precision and bias goals instead of a combined total error goal. This opinion recommends careful consideration of the differences in the concepts of accuracy and traceability and the appropriateness of different measures, particularly TAE as a measure of accuracy and MU as a measure of traceability. TAE is essential to manage quality within a medical laboratory and MU and trueness are essential to achieve comparability of results across laboratories. With this perspective, laboratory scientists can better understand the many measures and models needed for analytical quality management and assess their usefulness for practical applications in medical laboratories. © 2016 by De Gruyter 2016.


Westgard J.O.,University of Wisconsin - Madison | Westgard J.O.,Westgard QC Inc. | Westgard S.A.,Westgard QC Inc.
Clinical Chemistry and Laboratory Medicine | Year: 2015

Background: There is a need to assess the quality being achieved for laboratory examinations that are being utilized to support evidence-based clinical guidelines. Application of Six Sigma concepts and metrics can provide an objective assessment of the current analytical quality of different examination procedures. Methods: A "Sigma Proficiency Assessment Chart" can be constructed for data obtained from proficiency testing and external quality assessment surveys to evaluate the observed imprecision and bias of method subgroups and determine quality on the Sigma scale. Results: Data for hemoglobin A1c (HbA1c) from a 2014 survey by the College of American Pathologists (CAP) demonstrates that approximately two-thirds of the examination subgroups provide only two-Sigma quality when evaluated against the CAP requirement of an allowable total error of 6.0%. The weighted averages were 1.46 Sigma for a survey sample with an assigned value of 6.49% Hb (average bias 2.31%, CV 2.87%), 1.45 Sigma at 6.97% Hb (average bias 2.29%, CV 2.81%), and 1.75 at 9.65% Hb (average bias 1.55%, CV 2.71%). Maximum biases for examination subgroups were 5.7%, 5.8%, and 4.1%, respectively. Conclusions: Assessment of quality on the Sigma scale provides evidence of the analytical performance that is being achieved relative to requirements for intended use and should be useful for identifying and prioritizing improvements that are needed in the analytical quality of laboratory examinations. In spite of global and national standardization programs, bias is still a critical limitation of current HbA1c examination procedures. © 2015 by De Gruyter.


Objective: To assess the analytical performance of instruments and methods through external quality assessment and proficiency testing data on the Sigma scale. Design and methods: A representative report from five different EQA/PT programs around the world (2 US, 1 Canadian, 1 UK, and 1 Australasian) was accessed. The instrument group standard deviations were used as surrogate estimates of instrument imprecision. Performance specifications from the US CLIA proficiency testing criteria were used to establish a common quality goal. Then Sigma-metrics were calculated to grade the analytical performance. Results: Different methods have different Sigma-metrics for each analyte reviewed. Summary Sigma-metrics estimate the percentage of the chemistry analytes that are expected to perform above Five Sigma, which is where optimized QC design can be implemented. The range of performance varies from 37% to 88%, exhibiting significant differentiation between instruments and manufacturers. Median Sigmas for the different manufacturers in three analytes (albumin, glucose, sodium) showed significant differentiation. Conclusions: Chemistry tests are not commodities. Quality varies significantly from manufacturer to manufacturer, instrument to instrument, and method to method. The Sigma-assessments from multiple EQA/PT programs provide more insight into the performance of methods and instruments than any single program by itself. It is possible to produce a ranking of performance by manufacturer, instrument and individual method. Laboratories seeking optimal instrumentation would do well to consult this data as part of their decision-making process. To confirm that these assessments are stable and reliable, a longer term study should be conducted that examines more results over a longer time period. © 2016 The Canadian Society of Clinical Chemists.


Trademark
Westgard QC Inc. | Date: 2015-05-05

Digital materials, namely, computer software for testing purposes, namely, for testing the performance and integrity of scientific measurement procedures and laboratory measurement devices, audio files, and publications in the nature of brochures, worksheets, and articles, all the aforementioned goods being in the field of scientific and laboratory quality control procedures, and all being either downloadable or available on CDs or digital storage media. Books, posters, and printed instructional, educational, and teaching materials in the field of scientific and laboratory quality control procedures. Educational services, namely, conducting seminars and conferences in the field of scientific and laboratory quality control procedures and distribution of training material in connection therewith; providing on-line training courses in the field of scientific and laboratory quality control procedures. Consulting services in the field of scientific and laboratory quality control procedures; providing a website featuring information in the field of scientific and laboratory quality control procedures.


To assess the analytical performance of instruments and methods through external quality assessment and proficiency testing data on the Sigma scale.A representative report from five different EQA/PT programs around the world (2 US, 1 Canadian, 1 UK, and 1 Australasian) was accessed. The instrument group standard deviations were used as surrogate estimates of instrument imprecision. Performance specifications from the US CLIA proficiency testing criteria were used to establish a common quality goal. Then Sigma-metrics were calculated to grade the analytical performance.Different methods have different Sigma-metrics for each analyte reviewed. Summary Sigma-metrics estimate the percentage of the chemistry analytes that are expected to perform above Five Sigma, which is where optimized QC design can be implemented. The range of performance varies from 37% to 88%, exhibiting significant differentiation between instruments and manufacturers. Median Sigmas for the different manufacturers in three analytes (albumin, glucose, sodium) showed significant differentiation.Chemistry tests are not commodities. Quality varies significantly from manufacturer to manufacturer, instrument to instrument, and method to method. The Sigma-assessments from multiple EQA/PT programs provide more insight into the performance of methods and instruments than any single program by itself. It is possible to produce a ranking of performance by manufacturer, instrument and individual method. Laboratories seeking optimal instrumentation would do well to consult this data as part of their decision-making process. To confirm that these assessments are stable and reliable, a longer term study should be conducted that examines more results over a longer time period.

Loading Westgard QC Inc. collaborators
Loading Westgard QC Inc. collaborators