Biomedical subjects

James O Westgard

Publications and source records attributed to James O Westgard.

6 recordsLinked to original sources

PubMed2006

The quality of laboratory testing today: an assessment of sigma metrics for analytic quality using performance data from proficiency testing surveys and the CLIA criteria for acceptable performance.

To assess the analytic quality of laboratory testing in the United States, we obtained proficiency testing survey results from several national programs that comply with Clinical Laboratory Improvement Amendments (CLIA) regulations. We studied regulated tests (cholesterol, glucose, calcium, fibrinogen, and prothrombin time) and nonregulated tests (international normalized ratio [INR], glycohemoglobin, and prostate-specific antigen [PSA]). Quality was assessed on the sigma scale with a benchmark for minimum process performance of 3 sigma and a goal for world-class quality of 6 sigma. Based on the CLIA criteria for acceptable performance in proficiency testing (allowable total errors [TEa]), the national quality of cholesterol testing (TEa = 10%) estimated sigma values as 2.9 to 3.0; glucose (TEa = 10%), 2.9 to 3.3; calcium (TEa = 1.0 mg/dL), 2.8 to 3.0; prothrombin time (TEa = 15%), 1.8; INR (TEa = 20%), 2.4 to 3.5; fibrinogen (TEa = 20%), 1.8 to 3.2; glycohemoglobin (TEa = 10%), 1.9 to 2.6; and PSA (TEa = 10%), 1.2 to 1.8. The analytic quality of laboratory tests requires improvement in measurement performance and more intensive quality control monitoring than the CLIA minimum of 2 levels per day.

Benchmarking↗

PubMed2004

The truth about quality: medical usefulness and analytical reliability of laboratory tests.

BACKGROUND: In this age of evidence-based medicine, nothing is more important than the quality of laboratory tests. It is commonly thought that laboratory tests provide two-thirds to three-fourths of the information used for making medical decisions. If so, test results had better tell the truth about what is happening with our patients. METHODS: The age-old "truth standard" for the quality of evidence describes three dimensions that are important-a test should tell the truth, the whole truth, and nothing but the truth. This three-dimensional model can be used to characterize the clinical and analytical reliability of laboratory tests and guide the translation of outcome criteria, or quality goals, into practical specifications for method performance. RESULTS: Clinical reliability, or medical usefulness, should assess the correctness of patient classifications based on stated test interpretation guidelines, taking into account the precision and accuracy of the laboratory method, and allowing for the known within-subject biologic variation and the QC needed to detect method instability. Analytical reliability should assess the correctness of a test result based on a stated error limit, taking into account the precision and accuracy of the method and allowing for the QC necessary to detect method instability. These assessments challenge the reliability of current tests for cholesterol, glucose, and glycated hemoglobin in the implementation of U.S. national clinical guidelines. CONCLUSIONS: Evidence-based medicine must employ scientific methodology for translating test interpretation guidelines into practical, bench-level, operating specifications for the imprecision and inaccuracy allowable for a method and the QC necessary to detect method instability.

Chemistry, Clinical↗

PubMed2004

Design of internal quality control for reference value studies.

Internal quality control should assure that the desired quality goals are achieved during reference value studies. Quality goals are often stated in the form of allowable limits of error, such as an allowable total error or an allowable bias. For reference value studies, it may be more appropriate to utilize a goal for allowable bias. In either case, it is possible to calculate a metric in the form of the critical systematic error that can be used to guide selection or design of the internal quality control procedure. A graphical tool, called the critical-error graph, facilitates the selection by superimposing the calculated critical systematic error on the power curves of different control rules and numbers of control measurements. Examples are provided to illustrate the calculation of the critical systematic error from both an allowable total error goal and an allowable bias goal, using figures from an extensive tabulation of available total error and bias goals.

Bias↗

PubMed2003

Internal quality control: planning and implementation strategies.

The first essential in setting up internal quality control (IQC) of a test procedure in the clinical laboratory is to select the proper IQC procedure to implement, i.e. choosing the statistical criteria or control rules, and the number of control measurements, according to the quality required for the test and the observed performance of the method. Then the right IQC procedure must be properly implemented. This review focuses on strategies for planning and implementing IQC procedures in order to improve the quality of the IQC. A quantitative planning process is described that can be implemented with graphical tools such as power function or critical-error graphs and charts of operating specifications. Finally, a total QC strategy is formulated to minimize cost and maximize quality. A general strategy for IQC implementation is recommended that employs a three-stage design in which the first stage provides high error detection, the second stage low false rejection and the third stage prescribes the length of the analytical run, making use of an algorithm involving the average of normal patients' data.

Bias↗

PubMed2003

Designing quality control for neonatal screening assays.

The purpose of neonatal screening is to find those that have a high risk for a disorder and therefore need further action for diagnosis and treatment. The separation of the high-risk and low-risk groups is typically achieved by establishing cut-off values for interpretation of the test result. The guidelines should preferably include both a low cut-off and a high cut-off value, with a grey zone between them. The width of the grey zone can be used for defining the quality required by the assay. The present work is based on the use of the EZ Rules software (from Westgard QC, Madison, WI), which automatically selects control rules. The user enters the needed parameters, such as the precision (as % CV) and bias of the assay. By using the grey zone as the medical decision interval the program will calculate possible control rules for the assay. The program was used to calculate the control rule for the AutoDELFIA neoTSH assay (from PerkinElmer Wallac, Turku, Finland). The grey zone was taken as 10 - 20 mU/L TSH, which is the recommendation of the American Academy of Pediatrics (Pediat. 91, 1203 - 1209). The entered parameters were: a total imprecision of 9%, which is typically seen with the AutoDELFIA neoTSH kit, a bias of 0% and a preanalytical variation of 20%. With the number of controls chosen as two, as often is used, a 1 3.0 s rule can be applied. The program also gives alternative control rules. Many laboratories lack a documented definition of the required quality, and tend to use a 2 SD control rule, which however leads to many unnecessary rejections. The EZ Rules program provides a tool for selection of QC rules. With the quality of the AutoDELFIA neoTSH kit two controls and a 1 3.0 s rule is sufficient. Runs are rejected only if one control out of two exceeds the 3.0 SD limit.

Autoanalysis↗

PubMedDate not supplied

Evaluation of rule-based autoverification protocols.

Autoverification is a technique that can help save time and dollars in today's modern laboratory. Most laboratorians, however, are afraid to use it. The authors describe a rules-based system based on the quality of the laboratory's instrumentation and the quality of the results generated to help decide if the laboratory should pursue the advantages of autoverification. This project was developed through a grant from Ortho-Clinical Diagnostics, Inc., a Johnson & Johnson Company.

Autoanalysis↗