Assay performance is dependent on the accuracy and precision of a given method. These attributes can be combined into an analytical Sigma-metric, providing a simple value for laboratorians to use in evaluating a test method's capability to meet its analytical quality requirements. Sigma-metrics were determined for 37 clinical chemistry assays, 13 immunoassays, and 3 ICT methods on the Alinity ci system. METHODS: Analytical Performance Specifications were defined for the assays, following a rationale of using CLIA goals first, then Ricos Desirable goals when CLIA did not regulate the method, and then other sources if the Ricos Desirable goal was unrealistic. A precision study was conducted at Abbott on each assay using the Alinity ci system following the CLSI EP05-A2 protocol. Bias was estimated following the CLSI EP09-A3 protocol using samples with concentrations spanning the assay's measuring interval tested in duplicate on the Alinity ci system and ARCHITECT c8000 and i2000SR systems, where testing was also performed at Abbott. Using the regression model, the %bias was estimated at an important medical decisions point. Then the Sigma-metric was estimated for each assay and was plotted on a method decision chart. The Sigma-metric was calculated using the equation: Sigma-metric=(%TEa-|%bias|)/%CV. RESULTS: The Sigma-metrics and Normalized Method Decision charts demonstrate that a majority of the Alinity assays perform at least at five Sigma or higher, at or near critical medical decision levels. CONCLUSION: More than 90% of the assays performed at Five and Six Sigma. None performed below Three Sigma. Sigma-metrics plotted on Normalized Method Decision charts provide useful evaluations of performance. The majority of Alinity ci system assays had sigma values >5 and thus laboratories can expect excellent or world class performance. Laboratorians can use these tools as aids in choosing high-quality products, further contributing to the delivery of excellent quality healthcare for patients.
Assay performance is dependent on the accuracy and precision of a given method. These attributes can be combined into an analytical Sigma-metric, providing a simple value for laboratorians to use in evaluating a test method's capability to meet its analytical quality requirements. Sigma-metrics were determined for 37 clinical chemistry assays, 13 immunoassays, and 3 ICT methods on the Alinity ci system. METHODS: Analytical Performance Specifications were defined for the assays, following a rationale of using CLIA goals first, then Ricos Desirable goals when CLIA did not regulate the method, and then other sources if the Ricos Desirable goal was unrealistic. A precision study was conducted at Abbott on each assay using the Alinity ci system following the CLSI EP05-A2 protocol. Bias was estimated following the CLSI EP09-A3 protocol using samples with concentrations spanning the assay's measuring interval tested in duplicate on the Alinity ci system and ARCHITECT c8000 and i2000SR systems, where testing was also performed at Abbott. Using the regression model, the %bias was estimated at an important medical decisions point. Then the Sigma-metric was estimated for each assay and was plotted on a method decision chart. The Sigma-metric was calculated using the equation: Sigma-metric=(%TEa-|%bias|)/%CV. RESULTS: The Sigma-metrics and Normalized Method Decision charts demonstrate that a majority of the Alinity assays perform at least at five Sigma or higher, at or near critical medical decision levels. CONCLUSION: More than 90% of the assays performed at Five and Six Sigma. None performed below Three Sigma. Sigma-metrics plotted on Normalized Method Decision charts provide useful evaluations of performance. The majority of Alinity ci system assays had sigma values >5 and thus laboratories can expect excellent or world class performance. Laboratorians can use these tools as aids in choosing high-quality products, further contributing to the delivery of excellent quality healthcare for patients.
Authors: Jong Do Seo; Da Young Song; Youngwon Nam; Chihchiao Li; Seunghwan Kim; Joon Hee Lee; Kyunghoon Lee; Junghan Song; Sang Hoon Song Journal: Pract Lab Med Date: 2020-10-20