Brief Communication: Probability and the Westgard Rules. For 10X, the odds that the controls are on the. ![]() So we've catalogued some of the worst abuses of 'Westgard Rules.' What about the best uses? What's the best way to use 'Westgard Rules' - and When, Why, How and Who, too? Here is a list of 12 practices to make your use of 'Westgard Rules' better. • • • • • • • • • • • • • In a previous discussion, we described some of the involving the improper implementation and interpretation of 'Westgard Rules' by instruments, LIS devices, and data workstation QC software. Now that we've cleared the air about the 'worst practices', it's time to talk about 'best practices' for doing QC right. It's important to understand the problems (worst practices) in order to implement proper solutions (best practices). If your QC software is doing things wrong, no amount of effort on your part can correct for those problems. QC needs to be done right from the start. Define the quality that is needed for each test. Quality management begins with the knowledge of the quality that needs to be achieved. Sounds simple, doesn't it? But when I ask the laboratory professionals 'What quality is needed for a test?' The answer is seldom a numeric or quantitative definition of the quality requirement. That number could be in the form of a total allowable error (TE a), such as the CLIA proficiency testing criteria for acceptable performance. Or that number could be in the form of a clinical decision interval (D int), which is a gray zone of interpretation for patient treatment. This number comes from the physician and uses his/her diagnosis cutoffs as a way to figure out the level of quality needed in a method. A third possibility for that number is the biologic total error, as documented by a that has derived figures for the allowable bias and allowable imprecision from studies of individual biological variation. In any case, the sources of some of these numbers are here on the website or somewhere in your laboratory or hospital. Quality begins with defining the quality needed for each test. ![]() If you don't know the quality that is needed, then it doesn't make any difference how you do QC. It's all arbitrary! It's like taking a trip without knowing the destination. Or playing soccer without marking a goal. Or trying to call someone without knowing their phone number - you may get to talk to someone, but they may not care to talk to you. Resources: • • • • • 2. Know the performance of your method (CV, bias). It's hard to argue with this, too, particularly since CLIA requires that a laboratory validate the performance of its methods. You estimate method precision (CV) and accuracy (bias) by method validation experiments when you introduce any new method. For existing methods, the results observed on control materials being analyzed in your laboratory right now can be used to estimate the method's CV and results from proficiency testing or peer comparison studies can be used to estimate bias. Amplitube 3 authorization code keygens and hack. Why is this important? You need to know how well your method is performing. CV and bias are the characterstics that tell you how your method is performing. Resources: • The - to estimate inaccuracy • The - to estimate imprecision 3. Calculate the Sigma-metric for your testing process. It's useful to have a metric that tells you out-front whether or not your method performance is good enough to achieve the quality that is required. Why do you need to know this? If method performance is bad, no amount of QC can overcome the inherent lack of quality. If method performance is extremely good, only a little QC is needed to assure the necessary quality will be achieved. Here's the calculation: Sigma = (TE a - bias)/CV • Where TE a is the CLIA allowable total error (expressed in%), • Bias is the systematic error (also expressed in%) compared to a reference method or compared to peer methods in a proficiency testing survey or peer comparison survey, and • CV is the imprecision of your method (in%) as calculated from control measurements in your laboratory. Here's an example. The CLIA criterion for acceptable performance for cholesterol is 10%. If a laboratory method shows a bias of 2.0% on proficiency testing surveys and a CV of 2.0% on internal QC results, the Sigma-metric is 4 [(10-2)/2].
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