The applications of data on biological variation include assessment of the utility of population-based reference intervals, evaluation of the significance of change in serial results, and setting of analytical quality specifications. We investigated the biological variation of 19 biochemistry analytes and total T4, measured in serum from 7 clinically healthy domestic cats sampled once weekly for 5 weeks. Samples were frozen and analyzed in random order in the same analytical run. Results were analyzed for outliers, and the components of variance, subsequently generated by restricted maximum likelihood, were used to determine within-subject and between-subject variation (CV I and CV G, respectively), as well as analytical variation (CV A) for each analyte. Indices of individuality, reference change values, and analytical performance goals were calculated.
Download and Read Biological Variation From Principles To Practice Biological Variation From Principles To Practice It's coming again, the new collection that this.
The smallest CV I and CV G were found for calcium, chloride, and sodium, whereas the largest values were calculated for bile acids. Nine analytes (albumin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, cholesterol, creatinine, phosphate [phosphorus], total protein, total T4) demonstrated high individuality, indicating limited utility of population-based reference intervals. Individuality was low, and population-based reference intervals were thereby considered appropriate for 5 analytes (bile acids, calcium, fructosamine, glucose, potassium). The intermediate individuality observed for 4 analytes (creatine kinase, iron, magnesium, urea) indicated that population-based reference intervals should be used with caution.
Callum Fraser is recognized as the international expert on biologic variation. His new book from the AACC press: Biologic Variation: Principles and Practice is now the definitive reference on the subject. Fraser has let us post the Foreword to this book on our website. We want to call your attention to a new publication from AACC Press 'Biological Variation: From Principles to Practice.' This book is written by Callum Fraser, who is recognized worldwide as the authority on this subject.
Happily, with the permission of the AACC Press and Dr. Fraser himself, we can reprint the Foreword from the book. Foreword Many analytes of interest in the clinical laboratory can vary over an individual's lifetime, simply because of natural biological factors involved in the aging process. These variations may occur rapidly at critical points in the life cycle, such as during the neonatal period, childhood, puberty, menopause, or old age.
In addition, certain analytes have predictable biological rhythms or cycles. These cycles may be daily, monthly, or seasonal. Knowledge of these cycles is vital for good patient care.
For example, a patient sample must be collected at the time in the cycle that is appropriate for the clinical purpose to which the test result will be applied. Since developing good reference values is complex and time consuming, it is important to generate these values correctly, particularly at clinically important decision-making points. Moreover, the absence of an expected rhythm or cycle can give important clues about the presence of disease and is the simplest of dynamic function tests. Most analytes, however, do not have cyclical rhythms that are of major clinical importance. In fact, the variation can be described as random fluctuation around a homeostatic setting point.
We see this easily in practice. If we take a series of samples from one individual for a particular laboratory test, then the results are not all exactly the same number.
The test results of any person vary over time, due to three factors: • pre-analytical influences-those related to preparation of the individual for sampling, such as posture; and those influenced by sample collection itself, such as tourniquet application time, • analytical random error (precision)-and possibly systematic error (changes in bias due to calibration, for example), and • inherent biological variation around the homeostatic setting point (this is called within-subject [or intra-individual] biological variation). If we performed the same test on various individuals, we would find that the mean of each person's results would not all be exactly the same number.
Individual homeostatic setting points usually vary. This difference between individuals is called between-subject (or inter-individual) biological variation. In order to determine the magnitude of within-subject and between-subject components of biological variation in numerical terms, we could conduct a rather simple experiment along the following lines. • Recruit a small group of apparently healthy volunteers (or patients without any disease that affected the analyte under investigation).
• Take a series of samples from each individual at regular intervals while minimizing pre-analytical variation. • Store the samples for analysis.
• Perform the analysis in duplicate while minimizing analytical sources of variation. • Remove outliers, that is, numbers that are different from the bulk of the data set. • Determine the analytical, and within-subject and between-subject biological components of variation using simple statistical analysis of variance techniques (ANOVA). APPLICATION OF DATA ON RANDOM BIOLOGICAL VARIATION If we generated numerical data using this experimental technique,- we would then have quantitative knowledge of • average within-subject biological variation and • between-subject biological variation.
We rarely do this type of experimental work in our own laboratory because the literature contains large databases about the components of biological variation. These are easy to access. It is generally appropriate to use these data in everyday practice.
Data on the components of biological variation can be applied to set quality specifications for • precision, • bias, • total error allowable, • the allowable difference between methods, • use in proficiency testing programs (PT) or external quality assessment schemes (EQAS), and • reference methods. Data on within-subject biological variation and analytical precision can be used to • determine the change that must occur in an individual's serial results before the change is significant (the reference change value), • determine the statistical probability that a change in an individual's serial results is significant, and • generate objective delta-check values for use in quality management. Comparing within-subject and between-subject biological variation allows us to • decide the utility of traditional population-based reference values (often termed normal ranges), and • clarify why stratifying reference values according to age and sex, for example, improves clinical decision making. Data on biological variation can be used for other purposes, including • calculating the reliability coefficient used in epidemiology, • determining the number of samples needed to get an estimate of the homeostatic setting point within a certain percentage with a stated probability, and • deciding the best way to report test results, the best sample to collect, and the test procedure of greatest potential use. And, of course, generation and application of data on biological variation is an essential prerequisite in the evolution of any new test procedure.
BIOLOGICAL VARIATION AND THE QUEST FOR QUALITY Over time, we have come to realize that quality management involves much more than the simple statistical quality control techniques that we have performed every day at the bench for many years -- it requires incorporating and integrating quality laboratory practice, quality assurance, quality improvement, and quality planning as well as quality control. In short, quality management impacts all phases of obtaining a clinically appropriate and correctly interpreted laboratory result, including the pre-analytical, analytical, and post-analytical phases of our work. It is thus vital for those concerned with quality management to know how to generate or find, and then apply, numerical data on the components of biological variation in their everyday practice. Importantly, one must consider the influence of biological variation on laboratory tests and on the interpretation of laboratory results. Download Video Ss501 Love Like This. DEFINING QUALITY The International Organization for Standardization (ISO) defines quality as 'the totality of characteristics of an entity that bear on its ability to satisfy stated and implied needs.' This rather complex definition can be translated to mean-at least for us-that the quality of tests performed in laboratory medicine must allow our clinicians to practice good medicine. Before we can control, practice, assure, or improve laboratory quality, we must know exactly what level of quality we need to ensure satisfactory clinical decision making.
And, since a laboratory service includes much more than the technical analysis of samples, we must appreciate that the time of day or month when patient samples are obtained may influence the test result, and that biological variation influences the interpretation of numerical test results both in monitoring, in which serial results from an individual are assessed for change, and in diagnosis and case-finding, in which population-based reference values are most often used. USING THIS BOOK This book brings together modem and recent concepts on the generation and application of data on biological variation.