Using analytical performance specifications in a medical laboratory.

Analytical performance specifications (APS) are used for the quantitative assessment of assay analytical performance, with the aim of providing information appropriate for clinical care of patients. One of the major locations where APS are used is in the routine clinical laboratory. These may be used to assess and monitor assays in a range of settings including method selection, method verification or validation, external quality assurance, internal quality control and assessment of measurement uncertainty. The aspects of assays that may be assessed include imprecision, bias, selectivity, sample type, analyte stability and interferences. This paper reviews the practical use of APS in a routine clinical laboratory, using the laboratory I supervise as an example.

The effect of acute changes in glomerular filtration rate on common biochemical tests.

Objectives: To characterise the effect of acute kidney injury on the concentration of common biochemical analytes. Design: and methods: Pairs of serum or plasma samples from the same patients routinely submitted to the laboratory were subject to further analysis based on changes in serum creatinine within 72 h. Samples collected from patients on dialysis were excluded. Samples were measured for 28 biochemical analytes including electrolytes, liver function tests, iron studies, creatine kinase, amylase, lipase, parathyroid hormone, troponin T and troponin I, B-natriuretic peptide and NT pro B-natriuretic peptide. Results: 148 sample pairs were included with 99 having a rise in serum creatinine >50%, 18 with a fall of >50% and 31 with smaller changes. Acute changes in renal function were associated with changes in the concentration of several analytes, with the changes of the greatest magnitude observed in urea, phosphate, urate, parathyroid hormone, troponin T, BNP and NT-ProBNP. The remaining analytes did not show significant changes with changes in renal function. Conclusion: Acute changes in renal function are associated with significant changes in concentration of some serum/plasma biochemical analytes but not others. Expected changes in analyte concentration must be considered in the setting of acute kidney injury to avoid misinterpretation of blood test results.

Estimates of Within-Subject Biological Variation Derived from Pathology Databases: An Approach to Allow Assessment of the Effects of Age, Sex, Time between Sample Collections, and Analyte Concentration on Reference Change Values.

BACKGROUND: Within-subject biological variation data (CVI) are used to establish quality requirements for assays and allow calculation of the reference change value (RCV) for quantitative clinical laboratory tests. The CVI is generally determined using a large number of samples from a small number of individuals under controlled conditions. The approach presented here is to use a small number of samples (n = 2) that have been collected for routine clinical purposes from a large number of individuals. METHODS: Pairs of sequential results from adult patients were extracted from a routine pathology database for 29 common chemical and hematological tests. Using a statistical process to identify a central gaussian distribution in the ratios of the result pairs, the total result variation for individual results was determined for 26 tests. The CVI was then calculated by removing the effect of analytical variation. RESULTS: This approach produced estimates of CVI that, for most of the analytes in this study, show good agreement with published values. The data demonstrated minimal effect of sex, age, or time between samples. Analyte concentration was shown to affect the distributions with first results more distant from the population mean more likely to be followed by a result closer to the mean. DISCUSSION: The process described here has allowed rapid and simple production of CVI data. The technique requires no patient intervention and replicates the clinical environment, although it may not be universally applicable. Additionally, the effect of regression to the mean described here may allow better interpretation of sequential patient results.

Safe reading of chemical pathology reports: the RCPAQAP Report Assessment Survey.

Pathology reports are a vital component of the request-test-report cycle communicating pathology results to doctors to support clinical decision making. This should be done in a comprehensive, safe and time-efficient manner. As doctors may receive reports from different laboratories these goals can be achieved more readily if reports are formatted in the same way. This study evaluates the formatting of paper reports produced by Australian laboratories for numerical biochemistry results. As part of the RCPAQAP Liquid Serum Chemistry program in 2015, laboratories were invited to supply a routine paper report displaying the results. A total of 37 reports were received for analysis. These reports were assessed for variation in a range of components and, where possible, against relevant Australian standards and guidelines. In summary, there was a wide variation in most of the report components assessed including test names, result alignment, result flagging, sequence of data elements on the page, date formatting and patient name formatting. In most components there was also variation from the Standards. In order to ensure safe result transmission by printed reports there is a need to promote the adoption of current reporting standards and monitor compliance with similar external quality assurance programs.

Validating common reference intervals in routine laboratories.

Common reference intervals for numerical pathology tests have been proposed for many years as an improvement over the common situation where individual laboratories establish or select and validate their own intervals. However it is important that any intervals that are developed for common use are themselves validated for use in individual laboratories. There are three main aspects to consider, the appropriateness of the interval, methodological factors and population factors. Techniques for assessing method biases are reasonably straightforward with the use of shared samples and appropriate external quality assurance schemes. Validating the local population, which also encompasses the laboratory's method, can be done using a number of healthy subjects, the more the better, or by various "data mining" techniques using the results of tests performed on routine patients. In any of these methods there is the need to consider the selection of subjects, the statistical approach and the acceptance criteria. Only if a proposed common reference interval can be shown to be appropriate in routine laboratories can it become widely adopted and become truly "common".

Evidence-based approach to harmonised reference intervals.

Although we are in the era of evidence-based medicine, there is still a substantial gap between theory and current practice with the application of reference intervals as decision making tools. Different laboratories may have different reference intervals for the same tests using the same analytical methods and platforms. These differences have the potential to confuse physicians making the assessment and monitoring of patients more difficult by providing discordant information. This paper attempts to demonstrate how to use evidence-based approach for harmonising reference intervals. In order to consider harmonisation we must first have an appreciation of the various factors that influence the determination of that reference interval such as the choice of individuals within the population studied, biological variability of the analyte studied, partitioning, sample collection, analytical aspects such as bias and statistical models. An a priori approach for determining reference intervals, whilst recommended, may be beyond the scope of most laboratories and consideration should be given to the use of a validated indirect a posteriori approach. Regardless of method used, the continuing application of an evidence-based approach in harmonised reference intervals to meet the quality expectations of physicians should be pursued.

Critical difference calculations revised: inclusion of variation in standard deviation with analyte concentration.

BACKGROUND: The critical difference (CD), the smallest difference between sequential laboratory results which is associated with a true change in the patient, is commonly calculated by assuming the same standard deviation (SD) for the initial and subsequent measurements. The calculation of the CD is re-examined without making this assumption. METHODS: A formula for CD is developed, which specifies that even with the assumption of constant coefficient of variations (CV) at the two measurement concentrations used in the calculation, there will be different SDs due to different concentrations. RESULTS: The effect of removing the assumption of constant SD is to increase the CD for rises in analyte concentration and to decrease the CD for falls in concentration. These effects are caused by increased SD for the second measurement compared with the first when the second measurement is higher, and the reverse when the second is lower. CONCLUSIONS: Replacing the usual assumption of similar total result SD for both measurements included in the CD calculation with a calculation of the SD at both analyte concentrations leads to an increase in the magnitude of the CD for rises in analyte concentration and a decrease for falls in analyte concentration. This change is proposed for all forms of CD calculations.

Effect of the reporting-interval size on critical difference estimation: beyond "2.77".

BACKGROUND: The reporting interval is the bin size used to report numerical pathology results and must be determined for every analyte. The influence of the size of the reporting interval on the critical difference (CD) between two results from the same patient has not been addressed previously. METHODS: The effect of changing the reporting-interval size (RIS) on CDs was modeled by use of a spreadsheet application. The findings were applied to data on CDs with analytical precision values from our laboratory. RESULTS: As the RIS increases relative to the combined analytical and within-person biological variation, there is an approximately linear increase in the CD from the value determined by use of published techniques. The revised estimate is as follows: CD = 2(1/2) x z x (SDa(2) + SDi(2))(1/2) + 1.5 x RIS, where CD, SD, and RIS are all in the same units. This effect is seen for any probability associated with the critical difference and for both uni- and bidirectional changes. CONCLUSIONS: The choice of reporting interval should be made in the light of assay requirements. Where there is a clinical need for detection of small changes in analyte concentration, the reporting interval should be kept small relative to the combined variation attributable to assay precision and within-person biological variation.