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Reference intervals are a vital part of the information supplied by clinical laboratories to support interpretation of numerical pathology results such as are produced in clinical chemistry and hematology laboratories. The traditional method for establishing reference intervals, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. An alternative approach is to perform analysis of results generated as part of routine pathology testing and using appropriate statistical techniques to determine reference intervals. This is known as the indirect approach. This paper from a working group of the International Federation of Clinical Chemistry (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) aims to summarize current thinking on indirect approaches to reference intervals. The indirect approach has some major potential advantages compared with direct methods. The processes are faster, cheaper and do not involve patient inconvenience, discomfort or the risks associated with generating new patient health information. Indirect methods also use the same preanalytical and analytical techniques used for patient management and can provide very large numbers for assessment. Limitations to the indirect methods include possible effects of diseased subpopulations on the derived interval. The IFCC C-RIDL aims to encourage the use of indirect methods to establish and verify reference intervals, to promote publication of such intervals with clear explanation of the process used and also to support the development of improved statistical techniques for these studies.

Objective(s)To determine whether variation in cell-associated unspliced (CA-US) HIV RNA in HIV-infected individuals on antiretroviral therapy (ART) has a circadian basis.MethodsProspective observational study of HIV-infected individuals on ART. Blood was collected on three occasions and CA-US HIV RNA and mRNA of the circadian-locomotor-output-cycles-kaput (CLOCK)-associated genes quantified by real time PCR. CLOCK-associated proteins were over-expressed in a cell line stably transfected with an HIV long-terminal repeat (LTR) luciferase reporter.ResultsUsing a mixed effects model, there was a significant increase in log-CA-US RNA at the third visit compared with the first visit (effect size of 0.619 with standard error (SE) of 0.098, P

Reference Intervals (RIs) and clinical decision limits (CDLs) are a vital part of the information supplied by laboratories to support the interpretation of numerical clinical pathology results. RIs describe the typical distribution of results seen in a healthy reference population while CDLs are associated with a significantly higher risk of adverse clinical outcomes or are diagnostic for the presence of a specific disease. However, as the two concepts are sometimes confused, there is a need to clarify the differences between these terms and to ensure they are easily distinguished, especially because CDLs have a clinical association with specific diseases and risks, thereby implying that effective clinical interventions are available. It is important to note that, because population-based RIs are derived from the range of values expected in a typical community population, laboratory results that fall outside a RI do not necessarily indicate a disease but rather that additional medical follow-up and/or treatment may be warranted. In contrast, CDLs are associated with a risk of specific adverse outcomes, and are commonly used to interpret laboratory test results, including lipid parameters, glucose, hemoglobin A1c (HbA1c), and tumor markers, to determine risk of disease, to diagnose or to treat. In recent years, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) has focused primarily on RIs and has performed multicenter studies to obtain common RIs. However, the broader responsibility of the Committee, from its name, includes decision limits. C-RIDL now aims to emphasize the importance of the correct use of both RIs and CDLs and to encourage laboratories to specify the appropriate information to clinicians as needed. This review discusses RIs and CDLs in detail, describes the similarities and the differences between these two important tools in laboratory medicine, and clearly explains the processes used to define them. C-RIDL encourages the involvement of laboratory professionals in the establishment of both RIs and CDLs.

Scientific evidence supports the use of common reference intervals (RIs) for many general chemistry analytes, in particular those with sound calibration and traceability in place. Already the Nordic countries and United Kingdom have largely achieved harmonised RIs. Following a series of workshops organised by the Australasian Association of Clinical Biochemists (AACB) between 2012 and 2014 at which an evidence-based approach for determination of common intervals was developed, pathology organisations in Australia and New Zealand have reached a scientific consensus on what adult and paediatric intervals we should use across Australasia. The aim of this report is to describe the processes that the AACB and the Royal College of Pathologists of Australasia have taken towards recommending the implementation of a first panel of common RIs for use in Australasia.

Objective: Vitamin D deficiency is recognized as a global public health problem, but the population-based prevalence of deficiency and its determinants in Australian adults is not known. This study evaluated the vitamin D status of Australian adults aged ≥25 years and risk factors associated with vitamin D deficiency in this population. Design and Patients: We studied a national sample of 11 247 Australian adults enrolled in the 1999/2000 Australian Diabetes, Obesity and Lifestyle (AusDiab) study drawn from 42 randomly selected districts throughout Australia. Measurements: Serum concentrations of 25-hydroxyvitamin D [25(OH)D] were measured by immunoassay. Vitamin D deficiency was defined as a concentration < 50 nmol/l. Information on demographic and lifestyle factors was derived from interview-administered questionnaires. Results: The mean serum 25(OH)D concentration was 63 nmol/l (95% CI: 59–67 nmol/l). Only 4% of the population had a level < 25 nmol/l, but the prevalence of vitamin D deficiency ( < 50 nmol/l) was 31% (22% men; 39% women); 73% had levels < 75 nmol/l. The prevalence of vitamin D deficiency increased significantly with age, was greater in women, in those of non-Europid origin, in the obese and those who were physically inactive and with a higher level of education. Deficiency was also more common during winter and in people residing in southern Australia (latitude > 35°S); 42% of women and 27% of men were deficient during summer–autumn, which increased to 58% and 35%, respectively, during winter–spring. Conclusion: Vitamin D deficiency is common in Australia affecting nearly one-third of adults aged ≥25 years. This indicates that strategies are needed at the population level to improve vitamin D status of Australians.

Objective: To examine whether serum 25-hydroxyvitamin D (25OHD) and dietary calcium predict incident type 2 diabetes and insulin sensitivity. Research design and methods: A total of 6,537 of the 11,247 adults evaluated in 1999–2000 in the Australian Diabetes, Obesity and Lifestyle (AusDiab) study, returned for oral glucose tolerance test (OGTT) in 2004–2005. We studied those without diabetes who had complete data at baseline (n = 5,200; mean age 51 years; 55% were women; 92% were Europids). Serum 25OHD and energy-adjusted calcium intake (food frequency questionnaire) were assessed at baseline. Logistic regression was used to evaluate associations between serum 25OHD and dietary calcium on 5-year incidence of diabetes (diagnosed by OGTT) and insulin sensitivity (homeostasis model assessment of insulin sensitivity [HOMA-S]), adjusted for multiple potential confounders, including fasting plasma glucose (FPG).

Medicine has traditionally relied on heuristic approaches in which knowledge is acquired through experience and self-learning. Pathology is information rich with quantitative and qualitative measurements such as history, images, and physiological data from which diagnosis and treatment decisions are made. This information is readily linked to patient outcome data and is therefore potentially invaluable in improving treatment. Thus, pathology is ripe for the application of tools that can effectively turn this information into wisdom. Machine learning (ML), which is a core branch of artificial intelligence, is one of the tools that can take information from many sources to aid decision-making processes. It's a system that takes in data, finds patterns, trains itself using the data, and produces an outcome. It uses algorithms and statistical models to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. Essentially, an ML algorithm is used to create a model from a data set, from which predictions are made. The data used to develop the models are divided into a training set and a testing set. The training set is used to learn different predictive models, each characterized by a set of parameters, including a penalty/complexity parameter. The purpose of the model selection set is to assess the predictive accuracy of each model on blinded data to select the model with the lowest generalization error. This optimum model is the most likely to be true. Data mining uses many ML methods but with different goals. ML differs from data mining in that although both use the same methods and overlap substantially, ML focuses on prediction with known properties learned from the training data, whereas data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). The algorithms …

OBJECTIVE To evaluate A1C for screening and diagnosis of undiagnosed type 2 diabetes defined by oral glucose tolerance testing in clinical and general populations. RESEARCH DESIGN AND METHODS A1C cut offs (≤5.5% to rule out diabetes; ≥7.0% to rule in diabetes) were derived from a clinical group (Melbourne Pathology [MP] group: n = 2,494; undiagnosed diabetes 34.6%) and then evaluated in a population-based sample (AusDiab group: n = 6,015; undiagnosed diabetes 4.6%). RESULTS For diabetes in the MP and AusDiab groups, A1C at 5.5% gave sensitivities of 98.7 and 83.5%, while A1C at 7.0% gave specificities of 98.2 and 100%, respectively. Many (61.9–69.3%) with impaired A1C (5.6–6.9%) in both populations had abnormal glucose status. CONCLUSIONS A1C ≤5.5% and ≥7.0% predicts absence or presence of type 2 diabetes, respectively, while at A1C 6.5–6.9% diabetes is highly probable in clinical and population settings. A high proportion of people with impaired A1C have abnormal glucose status requiring follow-up.