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Reference intervals are established either by direct or indirect approaches. Whereas the definition of direct is well established, the definition of indirect is still a matter of debate. In this paper, a general definition that covers all indirect models presently in use is proposed. With the upcoming popularity of indirect models, it has become evident that further partitioning strategies are required to minimize the risk of patients' false classifications. With indirect methods, such partitions are much easier to execute than with direct methods. The authors believe that the future of reference interval estimation belongs to indirect models with big data pools either from one laboratory or combined from several regional centres (if necessary). Independent of the approach applied, the quality assurance of the pre-analytical and analytical phase, considering biological variables and other confounding factors, is essential. [ABSTRACT FROM AUTHOR]

Objective: To compare the performance of two anti‐Mullerian (AMH) assays over a range of concentrations, in samples collected from young women. Design: A cross‐sectional method‐comparison study of 168 non‐healthcare‐seeking women. Participants: Included women were aged 18–39 years, not recently pregnant, breast feeding or using systemic hormones. Measurements: Serum AMH levels were analysed with the Beckman Coulter Access 2 assay from fresh samples and the Ansh picoAMH assay using samples stored at −80°C, in a parallel setting. Comparisons between the two assays were examined using Bland‐Altman plots. Results: Participants had a mean ± SD age of 32.6 ± 5.4 years and body mass index of 28.1 ± 7.9 kg/m2, and 60.1% were parous. Although the assay results were highly correlated (Spearman correlation.982, P <.001), the relationship between the assays was nonlinear. Serum AMH values below 4 pmol/L were lower with the picoAMH assay compared with the Access AMH assay (mean difference in this range was −0.49 pmol/L), but for samples with a mean value above 10 pmol/L, the picoAMH assay consistently measured higher than the Access AMH assay (mean difference in this range was +8.2 pmol/L). As AMH concentrations increased the absolute discrepancy between the assays also increased. Conclusions: This study demonstrates that despite the high correlation between two commercially available AMH assays, the assays performed in a discordant manner at high and low concentrations. Hence, the results of these assays are not interchangeable, highlighting the need to establish specific reference limits for individual assays to guide clinical decision‐making and the challenge of establishing future universal cut‐offs for the application of AMH levels in clinical practice. [ABSTRACT FROM AUTHOR]

The Australasian Association of Clinical Biochemists (AACB) has over the past 5 years been actively working to achieve harmonized reference intervals (RIs) for common clinical chemistry analytes using an evidence-based checklist approach where there is sound calibration and metrological traceability. It has now recommended harmonized RIs for 18 common clinical chemistry analytes which are performed in most routine laboratories and these have been endorsed by the Royal College of Pathologists of Australasia (RCPA). In 2017 another group of analytes including urea, albumin and arterial blood gas parameters were considered and suggested harmonized RIs proposed. This report provides an update of those harmonization efforts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Gestational Diabetes Mellitus (GDM) increases the risk of type 2 diabetes. A register can be used to follow-up high risk women for early intervention to prevent progression to type 2 diabetes. We evaluate the performance of the world’s first national gestational diabetes register. Research design and methods: Observational study that used data linkage to merge: (1) pathology data from the Australian states of Victoria (VIC) and South Australia (SA); (2) birth records from the Consultative Council on Obstetric and Paediatric Mortality and Morbidity (CCOPMM, VIC) and the South Australian Perinatal Statistics Collection (SAPSC, SA); (3) GDM and type 2 diabetes register data from the National Gestational Diabetes Register (NGDR). All pregnancies registered on CCOPMM and SAPSC for 2012 and 2013 were included–other data back to 2008 were used to support the analyses. Rates of screening for GDM, rates of registration on the NGDR, and rates of follow-up laboratory screening for type 2 diabetes are reported. Results: Estimated GDM screening rates were 86% in SA and 97% in VIC. Rates of registration on the NGDR ranged from 73% in SA (2013) to 91% in VIC (2013). During the study period rates of screening at six weeks postpartum ranged from 43% in SA (2012) to 58% in VIC (2013). There was little evidence of recall letters resulting in screening 12 months follow-up. Conclusions: GDM Screening and NGDR registration was effective in Australia. Recall by mail-out to young mothers and their GP’s for type 2 diabetes follow-up testing proved ineffective. [ABSTRACT FROM AUTHOR]

Introduction: Dyslipidemia affects up to 75% of morbidly obese individuals and is a key driver of cardiovascular disease. Weight loss is an established strategy to improve metabolic risk, including dyslipidemia. We aimed to determine weight loss goals for resolution of serum lipid abnormalities, by measuring improvements during progressive weight loss in obese individuals.Methods: We performed a prospective cohort study of obese individuals with the metabolic syndrome undergoing adjustable gastric banding. Lipid levels were monitored monthly for 9 months, then three monthly until 24 months.Results: There were 101 participants included, age 47.4 ± 10.9 years with body mass index 42.6 ± 5.9 kg/m2. At 24 months, total body weight loss (TBWL) was 18.3 ± 7.9%. This was associated with significant improvements in high-density lipoprotein (HDL) (1.18 vs 1.47, p < 0.001), triglyceride (2.0 vs 1.4, p < 0.001), and total cholesterol to HDL ratio (TC:HDL) (4.6 vs 3.6, p < 0.001). Over this time, progressive and linear improvements in HDL, triglycerides, and TC:HDL were seen with incremental weight loss (observed at 2.5% TBWL intervals). Significant improvements occurred after a threshold weight loss of 7.5-12.5% TBWL was achieved, with odds ratio (OR) 1.48-2.50 for normalization. These odds improved significantly with increasing weight loss (OR 18.2-30.4 with > 25% TBWL). Despite significant weight loss, there was no significant change in low-density lipoprotein (LDL).Conclusion: Significant improvements in triglycerides, HDL, and TC:HDL occur after 7.5-12.5% TBWL, with ongoing benefit after greater weight loss. LDL needs to be addressed independently, as this was not observed to respond to weight loss alone.Trial registration number: Australian Clinical Trials Registry (ACTRN12610000049077) [ABSTRACT FROM AUTHOR]