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.

Bo Burla, Makoto Arita, Masanori Arita, Anne K. Bendt, Amaury Cazenave-Gassiot, Edward A. Dennis, Kim Ekroos, Xianlin Han, Kazutaka Ikeda, Gerhard Liebisch, Michelle K. Lin, Tze Ping Loh, Peter J. Meikle, Matej Orešič, Oswald Quehenberger, Andrej Shevchenko, Federico Torta, Michael J. O. Wakelam, Craig E. Wheelock, Markus R. Wenk

Despite intense interests in developing blood measurements of Alzheimer’s disease (AD), the progress has been confounded by limited sensitivity and poor correlation to brain pathology. Here, we present a dedicated analytical platform for measuring different populations of circulating amyloid β (Aβ) proteins – exosome-bound vs. unbound – directly from blood. The technology, termed amplified plasmonic exosome (APEX), leverages in situ enzymatic conversion of localized optical deposits and double-layered plasmonic nanostructures to enable sensitive, multiplexed population analysis. It demonstrates superior sensitivity (~200 exosomes), and enables diverse target co-localization in exosomes. Employing the platform, we find that prefibrillar Aβ aggregates preferentially bind with exosomes. We thus define a population of Aβ as exosome-bound (Aβ42+ CD63+) and measure its abundance directly from AD and control blood samples. As compared to the unbound or total circulating Aβ, the exosome-bound Aβ measurement could better reflect PET imaging of brain amyloid plaques and differentiate various clinical groups.

Background: Despite being the most performed laboratory endocrine investigation, the optimum use of thyroid tests (thyrotropin [TSH] and thyroid hormone [TH] measurement) is open to question and the interpretation of the results from these tests can be ambiguous. The American Thyroid Association (ATA) with its expertise support the endeavor of the U.S. Centers for Disease Control (CDC) and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) to improve and maintain standardization and harmonization of thyroid testing. ATA mandated an international interdisciplinary working group panel to survey the status of thyroid testing by reviewing the recent literature to revise or update the criteria as needed in mutual agreement and to inform clinical care. Summary: This review represents the conclusions on the clinical use of current routine TSH and TH (thyroxine [T4] and triiodothyronine [T3]) assays, taking into account geographic differences in disease prevalence and clinical and laboratory practice among writing members. The interaction between physiological, pathophysiological, and pharmacological factors and thyroid assays can affect their measurements and confound result interpretation. These factors need to be considered in the clinical context of the patient for appropriate test ordering and result interpretation. Despite significant advances in laboratory methods over the past 50 years, routine thyroid assays remain susceptible to idiosyncratic analytical interference that may produce spurious results. Improved standardization needs to be demonstrated through ongoing international efforts before results from different assays can be considered equivalent. Emerging technology (e.g., mass spectrometry) shows promise for improved analytical performance, but more evidence of its clinical utility and improved throughput is required before it can be considered for routine use. Close clinical–laboratory collaboration is encouraged to overcome and avoid the pitfalls in thyroid testing as well as resolve clinically discrepant results. The evidence base supporting the conclusions of this review is summarized in four detailed online technical supplements. Conclusions: Over the past five decades, testing for TSH, T4, and T3 has evolved from manual radioisotopic immunoassays to nonisotopic multiplexed immunometric assays using highly automated equipment. Despite these technical advances, physicians and laboratorians performing these analyses must understand limitations of these methods to properly order tests and interpret results.

For many years the concept of patient-based quality control (QC) has been discussed and implemented in hematology laboratories; however, the techniques have not been widely implemented in clinical chemistry. This is mainly because of the complexity of this form of QC, as it needs to be optimized for each population and often for each analyte. However, the clear advantages of this form of QC, together with the ongoing realization of the shortcomings of “conventional” QC, have driven a need to provide guidance to laboratories to assist in deploying patient-based QC. This overview describes the components of a patient-based QC system (calculation algorithm, block size, truncation limits, control limits) and the relationship of these to the analyte being controlled. We also discuss the need for patient-based QC system optimization using patient data from the individual testing laboratory to reliably detect systematic errors while ensuring that there are few false alarms. The term patient-based real-time quality control covers many activities that use data from patient samples to detect analytical errors. These activities include the monitoring of patient population parameters such as the mean or median analyte value or using single within-patient changes such as the delta check. In this report, we will restrict the discussion to population-based parameters. This overview is intended to serve as a guide for the implementation of a patient-based QC system. The report does not cover the clinical evaluation of the population.