Behind the scenes of EQA-characteristics, capabilities, benefits and assets of external quality assessment (EQA).

The main stakeholders in external quality assessment (EQA) programs are the participants, in whose interests these challenges are ultimately organised. EQA schemes in the medical field contribute to improving the quality of patient care by evaluating the analytical and diagnostic quality of laboratory and point-of-care tests (POCT) by independent third parties and, if necessary, pointing out erroneous measurement results and analytical or diagnostic improvement potential. Other benefits include the option of using EQA samples for other important laboratory procedures, such as the verification or validation of diagnostic medical devices (IVD-MDs), a contribution to the estimation of measurement uncertainty, a means of training and educating laboratory staff through educational EQA programmes or samples, or even for independent and documented monitoring of staff competence, such as on samples with unusual or even exceptional characteristics.

Behind the scenes of EQA - characteristics, capabilities, benefits and assets of external quality assessment (EQA).

This is the first in a series of five papers that detail the role and substantial impact that external quality assessment (EQA) and their providers' services play in ensuring diagnostic (IVD) performance quality. The aim is to give readers and users of EQA services an insight into the processes in EQA, explain to them what happens before EQA samples are delivered and after examination results are submitted to the provider, how they are assessed, what benefits participants can expect, but also who are stakeholders other than participants and what significance do EQA data and assessment results have for them. This first paper presents the history of EQA, insights into legal, financing and ethical matters, information technology used in EQA, structure and lifecycle of EQA programs, frequency and intensity of challenges, and unique requirements of extra-examination and educational EQA programs.

Behind the scenes of EQA - characteristics, capabilities, benefits and assets of external quality assessment (EQA).

External quality assessment (EQA) cycles are the smallest complete units within EQA programs that laboratories can use to obtain external assessments of their performance. In each cycle, several samples are distributed to the laboratories registered for participation, and ideally, EQA programs not only cover the examination procedures but also the pre- and post-examination procedures. The properties and concentration range of measurands in individual samples are selected with regard to the intended challenge for the participants so that each sample fulfils its purpose.

Behind the scenes of EQA - characteristics, capabilities, benefits and assets of external quality assessment (EQA).

External quality assessment (EQA) enhances patient safety through the evaluation of the quality of laboratory-based and point of care testing. Regulatory agencies and accreditation organizations utilize the results and the laboratory's response to them as part of assessing the laboratory's fitness to practice. In addition, where EQA samples are commutable and the assigned value has been determined using reference measurement procedures (RMPs), EQA data contributes to the verification of metrological traceability of assays as part of the post-market surveillance of diagnostic (IVD) medical devices (IVD-MDs).

Behind the scenes of EQA - characteristics, capabilities, benefits and assets of external quality assessment (EQA).

Providers of external quality assessment (EQA) programs evaluate data or information obtained and reported by participant laboratories using their routine procedures to examine properties or measurands in samples provided for this purpose. EQA samples must offer participants an equal chance to obtain accurate results, while being designed to provide results in clinically relevant ranges. It is the responsibility of the EQA provider to meet the necessary requirements for homogeneity, stability and some other properties of the EQA items in order to offer participants a fair, reliable and technically interesting EQA experience.

Quantification of Difference in Nonselectivity Between In Vitro Diagnostic Medical Devices.

Correct measurement results from in vitro diagnostic (IVD) medical devices (MD) are crucial for optimal patient care. The performance of IVD-MDs is often assessed through method comparison studies. Such studies can be compromised by the influence of various factors.

Effects on HbA1c of referral of type 2 diabetes patients to secondary care.

Aim: To study trajectories of HbA1c in type 2 diabetes (T2D) patients referred to diabetes outpatient clinics (DOCs), and to explore characteristics of referrals and patient pathways in patients treated in DOCs.

Methods: We retrospectively followed T2D patients from the Norwegian population-based ROSA 4 study to identify persons with T2D who were referred to a DOC. We used latent class trajectory modelling to identify subgroups of patients with similar patterns of HbA1c one year before to one year after the first consultation at a DOC.

Women Suffered More Than Men Both During and After the COVID-19 Pandemic-A Cross-Sectional Study Among 29,079 Patients With Type 2 Diabetes.

Objective: To investigate the gender differences and the disparities between infected and noninfected patients with type 2 diabetes (T2D) regarding patient-reported experiences during the COVID-19 pandemic in Norway.

Method: Register study using questionnaires sent electronically to patients with T2D, June 2022. The questionnaire included 82 questions covering COVID-19 disease, symptoms, medications, comorbidities, hospital care, possibility of working from home and information received from health authorities.

The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS).

Objectives: An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI).

A standard to report biological variation data studies - based on an expert opinion.

There is a need for standards for generation and reporting of Biological Variation (BV) reference data. The absence of standards affects the quality and transportability of BV data, compromising important clinical applications. To address this issue, international expert groups under the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have developed an online resource (https://tinyurl.

Bowel Dysfunction After Colon Cancer Surgery: A Prospective, Longitudinal, Multicenter Study.

Background: Longitudinal studies on functional outcomes after colon resection are limited.

Objective: To evaluate bowel dysfunction and related distress 1 and 3 years after colon resection using the low anterior resection syndrome score as well as specific validated items.

Design: This study presents the long-term results of bowel dysfunction and related distress based on the Quality of Life in Colon Cancer study, an observational, prospective multicenter study of patients with newly diagnosed colon cancer.

Outcome-based analytical performance specifications: current status and future challenges.

Analytical performance specifications (APS) based on outcomes refer to how 'good' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs.

Applying the Milan models to setting analytical performance specifications - considering all the information.

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors.

Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport.

Background: When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models.

A New Concept for Reference Change Values-Regression to the Population Mean.

Background: Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval.

A register-based study describing time trends in risk factor control and serious hypoglycaemia together with the effects of starting continuous glucose monitoring in people with type 1 diabetes in Norway.

Aims: To describe trends in risk factor control and serious hypoglycaemia in people with type 1 diabetes and to assess the effect of starting continuous glucose monitoring (CGM) in the real-world setting.

Methods: Two cross-sectional surveys including 5746 individuals in 2012 and 18,984 individuals in 2020 based on data recorded in the Norwegian Diabetes Register for Adults (NDR-A) and an analysis of a longitudinal cohort of 2057 individuals where data on CGM and HbA1c were available in the NDR-A in 2012 and 2020.

Results: In the cross-sectional surveys mean HbA1c decreased from 66 mmol/mol (99% CI 65, 66) (8.