Volume 62, Issue 12

Identification of the molecular culprits of allergic reactions leveraged molecular allergology applications in clinical laboratory medicine. Molecular allergology shifted the focus from complex, heterogeneous allergenic extracts, e.g. pollen, food, or insect venom, towards genetically and immunologically defined proteins available for in vitro diagnosis. Molecular allergology is a precision medicine approach for the diagnosis, stratification, therapeutic management, follow-up and prognostic evaluation of patients within a large range of allergic diseases. Exclusively available for in vitro diagnosis, molecular allergology is nonredundant with any of the current clinical tools for allergy investigation. As an example of a major application, discrimination of genuine sensitization from allergen cross-reactivity at the molecular level allows the proper targeting of the culprit allergen and thus dramatically improves patient management. This review aims at introducing clinical laboratory specialists to molecular allergology, from the biochemical and genetic bases, through immunological concepts, to daily use in the diagnosis and management of allergic diseases.

Recreational use of nitrous oxide (N 2 O) has become a major health issue worldwide, with a high number of clinical events, especially in neurology and cardiology. It is essential to be able to detect and monitor N 2 O abuse to provide effective care and follow-up to these patients. Current recommendations for detecting N 2 O in cases of recreational misuse and consumption markers are lacking. We aimed to update current knowledge through a review of the literature on N 2 O measurement and kinetics. We reviewed the outcomes of experiments, whether in preclinical models ( in vitro or in vivo ), or in humans, with the aim to identify biomarkers of intoxication as well as biomarkers of clinical severity, for laboratory use. Because N 2 O is eliminated 5 min after inhalation, measuring it in exhaled air is of no value. Many studies have found that urine and blood matrices concentrations are connected to ambient concentrations, but there is no similar data for direct exposure. There have been no studies on N 2 O measurement in direct consumers. Currently, patients actively abusing N 2 O are monitored using effect biomarkers (biomarkers related to the effects of N 2 O on metabolism), such as vitamin B12, homocysteine and methylmalonic acid.

The ultimate goal of value-based laboratory medicine is maximizing the effectiveness of laboratory tests in improving patient outcomes, optimizing resources and minimizing unnecessary costs. This approach abandons the oversimplified notion of test volume and cost, in favor of emphasizing the clinical utility and quality of diagnostic tests in the clinical decision-making. Several key elements characterize value-based laboratory medicine, which can be summarized in some basic concepts, such as organization of in vitro diagnostics (including appropriateness, integrated diagnostics, networking, remote patient monitoring, disruptive innovations), translation of laboratory data into clinical information and measurable outcomes, sustainability, reimbursement, ethics (e.g., patient empowerment and safety, data protection, analysis of big data, scientific publishing). Education and training are also crucial, along with considerations for the future of the profession, which will be largely influenced by advances in automation, information technology, artificial intelligence, and regulations concerning in vitro diagnostics. This collective opinion paper, composed of summaries from presentations given at the two-day European Federation of Laboratory Medicine (EFLM) Strategic Conference “A vision to the future: value-based laboratory medicine” (Padova, Italy; September 23–24, 2024), aims to provide a comprehensive overview of value-based laboratory medicine, projecting the profession into a more clinically effective and sustainable future.

Point-of-care testing (POCT), near-patient testing (NPT) and patient self-tests (PST) are diagnostic examinations performed at the time and place of patient care. While POCT and NPT are performed and analyzed by medical professionals, PST are based on samples and parameters directly collected and analyzed by lay users. These tests are spreading both in high income countries and in low to middle income countries as they are expected to improve healthcare efficiency and equity, by saving resources, releasing pressure from hospitals and reducing logistical barriers. However, accurate multidisciplinary assessment is mandatory to ensure that what they promise is real. We reviewed some important ethical aspects, international standards and regulations. The current risks associated with alternative ways of testing are explained by the principles of respect for patient autonomy and non-maleficence. Further evidence from multidisciplinary assessment is needed to evaluate pros and cons in light of the principles of beneficence and justice. Although POCT or NPT need common regulation and accurate provider training to ensure safe and appropriate interpretation of results, PST needs even more attention as they are subject to direct patient use. Randomized controlled trails including patient education should be conducted in order to provide reliable evidence on clinical outcomes, patient acceptance and cost-effectiveness. Mandatory regulation is needed to avoid harm and EU regulation should help different countries maintain a safe use of devices in a global population of producers and users.

The development of microRNA (miRNA)-based biomarkers has gained significant attention due to their potential diagnostic, prognostic and therapeutic applications. However, the reproducibility of miRNA biomarker research faces unique challenges, primarily due to the influence of pre-analytical and analytical factors. The absence of standardized procedures contributes to inconsistencies across studies, alongside challenges in reference gene selection, data analysis methods and miRNA profiling platforms. Inter-laboratory comparison trials, or ring trials, offer a strategic approach to address technical and biological variability in miRNA biomarker studies. These trials promote standardization, identify sources of variability and strengthen the correlation between miRNAs and clinical outcomes. Despite their underutilization in miRNA biomarker research, ring trials represent a valuable tool for enhancing reproducibility and expediting the translation of miRNA-based biomarkers into clinical applications.

The Sigma metric is widely used in laboratory medicine.

While Six Sigma is used in different disciplines to improve quality, Tony Badric and Elvar Theodorsson in a recent paper in CCLM have questioned Six Sigma application in medical laboratory concluding Six Sigma has provided no value to medical laboratory. In addition, the authors have expanded their criticism to Total Analytical Error (TAE) model and statistical quality control. To address their arguments, we have explained the basics of TAE model and Six Sigma and have shown the value of Six Sigma to medical laboratory.

Objectives Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by homozygous deletion and compound heterozygous mutations in survival motor neuron 1 ( SMN1 ), with severity tied to the copy number of survival motor neuron 2 ( SMN2 ). This study aimed to develop a rapid and comprehensive method for the diagnosis of SMA. Methods A total of 292 children with clinically suspected SMA and 394 family members were detected by the amplification refractory mutation system polymerase chain reaction-capillary electrophoresis (ARMS-PCR-CE) method, which targeted 19 reported mutations, and the results were compared with those in multiplex ligation-dependent probe amplification (MLPA). Individuals with identified point mutations were further confirmed by SMN1 long-range PCR and Sanger sequencing. Results A total of 202 children with SMA, 272 carriers, and 212 normal individuals were identified in this study. No difference was found in the R-value distribution of exons 7 and 8 in SMN1 and SMN2 among these cohorts, with coefficients of variation consistently below 0.08. To detect exon 7 and 8 copy numbers in SMN1 and SMN2 , the ARMS-PCR-CE results were concordant with those of MLPA. Approximately 4.95 % (10/202) of the study patients had compound heterozygous mutations. Conclusions The ARMS-PCR-CE assay is a comprehensive, rapid, and accurate diagnostic method for SMA that simultaneously detects copy numbers of exons 7 and 8 in SMN1 / SMN2 , as well as 19 point mutations in SMN1 and 2 enhancers in SMN2 . This approach can effectively reduce the time frame for diagnosis, facilitating early intervention and preventing birth defects.

Objectives Dihydropyrimidine dehydrogenase (DPD) deficiency is the main cause of severe fluoropyrimidine-related toxicities. The best strategy for identifying DPD-deficient patients is still not defined. The EMA recommends targeted DPYD genotyping or uracilemia (U) testing. We analyzed the concordance between both approaches. Methods This study included 19,376 consecutive French patients with pre-treatment plasma U, UH2 and targeted DPYD genotyping (*2A, *13, D949V, *7) analyzed at Eurofins Biomnis (2015–2022). Results Mean U was 9.9 ± 10.1 ng/mL (median 8.7, range 1.6–856). According to French recommendations, 7.3 % of patients were partially deficient (U 16–150 ng/mL) and 0.02 % completely deficient (U≥150 ng/mL). DPYD variant frequencies were *2A: 0.83 %, *13: 0.17 %, D949V: 1.16 %, *7: 0.05 % (2 homozygous patients with U at 22 and 856 ng/mL). Variant carriers exhibited higher U (median 13.8 vs. 8.6 ng/mL), and lower UH2/U (median 7.2 vs. 11.8) and UH2/U 2 (median 0.54 vs. 1.37) relative to wild-type patients (p<0.00001). Sixty-six% of variant carriers exhibited uracilemia <16 ng/mL, challenging correct identification of DPD deficiency based on U. The sensitivity (% patients with a deficient phenotype among variant carriers) of U threshold at 16 ng/mL was 34 %. The best discriminant marker for identifying variant carriers was UH2/U 2 . UH2/U 2 <0.942 (29.7 % of patients) showed enhanced sensitivity (81 %) in identifying deleterious genotypes across different variants compared to 16 ng/mL U. Conclusions These results reaffirm the poor concordance between DPD phenotyping and genotyping, suggesting that both approaches may be complementary and that targeted DPYD genotyping is not sufficiently reliable to identify all patients with complete deficiency.

Objectives Laboratory medical reports are often not intuitively comprehensible to non-medical professionals. Given their recent advancements, easier accessibility and remarkable performance on medical licensing exams, patients are therefore likely to turn to artificial intelligence-based chatbots to understand their laboratory results. However, empirical studies assessing the efficacy of these chatbots in responding to real-life patient queries regarding laboratory medicine are scarce. Methods Thus, this investigation included 100 patient inquiries from an online health forum, specifically addressing Complete Blood Count interpretation. The aim was to evaluate the proficiency of three artificial intelligence-based chatbots (ChatGPT, Gemini and Le Chat) against the online responses from certified physicians. Results The findings revealed that the chatbots’ interpretations of laboratory results were inferior to those from online medical professionals. While the chatbots exhibited a higher degree of empathetic communication, they frequently produced erroneous or overly generalized responses to complex patient questions. The appropriateness of chatbot responses ranged from 51 to 64 %, with 22 to 33 % of responses overestimating patient conditions. A notable positive aspect was the chatbots’ consistent inclusion of disclaimers regarding its non-medical nature and recommendations to seek professional medical advice. Conclusions The chatbots’ interpretations of laboratory results from real patient queries highlight a dangerous dichotomy – a perceived trustworthiness potentially obscuring factual inaccuracies. Given the growing inclination towards self-diagnosis using AI platforms, further research and improvement of these chatbots is imperative to increase patients’ awareness and avoid future burdens on the healthcare system.

Objectives This study aimed to assess the validity of external quality assessment (EQA) laboratory results across various cultural and environmental contexts and to identify potential improvement areas. Methods The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on Global Laboratory Quality (TF-GLQ) conducted a 2-year study (2022 and 2023) in which EQA materials, related software and online training was provided by a commercial vendor to 100 laboratories in ten IFCC member society countries. The results were analysed on a monthly basis by the TF-GLQ, to show the number of submissions per country, tests per lab, acceptability rates, random failures and to get a measure of which analytes performed poorly. Results The EQA material was dispatched on a quarterly basis. Some countries had problems with customs releasing the material in a timely manner, resulting in laboratories not receiving them on time leading to no submission. We report here the results for the second year of the survey. The number of examinations varied between laboratories, ranging from seven to 84 analytes. Of the ten countries surveyed, six averaged greater than 90 % acceptable results over the whole 12-months cycle, one had unacceptable results for two of the nine months they returned results and the other four were considered to not perform to an acceptable standard. Conclusions All 100 participating laboratories indicated satisfaction with the EQA survey and related services, including on-site training, and report handling. However, specimen receiving issues, suggest benefits in dispatching materials for a full 12-month cycle. Significant discrepancies in EQA performance indicate that four countries require long-term assistance, training and guidance. To ensure reliable patient results, promoting EQA in certain countries is essential to achieve the required level of quality.

Objectives Conventional autoverification rules evaluate analytes independently, potentially missing unusual patterns of results indicative of errors such as serum contamination by collection tube additives. This study assessed whether multivariate anomaly detection algorithms could enhance the detection of such errors. Methods Multivariate Gaussian, k-nearest neighbours (KNN) distance, and one-class support vector machine (SVM) anomaly detection models, along with conventional limit checks, were developed using a training dataset of 127,451 electrolyte, urea, and creatinine (EUC) results, with a 5 % flagging rate targeted for all approaches. The models were compared with limit checks for their ability to detect atypical EUC results from samples spiked with additives from collection tubes: EDTA, fluoride, sodium citrate, or acid citrate dextrose (n=200 per contaminant). The study additionally assessed the ability of the models to identify 127,449 single-analyte errors, a potential weakness of multivariate models. Results The KNN distance and SVM models outperformed limit checks for detecting all contaminants (p-values <0.05). The multivariate Gaussian model did not surpass limit checks for detecting EDTA contamination but was superior for detecting the other additives. All models surpassed limit checks for identifying single-analyte errors, with the KNN distance model demonstrating the highest overall sensitivity. Conclusions Multivariate anomaly detection models, particularly the KNN distance model, were superior to the conventional approach for detecting serum contamination and single-analyte errors. Developing multivariate approaches to autoverification is warranted to optimise error detection and improve patient safety.

Objectives Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. Methods The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. Results The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. Conclusions The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Objectives Even in the current era of hematology analyzer automation and peripheral equipment, quality control sample measurement remains a manual task, leading to variability in quality control data and increased workload. In this study, we evaluated the performance of quality control measurement using the BT-50 Transportation Unit (BT-50, Sysmex, Kobe, Japan), equipped with a scheduled automatic quality control function, to ensure measurement accuracy and streamline the workflow of hematology testing. Methods We evaluated the automatic measurement performance of quality control samples using the BT-50 for six representative blood test parameters: WBC (white blood cell), RBC (red blood cell), HGB (hemoglobin), HCT (hematocrit), PLT (platelet), and RET% (reticulocyte percent). We evaluated the equivalence and compared measurement accuracy between the BT-50 and the manual method. We then compared the variability to other laboratories and confirmed the stability of quality control samples. We also evaluated changes in workflow and staff resources before and after the introduction of the BT-50. Results The quality control measurement results for the BT-50 and the manual method were found to be equivalent for all six parameters. The variability measured by the BT-50 was lower for some parameters compared to the manual method. Furthermore, the workflow was streamlined by reducing manual processes, resulting in increased efficiency. Conclusions We confirmed the performance of quality control measurements using the schedule function of the BT-50. Introducing the BT-50 reduced the operator’s workload, improved operational efficiency, and promoted the standardization of quality control measurements.

Objectives Steroid hormone levels of patients may be monitored via dried blood spot (DBS) sampling at home. Stability of steroid hormones in DBS samples, however, needs to be established. Methods DBS samples from healthy volunteers were collected and stored at various temperatures. Steroid hormone concentrations in DBS were measured directly, at day 2, day 7 and day 14 following storage at 37 °C and after 7 days, 14 days, 3 months and 6 months following storage at −20 °C, 4 °C and room temperature (RT). Cortisol, cortisone, corticosterone, testosterone, androstenedione, and 17-hydroxyprogesterone (17-OHP) were assessed using LC-MS/MS. Results All steroids were stable (±15 %) up to 14 days when stored at 37 °C, except for cortisone (only stable until 2 days). All steroids were stable up to 6 months when stored at −20 °C, 4 °C and RT. However, there were some exceptions, for androstenedione at RT (only stable until 7 days), for 17-OHP when stored at −20 °C (only stable until 3 months), for cortisone at RT and 4 °C (only stable until 14 days), and cortisol at RT (only stable until 3 months). Conclusions Overall, we demonstrated stability of steroid hormone concentrations in DBS under various conditions which may be encountered during shipping to the diagnostic laboratory and during long-term storage before analysis.

Objectives The quantification of functional C1 inhibitor activity (fC1-INH) is an important tool to diagnose bradykinin-mediated angioedema (AE), whether hereditary or acquired. For that an accurate assay is necessary, therefore we evaluated the analytical performances of a fC1-INH chromogenic assay (Berichrom ® , Siemens) performed utilizing an Optilite turbidimeter (Binding Site). Methods fC1-INH was quantified by means of the chromogenic assay Berichrom ® . Internal quality controls were used to determine the precision of the assay. Stability under various storage and matrix conditions, uncertainty, linearity, interference (of hemolysis, lipemia, and icterus), agreement with the manual Technochrom ® assay, and diagnostic performances were further evaluated on samples from patients and healthy donors. Results The fC1-INH Berichrom ® assay presented good performances regarding intra- and inter-assay precision (CV: 1.3–4.5 % and 3.0–6.0 %, respectively), expanded uncertainty (5.5 % at normal level and 12.5 % at the clinical threshold) and linearity (rho 2 >0.99: range 7–130 % activity). Addition of interfering substances (hemoglobin <16 g/L, intralipid ® <12 g/L, and bilirubin <1 g/L) did not affect fC1-INH quantification. fC1-INH activity from healthy donors remained stable in citrate whole blood until 4 days at room temperature, and 7 days when plasma was collected. Agreement between the automated Berichrom ® assay and the manual Technochrom ® assay (n=47) was excellent as obtained with both quantitative (Deming regression and Bland–Altman difference plot) and qualitative (Kappa index=1) analyses. Finally, the diagnostic performance of the quantification of fC1-INH for AE evaluated on 81 patients revealed a sensitivity of 100 %, a specificity of 97.2 %, a positive predictive value of 83.3 % and a negative predictive value of 100 %. Conclusions The automated fC1-INH Berichrom ® assay showed good performance, both at the analytical and diagnostic/clinical levels that allowed its usage in a clinical laboratory for C1-INH-dependent bradykinin-mediated AE research in combination with quantitative C1-INH and C4 determinations.

Objectives Biological variation is a relevant component of diagnostic uncertainty. In addition to within-subject and between-subject variation, preanalytical variation also includes components that contribute to biological variability. Among these, daily recurring, i.e., diurnal physiological variation is of particular importance, as it contains both a random and a non-random component if the exact time of blood collection is not known. Methods We introduce four time-dependent characteristics (TDC) of diurnal variations for measurands to assess the relevance and extent of time dependence on the evaluation of laboratory results. Results TDC address (i) a threshold for considering diurnality, (ii) the expected relative changes per time unit, (iii) the permissible time interval between two blood collections at different daytimes within which the expected time dependence does not exceed a defined analytical uncertainty, and (iv) a rhythm-expanded reference change value. TDC and their importance will be exemplified by the measurands aspartate aminotransferase, creatine kinase, glucose, thyroid stimulating hormone, and total bilirubin. TDCs are calculated for four time slots that reflect known blood collection schedules, i.e., 07:00–09:00, 08:00–12:00, 06:00–18:00, and 00:00–24:00. The amplitude and the temporal location of the acrophase are major determinates impacting the diagnostic uncertainty and thus the medical interpretation, especially within the typical blood collection time from 07:00 to 09:00. Conclusions We propose to check measurands for the existence of diurnal variations and, if applicable, to specify their time-dependent characteristics as outlined in our concept.

Objectives Serum protein electrophoresis (SPE) in combination with immunotyping (IMT) is the diagnostic standard for detecting monoclonal proteins (M-proteins). However, interpretation of SPE and IMT is weakly standardized, time consuming and investigator dependent. Here, we present five machine learning (ML) approaches for automated detection of M-proteins on SPE on an unprecedented large and well-curated data set and compare the performance with that of laboratory experts. Methods SPE and IMT were performed in serum samples from 69,722 individuals from Norway. IMT results were used to label the samples as M-protein present (positive, n=4,273) or absent (negative n=65,449). Four feature-based ML algorithms and one convolutional neural network (CNN) were trained on 68,722 randomly selected SPE patterns to detect M-proteins. Algorithm performance was compared to that of an expert group of clinical pathologists and laboratory technicians (n=10) on a test set of 1,000 samples. Results The random forest classifier showed the best performance (F1-Score 93.2 %, accuracy 99.1 %, sensitivity 89.9 %, specificity 99.8 %, positive predictive value 96.9 %, negative predictive value 99.3 %) and outperformed the experts (F1-Score 61.2 ± 16.0 %, accuracy 89.2 ± 10.2 %, sensitivity 94.3 ± 2.8 %, specificity 88.9 ± 10.9 %, positive predictive value 47.3 ± 16.2 %, negative predictive value 99.5 ± 0.2 %) on the test set. Interestingly the performance of the RFC saturated, the CNN performance increased steadily within our training set (n=68,722). Conclusions Feature-based ML systems are capable of automated detection of M-proteins on SPE beyond expert-level and show potential for use in the clinical laboratory.

Objectives Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry detecting M-proteins (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to MRD-assessment in bone marrow. However, MS-MRD still comprises of manual steps that hamper upscaling of MS-MRD testing. Here, we introduce a proof-of-concept for a novel workflow using data independent acquisition-parallel accumulation and serial fragmentation (dia-PASEF) and automated data processing. Methods Using automated data processing of dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in MM patient follow-up samples. Assay performance of parallel reaction monitoring (prm)-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target selection. Results No significant differences were observed in lowest detectable concentration, linearity, and slope coefficient when comparing prm-PASEF and dia-PASEF measurements of serial dilutions of patient sera. To improve assay development times, we tested multiplexing patient intake sera for target selection which resulted in the selection of identical clonotypic peptides for both simplex and multiplex dia-PASEF. Furthermore, assay development times improved up to 25× when measuring multiplexed samples for peptide selection compared to simplex. Conclusions Dia-PASEF technology combined with automated data processing and multiplexed target selection facilitated the development of a faster MS-MRD workflow which benefits upscaling and is an important step towards the clinical implementation of MS-MRD.

Objectives This study performed an analytical validation study of the Mindray high-sensitivity cardiac troponin I (hs-cTnI) assay addressing limit of blank (LoB), limit of detection (LoD), precision, linearity, analytical specificity and sex-specific 99th percentile upper reference limits. Methods LoB, LoD, precision, linearity and analytical specificity were studied according to Clinical and Laboratory Standards Institute. We used one reagent lot and one CL1200i analyzer. Skeletal troponin I and T, cardiac troponin T, troponin C, actin, tropomyosin, myosin light chain, myoglobin and creatine kinase (CK-MB) were studied for cross-reactivity. Interference with biotin was examined. Lithium heparin samples (one freeze thaw cycle) from healthy males and females were measured to determine the 99th percentiles by using the non-parametric method. Analyses were performed before and after excluding subjects with clinical conditions and/or increased surrogate biomarkers. Results The Mindray hs-cTnI assay met criteria to be considered as a hs-cTn assay. LoB and LoD was <0.1 ng/L and 0.1 ng/L, respectively. Repeatability had a coefficient of variation 1.2–3.8 %, and within-laboratory imprecision 1.7–5.0 %. The measuring interval ranged from 1.1 to 28,180 ng/L. The analytical specificity was clinically acceptable for the interferents studied. After exclusions, the 99th percentile URLs obtained were 10 ng/L overall, 5 ng/L for females and 12 ng/L for males. Conclusions Analytical observations of the Mindray hs-cTnI assay demonstrated excellent LoB, LoD, precision, linearity and analytical specificity, that were in alignment with the manufacturer’s claims and regulatory guidelines for hs-cTnI. The assay is suitable for clinical investigation for patient-oriented studies.

Objectives Glycated albumin (GA) has potential value in the management of people with diabetes; however, to draw meaningful conclusions between clinical studies it is important that the GA values are comparable. This study investigates the standardization of the Norudia Glycated Albumin and Lucica Glycated Albumin-L methods. Methods The manufacturer reported imprecision was verified by performing CLSI-EP15-A3 protocol using manufacturer produced controls. The Japanese Clinical Chemistry Reference Material (JCCRM)611-1 was measured 20 times to evaluate the accuracy of both methods. GA was also measured in 1,167 patient samples and results were compared between the methods in mmol/mol and %. Results Maximum CV for Lucica was ≤0.6 % and for Norudia ≤1.8 % for control material. Results in mmol/mol and % of the JCCRM611-1 were within the uncertainty of the assigned values for both methods. In patient samples the relative difference in mmol/mol between the two methods ranged from −10.4 % at a GA value of 183 mmol/mol to +8.7 % at a GA value of 538 mmol/mol. However, the relative difference expressed in percentage units ranged from of 0 % at a GA value of 9.9 % to +1.7 % at a GA value of 30 %. Conclusions The results in mmol/mol between the two methods for the patient samples were significantly different compared to the results in %. It is not clear why patient samples behave differently compared to JCCRM611-1 material. Valuable lessons can be learnt from comparing the standardization process of GA with that of HbA 1c .

Objectives Internal and external quality assurance materials often use highly processed matrixes. This can render the materials non-commutable. Monitoring laboratory methods with patient medians helps in identifying and correcting systematic errors that may affect diagnostic accuracy. The aim of the present study was to use HbA 1c patient results for monitoring of method performance over time. Methods Test HbA 1c results from 2010 to 2022 was analyzed (n=722,553) regarding changes over time and seasonal variation. The HbA 1c testing was initially performed on a Cobas 501 instrument using immunological detection but in May 2017 the method was replaced by capillary electrophoresis on Capillarys 3 Tera. Results There was a steady decrease in HbA 1c values. From 2011 to 2021 the decrease was for 0.10 percentile 6.6 %, lower quartile 7.9 %, median 10.2 %, mean values 9 %, upper quartile 11.2 %, and 0.90 percentile 9.3 %. No clear shift in HbA 1c levels was observed due to the shift in methods. The median HbA 1c values per month was approximately 44 mmol/mol (6.2 %, DCCT/NGSP). The only month with a median HbA 1c that differed by more than 1 mmol/mol was July with a median value of 42 mmol/mol (6.0 %). Conclusions The patient data showed a similar decrease as in the National Diabetes Register which indicates that the method is stable over time without any sudden changes and that the seasonal variation is low. The continuous decrease in HbA 1c values over time is most likely to a shift towards earlier detection of patient with diabetes and improved treatment.