Immunoassays are currently the methods of choice for the measurement of a large panel of complex and heterogenous molecules owing to full automation, short turnaround time, high specificity and sensitivity. Despite remarkable performances, immunoassays are prone to several types of interferences that may lead to harmful consequences for the patient (e.g., prescription of an inadequate treatment, delayed diagnosis, unnecessary invasive investigations). A systematic search is only performed for some interferences because of its impracticality in clinical laboratories as it would notably impact budget, turnaround time, and human resources. Therefore, a case-by-case approach is generally preferred when facing an aberrant result. Hereby, we review the current knowledge on immunoassay interferences and present an algorithm for interference workup in clinical laboratories, from suspecting their presence to using the appropriate tests to identify them. We propose an approach to rationalize the attitude of laboratory specialists when faced with a potential interference and emphasize the importance of their collaboration with clinicians and manufacturers to ensure future improvements.

D-dimer is a multifaceted biomarker of concomitant activation of coagulation and fibrinolysis, which is routinely used for ruling out pulmonary embolism (PE) and/or deep vein thrombosis (DVT) combined with a clinical pretest probability assessment. The intended use of the tests depends largely on the assay used, and local guidance should be applied. D-dimer testing may suffer from diagnostic errors occurring throughout the pre-analytical, analytical, and post-analytical phases of the testing process. This review aims to provide an overview of D-dimer testing and its value in diagnosing PE and discusses the variables that may impact the quality of its laboratory assessment.

Health care workers (HCWs) are at the frontline for combatting the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. To describe recent or past infections, the novel development of serological assays enabled the assessment of the immune response developed in coronavirus disease (COVID-19). Here, we investigate SARS-CoV-2 seroprevalence in high-risk HCWs in a Belgian general hospital after both the first and the second waves. Three different immunoassays were used to determine immune response to SARS-CoV-2 in volunteer HCWs who worked in at least one COVID-19-dedicated ward [emergency department, intensive care unit (ICU) and internal medicine department] in our institution from 8 May 2020 to 19 May 2020 (n = 267) and from 18 January 2021 to 8 February 2021 (n = 189). Risk factors for seropositivity were also assessed using a questionnaire filled out by all participants. We report a steep increase in seroprevalence after the second wave and report a higher seropositivity in HCWs than in the general population. Furthermore, we show that ICU personnel and especially nurses exhibit a proportionally lower SARS-CoV-2 seroprevalence. This study documents the rapid increase in SARS-CoV-2 seroprevalence in highly exposed HCWs in a context of high viral circulation prior to vaccination campaigns. Most importantly, it suggests a lower occupational risk in ICU and illustrates the role of diagnostic labeling and use of personal protective equipment during the COVID-19 pandemic.

Objectives: Alpha-1 antitrypsin (A1AT) deficiency was first identified in patients with emphysema by the absence of the α₁ band on serum protein electrophoresis (SPE). Today, capillary zone electrophoresis is widely performed in laboratories. Here, we compared two SPE systems to detect decreased A1AT concentrations to optimize their use as a screening tool for A1AT deficiency. Methods: Serum protein electrophoresis was performed on 200 samples on the Capillarys 2 and the V8 Nexus. The latter presents two α₁ bands (α₁ band 1 and 2) while the Capillarys 2 has only one (Capillarys 2 total α₁). The measures of A1AT and α₁ acid glycoprotein (AAG) were performed as well as the phenotyping of M, S and Z alleles. Results: At a A1AT cutoff of 0.80 g/L, a cutoff of 1.21 g/L using the V8 Nexus α₁ band 2 corresponded to a 100% sensitivity and a 92.4% specificity while a 1.69% cutoff corresponded to a 100% sensitivity and a 92.4% specificity. The performance of the α₁ band 1 was suboptimal and rather corresponded to AAG. On the Capillarys 2, a cutoff of 2.0 g/L corresponded to a 75.0% sensitivity and a 86.6% specificity, while a 3.2% cutoff showed a 96.4% sensitivity and a 67.4% specificity. The V8 Nexus α₁ band 2 was the method the most correlated with A1AT (r=0.90–0.94). Conclusions: The V8 Nexus α₁ band 2 was the best predictor of A1AT deficiency, probably owing to a better resolution. The use of SPE was however unable to predict each phenotype. Phenotype or genotype studies are therefore still advisable in case of A1AT deficiency.