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1.
Clin Chem Lab Med ; 2024 May 13.
Article in English | MEDLINE | ID: mdl-38726766

ABSTRACT

OBJECTIVES: This study aimed to evaluate discrepancies in potassium measurements between point-of-care testing (POCT) and central laboratory (CL) methods, focusing on the impact of hemolysis on these measurements and its impact in the clinical practice in the emergency department (ED). METHODS: A retrospective analysis was conducted using data from three European university hospitals: Technische Universitat Munchen (Germany), Hospital Universitario La Paz (Spain), and Erasmus University Medical Center (The Netherlands). The study compared POCT potassium measurements in EDs with CL measurements. Data normalization was performed in categories for potassium levels (kalemia) and hemolysis. The severity of discrepancies between POCT and CL potassium measurements was assessed using the reference change value (RCV). RESULTS: The study identified significant discrepancies in potassium between POCT and CL methods. In comparing POCT normo- and mild hypokalemia against CL results, differences of -4.20 % and +4.88 % were noted respectively. The largest variance in the CL was a +4.14 % difference in the mild hyperkalemia category. Additionally, the RCV was calculated to quantify the severity of discrepancies between paired potassium measurements from POCT and CL methods. The overall hemolysis characteristics, as defined by the hemolysis gradient, showed considerable variation between the testing sites, significantly affecting the reliability of potassium measurements in POCT. CONCLUSIONS: The study highlighted the challenges in achieving consistent potassium measurement results between POCT and CL methods, particularly in the presence of hemolysis. It emphasised the need for integrated hemolysis detection systems in future blood gas analysis devices to minimise discrepancies and ensure accurate POCT results.

2.
Clin Chem Lab Med ; 58(12): 2113-2120, 2020 08 03.
Article in English | MEDLINE | ID: mdl-32745068

ABSTRACT

Objectives Serological assays for detection of SARS-CoV-2 antibodies are increasingly used during the COVID-19 pandemic caused by the SARS-Coronavirus-2. Here we evaluated the analytical and clinical performance of three commercially available SARS-CoV-2 antibody assays. Methods A total of 186 samples from 58 patients with PCR-confirmed COVID-19 infection were measured using SARS-CoV-2 antibody assays by Siemens Healthineers, Roche Diagnostics and Euroimmun. Additionally, 123 control samples, including samples collected before December 2019 and samples with potential cross-reactive antibodies were analyzed. Diagnostic specificity, sensitivity, agreement between assays and ROC curve-derived optimized thresholds were determined. Furthermore, intra- and inter-assay precision and the potential impact of interfering substances were investigated. Results SARS-CoV-2 antibody assays by Siemens and Roche showed 100% specificity. The Euroimmun assay had 98 and 100% specificity, when borderline results are considered as positive or negative, respectively. Diagnostic sensitivity for samples collected ≥14 days after PCR-positivity was 97.0, 89.4 and 95.5% using the Siemens, Roche and Euroimmun assay, respectively. Sensitivity of the Roche assay can be increased using an optimized cut-off index (0.095). However, a simultaneous decrease in specificity (98.4%) was observed. Siemens showed 95.8 and 95.5% overall agreement with results of Euroimmun and Roche assay, respectively. Euroimmun and Roche assay exhibited 92.6% overall agreement. Discordant results were observed in three COVID-19 patients and in one COVID-19 patient none of the investigated assays detected antibodies. Conclusions The investigated assays were highly specific and sensitive in detecting SARS-CoV-2 antibodies in samples obtained ≥14 days after PCR-confirmed infection. Discordant results need to be investigated in further studies.


Subject(s)
Antibodies, Viral/blood , Betacoronavirus/immunology , Serologic Tests/methods , Antibodies, Viral/immunology , Automation , Humans , ROC Curve , SARS-CoV-2
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