Background SARS‐CoV‐2 pandemic is currently ongoing, meanwhile vaccinations are rapidly underway in some countries. The quantitative immunoassays detecting antibodies against spike antigen of SARS‐CoV‐2 have been developed based on the findings that they have a better correlation with the neutralizing antibody. Methods The performances of the Abbott Architect SARS‐CoV‐2 IgG II Quant, DiaSorin LIAISON SARS‐CoV‐2 TrimericS IgG, and Roche Elecsys anti‐SARS‐CoV‐2 S were evaluated on 173 sera from 126 SARS‐CoV‐2 patients and 151 pre‐pandemic sera. Their correlations with GenScript cPass SARS‐CoV‐2 Neutralization Antibody Detection Kit were also analyzed on 173 sera from 126 SARS‐CoV‐2 patients. Results Architect SARS‐CoV‐2 IgG II Quant and Elecsys anti‐SARS‐CoV‐2 S showed the highest overall sensitivity (96.0%), followed by LIAISON SARS‐CoV‐2 TrimericS IgG (93.6%). The specificities of Elecsys anti‐SARS‐CoV‐2 S and LIAISON SARS‐CoV‐2 TrimericS IgG were 100.0%, followed by Architect SARS‐CoV‐2 IgG II Quant (99.3%). Regarding the correlation with cPass neutralization antibody assay, LIAISON SARS‐CoV‐2 TrimericS IgG showed the best correlation (Spearman rho = 0.88), followed by Architect SARS‐CoV‐2 IgG II Quant and Elecsys anti‐SARS‐CoV‐2 S (all rho = 0.87). Conclusions The three automated quantitative immunoassays showed good diagnostic performance and strong correlations with neutralization antibodies. These assays will be useful in diagnostic assistance, evaluating the response to vaccination, and the assessment of herd immunity in the future.

The Siemens severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG (sCOVG; Siemens Healthcare Diagnostics Inc., NY, USA) and Abbott SARS-CoV-2 IgG II Quant (CoV-2 IgG II; Abbott Laboratories, Sligo, Ireland), which are automated, quantitative SARS-CoV-2-binding antibody assays, have been recently launched. This study aimed to evaluate the humoral immune response of BNT162b2 and ChAdOx1 nCoV-19 vaccines using sCOVG and CoV-2 IgG II and compare the quantitative values with the results of the GenScript surrogate virus neutralization test (cPASS; GenScript, USA Inc., NJ, USA).

Digital morphology (DM) analyzers are widely applied in clinical practice. It is necessary to evaluate performances of DM analyzers by focusing on leukopenic samples. We evaluated the analytical performance, including precision, of a Sysmex DI-60 system (Sysmex, Kobe, Japan) on white blood cell (WBC) differentials in leukopenic samples. In a total of 40 peripheral blood smears divided into four groups according to WBC count (normal, mild, moderate, and severe leukopenia; each group n = 10), we evaluated precision of WBC preclassificaiton by DI-60. %coefficients of variation (%CVs) of precision varied for each sample and for each cell class; the fewer cells per slide, the higher %CV. The overall specificity and efficiency were high for all cell classes except plasma cells (95.9–99.9% and 90.0–99.4%, respectively). The largest absolute value of mean difference between DI-60 and manual count in each group was: 10.77, normal; 10.22, mild leukopenia; 19.09, moderate leukopenia; 47.74, severe leukopenia. This is the first study that evaluated the analytical performance of DI-60 on WBC differentials in leukopenic samples as the main subject. DI-60 showed significantly different performance depending on WBC count. DM analyzers should be evaluated separately in leukopenic samples, even if the overall performance was acceptable.

Objectives CellaVision DC-1 (DC-1, Sysmex, Kobe, Japan) is a newly launched digital morphology analyzer that was developed mainly for small to medium-volume laboratories. We evaluated the precision, qualitative performance, comparison of cell counts between DC-1 and manual counting, and turnaround time (TAT) of DC-1. Methods Using five peripheral blood smear (PBS) slides spanning normal white blood cell (WBC) range, precision and qualitative performance of DC-1 were evaluated according to the Clinical and Laboratory Standards Institute (CLSI) EP15-A3, EP15-Ed3-IG1, and EP12-A2 guidelines. Cell counts of DC-1 and manual counting were compared according to the CLSI EP 09C-ED3 guidelines, and TAT of DC-1 was also compared with TAT of manual counting. Results DC-1 showed excellent precision (%CV, 0.0–3.5%), high specificity (98.9–100.0%), and high negative predictive value (98.4–100.0%) in 18 cell classes (12 WBC classes and six non-WBC classes). However, DC-1 showed 0% of positive predictive value in seven cell classes (metamyelocytes, myelocytes, promyelocytes, blasts, plasma cells, nucleated red blood cells, and unidentified). The largest absolute mean differences (%) of DC-1 vs. manual counting was 2.74. Total TAT (min:s) was comparable between DC-1 (8:55) and manual counting (8:55). Conclusions This is the first study that comprehensively evaluated the performance of DC-1 including its TAT. DC-1 has a reliable performance that can be used in small to medium-volume laboratories for assisting PBS review. However, DC-1 may make unnecessary workload for cell verification in some cell classes.

Background: Biomarkers and clinical indices have been investigated for predicting mortality in patients with coronavirus disease . We explored the prognostic utility of procalcitonin (PCT), presepsin, and the Veterans Health Administration COVID-19 (VACO) index for predicting 30-day-mortality in COVID-19 patients.Methods: In total, 54 hospitalized COVID-19 patients were enrolled. PCT and presepsin levels were measured using the Elecsys BRAHMS PCT assay (Roche Diagnostics GmbH, Mannheim, Germany) and HISCL Presepsin assay (Sysmex, Kobe, Japan), respectively. The VACO index was calculated based on age, sex, and comorbidities. PCT and presepsin levels and the VACO index were compared using ROC curve, Kaplan-Meier method, and reclassification analysis for the 30-day mortality.Results: ROC curve analysis was used to measure PCT and presepsin levels and the VACO index to predict 30-day mortality; the optimal cut-off values were 0.138 ng/mL for PCT, 717 pg/mL for presepsin, and 12.1% for the VACO index. On Kaplan-Meier survival analysis, hazard ratios (95% confidence interval) were 15.9 (4.1-61.3) for PCT, 26.3 (6.4-108.0) for presepsin, and 6.0 (1.7-21.1) for the VACO index. On reclassification analysis, PCT and presepsin in addition to the VACO index significantly improved the prognostic value of the index.Conclusions: This study demonstrated the prognostic utility of measuring PCT and presepsin levels and the VACO index in COVID-19 patients. The biomarkers in addition to the clinical index were more useful than the index alone for predicting clinical outcomes in COVID-19 patients.