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BackgroundBrain natriuretic peptide (BNP) is an essential cardiac biomarker for diagnosing heart failure and for prognoses in patients with various cardiac diseases. However, measurement requires immunological assays that are not available in every hospital. Recently, a novel BNP kit (Nanopia BNP-A, Sekisui Inc.; BNPn) that uses general-purpose, automated, biochemical analyzers has become commercially available. We assessed how its accuracy and utility compare with those of conventional immunological tests.Methods and resultsWe retrospectively collected 1491 conventional BNP measurements (BNPc), which had been clinically indicated for BNP testing and for which residual samples were still stored in the laboratory. We measured BNP using the novel kit and determined the correlation of BNP levels between the two methods. We also assessed the predictive value of both BNP measurements for major cardiac events (MACEs). The analytical performance of both measuring methods was similar. Log-transformed BNP measured by both methods showed strong correlation (r = 0.92); however, log-transformed BNPn was significantly higher than log-transformed BNPc (pConclusionsA new BNP measurement using biochemical equipment provides prognostic value similar to that of conventional BNP analysis; thus, it should prove useful in hospitals in which conventional immunological examinations are not available.

Background Accurate timing of BNT162b2 boosters to prevent breakthrough infections of coronavirus disease 2019 (COVID-19) requires reliable estimates of immune status. We hypothesized that spike IgG levels at 3 months after two doses of the BNT162b2 vaccine might predict subsequent spike IgG levels. Methods and results Spike IgG levels were tested at 3, 6, and 8 months after the second dose of the BNT162b2 vaccine in 251 Japanese health care workers (median age: 39 years, female: 187). The median level of spike IgG was 2,882 AU/mL at 3 months. This decreased to 875 AU/mL at 6 months and 579 AU/mL at 8 months. There were good correlations of log-transformed spike IgG levels between 3 and 6 months (r = 0.86) and between 3 and 8 months (r = 0.82). The correlation further improved after excluding three subjects who had possible COVID-19 infections (r = 0.91, r = 0.86). Log-transformed spike IgG levels at 6 or 8 months yields the following equation: log spike IgG at 6 (8) months = 0.92 (0.86) X log spike IgG at 3 months– 0.23 (0.18). Predicted spike IgG at 6 months of ≥ 300 or < 300 AU/mL had 98% sensitivity, 47% specificity, and 94% accuracy for discriminating subjects whose actual spike IgG titers at 6 months were above or below 300 AU/mL. Corresponding values of predicted spike IgG at 8 months were 97%, 70%, and 93%, respectively. Conclusions We conclude that predictive formulae using spike IgG levels at 3 months after two-dose vaccination with BNT162b2 reliably estimate subsequent spike IgG levels up to 8 months and provide useful information in terms of vaccination booster timing.

BackgroundAdverse reactions are more common after the second injection of messenger RNA vaccines such as Pfizer/BioNTech's BNT162b2. We hypothesized that the degree and severity of reactogenicity after the second injection reflects the magnitude of antibody production against the SARS CoV-2 virus spike protein (spike IgG).Methods and resultsBlood samples were obtained from 67 Japanese healthcare workers three weeks after the first injection and two weeks after the second injection of the BNT162b2 vaccine to measure spike IgG levels. Using questionnaires, we calculated an adverse event (AE) score (0-11) for each participant. The geometric mean of spike IgG titers increased from 1,047 antibody units (AU/mL) (95% confidence interval (95% CI): 855-1282 AU/mL) after the first injection to 17,378 AU/mL (95% CI: 14,622-20,663 AU/mL) after the second injection. The median AE score increased from 2 to 5. Spike IgG levels after the second injection were negatively correlated with age and positively correlated with spike IgG after the first injection. AE scores after the second injection were not significantly associated with log-transformed spike IgG after the second injection, when adjusted for age, sex, AE score after the first injection, and log-transformed spike IgG after the first injection.ConclusionsAlthough the sample size was relatively small, reactogenicity after the second injection may not accurately reflect antibody production.

Background: Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) play important roles in diagnosis and management of cardiac diseases. However, the issue of the accuracy and reliability of LVEF and GLS remains to be solved. Image quality is one of the most important factors affecting measurement variability. The aim of this study was to investigate whether improved image quality could reduce observer variability. Methods: Two sets of three apical images were acquired using relatively old- and new-generation ultrasound imaging systems (Vivid 7 and Vivid E95) in 308 subjects. Image quality was assessed by endocardial border delineation index (EBDI) using a 3-point scoring system. Three observers measured the LVEF and GLS, and these values and inter-observer variability were investigated. Results: Image quality was significantly better with Vivid E95 (EBDI: 26.8 ± 5.9) than that with Vivid 7 (22.8 ± 6.3, P < 0.0001). Regarding the inter-observer variability of LVEF, the r-value, bias, 95% limit of agreement and intra-class correlation coefficient for Vivid 7 were comparable to those for Vivid E95. The % variabilities were significantly lower for Vivid E95 (5.3–6.5%) than those for Vivid 7 (6.5–7.5%). Regarding GLS, all observer variability parameters were better for Vivid E95 than for Vivid 7. Improvements in image quality yielded benefits to both LVEF and GLS measurement reliability. Multivariate analysis showed that image quality was indeed an important factor of observer variability in the measurement of LVEF and GLS. Conclusions: The new-generation ultrasound imaging system offers improved image quality and reduces inter-observer variability in the measurement of LVEF and GLS.