河合 秀樹

AIMS: The global distribution of lipoprotein(a) [Lp(a)] levels varies due to racial and ethnic differences. However, the clinical relevance of Lp(a) levels in Japanese patients has not been fully explored. METHODS: We investigated the association of Lp(a) levels, the Suita score, and the presence of high-risk plaque (HRP) as well as that of ≥ 50% stenosis, quantitative plaque volume, and the value of coronary artery calcium score in coronary computed tomographic angiography (CCTA), among 272 Japanese patients (mean age: 65 years) in whom serum Lp(a) levels were measured due to suspected coronary artery disease. HRP was defined as positive remodeling and/or low attenuation. Plaque volume was quantified as the percent plaque volume. RESULTS: HRP was identified in 33 (12.1%) patients. The prevalence of HRP, ≥ 50% stenosis, and percent plaque volume progressively increased with higher Lp (a) levels and Suita scores. In multivariate analyses, Lp(a) and the Suita score independently predicted HRP when assessed as continuous (p = 0.02, p＜0.001, respectively) or categorical variables (p = 0.005, p = 0.007, respectively). Patients in the highest tertile of Lp(a) and classified as high- or intermediate-risk by the Suita score had the highest HRP risk, whereas those in the lower 2 tertiles and low-risk group had the lowest. Incorporating Lp(a) into the Suita score improved the prediction of HRP beyond the Suita score alone (p = 0.005). CONCLUSIONS: The combinatorial value of assessing Lp(a) levels and Suita score may provide useful insight regarding Japanese patients undergoing CCTA for the prediction of HRP.

OBJECTIVES: A new classification of heart failure based on the effects of medication has recently come into use. According to this classification, heart failure is divided into heart failure with normal ejection fraction (HFnEF; defined as an EF ≥55% for men and ≥60% for women) and non-HFnEF. However, the characteristics of patients with HFnEF are still unclear. Accordingly, in this study, we sought to identify the background characteristics, including non-cardiac factors, of patients with HFnEF. METHODS: We retrospectively divided 304 eligible patients who were hospitalized for worsening heart failure at our institution between December 2020 and December 2022 into an HFnEF group (n=37) and a non-HFnEF group (n=267) and compared their demographic and clinical characteristics. RESULTS: There were more elderly patients in the non-HFnEF group, along with fewer patients with coronary artery disease and low serum hemoglobin and NT-proBNP levels and a higher proportion of patients with a low skeletal muscle index (<7.0 kg/m2 for men and <5.7 kg/m2 for women). Multivariate analysis with addition of patient sex identified a low skeletal muscle index (odds ratio 2.96, p<0.01) to be an independent determinant of HFnEF along with older age and low NT-proBNP. CONCLUSIONS: A low skeletal muscle index was significantly more common in patients with HFnEF than in those with non-HFnEF. Intensive nutrition and exercise therapy to increase skeletal muscle mass may improve the prognosis in patients with HFnEF who respond poorly to standard pharmacological treatment.

This study aimed to determine the optimal measurement conditions for accurate standardized uptake value (SUV) analysis of iodine-123 metaiodobenzylguanidine (¹²³I-MIBG) by examining the relationship between image convergence and quantitation. Single-photon emission computed tomography/computed tomography images were acquired using JS-10 and National Electrical Manufacturers Association (NEMA) body phantoms, with acquisition time per view varied (10, 30, 50, and 100 s/view). Image reconstruction was performed using three-dimensional-ordered subset expectation maximization, adjusting the product of subset and iteration (SI product; 60, 120, 180) and Gaussian filter parameters (8, 10, 12 mm). For the JS-10 phantom, we evaluated the dose linearity (DL), the recovery coefficient (RC) of individual rods, the scatter ratio (SR), and the coefficient of variation (CV). For the NEMA body phantom, we assessed the contrast-to-noise ratio (CNR) of the 17-mm-diameter hot sphere. We also evaluated the maximum and mean SUVs for all its hot spheres, and their relative standard error (RSE), using SUVs obtained at 100 s/view as reference. In the JS-10 phantom, the DL remained stable under all conditions. The RC decreased when the Gaussian filter was large and the SI product was small. A trade-off between the CV and the SR emerged, depending on the acquisition time and the SI product; optimal results were observed at 50 − 100 s/view and an SI product of 120 − 180. In the NEMA body phantom, contrast improved with acquisition times of ≥30 s/view, and the CNR increased as noise declined with longer acquisition times. At ≥50 s/view, variation in the maximum and mean SUVs decreased, with the RSE remaining below 5%. In conclusion, accurate SUV measurement with ¹²³I-MIBG requires an acquisition time of ≥50 s/view, an SI product of approximately 120, and a Gaussian filter of 10 − 12 mm. These findings provide a foundation for future studies comparing this method with the heart-to-mediastinum ratio, supporting its clinical application.

OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary computed tomography angiography (CCTA) in both in vitro and in vivo studies. MATERIALS AND METHODS: For the in vitro study, a total of three-vessel tube phantoms with diameters of 3 mm, 4 mm, and 5 mm and with simulated non-calcified stepped stenosis plaques with degrees of 0%, 25%, 50%, and 75% stenosis were scanned with area-detector CT (ADCT) and ultra-high-resolution CT (UHR-CT). Then, ADCT data were reconstructed using all methods, although UHR-CT data were reconstructed with hybrid-type IR, MBIR, and DLR. For the in vivo study, patients who had undergone CCTA at ADCT were retrospectively selected, and each CCTA data set was reconstructed with all methods. To compare the image noise and measurement accuracy at each of the stenosis levels, image noise, and inner diameter were evaluated and statistically compared. To determine the effect of HR-DLR on CAD-RADS evaluation accuracy, the accuracy of CAD-RADS categorization of all CCTAs was compared by using McNemar's test. RESULTS: The image noise of HR-DLR was significantly lower than that of others on ADCT and UHR-CT (p < 0.0001). At a 50% and 75% stenosis level for each phantom, hybrid-type IR showed a significantly larger mean difference on ADCT than did others (p < 0.05). At in vivo study, 31 patients were included. Accuracy on HR-DLR was significantly higher than that on hybrid-type IR, MBIR, or DLR (p < 0.0001). CONCLUSION: HR-DLR is potentially superior for coronary arterial stenosis evaluations to hybrid-type IR, MBIR, or DLR shown on CCTA. KEY POINTS: Question How do coronary arterial stenosis evaluations by hybrid-type IR, MBIR, DLR, and HR-DLR compare to coronary CT angiography? Findings HR-DLR showed significantly lower image noise and more accurate coronary artery disease reporting and data system (CAD-RADS) evaluation than others. Clinical relevance HR-DLR is potentially superior to other reconstruction methods for coronary arterial stenosis evaluations, as demonstrated by coronary CT angiography results on ADCT and as shown in both in vitro and in vivo studies.