Publications by authors named "Satomi Yashima"

Article Synopsis
  • * It analyzes 220 patients, revealing that a greater change in mitral valve annulus area after surgery is linked to a higher risk of FMS, with 6.4% of patients developing this condition.
  • * The research identifies a 56.2% threshold in the change ratio of mitral valve annulus area as a key predictor for FMS, suggesting preoperative measurements can help optimize surgical outcomes.
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Objective Although magnetic resonance imaging (MRI) is the gold standard for evaluating abnormal myocardial fibrosis and extracellular volume (ECV) of the left ventricular myocardium (LVM), a similar evaluation has recently become possible using computed tomography (CT). In this study, we investigated the diagnostic accuracy of a new 256-row multidetector CT with a low tube-voltage single energy scan and deep-learning-image reconstruction (DLIR) in detecting abnormal late enhancement (LE) in LVM. Methods We evaluated the diagnostic performance of CT for detecting LE in LVM and compared the results with those of MRI as a reference.

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Background: Extracellular volume fraction (ECV) on magnetic resonance imaging can predict prognosis after aortic valve replacement in patients with aortic stenosis (AS). However, the usefulness of ECV on computed tomography (CT) for patients who have undergone transcatheter aortic valve replacement (TAVR) is unclear, so we investigated whether ECV analysis on CT is associated with clinical outcomes in TAVR candidates.

Methods and results: We analyzed 127 patients with severe AS who underwent preoperative CT for TAVR.

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Recently, myocardial extracellular volume (ECV) analysis has been measurable on computed tomography (CT) using new software. We evaluated the use of cardiac CT to estimate the myocardial ECV of left ventricular (LV) myocardium (LVM) to predict reverse remodeling (RR) in cases of atrial fibrillation (AF) after catheter ablation (CA). Four hundred and seven patients underwent CA for AF in our institution from April 2014 to Feb 2021.

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We treated a female patient known to have a double-chambered right ventricle (DCRV) who presented with symptoms of an acute myocardial infarction (AMI). Emergent coronary artery catheterization revealed acute right coronary artery (RCA) occlusion and proximal left anterior descending (LAD) stenosis. We performed percutaneous coronary intervention (PCI) for the RCA occlusion.

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Article Synopsis
  • Cardiac computed tomography (CT) is effective for screening coronary artery stenosis, and a new software allows for extracellular volume fraction (ECV) analysis to assess patient prognosis, especially in dilated cardiomyopathy (DCM).
  • In a study of 70 DCM patients, the average ECV of the left ventricular myocardium was found to be 33.96%, with 30% experiencing major adverse cardiac events (MACE).
  • Higher ECV values, endo-systolic volume, and significant valvular disease rates were linked to increased MACE risk, with ECV ≥ 32.26% identified as a strong predictor of MACE outcomes.
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Objective High-quality images can be obtained with 320-slice computed tomography (CT) with model-based iterative reconstruction (MBIR). We therefore investigated the diagnostic accuracy of 320-slice CT with MBIR for detecting significant coronary artery stenosis. Methods This was a retrospective study of 160 patients who underwent coronary CT and invasive coronary angiography (ICA).

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We treated a man with co-incident Marfan-like connective tissue disease with morphologic left ventricular non-compaction (LVNC). He underwent valve-sparing aortic root replacement because of aortic root dilation at 43 years old. Pathological findings of the aorta revealed cystic medio-necrosis, consistent with Marfan syndrome.

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It is important to suppress retinal vascular changes for prevention of the onset and progression of diabetic retinopathy. In the present study, we investigated the dose-response effect of an aldose reductase (AR) inhibitor, fidarestat, on retinal vascular changes in the retinas of streptozotocin (STZ)-induced diabetic rats. Fidarestat (0.

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