心エコー図法による心不全の診断

The number of patients with heart failure is steadily increasing in Japan, and this situation is called the “heart failure pandemic”. Nowadays, echocardiography plays a center role in diagnosis of heart failure. It gives not only a definitive diagnosis of heart failure, but can also be used to determine its pathophysiology and the effect of treatment. Echocardiography can evaluate not only the morphology of the heart but also its function. The hemodynamic diagnosis of heart failure is made by demonstrating １） increased preload, ２） elevated left atrial pressure, and ３） decreased cardiac output. This article describes how to evaluate each of these including evaluation of left ventricular diastolic dysfunction. We also explain the clinical significance of preload stress echocardiography, which we are developing, in patients with heart failure. Although such echocardiographic diagnostic method is useful for understanding the condition of patients, it has become complicated, and it is difficult to make an accurate diagnosis unless a specialist in echocardiography. Recently, a new way of using ultrasound called “point-of-care ultrasonography（POCUS）” has been developed. This is an ultrasonography in which a physician who is not a specialist in ultrasonography can obtain information to be used as part of a physical examination and make on-site decisions. A diagnostic method for heart failure using POCUS is also described in this paper. In order to properly deal with heart failure, accurate evaluation of the pathology and selection of appropriate treatment, as well as picking up high-risk patients and initiating treatment early to prevent heart failure are essential. We would like to make widespread use of these echocardiographic techniques, which are useful for both understanding the pathology and determining the risk of heart failure, so that more patients can benefit

GLS following high-dose chemotherapy

Background Cardiac amyloidosis (CA) is a secondary form of cardiomyopathy where abnormal accumulation of amyloid protein in the myocardial interstitium causes cardiac hypertrophy and myocardial fibrosis. If primary CA advances to heart failure, most patients do not survive for very long after the diagnosis. Case summary A 40-year-old man was admitted to our hospital for dyspnoea, progressive anaemia, and decreased appetite. He has diagnosed with amyloid light-chain (AL) amyloidosis. Although BD treatment (bortezomib + dexamethasone) and medical treatment were started, there was no sign of improvement. Then, high-dose chemotherapy followed by autologous peripheral blood stem cell transplantation (auto-PBSCT) was initiated. Pretreatment echocardiography revealed typical findings of CA, such as ventricular wall thickening, valvular thickening, diastolic dysfunction, and pericardial effusion. Global longitudinal strain (GLS) was significantly reduced, and bull's-eye mapping showed typical apical sparing. After auto-PBSCT, GLS gradually improved and was almost normal after 2 years. Other echocardiographic parameters, functional status, and laboratory data also showed that there was significant regression of CA. Discussion Although the prognosis in primary CA is extremely poor, we achieved long-term survival in a patient with effective high-dose chemotherapy and auto-PBSCT. Global longitudinal strain may be a useful marker of prognosis, regression, and recovery

Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning

A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data

Deep Learning for Echocardiography

Objectives: The aim of this study was to evaluate whether a deep convolutional neural network (DCNN) could detect regional wall motion abnormalities (RWMAs) and differentiate groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with cardiologist/sonographer or resident readers. Background: An effective intervention for reduction of misreading of RWMAs is needed. We hypothesized that a DCNN trained with echocardiographic images may provide improved detection of RWMAs in the clinical setting. Methods: A total of 300 patients with history of myocardial infarction were enrolled. In this cohort, 100 each had infarctions of the left anterior descending branch (LAD), left circumflex branch (LCX), and right coronary artery (RCA). The age-matched 100 control patients with normal wall motion were selected from our database. Each case contained cardiac ultrasound images from short axis views at end-diastolic, mid-systolic and end-systolic phases. After 100 steps of training, diagnostic accuracies were calculated on the test set. We independently trained 10 versions of the same model, and performed ensemble predictions with them. Results: For detection of the presence of wall motion abnormality, the area under the receiver-operating characteristic curve (AUC) by deep learning algorithm was similar to that by cardiologist/sonographer readers (0.99 vs. 0.98, p =0.15), and significantly higher than the AUC by resident readers (0.99 vs. 0.90, p =0.002). For detection of territories of wall motion abnormality, the AUC by the deep learning algorithm was similar to the AUC by cardiologist/sonographer readers (0.97 vs. 0.95, p =0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, p =0.003). In a validation group from an independent site (n=40), the AUC by the DL algorithm was 0.90. Conclusions: Our results support the possibility of DCNN use for automated diagnosis of RWMAs in the field of echocardiography

Electrical cardiometry for hemodynamics

Few reports have focused on hemodynamics around delivery in pregnant women because of the difficulty of continuous and noninvasive measurement. Electrical cardiometry allows noninvasive continuous monitoring of hemodynamics and has recently been used in non-pregnant subjects. We compared the use of electrical cardiometry versus transthoracic echocardiography in healthy pregnant women and evaluated hemodynamics immediately after vaginal delivery. In Study 1, electrical cardiometry and transthoracic echocardiography were used to measure cardiac output in 20 pregnant women with threatened premature delivery. A significant correlation was found between the two methods, with electrical cardiometry showing the higher cardiac output. In Study 2, heart rate, stroke volume, and cardiac output were continuously measured in 15 women during vaginal delivery up to 2 h postpartum. Cardiac output increased markedly because of an increased heart rate and stroke volume at the time of newborn delivery. The heart rate then immediately returned to baseline, while cardiac output remained elevated for at least 2 h after delivery because of a sustained high stroke volume. Electrical cardiometry was as readily available as transthoracic echocardiography for evaluating hemodynamics and allowed for continuous measurement during labor. High intrapartum cardiac output was sustained for at least 2 h after vaginal delivery

シンボウ サイドウ ト ソクセンショウ : イツ ドノヨウナ チリョウ オ ハジメルカ

Atrial fibrillation（AF）is a common arrhythmia and the prevalence of this arrhythmia is increasing as aging. Secondary AF is defined as AF with organic heart disease and lone AF as AF without it. The AF is divided into paroxysmal, persistent and chronic by its onset and persistence. It is clinically important that any type AF causes thromboembolic stroke. The preventive Qumadin therapy should be applied to patients with AF. Transesophageal echocardiography has been utilized for the diagnosis of left atrial thrombus and for the prediction of stroke. CHADS２ score is a clinical prediction rule for estimating the risk of stroke in patients with non-rheumatic AF. Patients with CHADS２ score_１should be treated with Qumadin. Rhythm control and rate control are two different strategies for the treatment of AF. There is no evidence that indicate better choice between rhythm control and rate control. Recently, inhibitors of the renin-angiotensin system have a potential to prevent new onset of AF in patients who has risk factors

Effects of dabigatran on diabetic endothelial dysfunction

Diabetic patients have coagulation abnormalities, in which thrombin plays a key role. Whereas accumulating evidence suggests that it also contributes to the development of vascular dysfunction through the activation of protease-activated receptors (PARs). Here we investigated whether the blockade of thrombin attenuates endothelial dysfunction in diabetic mice. Induction of diabetes by streptozotocin (STZ) increased the expression of PAR1, PAR3, and PAR4 in the aorta. STZ-induced diabetic mice showed impairment of endothelial function, while the administration of dabigatran etexilate, a direct thrombin inhibitor, significantly attenuated endothelial dysfunction in diabetic mice with no alteration of metabolic parameters including blood glucose level. Dabigatran did not affect endothelium-independent vasodilation. Dabigatran decreased the expression of inflammatory molecules (e.g., MCP-1 and ICAM-1) in the aorta of diabetic mice. Thrombin increased the expression of these inflammatory molecules and the phosphorylation of IκBα, and decreased the phosphorylation of eNOSSer1177 in human umbilical endothelial cells (HUVEC). Thrombin significantly impaired the endothelium-dependent vascular response of aortic rings obtained from wild-type mice. Inhibition of NF-κB attenuated thrombin-induced inflammatory molecule expression in HUVEC and ameliorated thrombin-induced endothelial dysfunction in aortic rings. Dabigatran attenuated the development of diabetes-induced endothelial dysfunction. Thrombin signaling may serve as a potential therapeutic target in diabetic condition