Estimation Of Ejection Fraction Research Articles

Accuracy of visual estimation of ejection fraction in patients with heart failure using augmented reality glasses

ObjectiveLeft ventricular ejection fraction (LVEF) is measured to assess haemodynamic status and cardiac function. It may be difficult to accurately measure in patients with heart failure (HF) as they are...

Abstract 19052: Interferon B1A-Induced Dilated Cardiomyopathy

Introduction: Congestive heart failure is a leading causes of morbidity and mortality. Interferon therapy is a rare cause of dilated cardiomyopathy (DCM) and Heart Failure with Reduced Ejection Fraction (HFrEF). Specifically, interferon Alpha has been associated with cardiotoxicity. Here we describe a case of interferon B1A - induced HFrEF and DCM in a patient who took subcutaneous Interferon B1A for 11 years before developing clinical signs of heart failure. To our knowledge, only four other cases have been reported. Case Description: A 70-year-old man with a history of multiple sclerosis managed with Interferon B1A presented with shortness of breath, orthopnea, and lower extremity edema for 10 days. Physical examination revealed jugular venous distension and pitting edema. Brain natriuretic peptide (BNP) was 1580 pg/mL, troponin was 49 ng/L. Lipid panel, complete blood count, complete metabolic panel, Hemoglobin A1c, and thyroid stimulating hormone were within normal limits. Urine drug screen was negative. Chest X-ray revealed pulmonary vascular congestion and small pleural effusions bilaterally. A Transthoracic echocardiogram revealed mild aortic root dilatation (4.1cm), significant four-chamber dilatation, severely reduced left ventricular (LV) systolic function with an estimated ejection fraction of 20%, diastolic LV dysfunction, and moderately reduced systolic RV function, consistent with HFrEF and DCM (LVIDD 6.1cm). Coronary angiography showed normal coronary anatomy. The patient was started on guideline-directed medical therapy for HFrEF. Close follow-up was arranged to track improvement in systolic function after cessation of interferon b1a and consideration for Cardiac MRI and possible RV biopsy to evaluate for other causes of cardiomyopathy. Discussion/Conclusions: Due to the patient’s negative ischemic workup and absence of risk factors for cardiovascular disease, we strongly suspect that the patient’s cardiomyopathy was due to interferon B1A. With only 4 other cases of interferon B1A-induced DCM reported, it is a rare cause of medication-induced DCM. Interferon B1A should be considered a potential risk factor for developing heart failure, especially in high-risk patients.

Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks

Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejection fraction estimation from echocardiograms for disease tracking. Semi-supervised approaches for deep regression are notably under-explored compared to classification and segmentation tasks, however. Unlike classification tasks, which rely on thresholding functions for generating class pseudo-labels, regression tasks use real number target predictions directly as pseudo-labels, making them more sensitive to prediction quality. In this work, we propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME), which improves training by generating high-quality pseudo-labels and uncertainty estimates for heteroscedastic regression. Given that aleatoric uncertainty is only dependent on input data by definition and should be equal for the same inputs, we present a novel uncertainty consistency loss for co-trained models. Our consistency loss significantly improves uncertainty estimates and allows higher quality pseudo-labels to be assigned greater importance under heteroscedastic regression. Furthermore, we introduce a novel variational model ensembling approach to reduce prediction noise and generate more robust pseudo-labels. We analytically show our method generates higher quality targets for unlabeled data and further improves training. Experiments show that our method outperforms state-of-the-art alternatives on different tasks and can be competitive with supervised methods that use full labels. Code is available at https://github.com/xmed-lab/UCVME.

Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods.

Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson's method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.

The impact of real-time guiding by deep learning on test-retest variability of automated left ventricular systolic function measurements

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority. Background Left ventricular (LV) ejection fraction (EF) and left ventricular volumes are key parameters for characterisation of cardiac function. Test-retest variability of EF and LV end-diastolic volume (EDV) are dependent on image quality and analyses. Foreshortened LV recordings cause inaccuracies in estimation of EF and EDV. Real-time guiding of LV length using deep learning (DL) could improve standardization of image acquisition and reduce variability of automated analyses of EF (auto-EF) and EDV (auto-EDV). Purpose To study the impact of real-time feedback of LV length using a robust deep-learning (DL) software during echocardiography on variability of automated analyses of EF (auto-EF) and EDV (auto-EDV). Methods Patients with mixed cardiac pathology were consecutively included if they were in sinus rhythm without need for contrast. Three consecutive echocardiograms were performed; the first and second by two of three experienced sonographers (Sonographer 1 and 2) and the third (reference) by one of four cardiologists in random order, all affiliated to an EACVI accredited echocardiographic laboratory. All exams included the standard apical views, and the reference exams also included tri-plane recordings of the LV. All measurements were done retrospectively using automated algorithm for measuring. LV volumes were measured in four- and two-chamber views and averaged by the method of discs’ formula. LV EF was calculated as biplane measurements. The coefficients of variation (CV) between sonographers and cardiologists were compared for both LV EF and end-diastolic LV volume from biplane measurements. One-way ANOVA was used to estimate within-patient variation for automated and manual measurements. Results A total of 47 patients (47% women) were included with mean (SD) age 64 (15) years. There was no significant difference in CV for neither auto-EF nor auto-EDV using the DL tool (Table) compared to standard care. Manual and automated measurements of LV volumes and EF correlated well with R 0.91 and R 0.74, respectively (only data for EF shown in the figure). Conclusion Real-time guiding did not significantly reduce variation of automated analyses of LV volumes and EF when used by experienced sonographers. Further studies are needed to evaluate the effect of real-time guiding to optimize LV acquisition in inexperienced users.

The Case Files

A patient presented after a cardiac cath with a football-sized mass in her groinFigure 1.: Axial CT angiography demonstrating right anterior thigh hematoma measuring 11.9 cm by 15.4 cm by 21 cm with active arterial extravasation (arrow).FigureFigureFigureA 56-year-old woman presented with shortness of breath and a cough she had had for a month. She also had associated orthopnea, paroxysmal nocturnal dyspnea, and dyspnea on exertion. A workup revealed an NSTEMI and new systolic heart failure, dilated cardiomyopathy (estimated ejection fraction 18%), and a sizable left ventricular apical thrombus on echocardiogram. Cardiac catheterization was performed and complicated by stable focal dissection of the right femoral artery and external iliac artery. She was discharged home on warfarin and enoxaparin for the LV thrombus. The patient returned to the ED one week later with symptoms of right groin pain and generalized lethargy, and she was in hypovolemic shock and had a massive right groin mass. Bedside ultrasound of the mass revealed a large hyperechoic fluid collection prompting resuscitation with two units of packed red blood cells to treat suspected hemorrhagic shock. She was also started on norepinephrine infusion for blood pressure and MAP support. Anticoagulation reversal was administered with 10 mg of vitamin K and one unit of FFP. CT angiography of the mass was obtained (Figures 1 and 2), and the patient was immediately taken to the OR for right femoral groin exploration for suspected right femoral artery injury with active arterial extravasation.Figure 2.: Coronal CT angiography demonstrating right anterior thigh hematoma with active arterial extravasation (arrow).Massive Hematoma The patient was diagnosed with post-catheterization femoral artery dissection with massive hematoma and active hemorrhage. She presented with hypotension, progressing to hypovolemic shock requiring aggressive fluid resuscitation, blood transfusion, vasopressors, and anticoagulation reversal. CT angiography showed a mixed density hematoma measuring 11.9 cm by 15.4 cm by 21 cm, and a small central focus of contrast blush. The patient was taken to the OR, where the surgeon found a hematoma the size of a football, which is roughly 17 cm in diameter and roughly 28 cm long. The hematoma was evacuated, and the femoral artery and external iliac arteries were repaired. Hematomas are the most common complication after cardiac catheterization. One review found that the incidence of femoral hematomas was 2.8 percent while active femoral and retroperitoneal bleeds were even lower at 0.6 percent and 0.3 percent, respectively. Dissection of the femoral or iliac arteries is also extremely rare, with an incidence of 0.46 percent. (Glob J Health Sci. 2012;4[1]:65; https://bit.ly/42TUAv5.) A large multicenter cohort study investigating vascular complications following endovascular cardiac procedures found that the incidence of major vascular complications was only 1.6 percent, defined as major hematoma (>10 cm), unstable bleeding, retroperitoneal hematoma, pseudoaneurysm, and arteriovenous fistula within a 48-hour periprocedural window. (Rev Lat Am Enfermagem. 2018;26:e3060; https://bit.ly/3lXFhRx.) The study also found that most vascular complications took place within six hours of surgery, making this patient's prolonged presentation even more unexpected. Most hematomas are self-limited, but larger ones with bleeding can cause hemodynamic instability. This patient was on anticoagulation for an LV thrombus, which likely complicated the bleeding. This case highlighted the importance of physician awareness of an emergent, rare presentation of a common hospital procedure—the cardiac cath. Clinical suspicion, especially in the periprocedural setting, rapid identification of the bleeding source, and immediate imaging make the diagnosis. Patients who continue to deteriorate despite resuscitative efforts require operative management. (StatPearls. Jan. 16, 2023; http://bit.ly/3nnkmaL.) Share this article on Twitter and Facebook. Access the links in EMN by reading this on our website: www.EM-News.com. Comments? Write to us at [email protected].

Comparison of visual estimation and quantitative measurement of left ventricular ejection fraction in untrained perioperative echocardiographers

BackgroundPerioperative evaluation of the left ventricular systolic function is essential information to help diagnose and manage life-threatening perioperative emergencies. Although quantifying the left ventricular ejection fraction (LVEF) is recommended to determine the left ventricular function, it may not always be feasible in emergency perioperative settings. This study compared the visual estimation of LVEF (eyeballing) by noncardiac anesthesiologists with the quantitative LVEF measured using a modified Simpson’s biplane method.MethodsTransesophageal echocardiographic (TEE) studies of 35 patients were selected and 3 different echocardiographic views (the mid-esophageal four chamber view, the mid-esophageal two chamber view, and the transgastric mid-papillary short axis view) were recovered from each study and displayed in random order. Two cardiac anesthesiologists certified in perioperative echocardiography independently measured LVEF using the modified Simpson method and categorized LVEF into five grades: hyperdynamic LVEF, normal, mildly reduced LVEF, moderately reduced LVEF and severely reduced LVEF. Seven noncardiac anesthesiologists with limited experience in echocardiography also reviewed the same TEE studies and estimated the LVEF and graded LV function. The precision of the LV function classification and the correlation between visual estimation of LVEF and quantitative LVEF were calculated. The agreement of measurements between the two methods was also assessed.ResultsPearson’s correlation between the LVEF estimated by the participants and the quantitative LVEF using the modified Simpson method was 0.818 (p < 0.001). Of a total of 245 responses, 120 (49.0%) responses were correct grading of the LV function. Participants were able to classify the LV function more accurately in the LV function grades 1 and 5 (65.3%). The 95% level of agreement of the Bland-Altman method was − 11.3-24.5. -21.9-22.6, − 23.1-26.5, − 20.5-22.0 and − 26.6-11.1 for LV grade 1 to 5, respectively.ConclusionVisual estimation of LVEF in perioperative TEE has acceptable accuracy in untrained echocardiographers and can be used for rescue TEE.

Clinical Validation of an Artificial Intelligence–Based Tool for Automatic Estimation of Left Ventricular Ejection Fraction and Strain in Echocardiography: Protocol for a Two-Phase Prospective Cohort Study

Echocardiography (ECHO) is a type of ultrasonographic procedure for examining the cardiac function and morphology, with functional parameters of the left ventricle (LV), such as the ejection fraction (EF) and global longitudinal strain (GLS), being important indicators. Estimation of LV-EF and LV-GLS is performed either manually or semiautomatically by cardiologists and requires a nonnegligible amount of time, while estimation accuracy depends on scan quality and the clinician's experience in ECHO, leading to considerable measurement variability. The aim of this study is to externally validate the clinical performance of a trained artificial intelligence (AI)-based tool that automatically estimates LV-EF and LV-GLS from transthoracic ECHO scans and to produce preliminary evidence regarding its utility. This is a prospective cohort study conducted in 2 phases. ECHO scans will be collected from 120 participants referred for ECHO examination based on routine clinical practice in the Hippokration General Hospital, Thessaloniki, Greece. During the first phase, 60 scans will be processed by 15 cardiologists of different experience levels and the AI-based tool to determine whether the latter is noninferior in LV-EF and LV-GLS estimation accuracy (primary outcomes) compared to cardiologists. Secondary outcomes include the time required for estimation and Bland-Altman plots and intraclass correlation coefficients to assess measurement reliability for both the AI and cardiologists. In the second phase, the rest of the scans will be examined by the same cardiologists with and without the AI-based tool to primarily evaluate whether the combination of the cardiologist and the tool is superior in terms of correctness of LV function diagnosis (normal or abnormal) to the cardiologist's routine examination practice, accounting for the cardiologist's level of ECHO experience. Secondary outcomes include time to diagnosis and the system usability scale score. Reference LV-EF and LV-GLS measurements and LV function diagnoses will be provided by a panel of 3 expert cardiologists. Recruitment started in September 2022, and data collection is ongoing. The results of the first phase are expected to be available by summer 2023, while the study will conclude in May 2024, with the end of the second phase. This study will provide external evidence regarding the clinical performance and utility of the AI-based tool based on prospectively collected ECHO scans in the routine clinical setting, thus reflecting real-world clinical scenarios. The study protocol may be useful to investigators conducting similar research. DERR1-10.2196/44650.

Imaging modality for left ventricular ejection fraction estimation and effect of implantable cardioverter-defibrillator on mortality in patients with heart failure

Implantable cardioverter-defibrillators (ICDs) improve outcomes in patients with heart failure (HF) with left ventricular ejection fraction (LVEF) ≤35%. Less is known about whether outcomes varied between the 2 noninvasive imaging modalities used to estimate LVEF-2-dimensional echocardiography (2DE) and multigated acquisition radionuclide ventriculography (MUGA)-which use different principles (geometric vs count-based, respectively). The purpose of this study was to examine whether the effect of ICD on mortality in patients with HF and LVEF ≤35% varied on the basis of LVEF measured by 2DE or MUGA. Of the 2521 patients with HF with LVEF ≤35% in the Sudden Cardiac Death in Heart Failure Trial, 1676 were randomized to either placebo or ICD, of whom 1386 had LVEF measured by 2DE (n = 971) or MUGA (n = 415). Hazard ratios (HRs) and 97.5% confidence intervals (CIs) for mortality associated with ICD use were estimated overall, checking for interaction, and within the 2 imaging subgroups. Of the 1386 patients in the present analysis, all-cause mortality occurred in 23.1% (160 of 692) and 29.7% (206 of 694) of patients randomized to ICD or placebo, respectively (HR 0.77; 97.5% CI 0.61-0.97), which is consistent with that in 1676 patients in the original report. HRs (97.5% CIs) for all-cause mortality in the 2DE and MUGA subgroups were 0.79 (0.60-1.04) and 0.72 (0.46-1.11), respectively (P = .693 for interaction). Similar associations were observed for cardiac and arrhythmic mortalities. We found no evidence that in patients with HF and LVEF ≤35%, the effect of ICD on mortality varied by the noninvasive imaging method used to measure LVEF.

Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients

Significant interobserver and interstudy variability occurs for left ventricular functional indices despite standardization of measurement techniques. Artificial intelligence models trained on adult echocardiograms are not likely to be applicable to a pediatric population. We present EchoNet-Peds, a video-based deep learning algorithm, which matches human expert performance of left ventricular (LV) segmentation and ejection fraction (EF). A large pediatric dataset of 4,467 echocardiograms were used to develop EchoNet-Peds. EchoNet-Peds was trained on 80% of the data for segmentation of the left ventricle and estimation of left ventricular EF. The remaining 20% was used to fine tune and validate the algorithm. In both apical 4-chamber (A4C) and parasternal short-axis views (PSAX), EchoNet-Peds segments the left ventricle with a Dice similarity coefficient of 0.89. EchoNet-Peds estimates EF with a mean absolute error of 3.66% and can routinely identify pediatric patients with systolic dysfunction (area under the curve of 0.95). EchoNet-Peds was trained on pediatric echocardiograms and performed significantly better to estimate EF (p < 0.001) than an adult model applied to the same data. Accurate, rapid automation of EF assessment and recognition of systolic dysfunction in a pediatric population are feasible using EchoNet-Peds with the potential for far-reaching clinical impact. In addition, the first large pediatric dataset of annotated echocardiograms is now publicly available for efforts to develop pediatric-specific artificial intelligence algorithms.

Co-learning of appearance and shape for precise ejection fraction estimation from echocardiographic sequences.

Accurate estimation of ejection fraction (EF) from echocardiography is of great importance for evaluation of cardiac function. It is usually obtained by the Simpson's bi-plane method based on the segmentation of the left ventricle (LV) in two keyframes. However, obtaining accurate EF estimation from echocardiography is challenging due to (1) noisy appearance in ultrasound images, (2) temporal dynamic movement of myocardium, (3) sparse annotation of the full sequence, and (4) potential quality degradation during scanning. In this paper, we propose a multi-task semi-supervised framework, which is denoted as MCLAS, for precise EF estimation from echocardiographic sequences of two cardiac views. Specifically, we first propose a co-learning mechanism to explore the mutual benefits of cardiac segmentation and myocardium tracking iteratively on appearance level and shape level, therefore alleviating the noisy appearance and enforcing the temporal consistency of the segmentation results. This temporal consistency, as shown in our work, is critical for precise EF estimation. Then we propose two auxiliary tasks for the encoder, (1) view classification to help extract the discriminative features of each view, and automatize the whole pipeline of EF estimation in clinical practice, and (2) EF regression to help regularize the spatiotemporal embedding of the echocardiographic sequence. Both two auxiliary tasks can improve the segmentation-based EF prediction, especially for sequences of poor quality. Our method is capable of automating the whole pipeline of EF estimation, from view identification, cardiac structures segmentation to EF calculation. The effectiveness of our method is validated in aspects of segmentation, tracking, consistency analysis, and clinical parameters estimation. When compared with existing methods, our method shows obvious superiority for LV volumes on ED and ES phases, and EF estimation, with Pearson correlation of 0.975, 0.983 and 0.946, respectively. This is a significant improvement for echocardiography-based EF estimation and improves the potential of automated EF estimation in clinical practice. Besides, our method can obtain accurate and temporal-consistent segmentation for the in-between frames, which enables it for cardiac dynamic function evaluation.

Automatic Prediction of Paediatric Cardiac Output From Echocardiograms Using Deep Learning Models

<h2>Abstract</h2><h3>Background</h3> Cardiac output (CO) perturbations are common and cause significant morbidity and mortality. Accurate CO assessment is crucial for guiding treatment in anaesthesia and critical care, but measurement is difficult, even for experts. Artificial intelligence methods show promise as alternatives for accurate, rapid CO assessment. <h3>Methods</h3> We reviewed paediatric echocardiograms with normal CO and a dilated cardiomyopathy patient group with reduced CO. Experts measured the left ventricular outflow tract diameter, velocity time integral, CO, and cardiac index (CI). EchoNet-Dynamic is a deep learning model for estimation of ejection fraction in adults. We modified this model to predict the left ventricular outflow tract diameter and retrained it on paediatric data. We developed a novel deep learning approach for velocity time integral estimation. The combined models enable automatic prediction of CO. We evaluated the models against expert measurements. Primary outcomes were root-mean-squared error, mean absolute error, mean average percentage error, and coefficient of determination (<i>R</i>²). <h3>Results</h3> In a test set unused during training, CI was estimated with the root-mean-squared error of 0.389 L/min/m², mean absolute error of 0.321 L/min/m², mean average percentage error of 10.8%, and <i>R</i>² of 0.755. The Bland-Altman analysis showed that the models estimated CI with a bias of +0.14 L/min/m² and 95% limits of agreement -0.58 to 0.86 L/min/m². <h3>Conclusions</h3> Our model estimated CO with strong correlation to ground truth and a bias of 0.17 L/min, better than many CO measurements in paediatrics. Model pretraining enabled accurate estimation despite a small dataset. Potential uses include supporting clinicians in real-time bedside calculation of CO, identification of low-CO states, and treatment responses.

Myocardial Contraction Fraction is not a Predictor of Clinical Outcomes in Acute Systolic Heart Failure: A Brief Report.

The utility of myocardial contraction fraction (MCF), a volumetric measure of myocardial shortening, has not been well evaluated in patients with systolic heart failure (SHF). A single-center, retrospective cohort study of all adults admitted with acute SHF from 2013 to 2018 at an academic medical center. A chart review was performed to identify key echocardiographic transthoracic echocardiogram (TTE), laboratory, and demographic characteristics. MCF was calculated based on M-mode measurements of estimated stroke volume and myocardial volume based on admission TTE. The primary outcome was 30-day combined all-cause readmission/mortality and 365-day all-cause mortality. A total of 1282 patients were analyzed. The 30-day composite outcome occurred in 310 patients (24.2%), and all-cause death at 365 days occurred in 375 patients (29.3%). There was a weak correlation between the visually estimated ejection fraction (EF) and MCF (r = 0.356, P < 0.001). Neither MCF nor EF was associated with either component of the primary outcome. Other parameters on TTE that were associated with higher risk of primary outcome were higher tricuspid regurgitation (TR) velocity, larger left atrial (LA) diameter, and moderate or greater TR and mitral regurgitation (MR). Echocardiographic predictors of postdischarge adverse events among patients hospitalized with acute SHF include higher TR velocity, larger LA diameter, and at least moderate MR or TR. MCF does not correlate well with visually assessed EF among patients with acute SHF, and neither MCF nor EF provides prognostic information in this population.

Ten questions on cardiac magnetic resonance in patients with heart failure: from etiological diagnosis to prognostic stratification

Cardiac magnetic resonance (CMR) imaging has progressively become part of the imaging methods recommended in patients with heart failure. CMR represents the gold standard for assessing volumes, function, biventricular kinetics and providing tissue characterization through scans with and without contrast medium. In patients with heart failure with reduced ejection fraction (HFrEF) and ischemic dilated cardiomyopathy, CMR allows to search for viability, accurately estimate volumes and ejection fraction. It can assess scar extent for predicting response to cardiac resynchronization therapy and for establishing an indication for implanting a defibrillator in borderline cases. In patients with HFrEF and non-ischemic dilated cardiomyopathy, CMR helps to identify specific etiological subgroups and to estimate the arrhythmic risk beyond ejection fraction. In patients with heart failure with preserved ejection fraction, CMR offers the possibility of diagnosing specific phenotypes, including sarcomeric hypertrophic cardiomyopathy, amyloidosis or Fabry disease, and adds prognostic information. Both clinical and scientific interest in this imaging method is constantly expanding; the clinicians dealing with heart failure cannot fail to know the technique, the indications and all the potential that CMR can offer.

Improved Segmentation of Echocardiography With Orientation-Congruency of Optical Flow and Motion-Enhanced Segmentation.

Quantification of left ventricular (LV) ejection fraction (EF) from echocardiography depends upon the identification of endocardium boundaries as well as the calculation of end-diastolic (ED) and end-systolic (ES) LV volumes. It's critical to segment the LV cavity for precise calculation of EF from echocardiography. Most of the existing echocardiography segmentation approaches either only segment ES and ED frames without leveraging the motion information, or the motion information is only utilized as an auxiliary task. To address the above drawbacks, in this work, we propose a novel echocardiography segmentation method which can effectively utilize the underlying motion information by accurately predicting optical flow (OF) fields. First, we devised a feature extractor shared by the segmentation and the optical flow sub-tasks for efficient information exchange. Then, we proposed a new orientation congruency constraint for the OF estimation sub-task by promoting the congruency of optical flow orientation between successive frames. Finally, we design a motion-enhanced segmentation module for the final segmentation. Experimental results show that the proposed method achieved state-of-the-art performance for EF estimation, with a Pearson correlation coefficient of 0.893 and a Mean Absolute Error of 5.20% when validated with echo sequences of 450 patients.

Point-of-care ultrasound use in emergencies: what every anaesthetist should know.

Point-of-care ultrasound has been embraced by anaesthetists as an invaluable tool for rapid diagnosis of haemodynamic instability, to ensure procedural safety and monitor response to treatments. Increasingly available, affordable and portable, with emerging evidence of improved patient outcomes, point-of-care ultrasound has become a valuable tool in the emergency setting. This state-of-the-art review describes the feasibility of point-of-care ultrasound practice, training and maintenance of competence. It also describes the many uses of point-of-care ultrasound for the anaesthetist and describes the most salient point-of-care ultrasound views for anaesthetic emergencies including: undifferentiated shock; hypoxemia; and trauma. Procedural safety is also discussed in addition to relevant important governance aspects. Cardiac function should be assessed using the parasternal long axis, parasternal short basal/mid-papillary/apical, apical four chamber and subcostal four chamber views, and should include a visual estimation of global left ventricular ejection fraction. Other cardiovascular conditions that can be identified using point-of-care ultrasound include: pericardial effusion; cardiac tamponade; and pulmonary embolism. Pulmonary emergency conditions that can be diagnosed using point-of-care ultrasound include pneumothorax; pleural effusion; and interstitial syndrome. The extended focused assessment with sonography for trauma examination may of value in patients who are hypotensive in order to identify intra-abdominal haemorrhage, pneumothoraces and haemothoraces.

Abstract 11593: The Critical Role of Transthoracic Echocardiography in the Evaluation of Cardiogenic Shock and Takotsubo Cardiomyopathy

Introduction: Cardiogenic shock occurs in 10% of patients with takotsubo cardiomyopathy (TTC) as a result of severe systolic dysfunction, or left ventricular outflow tract (LVOT) obstruction. Methods: Case report and review of the literature. Results: A 62-year-old female with a history of hypertension and diabetes mellitus presented with lightheadedness, shortness of breath, and chest discomfort. She was hemodynamically unstable, tachycardic, and hypotensive (70/40 mmHg). Her EKG showed sinus tachycardia at 101 BPM with 1 mm ST depression in leads II, III, AVF; V3-V6. Chest radiograph revealed pulmonary edema. High-sensitive troponin was 146.7. She was intubated for respiratory distress and had rapidly increasing vasopressor requirements. Emergent coronary angiography revealed nonobstructive coronary artery disease. A left ventriculogram revealed apical ballooning with an estimated ejection fraction of 25%, consistent with TTC. Transthoracic echocardiogram (TTE) showed severe mid and apical LV hypokinesis with basal hyperkinesis. Additionally, systolic anterior motion (SAM) of the mitral valve (MV) was noted, with a peak LVOT gradient of 7 mmHg. The patient improved clinically, and all support was weaned off. However, she later acutely developed recurrent respiratory distress and hypotension. Repeat TTE showed LVOT obstruction with a gradient of 40 mmHg due to increased basal hyperkinesis and SAM. The choice of vasopressor was switched from norepinephrine to phenylephrine to reduce the obstruction. She clinically improved and metoprolol succinate was added to reduce basal hypercontractility and improve left ventricular filling. Conclusions: Differentiating the mechanism of shock in TTC can easily be accomplished with TTE, and is essential to guiding treatment. The initial acute systolic dysfunction was effectively managed with norepinephrine, dopamine, and an intra-aortic balloon pump. She later developed LVOT obstruction as her systolic function improved. This was recognized with TTE and led to an appropriate change in vasopressor, as norepinephrine would have worsened the obstruction. This highlights the importance of early TTE in patients presenting with undifferentiated shock, especially when TTC is suspected.

Management Challenge: An Atypical Variant of Takotsubo Presenting With Multiple Complications

An 84-year-old woman with depression, who witnessed the suicide of a close friend, presented with symptoms of chest pain, palpitations, and cold and clammy extremities. An electrocardiogram showed alternating tachycardia and bradycardia. Urgent transthoracic echocardiogram demonstrated left greater than right ventricular dysfunction, moderate mitral regurgitation, global hypokinesis, and an estimated ejection fraction of 20%. Cardiac catheterization demonstrated non-obstructive coronary artery disease and decreased cardiac output. Findings were consistent with Takotsubo cardiomyopathy complicated with cardiogenic shock, acute mitral regurgitation, and sinus node dysfunction. Management of this patient required the use of a mechanical device intra-aortic balloon pump, and pacemaker insertion for persistent symptomatic arrhythmia. This case highlights the challenging management of potentially fatal acute complications of Takotsubo cardiomyopathy and inadequate data on how to approach them.

Left ventricular strain in neonates using 2-dimensional speckle tracking: Normal range and relation with bi-plan ejection fraction.

Echocardiographic assessment of Left ventricular systolic function is traditionally being performed by estimation of fractional shortening (FS) and ejection fraction (EF). Speckle tracking echocardiography (STE) is a promising tool for assessment of myocardial function. The aim of this study is to evaluate the global longitudinal strain (GLS) using 2D-STE in healthy neonates to establish normal reference ranges. It is a retrospective study through an analysis of transthoracic echocardiogram of normal healthy neonates. We enrolled all neonates in our institution from January 1st, 2021 to February 28th, 2021. 2-D STE was used to assess left ventricular GLS from the apical views. 185 neonates were enrolled. Mean value for left ventricle GLS (%) was -19.9±1.2, GLS-derived EF (%) was 60.0±2.7; while the left ventricle EF by biplane Simpson's method (%) was 61.0±3. There is a good positive correlation between the Left Ventricle EF by biplane Simpson's method and EF by 2-D STE, which was statistically significant (r=.294,n=102,p=.003). Apical 4-chamber longitudinal strain and strain-derived EF is significantly correlated with GLS and bi-plan EF respectively. 2-STE is feasible technique for analyzing newborn myocardial systolic function. The normal range of GLS in neonates is not much different than reported for the pediatric. There is a good positive correlation between the Left Ventricle EF by 2-D STE and EF by biplane method.

Predictive Value of QRS Fraction for Cardiovascular Death in Patients with Heart Failure: A Prospective Cohort Study in Acute Decompensated Heart Failure (Heb-ADHF).

The QRS fraction is the ratio of the total amplitude of R waves to the total amplitude of QRS complexes ( R/QRS) on a 12-lead electrocardiogram. Our group has previously proposed calculation of the QRS fraction as a simple method for estimation of left ventricular ejection fraction. In this study, we explored the ability of the QRS fraction to predict cardiovascular death in patients with heart failure. The study had a prospective, observational design and collected epidemiological and follow-up data for 1715 patients with heart failure who were inpatients in the Department of Cardiology at the Second Hospital of Hebei Medical University between January 2017 and December 2018. The patients were stratified according to quartile of QRS fraction, namely, lower ( 43.8%, Q1 group) middle (43.8%-61.0%, Q2 group), and higher ( 61.0%, Q3 group). One thousand and fifty-one (61.28%) of the 1715 patients were male and the median follow-up duration was 261 days (interquartile range 39, 502). There were 341 (19.88%) deaths, including 282 (16.44%) with a cardiovascular cause. The Q1, Q2, and Q3 groups comprised 431 (25.13%), 850 (49.56%), and 434 (25.31%) patients, respectively. There were significant differences in cardiovascular mortality among the three QRS fraction subgroups (p 0.05). Kaplan-Meier survival curves of different QRS fraction levels showed significant diffference among patients with heart failure, especially among those with preserved ejection fraction (p = 0.025 and 0.031, log-rank test). Cox regression analysis showed that the QRS fraction was independently associated with the risk of cardiovascular death. The risk of cardiovascular death was lower in the Q2 and Q3 groups than in the Q1 group, with respective hazard ratios of 0.668 (95% confidence interval 0.457-0.974) and 0.538 (95% confidence interval 0.341-0.849). The QRS fraction may serve as a prognostic indicator of the long-term risk of cardiovascular death in patients with heart failure, especially those with preserved ejection fraction. ChiCTR-POC-17014020.