Estimation Of Ejection Fraction Research Articles

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment

Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model’s superior performance on segmentation and clinical function assessment.

Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance

BackgroundMachine learning (ML) algorithms can accurately estimate left ventricular ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of-care ultrasound (POCUS) is not well understood.ObjectivesWe evaluate the performance of an ML model for estimation of LVEF on cardiac POCUS compared with Level III echocardiographers’ interpretation and formal echo reported LVEF.MethodsClinicians at a tertiary care heart failure clinic prospectively scanned 138 participants using hand-carried devices. Video data were analyzed offline by an ML model for LVEF. We compared the ML model's performance with Level III echocardiographers' interpretation and echo reported LVEF.ResultsThere were 138 participants scanned, yielding 1257 videos. The ML model generated LVEF predictions on 341 videos. We observed a good intraclass correlation (ICC) between the ML model's predictions and the reference standards (ICC = 0.77–0.84). When comparing LVEF estimates for randomized single POCUS videos, the ICC between the ML model and Level III echocardiographers' estimates was 0.772, and it was 0.778 for videos where quantitative LVEF was feasible. When the Level III echocardiographer reviewed all POCUS videos for a participant, the ICC improved to 0.794 and 0.843 when only accounting for studies that could be segmented. The ML model's LVEF estimates also correlated well with LVEF derived from formal echocardiogram reports (ICC = 0.798).ConclusionOur results suggest that clinician-driven cardiac POCUS produces ML model LVEF estimates that correlate well with expert interpretation and echo reported LVEF.

Improvement in Echocardiographic Indexes of Systolic Heart Failure Post Kidney Transplantation - a retrospective analysis.

End-stage renal disease (ESRD) is a major risk factor for cardiovascular morbidity and mortality, which can be partially eliminated by kidney transplantation. Systolic heart failure might be considered as contraindication for kidney transplant although some patients demonstrate myocardial recovery post-transplant. We aim to identify and characterize the phenomenon of reverse myocardial remodelling in kidney transplanted patients. The study is a retrospective cohort of patients undergoing kidney transplant between 2016-2019 (n=604) at Rabin Medical Center. Patients were assessed according to availability of two echocardiographic examinations: pre- and post-kidney transplant. The change in estimated ejection fraction (EF) and possible predictors of myocardial recovery were examined. Data of 293 patients was available for the final analysis. Eighty-one (28%) patients had a LVEF improvement equal or above 5%, whereas 36 (12%) patients had a LVEF improvement 10% or more post transplantation. Twenty-five patients (8.5%) had moderate or severe systolic heart failure with LVEF reduced to 40% or less at baseline. 13 of them (52%) had a LVEF improvement of ≥5% and 10 patients (40%) had an improvement of ≥10% in their EF. Cox regression analyses identified female gender as the only independent variable associated with LVEF improvement of at least 10%. Conclusion Renal transplantation might lead to improved LV systolic function in some patients.

Visual Estimation of Left Ventricular Ejection Fraction by Emergency Medicine Residents is More Accurate in Emergency Department Patients

Visual Estimation of Left Ventricular Ejection Fraction by Emergency Medicine Residents is More Accurate in Emergency Department Patients

Single-access, non-contrast transcatheter aortic valve implantation, the ultimate minimalist approach: a case report

Abstract Background Transcatheter aortic valve implantation (TAVI) is an established treatment for patients with symptomatic severe aortic stenosis. Patients with previous renal transplant are considered as a high-risk cohort who may develop procedural complications related to vascular access and renal impairment post-TAVI. Case summary Herein, we report a case of an 88-year-old male who presented with progressive dyspnoea. His transthoracic echocardiogram revealed severe aortic stenosis with a peak gradient of 75 mmHg and impaired left ventricle systolic function (an estimated ejection fraction of 40%). He had a background of kidney transplant with progressive decline in renal function, requiring the formation of left arm arteriovenous fistula in preparation for future dialysis. He was successfully treated with TAVI using a single vascular access site without administering contrast media. Discussion Single-access, non-contrast TAVI is feasible when treating renal transplant patients with severe aortic stenosis and limited vascular access. The current minimalistic approach should be used only in highly selective patient cases.

Pragmatic Evaluation of a Deep-Learning Algorithm to Automate Ejection Fraction on Hand-Held, Point-of-Care Echocardiography in a Cardiac Surgical Operating Room

ObjectiveTest the correlation of ejection fraction (EF) estimated by a deep learning-based, automated algorithm (Auto EF) vs an EF estimated by Simpson's method. DesignProspective observational study. SettingSingle-center study at the Hospital of the University of Pennsylvania. ParticipantsStudy participants were ≥18 years of age, scheduled to undergo valve, aortic, coronary artery bypass graft (CABG), heart or lung transplant surgery. InterventionsThis non-interventional study involved acquiring apical 4 Chamber (A4C), transthoracic echocardiographic clips using Philips hand-held ultrasound device; Lumify. Measurements and Main ResultsIn the primary analysis of 54 clips, compared to the Simpson's method for EF estimation, bias was similar for Auto EF (-10.17%) and the experienced reader-estimated EF (-9.82%), but correlation was lower for Auto EF (r = 0.56) than the experienced reader-estimated EF (r = 0.80). In the secondary analyses, correlation between EF estimated by Simpson's method and Auto EF increased when applied to 27 acquisitions classified as adequate (r = 0.86) but decreased when applied to 27 acquisitions classified as inadequate (r = 0.46). ConclusionsApplied to acquisitions of adequate image quality, Auto EF produced a numerical EF estimate equivalent to Simpson's method. However, when applied to acquisitions of inadequate image quality, discrepancies arose between EF estimated by Auto EF and Simpson's method. Visual EF estimates by experienced readers correlated highly with Simpson's method in both variable and inadequate imaging conditions – emphasizing its enduring clinical utility. Trial RegistrationClinicaltrials.gov trial identifier: NCT04943965

Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography.

Characterizing left ventricular deformation and strain using 3D+time echocardiography provides useful insights into cardiac function and can be used to detect and localize myocardial injury. To achieve this, it is imperative to obtain accurate motion estimates of the left ventricle. In many strain analysis pipelines, this step is often accompanied by a separate segmentation step; however, recent works have shown both tasks to be highly related and can be complementary when optimized jointly. In this work, we present a multi-task learning network that can simultaneously segment the left ventricle and track its motion between multiple time frames. Two task-specific networks are trained using a composite loss function. Cross-stitch units combine the activations of these networks by learning shared representations between the tasks at different levels. We also propose a novel shape-consistency unit that encourages motion propagated segmentations to match directly predicted segmentations. Using a combined synthetic and in-vivo 3D echocardiography dataset, we demonstrate that our proposed model can achieve excellent estimates of left ventricular motion displacement and myocardial segmentation. Additionally, we observe strong correlation of our image-based strain measurements with crystal-based strain measurements as well as good correspondence with SPECT perfusion mappings. Finally, we demonstrate the clinical utility of the segmentation masks in estimating ejection fraction and sphericity indices that correspond well with benchmark measurements.

Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography.

The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depends on the quality of the acquired ultrasound data. This work reports on the development and validation of spatiotemporal deep learning models to assess the suitability of input ultrasound cine loops collected using a handheld echocardiography device for processing by an automated quantification algorithm (e.g. ejection fraction estimation). POC echocardiograms (n=885 DICOM cine loops from 175 patients) from two sites were collected using a handheld ultrasound device and annotated for image quality at the frame-level. Attributes of high-quality frames for left ventricular (LV) quantification included a temporally-stable LV, reasonable coverage of LV borders, and good contrast between the borders and chamber. Attributes of low-quality frames included temporal instability of the LV and/or imaging artifacts (e.g., lack of contrast, haze, reverberation, acoustic shadowing). Three different neural network architectures were investigated - (a) frame-level convolutional neural network (CNN) which operates on individual echo frames (VectorCNN), (b) single-stream sequence-level CNN which operates on a sequence of echo frames (VectorCNN+LSTM) and (c) two-stream sequence-level CNNs which operate on a sequence of echo and optical flow frames (VectorCNN+LSTM+Average, VectorCNN+LSTM+MinMax, and VectorCNN+LSTM+ConvPool). Evaluation on a sequestered test dataset containing 76 DICOM cine loops with 16,914 frames showed that VectorCNN+LSTM can effectively utilize both spatial and temporal information to regress the quality of an input frame (accuracy: 0.925, sensitivity = 0.860, specificity = 0.952), compared to the frame-level VectorCNN that only utilizes spatial information in that frame (accuracy: 0.903, sensitivity = 0.791, specificity = 0.949). Furthermore, an independent sample t-test indicated that the cine loops classified to be of adequate quality by the VectorCNN+LSTM model had a statistically significant lower bias in the automatically estimated EF (mean bias = - 3.73 ± 7.46 %, versus a clinically obtained reference EF) compared to the loops classified as inadequate (mean bias = -15.92 ± 12.17 %) (p = 0.007). Thus, cine loop stratification using the proposed spatiotemporal CNN model improves the reliability of automated point-of-care echocardiography image interpretation.

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8–9 am, and 10–11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.

Anesthesia in Low Ejection Fraction Patient : Case Report

Ejection fraction (EF) is the percentage of the difference between the end-diastolic volume and the end-systolic volume divided by the end-diastolic volume. This measurement is important in determining how well the heart is pumping out blood and in diagnosing heart failure (HF). Normally the percentage of EF is above 50%. A decrease in EF will make it difficult to perform anesthesia during surgery due to life-threatening irregular heartbeats, leading to sudden cardiac arrest and sudden death. Patients with a very low ejection fraction require identification of risk factors, preoperative evaluation and optimization, medical therapy, monitoring, and appropriate anesthetic technique and drugs. In this report, we present a 67-year-old woman with HFmrEF (estimated ejection fraction 46%) and coronary artery disease who underwent partial hip replacement surgery under general anesthesia. General anesthesia was performed on the patient and induction used fentanyl, midazolam, and atracurium. Management of patients is aimed at maintaining cardiac output and avoiding myocardial depression by maintaining a balance between oxygen supply and demand. Therefore good anesthetic management is needed to maintain the hemodynamic stability of the patient.

Estimation of Left Ventricular Ejection Fraction Based on Swin_Uniform

A new left ventricular ejection index prediction method was proposed by introducing a block attention mechanism, which helps to reduce overfitting problems and improve the power of the model to process new different samples. Swin Transformer is a Transformer architecture that retains the modeling advantages of self attention mechanism. Its hierarchical design has both global and local modeling capabilities, and can output multi resolution feature maps, which is suitable for EchoCoTr's need to extract multi-scale information from time series medical images. Meanwhile, the resources required of Swin Transformer at runtime are linearly related to images to be processed, which reduces computational complexity and is suitable for processing high-resolution sequence images in EchoCoTr[1]. Swin Transformer has better results than Transformer in both classification and detection tasks, which just goes to show that it is somewhat of a great application in the field of vision. This is beneficial for EchoCoTr's understanding of cardiac ultrasound sequences. Therefore, in the original UniFormer model structure of the EchoCoTr[1] model, a new Swin Transformer Block is added. The new model has stronger modeling capabilities. This means that better results can be obtained under the same computing resources.

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.