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Limited views are often obtained in the setting of cardiac ultrasound, however, the likelihood of missing left ventricular (LV) dysfunction based on a single view is not known. We sought to determine the echo views that were least likely to miss LV systolic dysfunction in consecutive transthoracic echocardiograms (TTEs). Structured data from TTEs performed at 2 hospitals from September 25, 2017, to January 15, 2019, were screened. Studies of interest were those with reported LV dysfunction. Views evaluated were the parasternal long-axis (PLAX), parasternal-short axis at mitral (PSAX M), papillary muscle (PSAX PM), and apical (PSAX A) levels, apical 2 (AP2), apical 3 (AP3), and apical 4 (AP4) chamber views. The probability that a view contained at least 1 abnormal segment was determined and analyzed with McNemar’s test for 21 adjusted pair-wise comparisons. There were 4102 TTE studies included for analysis. TTEs on males comprised 72.7% of studies with a mean LV ejection fraction of 42.8 ± 9.7%. The echo view with the greatest likelihood of encompassing an abnormal segment was the AP2 view with a prevalence of 93.4% (p 82% of the time in the setting of LV systolic dysfunction, with a prevalence of up to 93.4% in the apical windows. Supplementary Information The online version contains supplementary material available at 10.1007/s10554-021-02460-4.

The emerging market of cardiac handheld ultrasound (US) is on the rise. Despite the advantages in ease of access and the lower cost, a gap in image quality can still be observed between the echocardiography (echo) data captured by point-of-care ultrasound (POCUS) compared to conventional cart-based US, which limits the further adaptation of POCUS. In this work, we aim to present a machine learning solution based on recent advances in adversarial training to investigate the feasibility of translating POCUS echo images to the quality level of high-end cart-based US systems. We propose a constrained cycle-consistent generative adversarial architecture for unpaired translation of cardiac POCUS to cart-based US data. We impose a structured shape-wise regularization via a critic segmentation network to preserve the underlying shape of the heart during quality translation. The proposed deep transfer model is constrained to the anatomy of the left ventricle (LV) in apical two-chamber (AP2) echo views. A total of 1089 echo studies from 841 patients are used in this study. The AP2 frames are captured by POCUS (Philips Lumify and Clarius) and cart-based (Philips iE33 and Vivid E9) US machines. The dataset of quality translation comprises a total of 441 echo studies from 395 patients. Data from both POCUS and cart-based systems of the same patient were available in 122 cases. The deep-quality transfer model is integrated into a pipeline for an automated cardiac evaluation task, namely segmentation of LV in AP2 view. By transferring the low-quality POCUS data to the cart-based US, a significant average improvement of 30% and 34 mm is obtained in the LV segmentation Dice score and Hausdorff distance metrics, respectively. This paper presents the feasibility of a machine learning solution to transform the image quality of POCUS data to that of high-quality high-end cart-based systems. The experiments show that by leveraging the quality translation through the proposed constrained adversarial training, the accuracy of automatic segmentation with POCUS data could be improved.

Objectives The authors sought to prospectively determine the safety and efficacy of next-day discharge using the Vancouver 3M (Multidisciplinary, Multimodality, but Minimalist) Clinical Pathway. Background Transfemoral transcatheter aortic valve replacement (TAVR) is an alternative to surgery in high- and intermediate-risk patients; however, hospital stays average at least 6 days in most trials. The Vancouver 3M Clinical Pathway is focused on next-day discharge, made possible by the use of objective screening criteria as well as streamlined peri- and post-procedural management guidelines. Methods Patients were enrolled from 6 low-volume ( 200 TAVR/year) centers in Canada and the United States. The primary outcomes were a composite of all-cause death or stroke by 30 days and the proportion of patients successfully discharged home the day following TAVR. Results Of 1,400 screened patients, 411 were enrolled at 13 centers and received a SAPIEN XT (58.2%) or SAPIEN 3 (41.8%) valve (Edwards Lifesciences, Irvine, California). In centers enrolling exclusively in the study, 55% of screened patients were enrolled. The median age was 84 years (interquartile range: 78 to 87 years) with a median STS score of 4.9% (interquartile range: 3.3% to 6.8%). Next-day discharge home was achieved in 80.1% of patients, and within 48 h in 89.5%. The composite of all-cause mortality or stroke by 30 days occurred in 2.9% (95% confidence interval: 1.7% to 5.1%), with neither component of the primary outcome affected by hospital TAVR volume (p = 0.51). Secondary outcomes at 30 days included major vascular complication 2.4% (n = 10), readmission 9.2% (n = 36), cardiac readmission 5.7% (n = 22), new permanent pacemaker 5.7% (n = 23), and >mild paravalvular regurgitation 3.8% (n = 15). Conclusions Adherence to the Vancouver 3M Clinical Pathway at low-, medium-, and high-volume TAVR centers allows next-day discharge home with excellent safety and efficacy outcomes.

BACKGROUND In December of 2019, Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged, resulting in a global pandemic with 125 million cases worldwide. Few prospective echocardiographic studies have been performed to characterize cardiac manifestations of patients who have recovered from acute COVID-19 infection. There are limited data on the residual echocardiographic findings including strain analysis of post-COVID patients. One aim of our CIHR-funded study is to prospectively phenotype post-COVID patients using conventional echocardiography including strain imaging. METHODS AND RESULTS All patients discharged from hospital following acute COVID-19 infection are systematically followed in the Post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At about 4-12 weeks post diagnosis, patients undergo comprehensive echocardiographic assessment with conventional echocardiography including strain analysis. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. Left ventricular global longitudinal strain (GLS), was measured using a vendor-independent 2D speckle-tracking software (TomTec). A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation), while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% ICU admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of cohort defined in this study as EF≥54%, and 97% had normal RV systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS in septal and inferior segments were lower compared to the rest. In patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9% but LV GLS was abnormal in 46%. CONCLUSION Most post-COVID patients had normal LVEF at 4-12 weeks post positive test, but over half had abnormal LV GLS. Even when patients with pre-existing cardiac conditions were excluded, 46% had abnormal global strain. There appeared to be a high predilection for septal, and to a lesser extent, inferior wall involvement, giving an appearance of a “C” sign on the bullseye strain map. Further studies are needed to confirm whether these abnormalities are consistently found and to understand the long-term cardiac sequelae of SARS-CoV-2 infection.

BACKGROUND Point of care lung ultrasound (POCUS-L) and the interpretation of lung artifacts has recently emerged as key assessment for patients with acute respiratory illnesses. In the era of COVID-19, POCUS-L can be used to risk stratify, and diagnose COVID-19 pneumonia preceding a formal diagnosis by polymerase chain reaction (PCR). There has been recent development of artificial intelligence (AI) software to automate analysis of ultrasound images for clinicians without formal training. Given the magnitude of COVID-19, AI-assisted POCUS-L has the potential to change the diagnostic landscape empowering frontline clinicians from tertiary care centres to rural communities. While there have been published deep learning models for the interpretation of POCUS-L, images can be challenging for AI networks for 2 reasons: 1. large datasets are not currently available and 2. the presence of lung artifacts can be sparse within the numerous frames of a video. To overcome these limitations, we propose a knowledge transfer approach for the former, and an attention-based model for the latter. Using an attention-based model allows the network to narrow focus on relevant frames improving diagnostic precision and accuracy. METHODS AND RESULTS In our design we modify the current open-source state-of-the-art model (POCOVID-NET). A convolutional neural network (CNN) extracts spatially encoded features from POCUS-L images, which are fed to a novel attention-based transformer encoder to capture temporal information across frames, which then narrows focus to key frames. We guide the network learn relevant features (A and B lines) by training it on a pulmonary biomarker detection task from our own private dataset of lung images (Fig 2, step 1). We then transferred the knowledge learned and apply this novel attention-based model to a publicly available POCUS-L image dataset set consisting of patients with 1. healthy lungs 2. bacterial lobar pneumonia, and 3. COVID-19 viral pneumonia. We performed a performance study comparing diagnostic precision and recall compared to the POCOVID-Net model. Our novel attention-based model achieves 85% precision and 90% recall for COVID-19, an improvement of 4 % and 10% respectively over the previous model (Table 1). CONCLUSION In this study, our novel attention-based machine learning model outperformed the state-of-the-art model for COVID-19 diagnosis by POCUS-L. By integrating an attention mechanism across video frames, our models leverages both spatial and temporal information highlighting key frames used to make the prediction. To our knowledge, this is the highest performing model on this POCUS-L dataset diagnosing COVID-19.

In the midst of the COVID-19 pandemic, unprecedented pressure has been added to healthcare systems around the globe. Imaging is a crucial component in the management of COVID-19 patients. Point-of-care ultrasound (POCUS) such as hand-carried ultrasound emerges in the COVID-19 era as a tool that can simplify the imaging process of COVID-19 patients, and potentially reduce the strain on healthcare providers and healthcare resources. The preliminary evidence available suggests an increasing role of POCUS in diagnosing, monitoring, and risk-stratifying COVID-19 patients. This scoping review aims to delineate the challenges in imaging COVID-19 patients, discuss the cardiopulmonary complications of COVID-19 and their respective sonographic findings, and summarize the current data and recommendations available. There is currently a critical gap in knowledge in the role of POCUS in the COVID-19 era. Nonetheless, it is crucial to summarize the current preliminary data available in order to help fill this gap in knowledge for future studies.

Background Echocardiographic assessment of diastolic function is complex but can aid in the diagnosis of heart failure, particularly in patients with preserved ejection fraction. In 2016, the American Society of Echocardiography (ASE) and European Association of Cardiovascular Imaging (EACVI) published an updated algorithm for the evaluation of diastolic function. The objective of our study was to assess its impact on diastolic function assessment in a real-world cohort of echo studies. Methods We retrospectively identified 71,727 consecutive transthoracic echo studies performed at a tertiary care center between February 2010 and March 2016 in which diastolic function was reported based on the 2009 ASE Guidelines. We then programmed a software algorithm to assess diastolic function in these echo studies according to the 2016 ASE/EACVI Guidelines. Results When diastolic function assessment based on the 2009 guidelines was compared to that using the 2016 guidelines, there were significant differences in proportion of studies classified as normal (23% vs. 32%) or indeterminate (43% vs. 36%) function, and mild (23% vs. 23%), moderate (10% vs. 8%), or severe (1% vs. 2%) diastolic dysfunction, with poor agreement between the two methods (Kappa 0.323, 95% CI 0.318–0.328). Furthermore, within the subgroup of studies with preserved ejection fraction and no evidence of myocardial disease, there was significant reclassification from mild diastolic dysfunction to normal diastolic function. Conclusion The updated guidelines result in significant differences in diastolic function interpretation in the real world. Our findings have important implications for the identification of patients with or at risk for heart failure.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Atrial volumes indexed to body surface area (AVI) are robust predictors of nonvalvular atrial fibrillation (AF) recurrence after direct current cardioversion (DCCV). The incremental value of atrial emptying fraction (EmF) compared with atrial volumes as a predictor for recurrent AF after DCCV has not been evaluated. We sought to compare the predictive ability of baseline left atrial (LA) EmF, right atrial (RA) EmF, LAVI, and RAVI for post-DCCV AF recurrence at 6 months. The first 95 patients enrolled in the AF Clinic Registry with adequate echocardiogram imaging constituted the study cohort. Each patient underwent echocardiogram within 6 months before cardioversion. Maximal LAVI and RAVI, LA EmF, and RA EmF were performed offline using 4-chamber single-plane Simpson's method, averaged over 5 cycles. The mean age of the study cohort was 64 ± 12 years, and 67% were men. Only 28 patients (29%) who underwent DCCV remained in sinus rhythm at 6 months of follow-up. The remaining, 67 (71%) had reverted to AF or underwent ablation during the 6 months of follow-up. The overall performance for prediction of AF recurrence was greatest for RA EmF, area under the receiver operator characteristic curve (AUC): RA EmF 0.92, LA EmF 0.89, RAVI 0.76, and LAVI 0.63. RA and LA EmF AUCs were significantly higher than for LAVI or RAVI (max p = 0.02). In conclusion, although RAVI and LAVI are strong predictors of AF recurrence after DCCV, RA and LA EmF outperformed in this cohort.

ObjectivesThis study sought to determine whether volume loading alters the left atrial appendage (LAA) dimensions in patients undergoing percutaneous LAA closure.BackgroundPercutaneous LAA closure is increasingly performed in patients with atrial fibrillation and contraindications to anticoagulation, to lower their stroke and systemic embolism risk. The safety and efficacy of LAA closure relies on accurate device sizing, which necessitates accurate measurement of LAA dimensions. LAA size may change with volume status, and because patients are fasting for these procedures, intraprocedural measurements may not be representative of true LAA size.MethodsThirty-one consecutive patients undergoing percutaneous LAA closure who received volume loading during the procedure were included in this study. After an overnight fast and induction of general anesthesia, patients had their LAA dimensions (orifice and depth) measured by transesophageal echocardiography before and after 500 to 1,000 ml of intravenous normal saline, aiming for a left atrial pressure >12 mm Hg.ResultsSuccessful implantation of LAA closure device was achieved in all patients. The average orifice size of the LAA at baseline was 20.5 mm at 90°, and 22.5 mm at 135°. Following volume loading, the average orifice size of the LAA increased to 22.5 mm at 90°, and 23.5 mm at 135°. The average increase in orifice was 1.9 mm (p < 0.0001). The depth of the LAA also increased by an average of 2.5 mm after volume loading (p < 0.0001).ConclusionsIntraprocedural volume loading with saline increased the LAA orifice and depth dimensions during LAA closure. Operators should consider optimizing the left atrial pressure with volume loading before final device sizing.

Pulmonary artery sarcoma is a rare malignant neoplasm. Here, we describe a patient with a pulmonary artery sarcoma, which was only subtly visible and therefore not fully appreciated on initial transthoracic echocardiogram. Characterization of the tumor was aided by the use of multimodality imaging that included computed tomography, magnetic resonance imaging, and positron emission tomography. Familiarity with its appearance on multiple imaging modalities including echocardiography is important to ensure timely diagnosis, although the optimal treatment strategy is still unknown, and the prognosis remains poor.

The authors sought to prospectively determine the safety and efficacy of next-day discharge using the Vancouver 3M (Multidisciplinary, Multimodality, but Minimalist) Clinical Pathway.Transfemoral transcatheter aortic valve replacement (TAVR) is an alternative to surgery in high- and intermediate-risk patients; however, hospital stays average at least 6 days in most trials. The Vancouver 3M Clinical Pathway is focused on next-day discharge, made possible by the use of objective screening criteria as well as streamlined peri- and post-procedural management guidelines.Patients were enrolled from 6 low-volume (100 TAVR/year), 4 medium-volume, and 3 high-volume (200 TAVR/year) centers in Canada and the United States. The primary outcomes were a composite of all-cause death or stroke by 30 days and the proportion of patients successfully discharged home the day following TAVR.Of 1,400 screened patients, 411 were enrolled at 13 centers and received a SAPIEN XT (58.2%) or SAPIEN 3 (41.8%) valve (Edwards Lifesciences, Irvine, California). In centers enrolling exclusively in the study, 55% of screened patients were enrolled. The median age was 84 years (interquartile range: 78 to 87 years) with a median STS score of 4.9% (interquartile range: 3.3% to 6.8%). Next-day discharge home was achieved in 80.1% of patients, and within 48 h in 89.5%. The composite of all-cause mortality or stroke by 30 days occurred in 2.9% (95% confidence interval: 1.7% to 5.1%), with neither component of the primary outcome affected by hospital TAVR volume (p = 0.51). Secondary outcomes at 30 days included major vascular complication 2.4% (n = 10), readmission 9.2% (n = 36), cardiac readmission 5.7% (n = 22), new permanent pacemaker 5.7% (n = 23), andmild paravalvular regurgitation 3.8% (n = 15).Adherence to the Vancouver 3M Clinical Pathway at low-, medium-, and high-volume TAVR centers allows next-day discharge home with excellent safety and efficacy outcomes.

Aims Left atrial appendage (LAA) device imaging after endovascular closure is important to assess for device thrombus, residual leak, positioning, surrounding structures, and pericardial effusion. Cardiac CT angiography (CCTA) is well suited to assess these non-invasively. Methods and results We report our consecutive series of non-valvular atrial fibrillation patients who underwent CCTA post-LAA closure with Amplatzer Cardiac Plug (ACP), Amulet (second generation ACP), or WATCHMAN devices. Patients underwent CCTA typically 1–6 months post-implantation. Prospective cardiac-gated CCTA was performed with Toshiba 320-detector or Siemens 2nd generation 128-slice dual-source scanners, and images interpreted with VitreaWorkstation™. GFR

Fabry disease is an X-linked lysosomal storage disorder that results from a deficiency of α-galactosidase A. Increased left ventricular wall thickness has been the most commonly described cardiovascular manifestation of the disease. However, a variety of other structural and functional abnormalities have also been reported. Echocardiography is an effective noninvasive method of assessing the cardiac involvement of Fabry disease. A more precise and comprehensive characterization of Fabry cardiomyopathy using conventional and novel echocardiographic techniques may lead to earlier diagnosis, more accurate prognostication, and timely treatment. The aim of this review is to provide a comprehensive overview of the structural and functional abnormalities on echocardiography that have thus far been described in patients with Fabry disease and to highlight potential areas that would benefit from further research.

For patients with symptomatic, sustained atrial fibrillation (AF), a "pill-in-the-pocket" antiarrhythmic drug (PIP-AAD) strategy has been proposed to reduce emergency department (ED) use.To assess the clinical utility of a protocolled PIP-AAD approach within contemporary practice.Consecutive patients who hemodynamically tolerated symptomatic, sustained AF were prospectively managed with the PIP-AAD strategy. All patients were given an atrioventricular nodal blocker 30 minutes prior to a single oral dose of a class Ic antiarrhythmic drug. If the initial PIP-AAD in the ED was efficacious and tolerated, PIP-AADs were given out of hospital for subsequent sustained AF episodes. Usage and complications were systematically recorded.During a median follow-up period of 565 days, 43 of 80 patients presented to the ED for initial PIP-AAD. Sinus rhythm was restored without complication in 30 of 43 patients. The reasons for initial PIP-AAD failure were inefficacy (6 patients), significant hypotension (4 patients), conversion to flutter necessitating cardioversion (2 patients), and syncopal conversion pause (1 patient). For the 30 patients with successful initial PIP-AAD, 159 out-of-hospital PIP-AAD treatments occurred (mean 5.3 ± SD 1.3 per patient). Compared with ED visits in the period prior to PIP-AAD initiation, there was a significant reduction in visits (2.6 ± 3.0 vs. 0.4±0.9 ED visits per patient, P.001) and the need for cardioversion (2.3 ± 3.1 vs. 0.0 ± 0.2 treatments per patient, P .001). Adverse events associated with out-of-hospital PIP-AAD include presyncope (3 of 30 patients), syncope necessitating pacemaker implantation (1 patient), and conversion to flutter (1 patient).Out-of-hospital PIP-AAD can be an effective for highly selected patients; however, the rates of treatment failure and adverse events are clinically relevant, which limits the widespread application of a PIP-AAD approach.

Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert’s manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

Echocardiography (echo) is the most common test for diagnosis and management of patients with cardiac condi- tions. While most medical imaging modalities benefit from a relatively automated procedure, this is not the case for echo and the quality of the final echo view depends on the competency and experience of the sonographer. It is not uncommon that the sonographer does not have adequate experience to adjust the transducer and acquire a high quality echo, which may further affect the clinical diagnosis. In this work, we aim to aid the operator during image acquisition by automatically assessing the quality of the echo and generating the Automatic Echo Score (AES). This quality assessment method is based on a deep convolutional neural network, trained in an end-to-end fashion on a large dataset of apical four-chamber (A4C) echo images. For this project, an expert car- diologist went through 2,904 A4C images obtained from independent studies and assessed their condition based on a 6-scale grading system. The scores assigned by the expert ranged from 0 to 5. The distribution of scores among the 6 levels were almost uniform. The network was then trained on 80% of the data (2,345 samples). The average absolute error of the trained model in calculating the AES was 0.8 ± 0:72. The computation time of the GPU implementation of the neural network was estimated at 5 ms per frame, which is sufficient for real-time deployment.

Characterisation of cardiac cycle phase in echocardiography data is a necessary preprocessing step for developing automated systems that measure various cardiac parameters. Accurate characterisation is challenging, due to differences in appearance of the cardiac anatomy and the variability of heart rate in individuals. Here, we present a method for automatic recognition of cardiac cycle phase from echocardiograms by using a new deep neural networks architecture. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual echocardiogram frames, with recurrent neural networks (RNNs), which model the temporal dependencies between sequential frames. We demonstrate that such new architecture produces results that outperform baseline architecture for the automatic characterisation of cardiac cycle phase in large datasets of echocardiograms containing different levels of pathological conditions.

Echocardiography (echo) is a clinical imaging technique which is highly dependent on operator experience. We aim to reduce operator variability in data acquisition by automatically computing an echo quality score for real-time feedback. We achieve this with a deep neural network model, with convolutional layers to extract hierarchical features from the input echo cine and recurrent layers to leverage the sequential information in the echo cine loop. Using data from 509 separate patient studies, containing 2,450 echo cines across five standard echo imaging planes, we achieved a mean quality score accuracy of 85\(\%\) compared to the gold-standard score assigned by experienced echosonographers. The proposed approach calculates the quality of a given 20 frame echo sequence within 10 ms, sufficient for real-time deployment.

Background Data regarding the prevalence and spectrum of conditions associated with severe tricuspid regurgitation (TR) are limited to small cohorts. Methods We retrospectively identified all patients with severe native tricuspid valve regurgitation in a large echocardiogram database between January 2004 and December 2010. Patients were classified into 1 of 3 groups based on the echocardiogram results: (1) organic TR; (2) functional TR; and (3) idiopathic TR. Results Severe TR was identified in 768 (1.2%, 72 ± 16 years, 64.3% females) of 63, 472 consecutive patients referred for transthoracic echocardiography. The conditions associated with severe TR could be established in 91% of patients. The remaining 9% were classified as idiopathic severe TR with these patients being older (78 ± 10 years) and having a higher frequency of atrial fibrillation (63.8%) compared to patients with organic (65 ± 22 years; 31%) or functional severe TR (73 ± 16 years; 47.8%). Overall, organic severe TR was identified in 11.3% of all cases. Functional severe TR occurred in 79.7% of the overall cohort and was related to pulmonary hypertension and/or left-sided heart disease. Conclusion Severe TR occurred with a prevalence of 1.2% in our patients referred for echocardiography and was more common in females. Functional severe TR was the most common etiology with only a minority of cases secondary to organic severe TR. Idiopathic severe TR was found in a small proportion of patients who were older and more likely to have atrial fibrillation.

Background: Bicuspid aortic valve (BAV) is the leading cause of aortic stenosis in patients younger than the age of 50. A classification scheme of BAVs is based upon leaflet orientation: Type I (fusion of right and left coronary cusps) and Type II (fusion of right and noncoronary cusps). The correlation between BAV leaflet orientation and aortic root pathology however remains ill defined. Objective: The objective was to describe a potential relationship between BAV leaflet morphology and aortic root measurements in the ASTRONOMER study, a multicenter study to assess the effect of rosuvastatin on the progression of AS. Methods: BAV morphology was classified as Type I or Type II orientation based on the parasternal short-axis view. Echo measurements including left ventricular and aortic root dimensions were obtained. Results: The study population included 89 patients (56 ± 11 years; 44 males). There were 63 patients with Type I and 26 patients with Type II BAV. Baseline demographics, hemodynamics, and left heart dimensions were similar between both groups. Patients with Type I BAV had larger aortic annulus and ascending root dimensions compared to those patients with Type II BAV (P < 0.05). Conclusion: In patients with mild to moderate aortic stenosis due to a BAV, the presence of Type I valve orientation was associated with significantly greater aortic root parameters compared to Type II valve orientation. (ECHOCARDIOGRAPHY 2010;27:174-179)

Aims Mitral annular calcification (MAC) is characterized by calcium and lipid deposition in the annular fibrosa of the mitral valve. Although individuals with MAC are at increased risk of cardiovascular events, little is known about the significance of this finding in patients with concurrent aortic stenosis (AS). The aim of this study was to describe the association of baseline MAC and aortic valve morphology in asymptomatic patients enrolled in the ASTRONOMER study, a multicentre study to assess the effect of Rosuvastatin on the progression of AS. Methods and results At baseline, transthoracic echocardiography was performed with two-dimensional and Doppler imaging following a standardized protocol. Echo measurements including left ventricular (LV) dimensions and aortic root dimensions were obtained according to the ASE recommendations. MAC was identified by bright echoes at the base of the mitral leaflets or annulus on 2D imaging, and aortic valve calcification by visualization of bright echoes on the aortic valve leaflets. The degree of calcification was semi-quantitated from absent to severe. The study population included 219 patients (57 ± 14 years; 129 males), divided into two pre-specified categories; bicuspid ( n = 133) and tricuspid ( n = 86) aortic valves. Baseline LV dimensions, aortic valve haemodynamics, and cholesterol profiles were similar between the two groups at baseline. Individuals with tricuspid aortic valves were older, more hypertensive, with higher degrees of MAC and AV calcification ( P < 0.001). The higher degree of MAC persisted in patients with tricuspid AV after adjustment for age and systolic blood pressure ( P = 0.004). Conclusion In patients with asymptomatic mild to moderate AS, MAC is more prevalent in those individuals with tricuspid AV, independent of age, and systolic blood pressure. Whether the degree of MAC may be a surrogate for atherosclerosis, and predict the subset of patients who will respond to statin therapy in preventing the progression of AS, remains to be determined.

Tissue Doppler (TD) imaging is a novel echocardiographic technique that measures myocardial velocities. However, there are sparse data on TD imaging of the right ventricular (RV) free wall in the diagnosis and prognosis of RV myocardial infarction (MI) in inferior wall left ventricular MI. Fifty patients who had left ventricular MI underwent TD echocardiography and angiography within 48 hours of MI. For diagnosis, the ability of RV TD imaging to detect RV MI was assessed using coronary angiography as the reference standard. For prognosis, the ability of TD detection of RV dysfunction to predict cardiac death or rehospitalization at 1 year was determined. For diagnosis, the univariate predictors of RV MI included RV diastolic dimension (p = 0.001), TD imaging of tricuspid annular systolic velocity (p = 0.001), and early diastolic velocity (p = 0.002). On multivariate analysis, systolic annular velocity (p = 0.04) and RV dimension (p = 0.05) predicted RV MI. For prognosis, nonculprit coronary artery disease (p = 0.003), TD imaging of RV systolic annular velocity (p = 0.005), and early diastolic velocity (p = 0.01) were among the univariate predictors of cardiac death or rehospitalization. On multivariate analysis, nonculprit coronary artery disease (p = 0.02) and TD imaging of systolic annular velocity (p = 0.04) were independent predictors of outcome. Decreased RV systolic annular velocity on TD images detects RV MI in first left ventricular acute inferior MI and predicts cardiac death or rehospitalization at 1 year.

OBJECTIVES This study examined the effect of aspirin on the risk of embolic events in infective endocarditis (IE). BACKGROUND Embolism is a major complication of IE, and studies in animal models have shown that platelet inhibition with aspirin can lead to more rapid vegetation resolution and a lower rate of embolic events. METHODS We conducted a randomized, double-blinded, placebo-controlled trial of aspirin treatment (325 mg/day) for four weeks in patients with IE to test the hypothesis that the addition of aspirin would reduce the incidence of clinical systemic embolic events. Patients with perivalvular abscess were excluded. Serial cerebral computed tomograms and transesophageal echocardiograms were obtained in a subset of patients. RESULTS During the four-year study period, 115 patients were enrolled: 60 assigned to aspirin and 55 assigned to placebo. Embolic events occurred in 17 patients (28.3%) on aspirin and 11 patients (20.0%) on placebo, with an odds ratio (OR) of 1.62 (95% confidence interval [CI] 0.68 to 3.86, p = 0.29). There was a trend toward a higher incidence of bleeding in the patients taking aspirin versus placebo (OR 1.92, 95% CI 0.76 to 4.86, p = 0.075). Development of new intracranial lesions was similar in both groups. Aspirin had no effect on vegetation resolution and valvular dysfunction. CONCLUSIONS In endocarditis patients already receiving antibiotic treatment, the addition of aspirin does not appear to reduce the risk of embolic events and is likely associated with an increased risk of bleeding. Aspirin is not indicated in the early management of patients with IE.

Left ventricular filling and pulmonary artery pressure (PAP) have not been well described in patientsor = 90 years old. We reviewed our database to obtain echocardiograms with normal findings in this age group. The peak early and late mitral filling velocities, the early and late velocity-time integral (VTI), the mitral deceleration time, and the atrial filling fraction (late VTI/total VTI) were measured. PAP was obtained from the peak tricuspid regurgitation velocity. Of 252 echocardiograms performed on patientsor = 90 years old, 47 (19%) were normal. The mean +/- SD values were: ratio of peak early and late mitral filling velocities, 0.69 +/- 0.16; deceleration time, 240 milliseconds +/- 31; ratio of early and late VTI, 1.0 +/- 0.3; atrial filling fraction, 47% +/- 7; and PAP, 31 mm Hg +/- 6. Left ventricular filling in patients aged 90 to 100 years demonstrates increasingly impaired left ventricular relaxation from patients in their eighth and ninth decades. PAP continues to increase with age and PAPor = 40 mm Hg in the elderly could be considered normal.

Background Detecting bioprosthetic mitral valve dysfunction on transthoracic echocardiography can be challenging because of acoustic shadowing of regurgitant jets and a wide normal range of transvalvular gradients. Several studies in mechanical mitral valves have demonstrated the utility of the transthoracically derived parameters E (peak early mitral inflow velocity), pressure half-time, and the ratio of mitral inflow velocity-time integral (VTI MV ) to left ventricular outflow tract velocity-time integral (VTI LVOT ) in detecting significant prosthetic dysfunction. Uncertainty exists as to their applicability and appropriate cutoff levels in bioprosthetic valves. This study was designed to establish the accuracy and appropriate normal limits of routinely collected transthoracic Doppler parameters when used to assess bioprosthetic mitral valve function. Methods A total of 135 clinically stable patients with bioprosthetic mitral valves who had undergone both transthoracic and transesophageal echocardiography within a 6-month period were retrospectively identified from the past 11 years of the echocardiography database. Transthoracic findings were labeled as normal ( n = 81), regurgitant ( n = 44), or stenotic ( n = 10) according to the patient's transesophageal echocardiographic findings. Univariate and multivariate analyses were performed to identify Doppler parameters that detected dysfunction; then receiver operating characteristic curves were created to establish appropriate normal cutoff levels. Results The VTI MV /VTI LVOT ratio was the most accurate Doppler parameter at detecting valvular dysfunction, with a ratio of >2.5 providing sensitivity of 100% and specificity of 95%. E > 1.9 m/sec was slightly less accurate (93% sensitivity, 72% specificity), while a pressure half-time of >170 msec had both 100% specificity and sensitivity for detecting significant bioprosthetic mitral valve stenosis, (although it did not differentiate between regurgitant and normal). Conclusions This study demonstrates that Doppler parameters derived from transthoracic echocardiography can accurately detect bioprosthetic mitral valve dysfunction. These parameters, particularly a VTI MV /VTI LVOT ratio of >2.5, are a sensitive way of selecting patients to undergo more invasive examination with transesophageal echocardiography.

In the above-title paper [ibid., vol. 36, no. 6, pp. 1221-1230, Jun. 2017], the first footnote should have indicated the following information: A. H. Abdi and C. Luong are joint first authors.

Aim Echocardiographic methods for estimating right atrial (RA) volume have not been standardized. Our aim was to evaluate two-dimensional (2D) echocardiographic methods of RA volume assessment, using RA volume by magnetic resonance imaging (MRI) as the reference. Methods and results Right atrial volume was assessed in 51 patients (mean age 63 ± 14 years, 33 female) who underwent comprehensive 2D echocardiography and cardiac MRI for clinically indicated reasons. Echocardiographic RA volume methods included (1) biplane area length, using four-chamber view twice (biplane 4C-4C); (2) biplane area length, using four-chamber and subcostal views (biplane 4C-subcostal); and (3) single plane Simpson's method of disks (Simpson's). Echocardiographic RA volumes as well as linear RA major and minor dimensions were compared to RA volume by MRI using correlation and Bland-Altman methods, and evaluated for inter-observer reproducibility and accuracy in discriminating RA enlargement. All echocardiography volumetric methods performed well compared to MRI, with Pearson's correlation of 0.98 and concordance correlation ≥0.91 for each. For bias and limits of agreement, biplane 4C-4C (bias -4.81 mL/m(2) , limits of agreement ±9.8 mL/m(2) ) and Simpson's (bias -5.15 mL/m(2) , limits of agreement ±10.1 mL/m(2) ) outperformed biplane 4C-subcostal (bias -8.36 mL/m(2) , limits of agreement ±12.5 mL/m(2) ). Accuracy for discriminating RA enlargement was higher for all volumetric methods than for linear measurements. Inter-observer variability was satisfactory across all methods. Conclusions Compared to MRI, biplane 4C-4C and single plane Simpson's are highly accurate and reproducible 2D echocardiography methods for estimating RA volume. Linear dimensions are inaccurate and should be abandoned.

Background The value of right atrial volume as a predictor for recurrence of atrial fibrillation (AF) after direct current cardioversion (DCCV) is unknown. Methods We sought to compare the performance of right atrial volume indexed to body surface area (RAVI), left atrial diameter, left atrial volume indexed to body surface area (LAVI), and biatrial volume index (BAVI) for the prediction of AF recurrence at 6 months after DCCV. This study included the first 95 consecutive patients from the AF Clinic at a large tertiary care hospital who underwent DCCV and who had an echocardiogram available within 6 months before DCCV. Maximal LAVI, RAVI, and BAVI were determined from the echocardiogram before DCCV. Electrocardiographic and clinical data were acquired at baseline, before cardioversion, and at each clinic visit. Results Of the 95 patients (64 male; mean age, 63 ± 12 years), history of systemic hypertension, diabetes mellitus, heart failure, and transient ischemic attack/stroke was present in 60 (63%), 14 (15%), 27 (28%), and 5 (5%) patients, respectively. Mean duration from AF diagnosis to DCCV was 3.5 ± 5.0 years. At 6 months after DCCV, 53 (56%) had reverted to AF. RAVI had superior predictive ability (area under the receiver operator characteristic curve: RAVI, 0.77; left atrial diameter, 0.54; LAVI, 0.64; and BAVI, 0.70). RAVI ≥ 42 mL/m 2 provided the best accuracy for prediction of recurrence (76% accuracy, 71% sensitivity, 83% specificity, 90% positive predictive value, and 56% negative predictive value). Best accuracy for LAVI was ≥ 48 mL/m 2 (70% accuracy, 53% sensitivity, 79% specificity, 85% positive predictive value; 43% negative predictive value). Conclusions RAVI is superior to LAVI for the prediction of AF recurrence at 6 months after DCCV.