Improved Interobserver Variability and Accuracy of Echocardiographic Visual Left Ventricular Ejection Fraction Assessment through a Self-Directed Learning Program Using Cardiac Magnetic Resonance Images

Development of artificial intelligence-based slow-motion echocardiography and clinical usefulness for evaluating regional wall motion abnormalities

The diagnostic accuracy of exercise stress echocardiography (ESE) for myocardial ischemia requires improvement, given that it currently depends on the physicians' experience and image quality. To address this issue, we aimed to develop artificial intelligence (AI)-based slow-motion echocardiography using inter-image interpolation. The clinical usefulness of this method was evaluated for detecting regional wall-motion abnormalities (RWMAs). In this study, an AI-based echocardiographic image-interpolation pipeline was developed using optical flow calculation and prediction for in-between images. The accuracy for detecting RWMAs and image readability among 25 patients with RWMA and 25 healthy volunteers was compared between four cardiologists using slow-motion and conventional ESE. Slow-motion echocardiography was successfully developed for arbitrary time-steps (e.g., 0.125×, and 0.5×) using 1,334 videos. The RWMA detection accuracy showed a numerical improvement, but it was not statistically significant (87.5% in slow-motion echocardiography vs. 81.0% in conventional ESE; odds ratio: 1.43 [95% CI: 0.78-2.62], p = 0.25). Interreader agreement analysis (Fleiss's Kappa) for detecting RWMAs among the four cardiologists were 0.66 (95%CI: 0.55-0.77) for slow-motion ESE and 0.53 (95%CI: 0.42-0.65) for conventional ESE. Additionally, subjective evaluations of image readability using a four-point scale showed a significant improvement for slow-motion echocardiography (2.11 ± 0.73 vs. 1.70 ± 0.78, p < 0.001).In conclusion, we successfully developed slow-motion echocardiography using in-between echocardiographic image interpolation. Although the accuracy for detecting RWMAs did not show a significant improvement with this method, we observed enhanced image readability and interreader agreement. This AI-based approach holds promise in supporting physicians' evaluations.

Utility of left ventricular ejection fraction in atrial fibrillation patients without pre-existing heart failure

AIMS

Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus has been placed on the prevention of HF in patients with AF. Left ventricular ejection fraction (LVEF) is an established echocardiographic parameter in HF patients. We sought to investigate the association of LVEF with HF events in AF patients without pre-existing HF.

METHODS AND RESULTS

The Fushimi AF Registry is a community-based prospective survey of AF patients in Fushimi-ku, Japan. In this analysis, we excluded patients with pre-existing HF (defined as having one of the following: prior HF hospitalization, New York Heart Association class ≥ 2 in association with heart disease, or LVEF < 40%). Among 3233 AF patients without pre-existing HF, we investigated 2459 patients with the data of LVEF at enrolment. We divided the patients into three groups stratified by LVEF [mildly reduced LVEF (40-49%), below normal LVEF (50-59%), and normal LVEF (≥60%)] and compared the backgrounds and incidence of HF hospitalization between the groups. Of 2459 patients [mean age: 72.4 ± 10.5 years, female: 917 (37%), paroxysmal AF: 1405 (57%), and mean CHA2 DS2 -VASc score: 3.0 ± 1.6], the mean LVEF was 66 ± 8% [mildly reduced LVEF: 114 patients (5%), below normal LVEF: 300 patients (12%), and normal LVEF: 2045 patients (83%)]. Patients with lower LVEF demonstrated lower prevalence of female and paroxysmal AF (both P < 0.01), but age and CHA2 DS2 -VASc score were comparable between the three groups (both P > 0.05). During the median follow-up period of 6.0 years, 255 patients (10%) were hospitalized for HF (annual incidence: 1.9% per person-year). Multivariable Cox regression analysis demonstrated that lower LVEF strata were independently associated with the risk of HF [mildly reduced LVEF (40-49%): hazard ratio = 2.98, 95% confidence interval = 1.99-4.45 and below normal LVEF (50-59%): hazard ratio = 2.01, 95% confidence interval = 1.44-2.82, compared with normal LVEF (≥60%)] after adjustment by age, sex, type of AF, and CHA2 DS2 -VASc score. LVEF < 60% was significantly associated with the higher risk of HF hospitalization across all major subgroups without significant interaction (P for interaction; all P > 0.05). LVEF had an independent and incremental prognostic value for HF hospitalization in addition to natriuretic peptide levels in AF patients without pre-existing HF.

CONCLUSIONS

Lower LVEF was significantly associated with the higher incidence of HF hospitalization in AF patients without pre-existing HF, leading to the future risk stratification for and prevention of incident HF in AF patients.

ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram?

While multidisciplinary collaboration in echocardiography is not new, machine learning has the potential to further improve it. In this transcript of the ASE 2023 Annual Feigenbaum lecture, advancements in foundation models are discussed, including their advantages, current disadvantages, and future potential for echocardiography.

Association between implanted cardioverter-defibrillators and mortality for patients with left ventricular ejection fraction between 30% and 35

BACKGROUND

Consensus guidelines support the use of implanted cardioverter-defibrillators (ICD) for primary prevention of sudden cardiac death in patients with either non-ischaemic or ischaemic cardiomyopathy with left ventricular ejection fraction (LVEF) ≤35%. However, evidence from trials for efficacy specifically for patients with LVEF near 35% is weak. Past trials are underpowered for this population and future trials are unlikely to be performed.

METHODS

Patients with lowest LVEF between 30% and 35% without an ICD prior to the lowest-LVEF echo (defined as 'time zero') were identified by querying echocardiography data from 28 November 2001 to 9 July 2020 at the Massachusetts General Hospital linked to ICD treatment status. To assess the association between ICD and mortality, propensity score matching followed by Cox proportional hazards models considering treatment status as a time-dependent covariate was used. A secondary analysis was performed for LVEF 36%-40%.

RESULTS

Initially, 526 440 echocardiograms representing 266 601 unique patients were identified. After inclusion and exclusion criteria were applied, 6109 patients remained for the analytical cohort. In bivariate unadjusted comparisons, patients who received ICDs were substantially more often male (79.8% vs 65.4%, p<0.0001), more often white (87.5% vs 83.7%, p<0.046) and more often had a history of ventricular tachycardia (74.5% vs 19.1%, p<0.0001) and myocardial infarction (56.1% vs 38.2%, p<0.0001). In the propensity matched sample, after accounting for time-dependence, there was no association between ICD and mortality (HR 0.93, 95% CI 0.75 to 1.15, p=0.482).

CONCLUSIONS

ICD therapy was not associated with reduced mortality near the conventional LVEF threshold of 35%. Although this treatment design cannot definitively demonstrate lack of efficacy, our results are concordant with available prior trial data. A definitive, well-powered trial is needed to answer the important clinical question of primary prevention ICD efficacy between LVEF 30% and 35%.

Artificial intelligence-assisted interpretation of systolic function by echocardiogram

OBJECTIVE

Precise and reliable echocardiographic assessment of left ventricular ejection fraction (LVEF) is needed for clinical decision-making. Recently, artificial intelligence (AI) models have been developed to estimate LVEF accurately. The aim of this study was to evaluate whether an AI model could estimate an expert read of LVEF and reduce the interinstitutional variability of level 1 readers with the AI-LVEF displayed on the echocardiographic screen.

METHODS

This prospective, multicentre echocardiographic study was conducted by five cardiologists of level 1 echocardiographic skill (minimum level of competency to interpret images) from different hospitals. Protocol 1: Visual LVEFs for the 48 cases were measured without input from the AI-LVEF. Protocol 2: the 48 cases were again shown to all readers with inclusion of AI-LVEF data. To assess the concordance and accuracy with or without AI-LVEF, each visual LVEF measurement was compared with an average of the estimates by five expert readers as a reference.

RESULTS

A good correlation was found between AI-LVEF and reference LVEF (r=0.90, p<0.001) from the expert readers. For the classification LVEF, the area under the curve was 0.95 on heart failure with preserved EF and 0.96 on heart failure reduced EF. For the precision, the SD was reduced from 6.1±2.3 to 2.5±0.9 (p<0.001) with AI-LVEF. For the accuracy, the root-mean squared error was improved from 7.5±3.1 to 5.6±3.2 (p=0.004) with AI-LVEF.

CONCLUSIONS

AI can assist with the interpretation of systolic function on an echocardiogram for level 1 readers from different institutions.

Strain Imaging and Ventricular Arrhythmia

Ventricular arrhythmia is one of the main causes of sudden cardiac death. Hence, identifying patients at risk of ventricular arrhythmias and sudden cardiac death is important but can be challenging. The indication for an implantable cardioverter defibrillator as a primary preventive strategy relies on the left ventricular ejection fraction as a measure of systolic function. However, ejection fraction is flawed by technical constraints and is an indirect measure of systolic function. There has, therefore, been an incentive to identify other markers to optimize the risk prediction of malignant arrhythmias to select proper candidates who could benefit from an implantable cardioverter defibrillator. Speckle-tracking echocardiography allows for a detailed assessment of cardiac mechanics, and strain imaging has repeatedly been shown to be a sensitive technique to identify systolic dysfunction unrecognized by ejection fraction. Several strain measures, including global longitudinal strain, regional strain, and mechanical dispersion, have consequently been proposed as potential markers of ventricular arrhythmias. In this review, we will provide an overview of the potential use of different strain measures in the context of ventricular arrhythmias.

Quantification of left ventricular ejection fraction and cardiac output using a novel semi-automated echocardiographic method: a prospective observational study in coronary artery bypass patients

BACKGROUND

Echocardiographic quantification of ejection fraction (EF) by manual endocardial tracing requires training, is time-consuming and potentially user-dependent, whereas determination of cardiac output by pulmonary artery catheterization (PAC) is invasive and carries a risk of complications. Recently, a novel software for semi-automated EF and CO assessment (AutoEF) using transthoracic echocardiography (TTE) has been introduced. We hypothesized that AutoEF would provide EF values different from those obtained by the modified Simpson's method in transoesophageal echocardiography (TOE) and that AutoEF CO measurements would not agree with those obtained via VTI_LVOT in TOE and by thermodilution using PAC.

METHODS

In 167 patients undergoing coronary artery bypass graft surgery (CABG), TTE cine loops of apical 4- and 2-chamber views were recorded after anaesthesia induction under steady-state conditions. Subsequently, TOE was performed following a standardized protocol, and CO was determined by thermodilution. EF and CO were assessed by TTE AutoEF as well as TOE, using the modified Simpson's method, and Doppler measurements via velocity time integral in the LV outflow tract (VTI_LVOT). We determined Pearson's correlation coefficients r and carried out Bland-Altman analyses. The primary endpoints were differences in EF and CO. The secondary endpoints were differences in left ventricular volumes at end diastole (LVEDV) and end systole (LVESV).

RESULTS

AutoEF and the modified Simpson's method in TOE showed moderate EF correlation (r = 0.38, p < 0.01) with a bias of -12.6% (95% limits of agreement (95%LOA): -36.6 - 11.3%). AutoEF CO correlated poorly both with VTI_LVOT in TOE (r = 0.19, p < 0.01) and thermodilution (r = 0.28, p < 0.01). The CO bias between AutoEF and VTI_LVOT was 1.33 l min^-1 (95%LOA: -1.72 - 4.38 l min^-1) and 1.39 l min^-1 (95%LOA -1.34 - 4.12 l min^-1) between AutoEF and thermodilution, respectively. AutoEF yielded both significantly lower EF (EF_AutoEF: 42.0% (IQR 29.0 - 55.0%) vs. EF_{TOE Simpson}: 55.2% (IQR 40.1 - 70.3%), p < 0.01) and CO values than the reference methods (CO_{AutoEF biplane}: 2.30 l min^-1 (IQR 1.30 - 3.30 l min^-1) vs. CO_{VTI LVOT}: 3.64 l min^-1 (IQR 2.05 - 5.23 l min^-1) and CO_PAC: 3.90 l min^-1 (IQR 2.30 - 5.50 l min^-1), p < 0.01)).

CONCLUSIONS

AutoEF correlated moderately with TOE EF determined by the modified Simpson's method but poorly both with VTI_LVOT and thermodilution CO. A systematic bias was detected overestimating LV volumes and underestimating both EF and CO compared to the reference methods.

TRIAL REGISTRATION

German Register for Clinical Trials (DRKS-ID DRKS00010666, date of registration: 08/07/2016).

Watchful surgery in asymptomatic mitral valve prolapse

The most common organic etiology of mitral regurgitation is degenerative and consists of mitral valve prolapse (MVP). Volume overload because of mitral regurgitation is the most common complication of MVP. Advocating surgery before the consequences of volume overload become irreparable restores life expectancy, but carries a risk of mortality in patients who are often asymptomatic. On the other hand, the post-surgical outcome of symptomatic patients is dismal and life expectancy is impaired. In the present article, we aim to bridge the gap between these two therapeutic approaches, unifying the concepts of watchful waiting and early surgery in a "watchful surgery approach".

Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography

BACKGROUND

Left ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and complexities. We sought to assess whether a fully automated assessment of LV function could be reliably used in children and young adults.

METHODS

Fifty normal volunteers (22/28, female/male) were prospectively recruited for research echocardiography. LV volumes, EF, and strain were measured both manually and automatically. An experienced sonographer performed all the manual analysis and recorded the analysis timing. The fully automated analyses were accomplished by 5 groups of observers with different knowledge and medical background. AutoLV and AutoSTRAIN (TomTec) were employed for the fully automated LV analysis. The LV volumes, EF, strain, and analysis time were compared between manual and automated methods, and among the 5 groups of observers.

RESULTS

Software-determined endocardial border detection was achievable in all subjects. The analysis times of the experienced sonographer were significantly shorter for AutoLV and AutoSTRAIN than manual analyses (both p < 0.001). Strong correlations were seen between conventional EF and AutoLV (r = 0.8373), and between conventional three view global longitudinal strain (GLS) and AutoSTRAIN (r = 0.9766). The volumes from AutoLV and three view GLS from AutoSTRAIN had strong correlations among different observers regardless of level of expertise. EF from AutoLV analysis had moderately strong correlations among different observers.

CONCLUSION

Automated pediatric LV analysis is feasible in normal hearts. Machine learning-enabled image analysis saves time and produces results that are comparable to traditional methods.

Competency Assessment in Focused Cardiac Ultrasound—Can the Use of Sequential Testing Help Tailor Training Requirements?

Varying numbers of scans are required by different professional bodies before focused cardiac ultrasound (FCU) competence is assumed. It has been suggested that innovation in the assessment of FCU competence is needed and that competency assessment needs to be more individualized. We report our experience of how the use of sequential testing may help personalize the assessment of FCU competence.

Analysis of Differences in Assessment of Left Ventricular Function on Echocardiography and Nuclear Perfusion Imaging

Two widely used methods for left ventricular (LV) ejection fraction (EF) determination, echocardiography (echo) and gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), often have wide limits of agreement. Factors influencing discrepancies between core laboratory echo and MPI LVEF determinations were examined in a large series of heart failure (HF) subjects and normal controls. 879 HF and 101 control subjects had core lab analyses of echo and MPI (mean time between procedures 7-8 days). LVEF differences were analyzed using one-way analysis of variance and Bland-Altman plots. Relationships between LVEF differences and patient characteristics and outcome endpoints (mortality and arrhythmias) were explored with logistic regression, Cox proportional hazards models, and Kaplan-Meier survival analyses. There was a systematic difference between the 2 modalities; echo LVEF was higher with more severe LV dysfunction, MPI LVEF higher when systolic function was normal. LVEF results were within ±5% in only 37% of HF and 23% of control subjects. Considering discordance around the LVEF threshold 35%, there was disagreement between the 2 methods in 305 HF subjects (35%). Male gender (odds ratio (OR) = 0.200), atrial fibrillation (OR = 2.314), higher body mass index (OR = 1.051) and lower LV end-diastolic volume (OR = 0.985) were the strongest predictors of methodologic discordance. Cardiac event rates were highest if both LVEF values were ≤35% and lowest when both LVEF values were >35%. In conclusion, substantial disagreements between LVEF results by echo and MPI are common. HF patients with LVEF ≤35% by both techniques have the highest 2-year event risk.

Visual versus fully automated assessment of left ventricular ejection fraction

Introduction

The aim of this study is to compare three different echocardiographic methods commonly used in the assessment of left ventricle (LV) ejection fraction (EF).

Methods

All patients underwent full echocardiography including LVEF assessed using M-mode, automated EF (Auto-EF), and visual estimation by two readers.

Results

We enrolled 268 patients. Auto-EF measurement was feasible in 240 (89.5%) patients. The averaged LVEF was (52% ± 12) with the visual assessment, (51% ± 11) with Auto-EF and (57% ± 13) with M-mode. Using Bland-Altman analysis, we found that the difference between the mean visual and the Auto-EF was not significant (-0.3% [-0.5803-0.0053], P = 0.054). However, the mean EF was significantly different when comparing visual versus M-mode and Auto-EF versus M-mode with the mean differences: (-2.4365 [-2.9946--1.8783], P < 0.0001) and (-2.1490 [-2.7348--1.5631], P < 0.0001) respectively. Inter-observer variability analysis of the visual EF assessment between the two readers showed that intraclass correlation coefficient was 0.953, (95% confidence interval: 0.939-0.965, P < 0.0001), with excellent correlation between the two readers: R = 0.911, P < 0.0001).

Conclusion

The two-dimensional echocardiographic methods using Biplane Auto-EF or visual assessment were significantly comparable, whereas M-mode results in an overestimation of the LVEF.

Effectiveness of real-time tele-ultrasound for echocardiography in resource-limited medical teams

BACKGROUND

Echocardiography is a first-line tool for the screening of patients with cardiac dysfunction. However, the mastery of echocardiography requires significant training, and not all medical teams involve an echocardiography specialist. Telemedicine approaches can potentially improve the quality of echocardiography in resource-limited situations.

METHODS

We used a novel tablet-based hand-held ultrasound device that enables tele-ultrasound- a real-time video telecommunication with remote control for ultrasound images. A trainee scanned patients with various cardiovascular diseases and interpreted the images. Subsequently, the same trainee re-scanned the same patients and re-interpreted the same images using tele-ultrasound with an echocardiography specialist. An examination on the same patients by a blinded specialist was considered the gold standard.

RESULTS

We included 31 patients (median 77 [interquartile range 68-84] years old, 42% women). Mean absolute errors in left ventricular (LV) end-diastolic and end-systolic diameters, visual LV ejection fraction, and tricuspid annular plane systolic excursion decreased significantly after tele-ultrasound advice (5.9 mm, 5.8 mm, 8.6%, and 4.5 to 1.6 mm, 2.8 mm, 0.7%, and 1.8 mm, respectively, all p < 0.001), and intra-class correlation coefficients improved (0.76, 0.84, 0.68, and 0.44 to 0.96, 0.93, 0.99, and 0.90, respectively). Notably, with tele-advice, the trainee's examination showed perfect agreement with that of the specialist in classifying LV ejection fraction (> 50%, 50-35%, or > 35%) and identifying significant valvular heart diseases.

CONCLUSION

Real-time tele-ultrasound improved a trainee's echocardiography results to those of a specialist-level examination. This approach might be helpful in resource-limited medical teams where echocardiographic specialists are not readily available.

How to standardize the measurement of left ventricular ejection fraction

Despite recent advances in imaging for myocardial deformation, left ventricular ejection fraction (LVEF) is still the most important index for systolic function in daily practice. Its role in multiple fields (e.g., valvular heart disease, myocardial infarction, cancer therapy-related cardiac dysfunction) has been a mainstay in guidelines. In addition, assessment of LVEF is vital to clinical decision-making in patients with heart failure. However, notable limitations to LVEF include poor inter-observer reproducibility dependent on observer skill, poor acoustic windows, and variations in measurement techniques. To solve these problems, methods for standardization of LVEF by sharing reference images among observers and artificial intelligence for accurate measurements have been developed. In this review, we focus on the standardization of LVEF using reference images and automated LVEF using artificial intelligence.

Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution

BACKGROUND

We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function.

METHODS

Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%-73%), mildly-to-moderately (30%-52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians' interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system.

RESULTS

Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86-0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%.

CONCLUSIONS

The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.

Quality Assurance and Improvement Project in Echocardiography Laboratory: The Pivotal Importance of Organizational and Managerial Processes

Echocardiography plays a vital role in the diagnosis and management of cardiovascular conditions. Echocardiography use is progressively increasing nowadays, and this is correlated to the evolving echo indications, to the relatively new available echocardiography modes (tissue Doppler imaging, speckle tracking imaging, three-dimensional mode, etc.) and modalities (transthoracic, transesophageal, and intracardiac) along with the various available clinical approaches (point of care echo, portable echo, etc.). Quality assurance in echocardiography is correlated to appropriate use criteria, adequate equipment, standardization of performance and reporting, along with timely storage and archiving. Quality improvement plan must target strategic planning, with metrics and timeline for assessment and re-assessment of results. Improvement project aims to ensure and enhance conformity with appropriate use criteria and standardization, timely completion of exams and reports, detection of discrepancies, and continuous improvement of knowledge and skills. Strategic planning is essential in this context in order to develop organizational and managerial processes, with regular auditing for a highly professional and advanced level of echocardiography, while ensuring teamwork and standards of ethical values.

Global longitudinal strain to determine optimal timing for surgery in primary mitral regurgitation: A systematic review

BACKGROUND

Primary mitral regurgitation (PMR) results in adverse remodeling changes and left ventricular (LV) dysfunction. Assessing LV function has prognostic value in predicting morbidity and mortality. Indications for surgery include parameters such as LV ejection fraction (LVEF) and systolic dimensions. Current guidelines are limited in identifying patients at optimal time for surgery. Impaired postoperative LVEF indicates poor prognostic outcomes and subsequent heart failure. Global longitudinal strain (GLS) via speckle tracking echocardiography (STE) presents as a promising parameter to detect subclinical dysfunction in asymptomatic patients.

METHODS

Following PRISMA guidelines, a literature search was conducted with Cochrane Library, PudMed, SCOPUS, and Web of Science. Key MeSH terms included "mitral regurgitation," "mitral valve insufficiency," "global longitudinal strain," "deformation," "LV-GLS," and "GLS." Inclusion criteria included (1) patients with severe PMR, (2) mixed population of symptomatic and asymptomatic patients, (3) standardized methods in assessing LV systolic function using 2D-STE, (4) valve repair or replacement surgery, and (5) patient outcomes measured after surgery. Search returned 234 papers, 12 of which met the inclusion criteria and were subsequently reviewed.

RESULTS

Baseline GLS is an independent predictor of postoperative outcomes, ranging from -17.9 to -21.7% GLS. A significant negative correlation was observed between preoperative GLS and postoperative LVEF. Impaired baseline GLS was associated with higher mortality rates. Better long-term survival rates were seen in patients who underwent early surgery.

CONCLUSION

GLS shows sensitivity in predicting long-term postoperative outcomes. Further analysis is required to determine preoperative GLS threshold to identify asymptomatic patients at the optimal time for mitral valve surgery.

Venous Flow Variation Predicts Preoperative Pulmonary Venous Obstruction in Children with Total Anomalous Pulmonary Venous Connection

BACKGROUND

Identifying preoperative pulmonary venous obstruction in total anomalous pulmonary venous connection is important to guide treatment planning and risk prognostication. No standardized echocardiographic definition of obstruction exists in the literature. Definitions based on absolute velocities are affected by technical limitations and variations in pulmonary venous return. The authors developed a metric to quantify pulmonary venous blood flow variation: pulmonary venous variability index (PVVI). The aim of this study was to demonstrate its accuracy in defining obstruction.

METHODS

All patients with total anomalous pulmonary venous connection at a single institution were identified. Echocardiograms were reviewed, and maximum (V_max), mean (V_mean), and minimum (V_min) velocities along the pulmonary venous pathway were measured. PVVI was defined as (V_max - V_min)/V_mean. These metrics were compared with pressures measured on cardiac catheterization. Echocardiographic measures were then compared between patients with and without clinical preoperative obstruction (defined as a need for preoperative intubation, catheter-based intervention, or surgery within 1 day of diagnosis), as well as pulmonary edema by chest radiography and markers of lactic acidosis. One hundred thirty-seven patients were included, with 22 having catheterization pressure recordings.

RESULTS

V_max and V_mean were not different between patients with catheter gradients ≥ 4 and < 4 mm Hg, while PVVI was significantly lower and V_min higher in those with gradients ≥ 4 mm Hg. The composite outcome of preoperative obstruction occurred in 51 patients (37%). Absolute velocities were not different between patients with and without clinical obstruction, while PVVI was significantly lower in patients with obstruction. All metrics except V_max were associated with pulmonary edema; none were associated with blood gas metrics.

CONCLUSIONS

The authors developed a novel quantitative metric of pulmonary venous flow, which was superior to traditional echocardiographic metrics. Decreased PVVI was highly associated with elevated gradients measured by catheterization and clinical preoperative obstruction. These results should aid risk assessment and diagnosis preoperatively in patients with total anomalous pulmonary venous connection.

Training, competence, and quality improvement in echocardiography: the European Association of Cardiovascular Imaging Recommendations: update 2020

The primary mission of the European Association of Cardiovascular Imaging (EACVI) is ‘to promote excellence in clinical diagnosis, research, technical development, and education in cardiovascular imaging’. Echocardiography is a key component in the evaluation of patients with known or suspected cardiovascular disease and is essential for the high quality and effective practice of clinical cardiology. The EACVI aims to update the previously published recommendations for training, competence, and quality improvement in echocardiography since these activities are increasingly recognized by patients, physicians, and payers. The purpose of this document is to provide the general requirements for training and competence in echocardiography, to outline the principles of quality evaluation, and to recommend a set of measures for improvement, with the ultimate goal of raising the standards of echocardiographic practice. Moreover, the document aims to provide specific guidance for advanced echo techniques, which have dramatically evolved since the previous publication in 2009.

Improving reproducibility of left ventricular ejection fraction in pediatric oncology patients: less is more

Reproducible measurement of left ventricular (LV) systolic function by echocardiography is important to detect cancer therapy-related cardiac dysfunction (CTRCD). We hypothesized that limiting the number of imaging operators and use of a single vendor would improve reproducibility of these measures. A standard operating procedure (SOP) whereby LV measurements were standardized and a cardio-oncology imaging team (5 sonographers, 6 cardiologists) was established. All pediatric oncology patient echocardiograms were acquired on a single vendor platform. In total, 100 consecutive pre-SOP and 100 post-SOP studies were reviewed. LV end-diastolic dimension (LVEDD), posterior wall thickness (PW), shortening fraction (SF), and ejection fraction by Simpson's biplane (EF) were re-measured by 2 blinded readers, and compared to what was originally reported. Image quality was scored by number of LV segments imaged (grades 1-4). Inter-observer reproducibility pre/post-SOP was assessed with intra-class coefficient (α). Reducing the number of imaging operators improved image quality (Grade ≥ 3: 13% vs. 46%, p < 0.001). Reproducibility of PW and LVEDD marginally improved (PW: 0.78 to 0.82; LVEDD: 0.96 to 0.97), and SF improved significantly (α = 0.65 vs. 0.79, p < 0.001). Pre-SOP reproducibility of LV EF was poor (α = 0.65), but improved significantly post-SOP (α = 0.83, p < 0.001). Reproducibility of LV EF improved with higher image quality score. Limiting imaging operators and vendor platform for pediatric oncology echocardiograms improves image quality and reproducibility of LV EF. Establishing an SOP and a cardio-oncology echocardiography team may improve precision of measurements used to detect CTRCD.

A Teaching Intervention Increases the Performance of Handheld Ultrasound Devices for Assessment of Left Ventricular Ejection Fraction

Background

Few studies have demonstrated the utility of a teaching program for evaluation of left ventricular ejection fraction (LVEF) of echocardiographic images acquired with high-end machines. No study to date explored the value of similar programs when a handheld ultrasound device is used. The aim of this study was to determine whether a teaching intervention could improve the accuracy and the reliability of LVEF visual assessment of echocardiographic images acquired with HUD.

Materials and Methods

Twenty echocardiograms acquired with a hand-held ultrasound device with a spectrum of LVEF were presented to 26 participants with varying experience in echocardiography (range 2-12 years) for single-point LVEF visual estimates. After this baseline assessment, participants underwent three training sessions which included analysis of the individual baseline results and review and interpretation of additional 60 cases from the same platform. After 2 months, 20 new echocardiograms were presented to the same 26 participants for visual LVEF assessment. For each participant, the visual LVEF for each case was compared with the reference LVEF (quantitative measurements by experts), and a difference of > ±5% was considered a misclassification.

Results

The misclassification rate was 61% preintervention and decreased to 41% after intervention (P < 0.0001). The mean absolute differences in LVEF between visual estimates and reference before and after intervention for all readers were -7.9 ± 9.6 and -1.2 ± 7.8, respectively (P < 0.0001). Inter-rater repeatability analysis was performed using the intraclass correlation coefficient. The intraclass correlation coefficient for inter-rater reliability was fair preintervention (0.65, 95% confidence interval [CI] 0.59 0.71) and good after intervention (0.80, 95% CI 0.73 0.87), and there were no differences when categorized according to the level of experience.

Conclusions

A teaching intervention can improve the accuracy and the reliability in the visual LVEF assessment of images acquired with handheld ultrasound device.

Impact of a training program incorporating cardiac magnetic resonance imaging on the accuracy and reproducibility of two-dimensional echocardiographic measurements of left ventricular volumes and ejection fraction

Background

Left ventricular (LV) ejection fraction (LVEF) assessed by two-dimensional echocardiography (2DE) is the most widely used parameter for clinical decision-making, but reproducibility and accuracy problems remain. We evaluated the usefulness of a novel training program based on cardiac magnetic resonance (CMR) imaging to obtain more reliable values of 2DE-derived LVEF and LV volumes.

Methods

Fifty-four sonographers from five hospitals independently measured LV volumes and LVEF using the same 2DE images from 15 patients who underwent CMR and 2DE. After receiving a lecture from an expert on how to properly trace the LV endocardium, each sonographer voluntary performed the measurements using the same datasets, and was invited to perform the same analysis for additional patients. The effect of the training intervention was evaluated using the coefficient of variation (CV) and coverage probability (CP).

Results

Before the intervention, the LV volumes were significantly underestimated and the LVEF was significantly overestimated compared to the CMR results; however, these differences were reduced after the intervention. In particular, the CP (0.52 vs. 0.76, p < 0.001) for the LVEF showed significant improvement. However, the degree of improvement differed among institutions, and the CV actually became worse in two hospitals after the intervention. Level of experience and self-practice was associated with the reproducibility after the intervention.

Conclusions

A training program using CMR as a reference improved the accuracy of 2DE-determined LV measurements. Since the degree of improvements differed among hospitals, individualization of training programs and periodical objective evaluation may be required to reduce inter-institutional variability.

The Added Value of Cardiac Magnetic Resonance in Muscular Dystrophies

Muscular dystrophies (MD) represent a heterogeneous group of rare genetic diseases that often lead to significant weakness due to progressive muscle degeneration. In many forms of MD, cardiac manifestations including heart failure, atrial and ventricular arrhythmias and conduction abnormalities can occur and may be a predominant feature of the disease. Cardiac magnetic resonance (CMR) can assess cardiac anatomy, global and regional ventricular function, volumes and mass as well as presence of myocardial inflammation, infiltration or fibrosis. The role for cardiac MRI has been well-established in a wide range of muscular dystrophies related cardiomyopathies. CMR is a more sensitive technique than echocardiography for early diagnosis of cardiac involvement. It has also great potential to improve the prediction of long-term outcome, particularly the development of heart failure and arrhythmic events; however it still has to be validated by longitudinal studies including large populations. This review will outline the utility of CMR in patients with muscular dystrophies for assessment of myocardial involvement, risk stratification, and in guiding therapeutic management.

Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert

BACKGROUND

Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could circumvent border detection and instead would estimate the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images.

METHODS

Machine learning algorithm was developed and trained to automatically estimate LVEF on a database of >50 000 echocardiographic studies, including multiple apical 2- and 4-chamber views (AutoEF, BayLabs). Testing was performed on an independent group of 99 patients, whose automated EF values were compared with reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Consistency was assessed by mean absolute deviation among automated estimates from different combinations of apical views. Finally, sensitivity and specificity of detecting of EF ≤35% were calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers.

RESULTS

Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (mean absolute deviation =2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, limits of agreement =±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF ≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, limits of agreement =±13.4%, sensitivity 0.93, specificity 0.87.

CONCLUSIONS

Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared with reference values provided by an expert panel.

Incremental Predictive Value of Longitudinal Axis Strain and Late Gadolinium Enhancement Using Standard CMR Imaging in Patients with Aortic Stenosis

To analyse the predictive ability and incremental value of left ventricular longitudinal axis strain (LAS) and late gadolinium enhancement (LGE) using standard cardiovascular magnetic resonance (CMR) imaging for the diagnosis and prognosis of severe aortic stenosis (AS) in patients with an indication for aortic valve replacement. We conducted a prospective study on 52 patients with severe AS and 52 volunteers. The evaluation protocol included standard biochemistry tests, novel biomarkers of myocardial fibrosis, 12-lead electrocardiograms and 24-hour Holter, the 6-minute walk test and extensive echocardiographic and CMR imaging studies. Outcomes were defined as the composite of major cardiovascular events (MACEs). Among AS patients, most (n = 17, 77.2%) of those who exhibited LGE at CMR imaging had MACEs during follow-up. Kaplan–Meier curves for event-free survival showed a significantly higher rate of MACEs in patients with LGE (p < 0.01) and decreased LAS (p < 0.001). In Cox regression analysis, only reduced LAS (hazard ratio 1.33, 95% CI (1.01 to 1.74), p < 0.01) and the presence of LGE (hazard ratio 11.3, 95% CI (1.82 to 70.0), p < 0.01) were independent predictors for MACEs. The predictive value increased if both LGE and reduced LAS were added to left ventricular ejection fraction (LVEF). None of the biomarkers of increased collagen turnover exhibited any predictive value for MACEs. LAS by CMR is an independent predictor of outcomes in patients with AS and provides incremental value beyond the assessment of LVEF and the presence of LGE.

Teamwork using strain imaging in the echocardiographic assessment of right ventricular systolic function: A cardiac magnetic resonance imaging correlation study

AIM

The aim of this study was to investigate whether conventional echocardiographic assessment of right ventricular (RV) systolic function can be improved by the addition of RV strain imaging. Additionally, we also aimed to investigate whether dedicated reading sessions and education can improve echocardiographic interpretation of RV systolic function.

METHODS

Readers of varying expertise (staff echocardiologists, advanced cardiovascular imaging fellows, sonographers) assessed RV systolic function. In session 1, 20 readers graded RV function of 19 cases, using conventional measures. After dedicated education, in session 2, the same cases were reassessed, with the addition of RV strains. In session 3, 18 readers graded RV function of 20 additional cases, incorporating RV strains. Computer simulations were performed to obtain 230 random teams. RV ejection fraction (RVEF) by cardiac magnetic resonance (CMR) was the reference standard.

RESULTS

Correlation between RV GLS and CMR-derived RVEF was moderate: Spearman's rho: 0.70, n = 19, P < 0.001 (first two sessions); 0.55, n = 20, P < 0.05 (third session). Individual readers' assessment moderately correlated with RVEF (Spearman's rho first session: 0.67 ± 0.2; second session: 0.61 ± 0.2; and third session: 0.68 ± 0.09). Team estimates of RV systolic function showed consistently better correlation with RVEF, which were improved further by averaging across all readers. RV strain parameters influenced echocardiographic interpretation, with a net reclassification index of 8.0 ± 3.6% (P = 0.014).

CONCLUSIONS

The RV strain parameters showed moderate correlations with CMR-derived RVEF and appropriately influenced echocardiographic interpretation of RV systolic function. "Wisdom of the crowd" applied by averaging echocardiographic assessments of RV systolic function across teams of echocardiography readers, further improved echocardiographic assessment of RV systolic function.

Artificial intelligence and echocardiography

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

Improving Wait Time for Patients in a Pediatric Echocardiography Laboratory - a Quality Improvement Project

Supplemental Digital Content is available in the text.

Background:

Prolonged wait times for echocardiograms in the outpatient pediatric cardiology clinic led to patient and provider dissatisfaction at our institution. The aims of this project were to measure our baseline performance with regard to echocardiogram wait time (EWT), to implement a formal quality improvement (QI) program to improve EWT, and to measure the impact of QI on EWT.

Methods:

A QI team was formed comprising of cardiologists (A.P., T.S.), sonographer (A.W.), and QI mentor (J.M.). EWT was defined as time in minutes from initiation of the order in the electronic medical record to start of the echocardiogram. Goal EWT was set as ≤ 20 minutes for 90% patients. Flowcharts were created after process observation to identify sources of potential delay contributing to EWT. QI methodology such as driver diagrams were utilized to identify interventions, which were then implemented and studied as Plan-Do-Study-Act cycles.

Results:

Sequential interventions included early start time, huddles involving clinic and echo laboratory staff, patient tracking system, and repurposing of a clinic room for echo. EWT was tracked for 840 patients. Mean EWT was 22.5 ± 17.5 minutes at baseline and decreased to 15.3 ± 7.8 minutes postintervention (P < 0.001). Postintervention, 81% of the patients waited < 20 minutes for their echo, and 98% patients waited < 30 minutes, compared with baseline numbers of 62% and 76%, respectively (P < 0.001).

Conclusions:

We were able to utilize QI methodology to derive interventions and track changes, resulting in quantifiable improvement in EWT in a busy pediatric echo laboratory.

Biomarker Discovery in Cardio-Oncology

PURPOSE OF REVIEW

In this article, we review current and emerging approaches to biomarker discovery to facilitate early diagnosis of cancer therapy-associated cardiovascular toxicity.

RECENT FINDINGS

Although small studies have demonstrated an association between established biomarkers of cardiac injury (troponins and brain natriuretic peptide) and acute or subacute cardiotoxicity, there is insufficient evidence to support their use in routine clinical care. Preclinical studies to define the molecular mechanisms of cardiotoxicity, as well as the use of unbiased "omics" techniques in small patient cohorts, have yielded promising candidate biomarkers that have the potential to enrich current risk stratification algorithms. New biomarkers of cardiotoxicity have the potential to improve patient outcomes in cardio-oncology. Further studies are needed to assess the clinical relevance of molecular mechanisms described in animal models. Similarly, findings from "omics" platforms require validation in large patient cohorts before they can be incorporated into everyday practice.

Levels of agreement in the measurements of carotid artery ultrasound across a regional vascular network

INTRODUCTION

Duplex ultrasound is often the sole imaging modality used in diagnosing carotid artery disease. However, the reproducibility and repeatability of scientists in determining the peak systolic velocity and end diastolic velocity of the internal carotid artery and common carotid artery (CCA) is widely debated.

STUDY AIM

To investigate intra- and inter-operator variability in diagnostic ultrasound of the carotid arteries across a centralised vascular network using a healthy test subject. To identify potential causes of variability and highlight areas for improvement.

METHODS

Fifteen vascular scientists across four hospital Trusts in the Bristol, Bath and Weston vascular network measured the peak systolic velocity and end diastolic velocity of the internal carotid artery and common carotid artery in a subject using a single portable ultrasound machine. A double blind assessment of spectral Doppler images was performed by two vascular clinical scientists for optimal caliper placement, spectral gain and angle correction. Results were compared for intra- and inter-operator variability.

RESULTS

Initial quality assessment of the Doppler images revealed that three out of 15 scientists produced suboptimal results. Box plot analysis of the common carotid artery and internal carotid artery for each scientist revealed significant variance (ANOVA p < 0.05). However, a Levene's test revealed no single operator who consistently produced highly variable results (p = 0.569).

CONCLUSION

This study highlights the difficulty in obtaining consistent velocity measurements from a subject. Despite the variability in absolute peak systolic velocity and end diastolic velocity, scientists were generally consistent in obtaining an optimal spectral Doppler trace. Some issues with consistency were, however, identified which were subsequently addressed.

Improving the quality of preclinical research echocardiography: observations, training, and guidelines for measurement

Informal training in preclinical research may be a contributor to the poor reproducibility of preclinical cardiology research and low rates of translation into clinical research and practice. Mouse echocardiography is a widely used technique to assess cardiac structure and function in drug intervention studies using disease models. The interobserver variability of clinical echocardiographic measurements has been shown to improve with formalized training, but preclinical echocardiography lacks similarly critical standardization of training. The aims of this investigation were to assess the interobserver variability of echocardiographic measurements from studies in mice and address any technical impediments to reproducibility by implementing standardized guidelines through formalized training. In this prospective, single-site, observational cohort study, 13 scientists performing preclinical echocardiographic image analysis were assessed for measurement of short-axis M-mode-derived dimensions and calculated left ventricular (LV) mass. Ten M-mode images of mouse hearts acquired and analyzed by an expert researcher with a spectrum of LV mass were selected for assessment and validated by autopsy weight. After the initial observation, a structured formal training program was introduced, and accuracy and reproducibility were reevaluated. Mean absolute percentage error for expert-calculated LV mass was 6 ± 4% compared with autopsy LV mass and 25 ± 21% for participants before training. Standardized formal training improved participant mean absolute percentage error by ~30% relative to expert-calculated LV mass ( P < 0.001). Participants initially categorized with high-range error (25-45%) improved to low-moderate error ranges (<15-25%). This report reveals an example of technical skill training insufficiency likely endemic to preclinical research and provides validated guidelines for echocardiographic measurement for adaptation to formalized in-training programs. NEW & NOTEWORTHY The informal training common to academic/research institutions may be a contributor to the relatively poor reproducibility observed for preclinical cardiac research. In our observation of echocardiography analysis in murine models, we present evidence of moderate interobserver variability in standard preclinical research practice at an Australian heart research institute. These observations give rise to our recommendations for practical guidelines for echocardiography analysis in an adaptable approach to general preclinical research skill training. Listen to this article's corresponding podcast at https://ajpheart.podbean.com/e/preclinical-echocardiography-training-and-guidelines/ .

Podiatry Ankle Duplex Scan: Readily Learned and Accurate in Diabetes

We aimed to train podiatrists to perform a focused duplex ultrasound scan (DUS) of the tibial vessels at the ankle in diabetic patients; podiatry ankle (PodAnk) duplex scan. Thirteen podiatrists underwent an intensive 3-hour long simulation training session. Participants were then assessed performing bilateral PodAnk duplex scans of 3 diabetic patients with peripheral arterial disease. Participants were assessed using the duplex ultrasound objective structured assessment of technical skills (DUOSATS) tool and an "Imaging Score". A total of 156 vessel assessments were performed. All patients had abnormal waveforms with a loss of triphasic flow. Loss of triphasic flow was accurately detected in 145 (92.9%) vessels; the correct waveform was identified in 139 (89.1%) cases. Participants achieved excellent DUOSATS scores (median 24 [interquartile range: 23-25], max attainable score of 26) as well as "Imaging Scores" (8 [8-8], max attainable score of 8) indicating proficiency in technical skills. The mean time taken for each bilateral ankle assessment was 20.4 minutes (standard deviation ±6.7). We have demonstrated that a focused DUS for the purpose of vascular assessment of the diabetic foot is readily learned using intensive simulation training.

Assessing observer variability: a user's guide

Some form of the assessment of observer variability may be the most frequent statistical task in medical literature. Still, very little attempt is made to make the reported methods uniform and clear to the reader. This paper provides overview of various measures of observer variability, and a rationale of why using standard error of measurement (SEM) is preferable to other measures of observer variability. The supplemental file contains examples on how to design a proper repeatability and reproducibility assessment, determine appropriate sample size, and test for significance of its findings.

Trials of Quality Improvement in Imaging

Cardiovascular imaging plays a central role in the diagnosis and treatment of cardiovascular disease. Recently, increased emphasis has been placed on quality in cardiovascular imaging, and it is becoming a central priority for various stakeholders, including patients, physicians, and payers. The changing health care landscape and associated challenges imposed on cardiac imagers, including reductions in reimbursement and growing need for pre-authorization, have also helped bring quality metrics to the forefront. Continuous quality improvement initiatives provide the framework for the team of physicians, technical staff members, administrators, and other health care professionals to deliver high-quality care. Efforts to improve quality in cardiac imaging have started to form the foundation for numerous research studies in this arena, and although few in number, randomized control trials have begun to emerge. This review highlights quality improvement studies focusing on appropriate use education, reporting, and radiation dose reduction in cardiovascular imaging.

The Role of Automated 3D Echocardiography for Left Ventricular Ejection Fraction Assessment

Ejection fraction is one of the most powerful determinants of prognosis and is a crucial parameter for the determination of cardiovascular therapies in conditions such as heart failure, valvular conditions and ischaemic heart disease. Among echocardiographic methods, 3D echocardiography has been attributed as the preferred one for its assessment, given an increased accuracy and reproducibility. Full-volume multi-beat acquisitions are prone to stitching artefacts due to arrhythmias and require prolonged breath holds. Single-beat acquisitions exhibit a lower temporal resolution, but address the limitations of multi-beat acquisitions. If not fully automated, 3D echocardiography remains time-consuming and resource-intensive, with suboptimal observer variability, preventing its implementation in routine practice. Further developments in hardware and software, including fully automated knowledge-based algorithms for left ventricular quantification, may bring 3D echocardiography to a definite turning point.

The influence of cardiac function on coronary arterial enhancement at coronary computed tomography angiography: A cross-sectional study

Background:

The purpose of this study was to evaluate the influence of ejection fraction (EF) on peak aortic time (PAT) and peak aortic enhancement (PAE) during coronary computed tomography angiography (CTA).

Materials and Methods:

One-hundred and twenty patients (64 men, 56 women) underwent measurement of coronary CTA with a measurement of EF within 3 months of coronary CTA. Pearson's correlation coefficient analysis was used to investigate the relationships between EF, PAT and PAE, and peak attenuation of all coronary arteries.

Results:

The range of EF was (25%–70%) (mean: 55 ± 7.7). The range of PAT and PAE of ascending aorta on bolus test was 13–31 s (mean: 19.3 ± 2) and 153–435 HU (mean: 235 ± 40.6), respectively. Mean peak attenuation of ascending aorta, right coronary artery, left coronary artery, left circumflex artery, and left anterior descending were (561 ± 119), (476 ± 109), (505 ± 108), (467 ± 113), and (473 ± 104), respectively. There was a negative correlation between EF and PAT (r = −0.266, P = 0.003); however, there was no significant correlation between EF and PAE (r = −0.027, P = 0.767). In addition, there was no significant correlation between EF and the peak attenuation of coronary arteries.

Conclusion:

PAT was related to EF, but there was no relationship between PAE and EF. One of the explanation is that the left ventricular EF used for our study was assessed with echocardiography which is used roughly estimation of EF with interval of 5%–10% and may cause confounding results.

Intensive Simulation Training in Lower Limb Arterial Duplex Scanning Leads to Skills Transfer in Real-World Scenario

OBJECTIVES

To train novices to perform an abbreviated duplex lower limb ultrasound scan using simulation training and assess real-world skills transference.

METHODS

Novices undertook 3 days of simulation training. Their progress was assessed using the Duplex Ultrasound Objective Structured Assessment of Technical Skills (DUOSATS) for simulation and Cumulative Imaging Score (CIS). A final assessment day was held to assess DUOSATS for simulation and real patient scanning, CIS, cumulative diagnostic accuracy, and sections A and B of the Society of Vascular Technology examination. MSc students in vascular ultrasound were also assessed for comparison.

RESULTS

A total of 17 novices and 7 MSc students with 3-month training participated. Novices improved DUOSATS for simulation scores between days 1, 3, and 4: 18 (17-19) vs 27 (25-28) vs 30 (28-32), (median [interquartile range], p < 0.001). Novices improved in CIS between days 1 and 3: 10 (10-13) vs 21 (19-21), p < 0.001, with a decline on day 4: 15.3 (11.3-18.3), p < 0.001. On the final assessment day, there were no significant differences between novices and MSc students in: DUOSATS for simulation scores (30 [28-32] vs 31 [6-31.5], p = 0.85); DUOSATS for patient assessment (31 [28.7-33.7] vs 26.7 [24.5-35.7], p = 0.41); CIS (15.3 [11.3-18.7] vs 20.7 [12.3-22.2], p = 0.2), respectively. However, novices performed better in section B of the Society of Vascular Technology examination compared with MSc students (72.9% vs 54.3%, p < 0.001). Novices also demonstrated a higher diagnostic accuracy when compared with MSc students (65.7% of arterial segments correctly assessed vs 47.6%, respectively [p = 0.044]).

CONCLUSIONS

Intensive simulation of 3-day training achieved real patient-based assessments that were comparable to MSc students who were 3 months into their traditional training program.

Cardiovascular surveillance in breast cancer treatment: A more individualized approach is needed

Newly developed treatment strategies for breast cancer have reduced mortality rates over the past decades. Patients with breast cancer represent a heterogeneous population. Differences in the severity of the disease require diverse treatment options. Women have distinct individual risk patterns for cardiovascular disease that may affect their susceptibility to cardiotoxicity during therapy. While breast cancer treatment is targeted more on tumor and patient characteristics, a tailored individual approach with early and late cardiosurveillance is not yet implemented in routine care. Newly available cardiac imaging techniques are better suited to the early detection of cardiotoxicity and should be used more often in those patients at highest risk, as the early intervention afforded will improve their quality of life and prognosis.