Retention of diagnostic imaging staff is a growing concern within the NHS, with echocardiography services facing particular pressure due to high demand, workforce shortages, and post-pandemic backlogs. While factors influencing NHS staff retention are well documented, limited research has focused specifically on echocardiography staff, a highly skilled and specialised workforce. This study aimed to explore intention to leave among NHS echocardiography staff and to identify factors that encourage retention, alongside perceived barriers to staying within the role or organisation. As a part of business as usual, Trust staff support psychologists offered to meet with echocardiography colleagues for a general support session and to advertise the services they offer. The psychological support aspect of the team session has an evidence base in the Trust that was used to form the session content. All echocardiography staff were invited to attend a facilitated two-hour group or individual sessions combining survey completion, structured discussion, and psychological support. Quantitative data on job satisfaction and intention to leave were collected alongside qualitative discussion data, which were anonymised and analysed thematically. This mixed-methods service evaluation was conducted within a large acute NHS Trust. Forty-five staff participated, including Band 7 echocardiographers (n = 37) and Band 8 team leaders (n = 8). Retention intent differed by role: 88
Abstract Background Neonatologist-performed echocardiography (NPE) is increasingly practiced. NPE operators typically use visual estimation of systolic function and, if indicated, confirm function using quantitative echocardiographic markers. This exploratory study aims to compare the concordance between qualitative and quantitative echocardiographic markers among NPE and non-NPE operators. Methods We studied a cohort of newborn infants, undergoing routine NPE by the authors at Homerton University Hospital and Aberdeen Maternity Hospital from April 2024 to October 2024. Blinded NPE and non-NPE operators independently assessed left and right ventricular systolic function visually. We compared visual estimation with standard quantitative echocardiographic markers such as shortening fraction, Simpson’s ejection fraction and global longitudinal strain for left ventricle and tricuspid annular plane systolic excursion and fractional area change for right ventricle. We examined the concordance between visual estimation and quantitative echocardiographic markers for biventricular systolic function. Results Sixty infants with a mean (SD) gestation and birth weight of 34 (6.2) weeks and 2311 (1319) grams respectively were examined. Sensitivity testing for all echocardiographic parameters ranged from 25% to 80% whereas specificity ranged from 38% to 78%. Sensitivity increased with worsening grades of LV dysfunction defined using shortening fraction. While concordance between NPE and non-NPE operator was generally limited across most echocardiographic parameters, test-retest reliability was acceptable for several quantitative measures, including Simpson’s ejection fraction, global longitudinal strain and right ventricular fractional area change. Conclusion Visual estimation may be a useful bedside tool for detecting severe ventricular dysfunction in newborn infants; however; quantitative echocardiographic measures are likely to improve the assessment of mild to moderate dysfunction. Further studies should confirm these findings in a larger cohort with a higher representation of infants with ventricular dysfunction.
Abstract Background Atrial functional tricuspid regurgitation (AFTR) is increasingly recognized as a result of atrial remodelling in patients with atrial fibrillation (AF). However, prognostic predictors in this population remain incompletely defined. Objectives To evaluate echocardiographic characteristics and identify predictors of all-cause mortality in patients with AFTR. Methods We retrospectively studied consecutive patients with AF who underwent transthoracic echocardiography between 2017 and 2025. Inclusion required significant right atrial remodelling, defined as a right atrial area > 25 cm 2 . Patients with at least moderate functional tricuspid regurgitation comprised the AFTR cohort, while patients with mild/trivial/no tricuspid regurgitation served as controls. Patients with primary valvular disease, cardiomyopathy, congenital heart disease, device-related valve disease, RV dilatation, or more than mild RV systolic dysfunction were excluded. Cox proportional hazards regression was performed to identify predictors of mortality. Results A total of 152 patients were included, comprising 120 patients with AFTR and 32 controls. Compared with controls, patients with AFTR demonstrated larger atrial dimensions, higher right-sided hemodynamic-burden, impaired RV longitudinal strain, and reduced reservoir RA strain. During a median follow-up of 6.3 years, 53 deaths (45%) occurred among patients with AFTR. In univariable Cox analysis, age (HR 1.02, 95% CI 1.00-1.04; p = 0.016), TR Vmax (HR 1.59, 95% CI 1.07–2.35; p = 0.020), and lower reservoir RA strain (HR 0.90, 95% CI 0.84–0.96; p = 0.003) were associated with mortality. In multivariable analysis, reservoir RA strain remained independently associated with mortality (adjusted HR 0.88, 95% CI 0.81–0.95; p < 0.001). Conclusion AFTR is characterized by marked biatrial remodelling and impaired right atrial function. Reduced RA reservoir strain was independently associated with mortality, supporting a potential role for RA deformation imaging in risk stratification in AFTR.
Assessment of maximally provoked LVOT obstruction (LVOTO) guides treatment decisions in obstructive hypertrophic cardiomyopathy (HCM). Guidelines recommend various maneuvers, with exercise stress echocardiography (ESE) as the preferred method if others fail. Comparative data are limited. Therefore, in this prospective study, 20 patients with obstructive HCM underwent transthoracic echocardiography (TTE) with continuous wave Doppler measuring LVOT gradients at rest, during the Valsalva maneuver, handgrip exercise at 30
Abstract Background Patient selection for transcatheter mitral valve edge-to-edge repair (MTEER) remains challenging, particularly in individuals with complex mitral valve anatomy. Conventional risk scores incorporate limited clinical variables and do not adequately account for detailed echocardiographic features, resulting in suboptimal prediction of procedural success. Objectives To develop and evaluate machine learning (ML) and deep learning (DL) models integrating clinical and echocardiographic data to predict procedural success following MTEER. Methods Consecutive patients undergoing MTEER at a single tertiary center between 2014 and 2022 were retrospectively analyzed. Procedural success was defined as residual mitral regurgitation ≤ mild and mean mitral valve gradient < 5 mmHg at the end of the procedure. Pre-procedural transesophageal echocardiographic (TEE) videoclips were used to generate image datasets for ML and DL model training. Machine Learning algorithms (Decision Tree, Random Forrest, Gradient Boosting, k-Nearest Neighbors, Support Vector Machines) were used to predict the primary outcome from patient demographics and baseline TEE measurements. Inception, Xception, and MobileNet architectures were evaluated using 10-fold cross-validation with strict patient-level data separation. Model performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results Among 467 included patients, procedural success was achieved in 90.7%. Deep learning models demonstrated superior performance compared with logistic regression and ML. The Xception architecture achieved the highest discriminatory ability (AUC 0.76), with favorable accuracy and sensitivity for predicting procedural success. Conclusions Artificial intelligence has the potential to improve prediction of procedural outcomes following MTEER. Deep learning models that integrate echocardiographic imaging and clinical data can accurately predict procedural success and may enhance pre-procedural risk stratification, patient selection, and Heart Team decision-making.
