Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study.

BACKGROUND: Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower. METHODS: We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0-1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov, NCT04601415. FINDINGS: Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81-0·89), sensitivity of 84·8% (76·2-91·3), and specificity of 69·5% (66·4-72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81-0·89), sensitivity of 82·7% (72·7-90·2), and specificity of 79·9% (77·0-82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88-0·95), sensitivity of 91·9% (78·1-98·3), and specificity of 80·2% (75·5-84·3). INTERPRETATION: A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment. FUNDING: NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research.

Adapting the role of handheld echocardiography during the COVID-19 pandemic: A practical guide.

The COVID-19 pandemic has altered our approach to inpatient echocardiography delivery. There is now a greater focus to address key clinical questions likely to make an immediate impact in management, particularly during the period of widespread infection. Handheld echocardiography (HHE) can be used as a first-line assessment tool, limiting scanning time and exposure to high viral load. This article describes a potential role for HHE during a pandemic. We propose a protocol with a reporting template for a focused core dataset necessary in delivering an acute echocardiography service in the setting of a highly contagious disease, minimising risk to the operator. We cover the scenarios typically encountered in the acute cardiology setting and how an expert trained echocardiography team can identify such pathologies using a limited imaging format and include cardiac presentations encountered in those patients acutely unwell with COVID-19.

Mitral valve repair results in better right ventricular remodelling than valve replacement for degenerative mitral regurgitation: a three-dimensional echocardiographic study.

INTRODUCTION: Right ventricular (RV) remodelling may be an important determinant of clinical outcome in patients undergoing mitral valve surgery for mitral regurgitation. In the present study we hypothesised that, compared to valve replacement, mitral valve repair for degenerative mitral regurgitation may result in better RV remodelling, as assessed by real-time, three-dimensional echocardiography (RT3DE). METHODS: Forty unselected patients with degenerative mitral valve regurgitation were recruited prospectively. Two-dimensional (2DE) and RT3DE studies were performed prior to surgery and 6 months postoperatively. RV volumes, stroke volume, ejection fraction and mass, as well as RV pressures were calculated. Regression analysis was used to demonstrate the effect of surgical mitral repair and replacement on reverse RV remodelling. RESULTS: Twenty-one patients underwent mitral valve repair and 19 valve replacement. Mean age was 59.5 ± 15.4 years. Five patients who underwent repair (23.8%) developed recurrent MR within 6 months postoperatively. RV systolic pressure was reduced from 39.3 ± 11.9 mmHg, to 25.4 ± 8.3 mmHg after surgery (p=0.027). Compared to preoperative volumes, 6 months after surgery there was a significant reduction in RV diastolic volume and stroke volume (from 106.4 ± 16.3 ml to 80.4 ± 12.1 ml and from 69.2 ± 15.4 ml to 52.2 ± 14.1 ml, respectively, p<0.001), and an increase in RV ejection fraction (from 54.5 ± 9.2% to 67.3 ± 8.5%, p<0.001). Over a 6-month follow-up period there were no deaths. Overall, the functional class was significantly improved in 39/40 patients (97.5%) but there was no difference between the repair and replacement groups. Using a multivariate regression analysis model including all parameters composing RV remodelling postoperatively, mitral valve repair was the strongest predictor of reverse RV remodelling (reduction of RV end-diastolic volume, p<0.01; reduction of RV mass, p<0.01; reduction of tricuspid regurgitant velocity, p=0.019). CONCLUSIONS: Mitral valve repair leads to more favourable reverse RV remodelling, assessed by RT3DE, compared to valve replacement. This may have important clinical implications.

Congenital tricuspid valve blood cyst: imaging with three-dimensional contrast echocardiography.

We describe the case of an 88-year-old woman who presented in WHO functional class III and breathless on exertion. She was investigated for pulmonary hypertension. However, two-dimensional echocardiography demonstrated a rounded mass attached to the septal leaflet of the tricuspid valve. Real-time three-dimensional echocardiography with contrast microbubbles was the imaging modality of choice to characterise the cyst and to guide the decision as to the best therapeutic approach for the patient.