Echocardiographic assessment of cardiac function in patients with atrial fibrillation

Echocardiography plays an essential role in the management of patients with atrial fibrillation (AF) and the diagnosis of heart failure in these patients. Assessment of systolic and diastolic function is challenging in AF due to the irregular RR interval, resulting in variability from beat to beat. In this thesis, I have compared the reproducibility and validity of an index beat approach (similar RR intervals for the two prior beats before measurement) versus conventional averaging of three, five and ten consecutive beats in patients with permanent AF and symptoms of heart failure. Transthoracic echocardiography was performed at baseline in 160 patients enrolled in the RAte control Therapy Evaluation in permanent AF randomised controlled trial (NCT02391337). Measurements of Simpson’s biplane left ventricular ejection fraction (LVEF), global longitudinal strain (GLS) and the diastolic parameter E/e’ were obtained using three index beats and 3, 5 and 10 consecutive beats. All measurements were analysed offline with the analyser blinded to clinical details and with no pre-exclusions to image quality. The index beat method was shown to have a significantly lower within beat variability compared to consecutive beats and a single index beat measuring GLS and E/e’ was more reproducible or equally reproducible to averaging 10 consecutive beats when assessing intra and inter-operator reproducibility. Using a single index beat did not impact on the validity of LVEF, GLS or E/e’ when correlated with natriuretic peptides and substantially shortened the time taken for measurement of E/e’ (64% quicker than assessing 10 consecutive beats). This approach can enhance the reliability and efficiency of measurements for both systolic and diastolic left ventricular function in patients with AF

Cardiac imaging to assess left ventricular systolic function in atrial fibrillation

The validity and reproducibility of systolic function assessment in patients with atrial fibrillation (AF) using cardiac magnetic resonance, echocardiography, nuclear imaging and computed tomography is unknown. A prospectively-registered systematic review was performed, including 24 published studies with patients in AF at the time of imaging and reporting validity or reproducibility data on left ventricular systolic parameters (PROSPERO: CRD42018091674). Data extraction and risk of bias were performed by 2 investigators independently and synthesized qualitatively. In 3 cardiac magnetic resonance studies (40 AF patients), left ventricular ejection fraction and stroke volume measurements correlated highly with catheter angiography (r ≥0.85), and intra- and/or interobserver variability were low. From 3 nuclear studies (171 AF patients), there were no external validation assessments but intra and/or interobserver and intersession variability were low. In 18 echocardiography studies (2,566 AF patients), 2 studies showed high external validity of global longitudinal strain and tissue Doppler s’ with angiography-derived dP/dt (r ≥0.88). Global longitudinal strain and myocardial performance index were both associated with adverse cardiovascular events. Reproducibility of echocardiography was better when selecting an index-beat (where 2 preceding R-to-R intervals are similar) compared to averaging of consecutive beats. There were no studies relating to computed tomography. Most studies were small and biased by selection of patients with good quality images, limiting clinical extrapolation of results. The validity of systolic function measurements in patients with AF remains unclear due to the paucity of good-quality data

Development of automated neural network prediction for echocardiographic left ventricular ejection fraction

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF).Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey’s method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline’s accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF.Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson’s correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment.Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function

Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

OBJECTIVES: Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection. METHODS: This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool. RESULTS: 28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias. CONCLUSION: PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection

Beta-blockers for heart failure with reduced, mid-range, and preserved ejection fraction:An individual patient-level analysis of double-blind randomized trials

Aims: Recent guidelines recommend that patients with heart failure and left ventricular ejection fraction (LVEF) 40-49% should be managed similar to LVEF ≥ 50%. We investigated the effect of beta-blockers according to LVEF in double-blind, randomized, placebo-controlled trials.  Methods and results: Individual patient data meta-analysis of 11 trials, stratified by baseline LVEF and heart rhythm (Clinicaltrials.gov: NCT0083244; PROSPERO: CRD42014010012). Primary outcomes were all-cause mortality and cardiovascular death over 1.3 years median follow-up, with an intention-to-treat analysis. For 14 262 patients in sinus rhythm, median LVEF was 27% (interquartile range 21-33%), including 575 patients with LVEF 40-49% and 244 ≥ 50%. Beta-blockers reduced all-cause and cardiovascular mortality compared to placebo in sinus rhythm, an effect that was consistent across LVEF strata, except for those in the small subgroup with LVEF ≥ 50%. For LVEF 40-49%, death occurred in 21/292 [7.2%] randomized to beta-blockers compared to 35/283 [12.4%] with placebo; adjusted hazard ratio (HR) 0.59 [95% confidence interval (CI) 0.34-1.03]. Cardiovascular death occurred in 13/292 [4.5%] with beta-blockers and 26/283 [9.2%] with placebo; adjusted HR 0.48 (95% CI 0.24-0.97). Over a median of 1.0 years following randomization (n = 4601), LVEF increased with beta-blockers in all groups in sinus rhythm except LVEF ≥50%. For patients in atrial fibrillation at baseline (n = 3050), beta-blockers increased LVEF when < 50% at baseline, but did not improve prognosis.  Conclusion: Beta-blockers improve LVEF and prognosis for patients with heart failure in sinus rhythm with a reduced LVEF. The data are most robust for LVEF < 40%, but similar benefit was observed in the subgroup of patients with LVEF 40-49%

A patient-centred model to quality assure outputs from an echocardiography department: consensus guidance from the British Society of Echocardiography

Background: Quality assurance (QA) of echocardiographic studies is vital to ensure that clinicians can act on findings of high quality to deliver excellent patient care. To date, there is a paucity of published guidance on how to perform this QA. The British Society of Echocardiography (BSE) has previously produced an Echocardiography Quality Framework (EQF) to assist departments with their QA processes. This article expands on the EQF with a structured yet versatile approach on how to analyse echocardiographic departments to ensure high-quality standards are met. In addition, a process is detailed for departments that are seeking to demonstrate to external bodies adherence to a robust QA process. Methods: The EQF consists of four domains. These include assessment of Echo Quality (including study acquisition and report generation); Reproducibility & Consistency (including analysis of individual variability when compared to the group and focused clinical audit), Education & Training (for all providers and service users) and Customer & Staff Satisfaction (of both service users and patients/their carers). Examples of what could be done in each of these areas are presented. Furthermore, evidence of participation in each domain is categorised against a red, amber or green rating: with an amber or green rating signifying that a quantifiable level of engagement in that aspect of QA has been achieved. Conclusion: The proposed EQF is a powerful tool that focuses the limited time available for departmental QA on areas of practice where a change in patient experience or outcome is most likely to occur

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Background: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation. Methods: Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012). Findings: 15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials. Interpretation: An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality