Feasibility study of Glucagon-like peptide-1 analogues for the optimization of Outcomes in obese patients undergoing AbLation for Atrial Fibrillation (GOAL-AF) protocol

BackgroundCatheter ablation for atrial fibrillation is recommended for symptomatic patients after failed medical therapy. Ablation has a higher failure rate in obese patients, and both the prevalence of atrial fibrillation and obesity are increasingly globally. The outcome of ablation can be improved if obese patients can achieve goal-oriented weight reduction prior to ablation. Conventional weight loss strategies, however, can be difficult to access and can delay ablation, thereby risking a lower chance of maintaining sinus rhythm. Effective weight-loss medications, such as the glucagon-like peptide inhibitor-1 drugs, offer the potential for incremental impact on weight loss over a shorter period of time as a bridging therapy. The aim of this study is to assess the feasibility of using liraglutide, a glucagon-like peptide inhibitor-1, in producing weight loss in obese patients before catheter ablation.MethodsThe study is an open-label, uncontrolled, prospective single-centre feasibility study of daily liraglutide injections in the treatment of obese patients for at least 13 weeks before and 52 weeks after AF ablation. Adult patients with symptomatic AF whose body mass index ≥ 30 will be recruited from those planning to undergo ablation. Feasibility will be determined based on the recruitment rate, adherence to the medication, and the amount of weight loss achieved over the study period. Exploratory outcomes include changes in atrial structure, function, and fibrosis with weight loss evaluated by cardiac magnetic resonance imaging, electroanatomic mapping, and patient-reported outcome measure.DiscussionThis study will allow us to determine whether the use of liraglutide in obese patients with atrial fibrillation undergoing ablation is feasible with adequate recruitment. The additional information on adherence and average weight loss over the study period will inform the design of a future definitive randomized controlled trial.Trial registrationClinicalTrials.gov (NCT05221229). Registered on 2 February 2022.Trial fundingMetchley Park Medical Society and University of Birmingham Starter Fellowship, British Heart Foundation Accelerator Grant, Abbott Investigator-Initiated Study Grant

Reference values for mitral and tricuspid annular dimensions using two-dimensional echocardiography

Only limited data are available from which normal ranges of mitral annular (MA) and tricuspid annular (TA) dimensions have been established. Normative data are important to assist the echocardiographer in defining the mechanism of atrioventricular valve regurgitation and to inform surgical planning. This study was conceived to establish normal MA and TA dimensions. Consecutive healthy subjects over the age of 60 were randomly recruited from the community as part of a screening project within South Birmingham. MA and TA dimensions in end-systole and end-diastole were evaluated in the parasternal and apical acoustic windows views using transthoracic echocardiography. Gender (males (M) and females (F))-specific dimensions were then assessed. A total of 554 subjects were screened and 74 with pathology considered to have an effect on annular dimensions were excluded from analysis. The mean age of the remaining 480 subjects was 70±7 years and the majority were female (56%). Dimensions were larger in men than in women and greater at end-diastole than end-systole (both P<0.05). Mean MA diameters at end-systole in the parasternal long axis view (cm) were 3.44 cm (M) and 3.11 cm (F) and at end-diastole 3.15 cm (M) and 2.83 cm (F) respectively. Mean TA diameters (cm) at end-systole in the apical 4 chamber view were 2.84 (M) and 2.80 (F) and at end-diastole 3.15 (M) and 3.01 (F) respectively. The reference ranges derived from this study differ from current published standards and should help to improve distinction of normal from pathological findings, in identifying aetiology and defining the mechanism of regurgitation

Progressive breathlessness in an Afro Caribbean hypertensive subject

The sensitivity and specificity of structural assessment of the heart by echocardiography in black hypertensive patients presenting with symptoms of heart failure is often incomplete. Cardiovascular magnetic resonance, mainly by virtue of its ability to characterize myocardial tissue composition, may be of value in differentiating some of the common pathologies noninvasively. We present an illustrative case of hypertrophic cardiomyopathy in a British Afro Caribbean hypertensive patient where at least some features of familial amyloidosis were present on screening echocardiography. Cardiovascular magnetic resonance examination of this case established not only the usefulness of this technique, but also highlighted the importance of recognizing the variations and departure from the usual which one associates with hypertrophic cardiomyopathy, so as to arrive at the final diagnosis

Aortic calcification and femoral bone density are independently associated with left ventricular mass in patients with chronic kidney disease

Background Vascular calcification and reduced bone density are prevalent in chronic kidney disease and linked to increased cardiovascular risk. The mechanism is unknown. We assessed the relationship between vascular calcification, femoral bone density and left ventricular mass in patients with stage 3 non-diabetic chronic kidney disease in a cross-sectional observational study. Methodology and Principal Findings A total of 120 patients were recruited (54% male, mean age 55±14 years, mean glomerular filtration rate 50±13 ml/min/1.73 m2). Abdominal aortic calcification was assessed using lateral lumbar spine radiography and was present in 48%. Mean femoral Z-score measured using dual energy x-ray absorptiometry was 0.60±1.06. Cardiovascular magnetic resonance imaging was used to determine left ventricular mass. One patient had left ventricular hypertrophy. Subjects with aortic calcification had higher left ventricular mass compared to those without (56±16 vs. 48±12 g/m2, P = 0.002), as did patients with femoral Z-scores below zero (56±15 vs. 49±13 g/m2, P = 0.01). In univariate analysis presence of aortic calcification correlated with left ventricular mass (r = 0.32, P = 0.001); mean femoral Z-score inversely correlated with left ventricular mass (r = −0.28, P = 0.004). In a multivariate regression model that included presence of aortic calcification, mean femoral Z-score, gender and 24-hour systolic blood pressure, 46% of the variability in left ventricular mass was explained (P<0.001). Conclusions In patients with stage 3 non-diabetic chronic kidney disease, lower mean femoral Z-score and presence of aortic calcification are independently associated with increased left ventricular mass. Further research exploring the pathophysiology that underlies these relationships is warranted

Narrative review of the role of artificial intelligence to improve aortic valve disease management

Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery