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Background Most people who begin statins abandon them, most commonly because of side effects. Objectives The purpose of this study was to assess daily symptom scores on statin, placebo, and no treatment in participants who had abandoned statins. Methods Participants received 12 1-month medication bottles, 4 containing atorvastatin 20 mg, 4 placebo, and 4 empty. We measured daily symptom intensity for each using an app (scale 1-100). We also measured the “nocebo” ratio: the ratio of symptoms induced by taking statin that was also induced by taking placebo. Results A total of 60 participants were randomized and 49 completed the 12-month protocol. Mean symptom score was 8.0 (95% CI: 4.7-11.3) in no-tablet months. It was higher in statin months (16.3; 95% CI: 13.0-19.6; P < 0.001), but also in placebo months (15.4; 95% CI: 12.1-18.7; P < 0.001), with no difference between the 2 (P = 0.388). The corresponding nocebo ratio was 0.90. In the individual-patient daily data, neither symptom intensity on starting (OR: 1.02; 95% CI: 0.98-1.06; P = 0.28) nor extent of symptom relief on stopping (OR: 1.01; 95% CI: 0.98-1.05; P = 0.48) distinguished between statin and placebo. Stopping was no more frequent for statin than placebo (P = 0.173), and subsequent symptom relief was similar between statin and placebo. At 6 months after the trial, 30 of 60 (50%) participants were back taking statins. Conclusions The majority of symptoms caused by statin tablets were nocebo. Clinicians should not interpret symptom intensity or timing of symptom onset or offset (on starting or stopping statin tablets) as indicating pharmacological causation, because the pattern is identical for placebo. (Self-Assessment Method for Statin Side-effects Or Nocebo [SAMSON]; NCT02668016), Central Illustration

Pacing therapy aims to improve overall cardiac function by normalizing cardiac electrical activation. Although hemodynamic measurements allow the impact of cardiac pacing on cardiac function to be quantified, the protocol is crucial to minimize the effect of noise and achieve greater precision. Multiple steps can be undertaken to optimize accuracy of hemodynamic measurements. These include comparing with a reference state, using an average of a set number of beats, making repeated measurements, ensuring all beats are included, and pacing at faster heart rates. These measurements can aid comparison between different pacing modalities and guide optimal programming.

Management of stable coronary artery disease (CAD) centers on medication to prevent myocardial infarction and death. Many anti-anginal medications also have benefit for reducing symptoms, and have been proven to be effective against placebo control. Before effective preventive medications were available, patients with stable CAD often underwent revascularization with coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), on the plausible assumption that these procedures would prevent adverse events and reduce symptoms. However, recent randomized controlled trials have cast doubt on these assumptions.Considering results from the recent ISCHEMIA trial, we discuss the evidence base that underpins revascularization for stable CAD in contemporary practice. We also focus on patient groups at high risk of myocardial infarction and death, for whom revascularization is often recommended. We outline the areas of uncertainty, unanswered research questions, and key areas of potential miscommunication in doctor-patient consultations.

Aims The traditional approach to documenting angina outcomes in clinical trials is to ask the patient to recall their symptoms at the end of a month. With the ubiquitous availability of smartphones and tablets, daily contemporaneous documentation might be possible. Methods and results The ORBITA-2 symptom smartphone app was developed with a user-centred iterative design and testing cycle involving a focus group of previous ORBITA participants. The feasibility and acceptability were assessed in an internal pilot of participants in the ongoing ORBITA-2 trial. Seven days of app entries by ORBITA-2 participants were compared with subsequent participant recall at the end of the 7-day period. The design focus group tested a prototype app. They reported that the final version captured their symptoms and was easy to use. In the completion assessment group, 141 of 142 (99%) completed the app in full and 47 of 141 (33%) without reminders. In the recall assessment group, 29 of 29 (100%) participants said they could recall the previous day’s symptoms, and 82% of them recalled correctly. For 2 days previously, 88% said they could recall and of those, 87% recalled correctly. The proportion saying they could recall their symptoms fell progressively thereafter: 89, 67, 61, 50%, and at 7 days, 55% (P Conclusion Episode counts of angina are difficult to recall after a few days. For trials such as ORBITA-2 focusing on angina, daily symptom collection via a smartphone app will increase the validity of the results.

The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency. Electronic supplementary material The online version of this article (10.1007/s10554-020-02050-w) contains supplementary material, which is available to authorized users.

Background Previous trials suggest lower long-term risk of mortality after invasive rather than non-invasive management of patients with non-ST elevation myocardial infarction (NSTEMI), but the trials excluded very elderly patients. We aimed to estimate the effect of invasive versus non-invasive management within 3 days of peak troponin concentration on the survival of patients aged 80 years or older with NSTEMI. Methods Routine clinical data for this study were obtained from five collaborating hospitals hosting NIHR Biomedical Research Centres in the UK (all tertiary centres with emergency departments). Eligible patients were 80 years old or older when they underwent troponin measurements and were diagnosed with NSTEMI between 2010 (2008 for University College Hospital) and 2017. Propensity scores (patients' estimated probability of receiving invasive management) based on pretreatment variables were derived using logistic regression; patients with high probabilities of non-invasive or invasive management were excluded. Patients who died within 3 days of peak troponin concentration without receiving invasive management were assigned to the invasive or non-invasive management groups based on their propensity scores, to mitigate immortal time bias. We estimated mortality hazard ratios comparing invasive with non-invasive management, and compared the rate of hospital admissions for heart failure. Findings Of the 1976 patients with NSTEMI, 101 died within 3 days of their peak troponin concentration and 375 were excluded because of extreme propensity scores. The remaining 1500 patients had a median age of 86 (IQR 82–89) years of whom (845 [56%] received non-invasive management. During median follow-up of 3·0 (IQR 1·2–4·8) years, 613 (41%) patients died. The adjusted cumulative 5-year mortality was 36% in the invasive management group and 55% in the non-invasive management group (adjusted hazard ratio 0·68, 95% CI 0·55–0·84). Invasive management was associated with lower incidence of hospital admissions for heart failure (adjusted rate ratio compared with non-invasive management 0·67, 95% CI 0·48–0·93). Interpretation The survival advantage of invasive compared with non-invasive management appears to extend to patients with NSTEMI who are aged 80 years or older. Funding NIHR Imperial Biomedical Research Centre, as part of the NIHR Health Informatics Collaborative.

Background His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts. Objective The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation. Methods We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset. Results The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network’s performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P, Graphical abstract

The role of percutaneous coronary interventions in addition to medical therapy for patients with stable coronary artery disease continues to be debated in routine clinical practice, despite more than 2 decades of randomized controlled trials. The residual uncertainty arises from particular challenges facing revascularization trials. Which endpoint do doctors care about, and which do patients care about? Which participants should be enrolled? What background medical therapy should we use? When is placebo control relevant? In this paper, we discuss how these questions can be approached and examine the merits and disadvantages of possible options. Engaging multiple stakeholders, including patients, researchers, regulators, and funders, to ensure the design elements are methodologically valid and clinically meaningful should be an aspirational goal in the development of future trials.

Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic cases. The results were compared with the results from the classic (standard) two-dimensional block matching. The proposed method presented an average displacement error of 0.57 pixels, while classic block matching provided an average error of 1.15 pixels. When estimating the segmental/regional longitudinal strain in healthy cases, the proposed method, with an average of 0.32 ± 0.53, outperformed the classic counterpart, with an average of 3.43 ± 2.84. A similar superior performance was observed in ischaemic cases. This method does not require any additional ad hoc filtering process. Therefore, it can potentially help to reduce the variability in the strain measurements caused by various post-processing techniques applied by different implementations of the speckle tracking. Graphical Abstract Standard block matching versus proposed iterative block matching approach.

Aims The optimal method of revascularization for patients with left main coronary artery disease (LMCAD) is controversial. Coronary artery bypass graft surgery (CABG) has traditionally been considered the gold standard therapy, and recent randomized trials comparing CABG with percutaneous coronary intervention (PCI) with drug-eluting stents (DES) have reported conflicting outcomes. We, therefore, performed a systematic review and updated meta-analysis comparing CABG to PCI with DES for the treatment of LMCAD. Methods and results We systematically identified all randomized trials comparing PCI with DES vs. CABG in patients with LMCAD. The primary efficacy endpoint was all-cause mortality. Secondary endpoints included cardiac death, myocardial infarction (MI), stroke, and unplanned revascularization. All analyses were by intention-to-treat. There were five eligible trials in which 4612 patients were randomized. The weighted mean follow-up duration was 67.1 months. There were no significant differences between PCI and CABG for the risk of all-cause mortality [relative risk (RR) 1.03, 95% confidence interval (CI) 0.81–1.32; P = 0.779] or cardiac death (RR 1.03, 95% CI 0.79–1.34; P = 0.817). There were also no significant differences in the risk of stroke (RR 0.74, 95% CI 0.35–1.50; P = 0.400) or MI (RR 1.22, 95% CI 0.96–1.56; P = 0.110). Percutaneous coronary intervention was associated with an increased risk of unplanned revascularization (RR 1.73, 95% CI 1.49–2.02; P Conclusion The totality of randomized clinical trial evidence demonstrated similar long-term mortality after PCI with DES compared with CABG in patients with LMCAD. Nor were there significant differences in cardiac death, stroke, or MI between PCI and CABG. Unplanned revascularization procedures were less common after CABG compared with PCI. These findings may inform clinical decision-making between cardiologists, surgeons, and patients with LMCAD.

PURPOSE: To assess whether the semisupervised natural language processing (NLP) of text from clinical radiology reports could provide useful automated diagnosis categorization for ground truth labeling to overcome manual labeling bottlenecks in the machine learning pipeline. MATERIALS AND METHODS: In this retrospective study, 1503 text cardiac MRI reports from 2016 to 2019 were manually annotated for five diagnoses by clinicians: normal, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, myocardial infarction (MI), and myocarditis. A semisupervised method that uses bidirectional encoder representations from transformers (BERT) pretrained on 1.14 million scientific publications was fine-tuned by using the manually extracted labels, with a report dataset split into groups of 801 for training, 302 for validation, and 400 for testing. The model's performance was compared with two traditional NLP models: a rule-based model and a support vector machine (SVM) model. The models’ F1 scores and receiver operating characteristic curves were used to analyze performance. RESULTS: After 15 epochs, the F1 scores on the test set of 400 reports were as follows: normal, 84%; DCM, 79%; hypertrophic cardiomyopathy, 86%; MI, 91%; and myocarditis, 86%. The pooled F1 score and area under the receiver operating curve were 86% and 0.96, respectively. On the same test set, the BERT model had a higher performance than the rule-based model (F1 score, 42%) and SVM model (F1 score, 82%). Diagnosis categories classified by using the BERT model performed the labeling of 1000 MR images in 0.2 second. CONCLUSION: The developed model used labels extracted from radiology reports to provide automated diagnosis categorization of MR images with a high level of performance. Keywords: Semisupervised Learning, Diagnosis/Classification/Application Domain, Named Entity Recognition, MRI Supplemental material is available for this article. © RSNA, 2021

Background The placebo effect is a well described phenomenon in blinded studies evaluating anti-anginal therapeutics, although its impact on clinical research metrics remains unknown. We conducted a systematic review and meta-analysis to quantify the impact of placebo on endpoints of symptoms, life-quality and functional outcomes in randomized placebo-controlled trials (RCTs) of symptomatic stable coronary artery disease. Methods We systematically reviewed MEDLINE, EMBASE, and the Cochrane database for double-blind RCTs of anti-angina therapeutics. Patients randomized to the placebo-arm were the study population. Main outcomes were the changes in exercise performance (exercise treadmill test [ETT] parameters), quality of life (Seattle Angina Questionnaire domains), symptoms (Canadian Cardiovascular Society angina class) and drug usage (nitroglycerin tabs/week) between baseline and following placebo. The primary outcome was ETT total duration time. Data were pooled with a random effect model. Results Seventy-eight RCTs (83% drug-controlled, 17% procedure-controlled) were included encompassing 4,925 patients randomized to placebo. ETT total duration time was significantly improved following placebo as compared to baseline (mean [95% confidence interval]: 29.2 [20.6-37.8] seconds) with evidence of high heterogeneity (I 2 = 98%) At subgroup analysis, crossover design was associated with a smaller placebo effect on ETT performance than parallel study design (p for interaction=0.001). A significant placebo effect was observed for all secondary outcomes with overall high heterogeneity. Conclusion A substantial placebo effect was present in angina RCTs across a variety of functional and life-quality metrics. High variability in placebo effect size was present, mostly unexplained by differences in study and patient characteristics (PROSPERO CRD42019132797).

Objectives-\ud Adenosine hyperemia is an integral component of the physiological assessment of obstructive coronary artery disease in patients with chronic coronary syndrome (CCS). The aim of this study was to compare systemic, coronary and microcirculatory hemodynamics between intravenous (IV) adenosine hyperemia versus physical exercise stress in patients with CCS and coronary stenosis.\ud Methods-\ud Twenty-three patients (mean age, 60.6 ± 8.1 years) with CCS and single-vessel coronary stenosis underwent cardiac catheterization. Continuous trans-stenotic coronary pressure-flow measurements were performed during: i) IV adenosine hyperemia, and ii) physical exercise using a catheter-table-mounted supine ergometer. Systemic, coronary and microcirculatory hemodynamic responses were compared between IV adenosine and exercise stimuli.\ud Results-\ud Mean stenosis diameter was 74.6% ± 10.4. Median (interquartile range) FFR was 0.54 (0.44–0.72). At adenosine hyperemia versus exercise stress, mean aortic pressure (Pa, 91 ± 16 mmHg vs 99 ± 15 mmHg, p < 0.0001), distal coronary pressure (Pd, 58 ± 21 mmHg vs 69 ± 24 mmHg, p < 0.0001), trans-stenotic pressure ratio (Pd/Pa, 0.63 ± 0.18 vs 0.69 ± 0.19, p < 0.0001), microvascular resistance (MR, 2.9 ± 2.2 mmHg.cm−1.sec−1 vs 4.2 ± 1.7 mmHg.cm−1.sec−1, p = 0.001), heart rate (HR, 80 ± 15 bpm vs 85 ± 21 bpm, p = 0.02) and rate-pressure product (RPP, 7522 ± 2335 vs 9077 ± 3200, p = 0.0001) were all lower. Conversely, coronary flow velocity (APV, 23.7 ± 9.5 cm/s vs 18.5 ± 6.8 cm/s, p = 0.02) was higher. Additionally, temporal changes in Pa, Pd, Pd/Pa, MR, HR, RPP and APV during IV adenosine hyperemia versus exercise were all significantly different (p < 0.05 for all).\ud Conclusions-\ud In patients with CCS and coronary stenosis, invasive hemodynamic responses differed markedly between IV adenosine hyperemia versus physical exercise stress. These differences were observed across systemic, coronary and microcirculatory hemodynamics.

Background Device-based algorithms offer the potential for automated optimization of cardiac resynchronization therapy (CRT), but the process for accepting them into clinical use is currently still ad-hoc, rather than based on pre-clinical and clinical testing of specific features of validity. We investigated how the QuickOpt-guided VV delay (VVD) programming performs against the clinical and engineering heuristic of QRS complex shortening by CRT. Methods A prospective, 2-center study enrolled 37 consecutive patients with CRT. QRS complex duration (QRSd) was assessed during intrinsic atrioventricular conduction, synchronous biventricular pacing, and biventricular pacing with QuickOpt-proposed VVD. The measurements were done manually by electronic calipers in signal-averaged and magnified 12-lead QRS complexes. Results Native QRSd was 174 ± 22 ms. Biventricular pacing with empiric AVD and synchronous VVD resulted in QRSd 156 ± 20 ms, a significant narrowing from the baseline QRSd by 17 ± 27 ms, P = 0.0003. In 36 of 37 patients, the QuickOpt algorithm recommended left ventricular preexcitation with VVD of 42 ± 18 ms (median 40 ms; interquartile range 30–55 ms, P Conclusions QuickOpt algorithm systematically favours a left-preexcitation VVD which translates into a significant prolongation of the QRSd compared to synchronous biventricular pacing. There is no reason to believe that a manipulation that systematically widens QRSd should be considered to optimize physiology. Device-based CRT optimization algorithms should undergo systematic mechanistic pre-clinical evaluation in various scenarios before they are tested in large clinical studies.

Background Standalone thoracoscopic surgical ablation may be more effective than catheter ablation in patients with long-standing persistent atrial fibrillation. Objectives To determine whether or not surgical ablation is clinically superior to catheter ablation as the first-line treatment strategy in long-standing persistent atrial fibrillation. Design This was a prospective, multicentre, randomised control trial. Setting Four NHS tertiary centres in England. Participants Adults with long-standing persistent atrial fibrillation, who had European Heart Rhythm Association symptom scores > 2 and who were naive to previous catheter ablation or thoracic/cardiac surgery. Interventions Minimally invasive thoracoscopic surgical ablation and conventional catheter ablation (control intervention). Main outcome measures The primary outcome was freedom from atrial fibrillation/tachycardia ≥ 30 seconds after a single procedure without antiarrhythmic drugs (class 1C/3) at 1 year, excluding a 3-month blanking period. The secondary outcomes include the intervention-related major complication rate; clinical success (≥ 75% reduction in arrhythmia burden); and changes in symptoms, quality of life and cost-effectiveness. Methods Patients (n = 120) were randomised to surgical ablation (n = 60) or catheter ablation (n = 60). An implanted loop recorder provided continuous cardiac monitoring following ablation. Follow-up visits were at 3, 6, 9 and 12 months. Loop recorder data were reviewed monthly by a physiologist who was blinded to the randomisation outcome. Results The study treatment was received by 55 patients in the surgical ablation arm and 60 patients in the catheter ablation arm; five patients withdrew from surgical ablation before treatment. Data from randomised and treated patients were analysed as per intention to treat. Patients had a mean age of 62.3 (standard deviation 9.6) years, were predominantly male (74%), had a mean left atrial diameter of 44.6 mm (standard deviation 6 mm) and were in continuous atrial fibrillation for 22 months (range 16–31 months). At 12 months, 26% of patients in the surgical ablation arm (14/54) and 28% of patients in the catheter ablation arm (17/60) were free from atrial arrhythmias after a single procedure without antiarrhythmic drugs (odds ratio 1.13, 95% confidence interval 0.46 to 2.83; p = 0.84). An arrhythmia burden reduction of ≥ 75% was seen in 36 out of 54 (67%) patients in the surgical ablation arm, compared with 46 out of 60 (77%) patients in the catheter ablation arm (odds ratio 1.64, 95% confidence interval 0.67 to 4.08; p = 0.3). Procedure-related serious complications within 30 days of the intervention occurred in 15% (8/55) of patients in the surgical ablation arm (including one death) compared with 10% (6/60) of patients in the catheter ablation arm (p = 0.46). Surgical ablation was associated with significantly higher costs (£23,221 vs. £18,186; p = 0.02) and fewer quality-adjusted life-years than catheter ablation (0.76 vs. 0.83; p = 0.02). Limitations This study was conducted in four highly specialised cardiology centres that have substantial experience in both treatment modalities; therefore, the results may not be widely generalisable. The study was not powered to detect small differences in efficacy. Conclusions We found no evidence to suggest that standalone thoracoscopic surgical ablation outcomes were superior to catheter ablation outcomes in achieving freedom from atrial arrhythmia after a single procedure without antiarrhythmic drugs. Moreover, surgical ablation is associated with a longer hospital stay, smaller improvements in quality of life and higher health-care costs than catheter ablation (standard care therapy). Future work Evaluation of the impact of ablation treatments on sinus rhythm maintenance and quality of life with extended follow-up to 3 years. Model-based economic analysis to estimate long-term benefits, harms and costs of surgical and catheter ablation compared with antiarrhythmic drug therapy in long-standing persistent atrial fibrillation patients. Trial registration Current Controlled Trials ISRCTN18250790 and ClinicalTrials.gov NCT02755688. Funding This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a Medical Research Council (MRC) and National Institute for Health Research (NIHR) partnership. This study was supported by the UK Clinical Research Collaboration-registered King’s Clinical Trials Unit at King’s Health Partners, which is part funded by the NIHR Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and King’s College London and the NIHR Evaluation, Trials and Studies Coordinating Centre. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 18. See the NIHR Journals Library website for further project information.

Aim: Malondialdehyde-modified low-density lipoprotein (MDA-LDL) forms a significant component of oxidised LDL. The effects of exercise on levels of MDA-LDL and anti-MDA-LDL antibodies are not well-understood. Furthermore, it is not known whether these can be modified in patients with coronary artery disease by percutaneous coronary intervention (PCI).Methods: The Objective Randomised Blinded Investigation with optimal medical Therapy of Angioplasty in stable angina (ORBITA) trial was the first blinded, multi-centre randomised trial of PCI vs. placebo procedure for angina relief. Serum samples were available at four time-points: pre-randomisation pre- (P1) and post- (P2) exercise and post-randomisation (6-weeks following the PCI or placebo procedure), pre- (P3) and post- (P4) exercise. ELISAs were performed using laboratory-developed assays for MDA-LDL (adjusted for Apolipoprotein B) and anti-MDA-LDL antibodies.Results: One hundred ninety-six of the 200 patients (age 66.1 [SD 8.99] years, 28% female) with severe single vessel coronary artery disease suitable for PCI enrolled in the ORBITA trial had blood available for analysis. With exercise at pre-randomisation (P2–P1) there was no significant change in adjusted MDA-LDL (−0.001, 95% CI −0.004 to 0.001; p = 0.287); however, IgG and IgM anti-MDA-LDL significantly declined (−0.022, 95% CI −0.029 to −0.014, p < 0.0001; −0.016, 95% CI −0.024 to −0.008, p = 0.0002, respectively). PCI did not have a significant impact on either the pre-exercise values (P3 controlling for P1) of MDA-LDL (p = 0.102), IgG (p = 0.444) or IgM anti-MDA-LDL (p = 0.909). Nor did PCI impact the exercise induced changes in these markers (P4 controlling for P1, P2, and P3) for MDA-LDL (p = 0.605), IgG (p = 0.725) or IgM anti-MDA-LDL (p = 0.171). Pre-randomisation ischaemia on stress echo did not impact these interactions.Conclusions: Exercise results in an acute reduction in anti-oxLDL antibodies in patients with severe single vessel coronary disease, possibly indicating an induction in homoeostatic clearance via the innate immune system. However, PCI did not ameliorate this effect.

Background Left ventricular hypertrophy (LVH) occurs in both aortic stenosis (AS) and hypertension (HT) due to an increase in afterload. However, in AS there is an increase in resting coronary flow (per gram of LV) while in HT it is reduced. Wave intensity analysis (WIA) is a well-established method of characterising and quantifying the energies that drive coronary flow. Energies propagating from the proximal vessel (aorta and systemic arteries) interact with energies travelling from the distal end (myocardial microcirculation). WIA allows the separation of these energies into the waves that drive cyclic changes in coronary flow. Purpose We aimed to compare coronary flow patterns in LVH secondary to AS with coronary flow patterns in LVH secondary to HT. Methods Thirty-one participants were recruited (mean age 63, 18 female), 10 with LVH and severe AS, 11 with LVH and HT, and 10 with no LVH and no AS. Participants underwent invasive pressure and Doppler velocity measurements in each of the left coronary arteries and echocardiography. We applied WIA. Results Mean resting coronary flow per gram of LV tissue (Fig. 1) was increased in participants with LVH secondary to AS (1.62±0.60 ml/min/g) and reduced in participants with LVH secondary to HT (0.49±0.27 ml/min/g), compared to participants with no LVH and no AS (1.47±0.73 ml/min/g). We observed marked differences between the magnitudes of the waves driving coronary flow in the three groups (Fig. 2). Forward and backward travelling waves are shown above and below the line respectively. Waves causing acceleration of coronary forward flow are shown as black and waves causing deceleration are shown in white. Wave 6, the backwards decompression wave (BDW), is particularly important for myocardial perfusion. The BDW corresponds to the diastolic 'suction' of blood down the coronary arteries during myocardial relaxation. The energy of the BDW was increased in LVH secondary to AS (31.1x103 W m–2 s–2) but was reduced in LVH secondary to HT (12.3x103 W m–2 s–2) (p The energy of the BDW correlated with LV cavity pressure (r=0.84, p Conclusions In hypertension, LVH is associated with reduced mean coronary flow and reduced myocardial “suction” during diastole, presumably by the reduction in myocardial compliance associated with diastolic dysfunction. However, in AS the large pressure gradient between the LV cavity and the aorta results in a large contractile force which is generated in systole and then released in diastole. This large diastolic force overwhelms any local impairment caused by the hypertrophied myocardium and contributes to high resting coronary flow in AS. Funding Acknowledgement Type of funding sources: None.

Background and purpose Artificial intelligence (AI) has the potential to greatly improve efficiency and reproducibility of quantification in echocardiography, but to gain widespread use it must both meet expert standards of excellence and have a transparent methodology. We developed an online platform to enable multiple collaborators to annotate medical images for training and validating neural networks. Methods Using our online collaborative platform 9 expert echocardiographers labelled 2056 images that comprised the training dataset. They labelled the four points from where the standard parasternal long axis (PLAX) measurements (interventricular septum, posterior wall, left ventricular dimension) would be made. Using these labelled images we trained a 2d convolutional neural network to replicate these labels. Separately, we curated an external validation dataset of the systolic and diastolic frames of 100 PLAX acquisitions. Each of these images were labelled twice by 13 different experts, and the average of the 26 measurements was taken as the consensus standard. We then compared the individual experts and the AI measurements on the external validation dataset to the consensus standard, and calculated the precision standard deviation (SD) of the signed differences from the consensus standard. Results For diastolic septum thickness, the AI had a precision SD of 1.8 mm (ICC 0.81; 95% CI 0.73 to 0.97), compared with 2.0 mm for the individual experts (ICC 0.64; 95% CI 0.57 to 0.72). For diastolic posterior wall thickness, the AI had a precision SD 1.4 mm (ICC 0.54; 95% CI 0.38 to 0.66), and the individual experts 2.2 mm (ICC 0.37; 95% CI 0.29 to 0.46). The AI's precision SD for left ventricular internal dimension was 3.5 mm (ICC 0.93, 95% CI 0.90 to 0.94), and for individual experts was 4.4mm (ICC 0.82, 95% CI 0.78 to 0.95). Both the experts and AI performed better in diastole than systole (precision SD AI 2.5mm vs 4.3mm, p Conclusions AI trained by a group of echocardiography experts was able to perform PLAX measurements which matched the reference standard more closely than any individual expert's own measurements. This open, collaborative approach may be a model for the development of AI that is explainable to, and trusted by clinicians. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIHR Imperil BRC ITMATDr Howard was additionally funded by Wellcome. Online collaborative platformResults of AI and experts

Background Left ventricular longitudinal strain has been reported to deliver reproducibility, sensitivity and prognostic value over and above ejection fraction. However, it currently relies on uninspectable proprietary algorithms and suffers from a lack of widespread clinical use. Uptake may be improved by increasing user trust through greater transparency. Purpose We therefore developed a machine-learning based method, trained, and validated with accredited experts from our AI Echocardiography Collaborative. We make the dataset, code, and trained network freely available under an open-source license. Methods AI enables strain to be calculated without relying on speckle tracking by directly locating key points and borders across frames. Strain can then be calculated as the fractional shortening of the left ventricular perimeter. We first curated a dataset of 7523 images, including 2587 apical four chamber, each labelled by a single expert from our collaboration of 17 hospitals, using our online platform (Figure 1). Using both this dataset and a semi-supervised approach, we trained a 3d convolutional neural network to identify the annulus, apex, and the endocardial border throughout the cardiac cycle. Separately, we constructed an external validation dataset of 100 apical 4 chamber video-loops. The systolic and diastolic frame were identified, and each image was separately labelled by 11 experts. From these labels we then derived the expert consensus strain for each of the 100 video loops. These experts also ordered all 100 echocardiograms by their visual grading of left ventricular longitudinal function. Finally, a single expert calculated strain using two different proprietary commercial packages (A and B). Results Consensus strain measurements (obtained by averaging individual assessments by the 11 experts) across the 100 cases ranged from −4% to −27%, with strong correlations with the individual experts and machine methods (Figure 2). Using each cases' consensus across experts as the gold standard, median error from consensus was 3.1% for individual experts, 3.4% for Propriety A, 2.6% for Proprietary B, 2.6% for our AI. Using the visual grading of longitudinal strain as the reference, the 11 individual experts and 4 machine methods each showed significant correlation: coefficients ranged from 0.55 to 0.69 for experts, and for Proprietary A was 0.68, Proprietary B 0.69, and our AI 0.69. Conclusions Our open-source, vendor-independent AI-based strain measure automatically produces values that agree with expert consensus, as strongly as the individual experts do. It also agrees with the subjective visual ranking by longitudinal function. Our open-source AI strain performs at least as well as closed-source speckle-based approaches, and may enable increased clinical and research use of longitudinal strain. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIHR Imperial BRC ITMAT.Dr Howard was additionally funded by Wellcome. Figure 1. Collaborative online platformFigure 2. Correlations between strain methods

During viral pandemics, filtering facepiece (FFP) masks together with eye protection form the essential components of personal protective equipment (PPE) for healthcare workers. There remain concerns regarding insufficient global supply and imperfect protection offered by currently available PPE strategies. A range of full-face snorkel masks were adapted to accept high grade medical respiratory filters using bespoke-designed 3D-printed connectors. We compared the protection offered by the snorkel to that of standard PPE using a placebo-controlled respirator filtering test as well as a fluorescent droplet deposition experiment. Out of the 56 subjects tested, 42 (75%) passed filtering testing with the snorkel mask compared to 31 (55%) with a FFP3 respirator mask (p = 0.003). Amongst the 43 subjects who were not excluded following a placebo control, 85% passed filtering testing with the snorkel versus to 68% with a FFP3 mask (p = 0.008). Following front and lateral spray of fluorescence liquid particles, the snorkel mask also provided superior protection against droplet deposition within the subject's face, when compared to a standard PPE combination of FFP3 masks and eye protection (3.19x108 versus 6.81x108 fluorescence units, p0.001). The 3D printable adaptors are available for free download online at https://www.ImperialHackspace.com/COVID-19-Snorkel-Respirator-Project/.Full-face snorkel masks adapted as particulate respirators performed better than a standard PPE combination of FFP3 mask and eye protection against aerosol inhalation and droplet deposition. This adaptation is therefore a promising PPE solution for healthcare workers during highly contagious viral outbreaks.

BackgroundArtificial intelligence (AI) technologies are increasingly used in clinical practice. Although there is robust evidence that AI innovations can improve patient care, reduce clinicians’ workload and increase efficiency, their impact on medical training and education remains unclear.MethodsA survey of trainee doctors’ perceived impact of AI technologies on clinical training and education was conducted at UK NHS postgraduate centers in London between October and December 2020. Impact assessment mirrored domains in training curricula such as ‘clinical judgement’, ‘practical skills’ and ‘research and quality improvement skills’. Significance between Likert-type data was analysed using Fisher’s exact test. Response variations between clinical specialities were analysed using k-modes clustering. Free-text responses were analysed by thematic analysis.ResultsTwo hundred ten doctors responded to the survey (response rate 72%). The majority (58%) perceived an overall positive impact of AI technologies on their training and education. Respondents agreed that AI would reduce clinical workload (62%) and improve research and audit training (68%). Trainees were skeptical that it would improve clinical judgement (46% agree,p = 0.12) and practical skills training (32% agree,p ConclusionsTrainee doctors have an overall positive perception of AI technologies’ impact on clinical training. There is optimism that it will improve ‘research and quality improvement’ skills and facilitate ‘curriculum mapping’. There is skepticism that it may reduce educational opportunities to develop ‘clinical judgement’ and ‘practical skills’. Medical educators should be mindful that these domains are protected as AI develops. We recommend that ‘Applied AI’ topics are formalized in curricula and digital technologies leveraged to deliver clinical education.

Background His bundle pacing (HBP) can be achieved in 2 ways: selective HBP (S-HBP), where the His bundle is captured alone, and nonselective HBP (NS-HBP), where local myocardium is also captured, resulting a pre-excited electrocardiogram appearance. Objective We assessed the impact of this ventricular pre-excitation on left and right ventricular dyssynchrony. Methods We recruited patients who displayed both S-HBP and NS-HBP. We performed noninvasive epicardial electrical mapping for left and right ventricular activation time (LVAT and RVAT) and pattern. Results Twenty patients were recruited. In the primary analysis, the mean within-patient change in LVAT from S-HBP to NS-HBP was -5.5 ms (95% confidence interval: -0.6 to -10.4, noninferiority P < .0001). NS-HBP did not prolong RVAT (4.3 ms, -4.0 to 12.8, P = .296) but did prolong QRS duration (QRSd, 22.1 ms, 11.8 to 32.4, P = .0003). In patients with narrow intrinsic QRS (n = 6), NS-HBP preserved LVAT (-2.9 ms, -9.7 to 4.0, P = .331) but prolonged QRS duration (31.4 ms, 22.0 to 40.7, P = .0003) and mean RVAT (16.8 ms, -5.3 to 38.9, P = .108) compared to S-HBP. Activation pattern of the left ventricular surface was unchanged between S-HBP and NS-HBP, but NS-HBP produced early basal right ventricular activation that was not seen in S-HBP. Conclusion Compared to S-HBP, local myocardial capture during NS-HBP produces pre-excitation of the basal right ventricle resulting in QRS duration prolongation. However, NS-HBP preserves the left ventricular activation time and pattern of S-HBP. Left ventricular dyssynchrony is not an important factor when choosing between S-HBP and NS-HBP in most patients., Graphical abstract

Supplemental Digital Content is available in the text., Background: Physiological assessment with pressure wire pullback can characterize coronary artery disease (CAD) with a focal or diffuse pattern. However, the clinical relevance of this distinction is unknown. We use data from the ORBITA trial (Objective Randomised Blinded Investigation With Optimal Medical Therapy of Angioplasty in Stable Angina) to test if the pattern of CAD predicts the placebo-controlled efficacy of percutaneous coronary intervention (PCI) on stress echocardiography ischemia and symptom end points. Methods: One hundred sixty-four patients in ORBITA underwent blinded instantaneous wave-free ratio (iFR) pullback assessment before randomization. Focal disease was defined as a ≥0.03 iFR unit drop within 15 mm, rather than over a longer distance. Analyses were performed using regression modeling. Results: In the PCI arm (n=85), 48 were focal and 37 were diffuse. In the placebo arm (n=79), 35 were focal and 44 were diffuse. Focal stenoses were associated with significantly lower fractional flow reserve (FFR) and iFR values than diffusely diseased vessels (mean FFR and iFR, focal 0.60±0.15 and 0.65±0.24, diffuse 0.78±0.10 and 0.88±0.08, respectively, P

Background: Right ventricular (RV) coupling to the pulmonary circulation influences the response of the RV to the increased afterload caused by pulmonary hypertension (PH), which ultimately determines prognosis. A methodology that accounts for pulsatile flow is required when assessing ventriculo-arterial coupling. We applied wave intensity analysis (WIA) methods to assess the compliance of the main pulmonary artery (PA) in patients with or without PH and compared this to PA distensibility, RV function and clinical outcomes. Methods: High-fidelity blood pressure and Doppler flow velocity tracings were obtained simultaneously during cardiac catheterisation for suspected PH. RV volumes, main PA distensibility and ventriculo-arterial coupling (Emax/Ea) were analysed using cardiovascular magnetic resonance. Results: The study included 17 ​PH patients and 6 controls. Wave speed, reservoir and excess pressure were higher in PH patients compared to controls (p ​

Results of trial and registry studies have shown lower long-term mortality after invasive management than after conservative management among patients with non-ST elevation myocardial infarction (NSTEMI). Elderly patients were majorly underrepresented in these previous studies. Using data from the

The clinical importance of troponin level in patients presenting with atrial fibrillation (AF) is uncertain. We investigated the relationship between troponin level, angiography and mortality in patients undergoing troponin testing with a primary diagnosis of AF. We used the National Institute for

N-of-1 Trial of a Statin, Placebo, or No Treatment Patients who had discontinued statins because of side effects received four bottles of a statin, four bottles of placebo, and four empty bottles, ...

Objectives: This study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping. Background: Damping of aortic pressure during coronary angiography must be identified to avoid serious complications and make accurate coronary physiology measurements. There are currently no automated methods to do this, and so identification of damping requires constant monitoring, which is prone to human error. Methods: The neural network was trained and tested versus core laboratory expert opinions derived from 2 separate datasets. A total of 5,709 aortic pressure waveforms of individual heart beats were extracted and classified. The study developed a recurrent convolutional neural network to classify beats as either normal, showing damping, or artifactual. Accuracies were reported using the opinions of 2 independent core laboratories. Results: The neural network was 99.4% accurate (95% confidence interval: 98.8% to 99.6%) at classifying beats from the testing dataset when judged against the opinions of the internal core laboratory. It was 98.7% accurate (95% confidence interval: 98.0% to 99.2%) when judged against the opinions of an external core laboratory not involved in neural network training. The neural network was 100% sensitive, with no beats classified as damped misclassified, with a specificity of 99.8%. The positive predictive and negative predictive values were 98.1% and 99.5%. The 2 core laboratories agreed more closely with the neural network than with each other. Conclusions: Arterial waveform analysis using neural networks allows rapid and accurate identification of damping. This demonstrates how machine learning can assist with patient safety and the quality control of procedures.

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Background; Left bundle area pacing is a novel technique that provides direct stimulation of cardiac conduction tissue in order to deliver physiological ventricular activation. The approach for left bundle area pacing is transseptal lead implantation, where the lead is advanced from the right ventricular side of the septum to the left ventricular side to capture the proximal left bundle. Observational data suggests that whilst this is a safe and feasible method, implant success rate is not 100%, and appears to be lower in patients with a cardiac resynchronization therapy (CRT) indication rather than a bradycardia indication for pacing. The mechanisms for failure to advance the lead through the ventricular septum are not well understood. Purpose; We used pre-procedural CMR to determine whether there are features which can help identify patients where lead implantation may be challenging. We assessed whether the extent and location of septal late gadolinium enhancement identified patients in whom left bundle area pacing will be challenging. We hypothesized that the presence of extensive scar in the septum impedes advancing the lead to the left ventricular septum and prevents capture of the left bundle. Methods; Patients underwent cardiac MRI including motion corrected free-breathing late gadolinium enhancement imaging1 before implantation. Scar was quantified using the full height half maximum method and expressed as the overall proportion of myocardial mass in the basal anteroseptal and basal inferoseptal segments, as shown in Figure 1. Left bundle area pacing was then attempted in patients with a CRT indication for pacing. We compared the extent of septal scar between patients in whom left bundle area pacing was achieved and those where there was failure to advance the lead deep into the septum. Results; 12 patients (11 male, 1 female), with average age 72 (IQR 63 to 78) and LVEF 30% (IQR 26 to 33) were studied. There was failure to advance the lead deep into the septum in 4 patients. There was a significantly higher basal septal scar burden in those patients where there was failure to advance the left bundle lead compared to those in which left bundle capture was achieved as shown in Figure 2 (median 55% and 5% respectively, p-value 0.02 by Wilcoxon signed rank test). Conclusion; The presence and extent of late gadolinium enhancement in the basal septum appears to be an important determinant of successful implantation of left bundle pacing lead using current implant technology. This may be because extensive septal scar prevents advancement of the pacing lead through the septum. Cardiac MRI before left bundle area pacing is likely to be useful in procedural planning.

Background We have previously shown that tissue Doppler assessments of left ventricular (LV) diastolic function predict cardiac events in a hypertensive population over a period of 4 years. These out-performed traditional echocardiographic measures in a well-treated hypertensive population. Purpose We aimed to test whether tissue Doppler assessment of LV diastolic function would predict cardiovascular (CV) mortality in the Hypertension Associated Cardiovascular Disease sub-study of the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT). Methods ASCOT was a multicentre randomised trial with a 2x2 factorial design. Inclusion criteria for the study included hypertension and three other CV risk factors, including male sex and age over 55. Protocols, including for echocardiography, have been detailed previously.This study comprised the 519 patients recruited to the St Mary’s Hospital site of the ASCOT study, who were followed for a median of 19 years with mortality flagged by the Office for National Statistics. We have used all reported deaths on or before 31st January 2019. CV deaths include deaths due to coronary heart disease (CHD), stroke and other CV causes. Echocardiography was performed one year after blood pressure control. mean tissue Doppler E’ was calculated as the average of septal, lateral and inferior wall measurements over three cycles. The ratio of the transmitral Doppler E wave velocity and the composite mean of E’ was used to calculate E/E’ ratio. Statistical analysis was performed using Python including multivariable Cox proportional hazards regression. A two-sided P-value

Background: The mechanisms underlying atrial fibrillation (AF) remain controversial. We developed Representation of Electrical Tracking or Origin-Mapping to characterize activation wavefronts by direction and uniformity, accumulating data as an orbital plot and analyzed as frequency histograms. We applied this technique to patients undergoing AF ablation to determine if AF activation is random. Methods: Patients undergoing persistent AF ablation were recruited, and an AFocusII was positioned at multiple left atrial locations and kept steady for 1 minute to collect electrograms. The AFocusII was returned to the original site and position after >10 minutes for a repeat 1-minute data collection. Data were exported to custom Representation of Electrical Tracking or Origin-Mapping software, and 30 seconds consecutive time windows at each location were studied using frequency histograms of wavefronts. R50 (the range in degrees containing 50% of the total activation) was used as a method to enable statistical comparisons of activation patterns. Electrogram characterization into categories of complex fractionated atrial electrograms by Ensite Precision was subjected to similar analysis. Results: Consecutive 30 seconds segments were studied at 161 locations in 18 pts. Mean overlap between frequency histograms was 79.5%±7.7 (95% CI, 78.3–80.7). Nine patients underwent delayed mapping at the same location, and mean overlap between the first 30 seconds and >10 minute interval was 73%±11.8 and 71.9%±13.6 for consecutive 30-second segments. Stability was confirmed using R50 (Bland-Altman mean difference: 0.87°; limits of agreement: −34.0 to 36.0; r =0.005; P =0.95). A greater variance in R50 was observed between different locations within a patient than the variance within the same locations (intraclass correlation=0.765; P P r =0.36, P Conclusions: There appears to be preferential activation patterns during persistent AF indicating spatiotemporal stability. This has important implication to our mechanistic understanding of persistent AF.

Supplemental Digital Content is available in the text., Background: Artificial intelligence (AI) for echocardiography requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. Methods: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729–0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379–0.661]), versus 2.2 mm for individuals (0.366 [0.288–0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly.

Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): British Heart Foundation Background Cardiac resynchronization therapy delivered via biventricular pacing is thought to improve haemodynamic function through resynchronization of ventricular activation. Biventricular pacing also improves ventricular filling by shortening atrioventricular delay. Quantifying the relative contributions of these two mechanisms requires atrioventricular delay to be altered while left bundle branch block is preserved. This occurs when the His bundle is paced at an output below the left bundle branch block correction threshold. Purpose We performed His bundle pacing with preservation of left bundle branch block to measure the relative contributions of atrioventricular delay shortening and ventricular resynchronisation to the overall haemodynamic benefit of biventricular pacing. Methods Patients with left bundle branch block referred for conventional cardiac resynchronization therapy with biventricular pacing were recruited. Using a high precision, beat-by-beat systolic blood pressure assessment protocol, we assessed the haemodynamic effects of biventricular pacing and temporary His bundle pacing with left bundle branch block preservation at a full range of atrioventricular delays. We used non-invasive epicardial mapping (ECGI) to assess left ventricular activation time. Left bundle branch block preservation was defined as Results In 19 patients, His bundle pacing with preservation of left bundle branch block produced a peak systolic blood pressure improvement of 5.1mmHg (95% confidence interval: 2.2 to 8.0, p = 0.0013) compared to AAI pacing. In 16 of these patients, biventricular pacing was performed and produced a peak systolic blood pressure improvement of 7.1mmHg (3.8 to 10.4, p Conclusion Biventricular pacing in left bundle branch block improves haemodynamic function through ventricular resynchronization and shortening of atrioventricular delay. The majority of benefit appears to be produced by atrioventricular delay shortening. When left bundle branch block is not corrected, His bundle pacing may still produce considerable haemodynamic improvement through this mechanism. Abstract Figure.

Purpose: Echocardiography is the most commonly used modality for assessing the heart in clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from different orientations and positions, thereby creating different viewpoints for assessing the cardiac function. The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis.\ud \ud Approach: In this study, convolutional neural networks are used for the automated identification of 14 different anatomical echocardiographic views (larger than any previous study) in a dataset of 8732 videos acquired from 374 patients. Differentiable architecture search approach was utilized to design small neural network architectures for rapid inference while maintaining high accuracy. The impact of the image quality and resolution, size of the training dataset, and number of echocardiographic view classes on the efficacy of the models were also investigated.\ud \ud Results: In contrast to the deeper classification architectures, the proposed models had significantly lower number of trainable parameters (up to 99.9% reduction), achieved comparable classification performance (accuracy 88.4% to 96%, precision 87.8% to 95.2%, recall 87.1% to 95.1%) and real-time performance with inference time per image of 3.6 to 12.6 ms.\ud \ud Conclusion: Compared with the standard classification neural network architectures, the proposed models are faster and achieve comparable classification performance. They also require less training data. Such models can be used for real-time detection of the standard views.

Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): NIHR Imperial Biomedical Research Centre Introduction Implantable Cardioverter-Defibrillators (ICDs) cannot distinguish between ventricular tachycardia (VT) with haemodynamic compromise from haemodynamically tolerated VT to ensure that therapies are delivered only when necessary. Currently, unnecessary therapies are reduced by longer duration thresholds and higher rate thresholds. This can result in ICDs withholding or delaying therapies during haemodynamically compromising VT while potentially still providing therapies during rapid or prolonged VT that is haemodynamically well tolerated. Laser doppler perfusion monitoring (LDPM) allows assessment of peripheral blood flow as a surrogate for haemodynamic status. We have previously demonstrated that laser doppler perfusion signals, analysed using an electro-mechanical coupling algorithm (SafeShock), can reliably identify loss of perfusion during ventricular fibrillation (VF), as well as discriminate VF from simulated lead fractures and T wave over-sensing. The utility of LDPM signals in VT, however, has not been established. Purpose In this study we assessed the utility of LDPM using the SafeShock algorithm to discriminate haemodynamically tolerated VT from VT with haemodynamic compromise. Methods Recruited participants underwent a rapid ventricular pacing protocol to simulate VT at different rates. Pacing was performed using the right ventricular lead of an implanted pacing device or a temporary pacing wire in the right ventricular apex. 3-lead ECG, blood pressure (either invasively using a radial artery catheter or non-invasively using beat-by-beat finometry) and LDPM signal were continuously recorded during the protocol. LDPM signals during simulated VT were analysed using the SafeShock electro-mechanical algorithm and compared to blood pressure change from baseline intrinsic rhythm to simulated VT. Results We obtained 588 recordings of simulated VT in 56 patients at rates of 100 bpm, 120 bpm, 140 bpm, 160 bpm, 180 bpm and 200 bpm. Percentage change in systolic blood pressure from baseline to VT correlated with LDPM-derived perfusion value during VT (Spearman’s Rho = 0.7786, p < 0.0001). Using a cut-off of 5 units, perfusion value predicted a 20% drop in systolic blood pressure in VT with an accuracy of 89.4% (sensitivity 94.8%, specificity 83.6%, p value Conclusions Peripheral perfusion measurements, analysed using an electro-mechanical algorithm, can accurately discriminate haemodynamically tolerated VT from VT with haemodynamic compromise. ICDs with integrated LDPM sensors and algorithms could make therapy decisions based on the circulatory status of patients with arrhythmias not just rate and duration parameters. This could reduce unnecessary therapies while facilitating prompt treatment of compromising arrhythmias. Abstract Figure 1

Funding Acknowledgements Type of funding sources: Other. Main funding source(s): BRAVO trial: BHF SP/10/002/28189, FS/10/038, FS/11/92/29122, FS/13/44/30291) National Institute for Health Research Imperial Biomedical Research Centre. HOPE-HF trial: British Heart Foundation (CS/15/3/31405, FS/13/44/30291, FS/15/53/31615, FS/14/27/30752, FS/10/038). Introduction The optimal atrioventricular (AV) delay for implantable cardiac devices can be derived by echocardiography or beat-by-beat blood pressure measurements. However, both of these approaches are labour intensive and neither could be incorporated into an implantable cardiac device for frequent repeated optimisations. Laser Doppler perfusion monitoring (LDPM) measures blood flow through tissue. LDPM has been miniaturised ready to be incorporated into future implantable cardiac devices. Purpose We studied if LDPM is a clinically reliable alternative method to blood-pressure measurements to determine optimal AV delay. Methods Data from 58 patients undergoing 94 clinical AVD optimisations using LDPM and simultaneous non-invasive beat-by-beat blood pressure was obtained. The optimal AV delay for each method and for each optimisation was determined using a curve of haemodynamic response to switching from AAI (reference state) to DDD (test state) at a series of AV delays (40, 80, 120, 160, 200, 240 ms). We then compared the derived optimal AV delays between the two measurement approaches. We also assessed the impact of the paced heart-rate on agreement between laser Doppler and Blood-Pressure derived optimal AV delays. Results The AV delay derived using LDPM was not clinically significant different from that derived by blood pressure changes. The median difference was -9ms (IQR -26 to 7, p = 0.05). Variability between the two methods was low (median absolute deviation 17ms). Optimisations performed at higher heart-rates resulted in a non-significant smaller difference between the LDPM and blood-pressure derived AV delays (median absolute deviation 12 vs 22 ms, p = 0.11). Conclusions Optimal AVDs derived from non-invasive blood-pressure or laser Doppler perfusion methods are clinically equivalent. The addition of laser Doppler to future implantable cardiac devices may enable devices to dynamically and reliably optimise AV delays. Abstract Figure 1

Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): British Heart Foundation Background: His bundle pacing can be achieved in two ways selective His bundle pacing, where the His bundle is captured alone, and non-selective His bundle pacing, where local myocardium is also captured resulting a pre-excited ECG appearance. We assessed the impact of this ventricular pre-excitation on left and right ventricular dys-synchrony. Methods We recruited patients who displayed both selective and non-selective His bundle pacing. We performed non-invasive epicardial electrical mapping to determine left and right ventricular activation times and patterns. Results In the primary analysis (n = 20, all patients), non-selective His bundle pacing did not prolong LVAT compared to select His bundle pacing by a pre-specified non-inferiority margin of 10ms (LVAT prolongation: -5.5ms, 95% confidence interval (CI): -0.6 to -10.4, non-inferiority p In patients with narrow intrinsic QRS (n = 6), non-selective His bundle pacing preserved left ventricular activation time (-2.9ms, 95%CI: -9.7 to 4.0, p = 0.331) but prolonged QRS duration (31.4ms, 95%CI: 22.0 to 40.7, p = 0.0003) and mean right ventricular activation time (16.8ms, 95%CI: -5.3 to 38.9, p = 0.108) compared to selective His bundle pacing. Activation pattern of the left ventricular surface was unchanged between selective and non-selective His bundle pacing. Non-selective His bundle pacing produced early basal right ventricular activation, which was not observed with selective His bundle pacing. Conclusions Compared to selective His bundle pacing, local myocardial capture during non-selective His bundle pacing produces right ventricular pre-excitation resulting in prolongation of QRS duration. However, non-selective His bundle pacing preserves the left ventricular activation time and pattern of selective His bundle pacing. When choosing between selective and non-selective His bundle pacing, left ventricular dyssynchrony is not an important factor. Abstract Figure: Selective vs Non-Selective HBP

Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): British Heart Foundation Imperial Centre of Research Excellence Background/Introduction ECG algorithms for identifying accessory pathway (AP) locations are inaccurate and difficult to use. Human expert interpretation is poorly reproducible. Artificial intelligence (AI) techniques such as machine learning can improve accuracy in classification tasks by eschewing theory-driven predictions. More reproducible and accurate AP localisation could shorten procedure time and personalise ablation consent. Purpose We developed a neural network to perform AP localisation using 12-lead ECGs. Its decision-making process was analysed to enable explainability of the neural network. Methods A convolutional neural network was trained on raw, digital, intra-procedural 12-lead ECGs of patients with manifest APs who underwent successful ablation. ECGs were labelled with AP locations as left-sided, septal or right-sided using procedure reports, fluoroscopy and electro-anatomical maps. Accuracy of the neural network was assessed via 4-fold cross-validation and was compared to the Arruda algorithm. Five cardiologists were also assessed for their accuracy in determining locations in sub-groups of cases. The neural network was retrospectively analysed to identify areas of ECGs most influential to its predictions using importance mapping. Results In 156 cases, accuracy of the neural network (92.9%) was significantly higher than the Arruda algorithm (76.9%; p The figure shows (A) architecture of the neural network, (B) accuracy of the neural network, Arruda algorithm and five cardiologists, (*, p = 0.05 – 0.01; **, p = 0.01 – 0.001; ***, p = 0.001 - 0.0001; ****, p Conclusion AI ECG interpretation allows accurate, reproducible prediction of AP locations, superior to conventional algorithms and human interpretation. Although AI decision-making is thought of as a ‘black box’, explanation visualisation techniques such as importance mapping allow humans to understand aspects of how a neural network make decisions. A prospectively validated neural network could be integrated into clinical practice to improve pre-procedural AP localisation. Abstract Figure. Summary of results

Background\ud Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, such as myocardial strain. Therefore, the ability to automatically detect frames of interest is highly desirable.\ud \ud Methods\ud We have developed deep neural networks, trained and tested on multi-centre patient data, for the accurate identification of end-diastolic and end-systolic frames in apical four-chamber 2D multibeat cine loop recordings of arbitrary length. Seven experienced cardiologist experts independently labelled the frames of interest, thereby providing infallible annotations, allowing for observer variability measurements.\ud \ud Results\ud When compared with the ground-truth, our model shows an average frame difference of −0.09 ± 1.10 and 0.11 ± 1.29 frames for end-diastolic and end-systolic frames, respectively. When applied to patient datasets from a different clinical site, to which the model was blind during its development, average frame differences of −1.34 ± 3.27 and −0.31 ± 3.37 frames were obtained for both frames of interest. All detection errors fall within the range of inter-observer variability: [-0.87, −5.51]±[2.29, 4.26] and [-0.97, −3.46]±[3.67, 4.68] for ED and ES events, respectively.\ud \ud Conclusions\ud The proposed automated model can identify multiple end-systolic and end-diastolic frames in echocardiographic videos of arbitrary length with performance indistinguishable from that of human experts, but with significantly shorter processing time.

Introduction During viral pandemics, filtering facepiece (FFP) masks together with eye protection form the essential components of personal protective equipment (PPE) for healthcare workers. There remain concerns regarding insufficient global supply and imperfect protection offered by currently available PPE strategies. A range of full-face snorkel masks were adapted to accept high grade medical respiratory filters using bespoke-designed 3D-printed connectors. We compared the protection offered by the snorkel to that of standard PPE using a placebo-controlled respirator filtering test as well as a fluorescent droplet deposition experiment. Out of the 56 subjects tested, 42 (75%) passed filtering testing with the snorkel mask compared to 31 (55%) with a FFP3 respirator mask (p = 0.003). Amongst the 43 subjects who were not excluded following a placebo control, 85% passed filtering testing with the snorkel versus to 68% with a FFP3 mask (p = 0.008). Following front and lateral spray of fluorescence liquid particles, the snorkel mask also provided superior protection against droplet deposition within the subject’s face, when compared to a standard PPE combination of FFP3 masks and eye protection (3.19x108 versus 6.81x108 fluorescence units, phttps://www.ImperialHackspace.com/COVID-19-Snorkel-Respirator-Project/. Conclusion Full-face snorkel masks adapted as particulate respirators performed better than a standard PPE combination of FFP3 mask and eye protection against aerosol inhalation and droplet deposition. This adaptation is therefore a promising PPE solution for healthcare workers during highly contagious viral outbreaks.

ObjectivesThe clinical impact of SARS-CoV-2 has varied across countries with varying cardiovascular manifestations. We review the cardiac presentations, in-hospital outcomes and development of cardiovascular complications in the initial cohort of SARS-CoV-2 positive patients at Imperial College Healthcare National Health Service Trust, UK.MethodsWe retrospectively analysed 498 COVID-19 positive adult admissions to our institute from 7 March to 7 April 2020. Patient data were collected for baseline demographics, comorbidities and in-hospital outcomes, especially relating to cardiovascular intervention.ResultsMean age was 67.4±16.1 years and 62.2% (n=310) were male. 64.1% (n=319) of our cohort had underlying cardiovascular disease (CVD) with 53.4% (n=266) having hypertension. 43.2%(n=215) developed acute myocardial injury. Mortality was significantly increased in those patients with myocardial injury (47.4% vs 18.4%, pConclusionHypertension, pre-existing CVD and acute myocardial injury were associated with increased in-hospital mortality in our cohort of COVID-19 patients. However, only a low number of patients required invasive cardiac intervention.

Recent randomised controlled trials, such as ISCHEMIA and ORBITA, have overturned most of what we were taught in medical school about hospital procedures considered necessary for patients with stable coronary artery disease. In this article, we discuss what these trials mean for physicians and patients considering revascularisation procedures with the hope of reducing the risk of death or alleviating angina.

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.

ObjectiveTo evaluate the cost-effectiveness of percutaneous coronary intervention (PCI) compared with placebo in patients with single-vessel coronary artery disease and angina despite anti-anginal therapy.DesignA cost-effectiveness analysis comparing PCI with placebo. A Markov model was used to measure incremental cost-effectiveness, in cost per quality-adjusted life-years (QALYs) gained, over 12 months. Health utility weights were estimated using responses to the EuroQol 5-level questionnaire, from the Objective Randomised Blinded Investigation with optimal medical Therapy of Angioplasty in stable angina trial and UK preference weights. Costs of procedures and follow-up consultations were derived from Healthcare Resource Group reference costs and drug costs from the National Health Service (NHS) drug tariff. Probabilistic sensitivity analysis was undertaken to test the robustness of results to parameter uncertainty. Scenario analyses were performed to test the effect on results of reduced pharmaceutical costs in patients undergoing PCI, and the effect of patients crossing over from placebo to PCI due to refractory angina within 12 months.SettingFive UK NHS hospitals.Participants200 adult patients with stable angina and angiographically severe single-vessel coronary artery disease on anti-anginal therapy.InterventionsAt recruitment, patients received 6 weeks of optimisation of medical therapy for angina after which they were randomised to PCI or a placebo procedure.Outcome measuresIncremental cost-effectiveness ratio (ICER) expressed as cost (in £) per QALY gained for PCI compared with placebo.ResultsThe estimated ICER is £90 218/QALY gained when using PCI compared with placebo in patients receiving medical treatment for angina due to single-vessel coronary artery disease. Results were robust under sensitivity analyses.ConclusionsThe ICER for PCI compared with placebo, in patients with single-vessel coronary artery disease and angina on anti-anginal medication, exceeds the threshold of £30 000 used by the National Institute of Health and Care Excellence when undertaking health technology assessment for the NHS context.Trial registration: The ORBITA study is registered with ClinicalTrials.gov, number NCT02062593.