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Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.

To assess whether electronic (e-) coaching, using personalized web-based lifestyle and risk factor counselling with additional email prompts, provides additional risk reduction when added to standard of care (SOC) in individuals at increased risk. Between June 2013 and May 2015, 402 participants were allocated 1:1 to e-coaching and SOC versus SOC. Participants free of manifest cardiovascular disease, with internet access, and a 10-year QRISK2 cardiovascular risk of ≥10% were enrolled. Change in oscillometric carotid-femoral pulse wave velocity (PWV) from baseline to six months was the primary endpoint. Secondary outcomes included change in blood pressure (BP), weight, and risk scores. Analysis was by intention to treat. Mean (±SD) age was 65.5 (5.6) years with 37% females. Primary outcome data were available for 94%. There was no difference in PWV reductions between e-coaching and standard of care groups (-0.16 m/s vs. -0.25 m/s, 95% confidence interval -0.39 to 0.22, p = 0.56). There were no differences in the improvement between groups for BP, weight, Framingham, or QRISK2 scores. Pulse wave velocity change was more favorable in those with a higher level of education (p = 0.04), but was not associated with age, gender, presence of diabetes, baseline QRISK2 score, or logins to the website. In individuals at increased cardiovascular risk, a comprehensive 'health check' program modestly reduced future risk. Personalized e-coaching did not provide added risk reduction. Currently there is no evidence to routinely recommend e-coaching in cardiovascular health check programs. HAPPY London ClinicalTrials.gov: NCT01911910.

Background: Pericardial adipose tissue (PAT) may represent a novel risk marker for cardiovascular disease. However, absence of rapid radiation-free PAT quantification methods has precluded its examination in large cohorts. Objectives: We developed a fully automated quality-controlled tool for cardiovascular magnetic resonance (CMR) PAT quantification in the UK Biobank (UKB). Methods: Image analysis comprised contouring an en-bloc PAT area on four-chamber cine images. We created a ground truth manual analysis dataset randomly split into training and test sets. We built a neural network for automated segmentation using a Multi-residual U-net architecture with incorporation of permanently active dropout layers to facilitate quality control of the model's output using Monte Carlo sampling. We developed an in-built quality control feature, which presents predicted Dice scores. We evaluated model performance against the test set ( n = 87), the whole UKB Imaging cohort ( n = 45,519), and an external dataset ( n = 103). In an independent dataset, we compared automated CMR and cardiac computed tomography (CCT) PAT quantification. Finally, we tested association of CMR PAT with diabetes in the UKB ( n = 42,928). Results: Agreement between automated and manual segmentations in the test set was almost identical to inter-observer variability (mean Dice score = 0.8). The quality control method predicted individual Dice scores with Pearson r = 0.75. Model performance remained high in the whole UKB Imaging cohort and in the external dataset, with medium–good quality segmentation in 94.3% (mean Dice score = 0.77) and 94.4% (mean Dice score = 0.78), respectively. There was high correlation between CMR and CCT PAT measures (Pearson r = 0.72, p -value 5.3 ×10 −18 ). Larger CMR PAT area was associated with significantly greater odds of diabetes independent of age, sex, and body mass index. Conclusions: We present a novel fully automated method for CMR PAT quantification with good model performance on independent and external datasets, high correlation with reference standard CCT PAT measurement, and expected clinical associations with diabetes.

The incidence and prevalence of valvular heart disease (VHD) is increasing and has been described as the next cardiac epidemic. Advances in imaging and therapeutics have revolutionized how we assess and treat patients with VHD. Although echocardiography continues to be the first-line imaging modality to assess the severity and the effects of VHD, advances in cardiac computed tomography (CT) now provide novel insights into VHD. Transcatheter valvular interventions rely heavily on CT guidance for procedural planning, predicting and detecting complications, and monitoring prosthesis. This review focuses on the current role and future prospects of CT in the assessment of aortic and mitral valves for transcatheter interventions, prosthetic valve complications such as thrombosis and endocarditis, and assessment of the myocardium.

ObjectivesThis study evaluates predictors of conduction abnormalities (CA) following transcatheter aortic valve implantation (TAVI) in patients with bicuspid aortic valves (BAV).BackgroundTAVI is associated with CA that commonly necessitate a permanent pacemaker. Predictors of CA are well established among patients with tricuspid aortic valves but not in those with BAV.MethodsThis is a single-centre, retrospective, observational study of patients with BAV treated with TAVI. Pre-TAVI ECG and CT scans and procedural characteristics were evaluated in 58 patients with BAV. CA were defined as a composite of high-degree atrioventricular block, new left bundle branch block with a QRS >150 ms or PR >240 ms and right bundle branch block with new PR prolongation or change in axis. Predictors of CA were identified using regression analysis and optimum cut-off values determined using area under the receiver operating characteristic curve analysis.ResultsCA occurred in 35% of patients. Bioprosthesis implantation depth, the difference between membranous septum (MS) length and implantation depth (δMSID) and device landing zone (DLZ) calcification adjacent to the MS were identified as univariate predictors of CA. The optimum cut-off for δMSID was 1.25 mm. Using this cut-off, low δMSID and DLZ calcification adjacent to MS predicted CA, adjusted OR 8.79, 95% CI 1.88 to 41.00; p=0.01. Eccentricity of the aortic valve annulus, type of BAV and valve calcium quantity and distribution did not predict CA.ConclusionsIn BAV patients undergoing TAVI, short δMSID and DLZ calcification adjacent to MS are associated with an increased risk of CA.

The coronavirus disease-2019 (COVID-19) pandemic has had an unprecedented impact leading to novel adaptations in post-graduate medical education for cardiovascular and general internal medicine. Whilst the results of initial community COVID-19 vaccination are awaited, continuation of multimodality teaching and training that incorporates telelearning will have enduring benefit to post-graduate education and will place educational establishments in good stead to nimbly respond in future pandemic-related public health emergencies. With the rise in innovative virtual learning solutions, medical educators will have to leverage technology to develop electronic educational materials and virtual courses that facilitate adult learning. Technology-enabled virtual learning is thus a timely progression of hybrid classroom initiatives that are already adopted to varying degrees, with a need for faculty to serve as subject matter experts, to host and moderate online discussions, and to provide feedback and overall mentorship. As an extension from existing efforts, simulation-based teaching (SBT) and learning and the use of mixed reality technology should also form a greater core in the cardiovascular medicine curriculum. We highlight five foundational themes for building a successful e-learning model in cardiovascular and general post-graduate medical training: (1) digital solutions and associated infrastructure; (2) equity in access; (3) participant engagement; (4) diversity and inclusion; and (5) patient confidentiality and governance framework. With digitalisation impacting our everyday lives and now how we teach and train in medicine, these five guiding principles provide a cognitive scaffold for careful consideration of the required ecosystem in which cardiovascular and general post-graduate medical education can effectively operate. With due consideration of various e-learning options and associated infrastructure needs; and adoption of strategies for participant engagement under sound and just governance, virtual training in medicine can be effective, inclusive and equitable through the COVID-19 era and beyond.

Objectives: To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). Methods: We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Results: Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Conclusions: Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up.

A 40-year-old woman presented to our outpatient clinic for routine follow-up. 6 years earlier she had been diagnosed with a stenotic bicuspid aortic valve, which was treated with a 25 mm bioprosthesis implantation.

Patients with chronic kidney disease (CKD) are at risk of contrast-induced nephropathy (CIN) if given iodinated contrast. [...]recently, there has been little or no known risk with gadolinium, but since the description of nephrogenic systemic fibrosis (NSF), 3 4 a rare disorder that occurs in some patients with CKD receiving gadolinium, relative risks of these contrast agents have become less clear ( table 1 ). Gadolinium Iodinated contrast (Gadolinium) Previous exposures to gadolinium (total lifetime dose) Severe 'proinflammatory' events at time of administration (forms of tissue injury-eg, surgery, sepsis/major infection, thromboembolic events associated with organ or limb ischaemia) Diabetes Dehydration Nephrotoxic drugs Large contrast volumes Multiple myeloma Advanced age Typically begins with swelling of the lower limbs followed by skin induration Fibrotic changes may affect muscles, heart, liver and lungs, which may account for an increased mortality of these patients 4 Increase in serum creatinine of 0.5 mg/dl (44 μmol/l) in the 3 days following contrast injection In patients with advanced CKD, even small changes in serum creatinine can represent significant decrease in the GFR.