Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices.

AIMS: Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target either employ invasive electrophysiological (EP) mapping or non-invasive modalities utilizing the electrocardiogram (ECG). Their success relies on localizing sites critical to the maintenance of the clinical arrhythmia, not always recorded on the 12-lead ECG. Targeting the clinical VT by utilizing electrograms (EGM) recordings stored in implanted devices may aid ablation planning, enhancing safety and speed and potentially reducing the need of VT induction. In this context, we aim to develop a non-invasive computational-deep learning (DL) platform to localize VT exit sites from surface ECGs and implanted device intracardiac EGMs. METHODS AND RESULTS: A library of ECGs and EGMs from simulated paced beats and representative post-infarct VTs was generated across five torso models. Traces were used to train DL algorithms to localize VT sites of earliest systolic activation; first tested on simulated data and then on a clinically induced VT to show applicability of our platform in clinical settings. Localization performance was estimated via localization errors (LEs) against known VT exit sites from simulations or clinical ablation targets. Surface ECGs successfully localized post-infarct VTs from simulated data with mean LE = 9.61 ± 2.61 mm across torsos. VT localization was successfully achieved from implanted device intracardiac EGMs with mean LE = 13.10 ± 2.36 mm. Finally, the clinically induced VT localization was in agreement with the clinical ablation volume. CONCLUSION: The proposed framework may be utilized for direct localization of post-infarct VTs from surface ECGs and/or implanted device EGMs, or in conjunction with efficient, patient-specific modelling, enhancing safety and speed of ablation planning.

Specialist valve clinic in a cardiac centre: 10-year experience.

Aims: Guidelines recommend specialist valve clinics as best practice for the assessment and conservative management of patients with heart valve disease. However, there is little guidance on how to set up and organise a clinic. The aim of this study is to describe a clinic run by a multidisciplinary team consisting of cardiologists, physiologist/scientists and a nurse. Methods: The clinical and organisational aims of the clinic, inclusion and exclusion criteria, and links with other services are described. The methods of training non-clinical staff are detailed. Data were prospectively entered onto a database and the study consisted of an analysis of the clinical characteristics and outcomes of all patients seen between 1 January 2009 and 31 December 2018. Results: There were 2126 new patients and 9522 visits in the 10-year period. The mean age was 64.8 and 55% were male. Of the visits, 3587 (38%) were to the cardiologists, 4092 (43%) to the physiologist/scientists and 1843 (19%) to the nurse. The outcomes from the cardiologist clinics were cardiology follow-up in 460 (30%), referral for surgery in 354 (23%), referral to the physiologist/scientist clinic in 412 (27%) or to the nurse clinic in 65 (4.3%) and discharge in 230 (15%). The cardiologist needed to see 6% from the nurse clinic and 10% from the physiologist/scientist clinic, while advice alone was sufficient in 10% and 9%. Conclusion: A multidisciplinary specialist valve clinic is feasible and sustainable in the long term.

Secondary mitral regurgitation: pathophysiology, proportionality and prognosis.

Secondary mitral regurgitation (SMR) occurs as a result of multifactorial left atrioventricular dysfunction and maleficent remodelling. It is the most common and undertreated form of mitral regurgitation (MR) and is associated with a very poor prognosis. Whether SMR is a bystander reflecting the severity of the cardiomyopathy disease process has long been the subject of debate. Studies suggest that SMR is an independent driver of prognosis in patients with an intermediate heart failure (HF) phenotype and not those with advanced HF. There is also no universal agreement regarding the quantitative thresholds defining severe SMR and indeed there are challenges with echocardiographic quantification. Until recently, no surgical or transcatheter intervention for SMR had demonstrated prognostic benefit, in contrast with HF medical therapy and cardiac resynchronisation therapy. In 2018, the first two randomised controlled trials (RCTs) of edge-to-edge transcatheter mitral valve repair versus guideline-directed medical therapy in HF (Percutaneous Repair with the MitraClip Device for Severe (MITRA-FR), Transcather mitral valve repair in patients with heart failure (COAPT)) reported contrasting yet complimentary results. Unlike in MITRA-FR, COAPT demonstrated significant prognostic benefit, largely attributed to the selection of patients with disproportionately severe MR relative to their HF phenotype. Consequently, quantifying the degree of SMR in relation to left ventricular volume may be a useful discriminator in predicting the success of transcatheter intervention. The challenge going forward is the identification and validation of such parameters while in parallel maintaining a heart-team guided holistic approach.