Conflicts of interest in electrophysiology and devices presentations.

AIMS: Industry collaboration with arrhythmia and devices research is common. However, this results in conflicts of interest (CoI) for researchers that should be disclosed. This study aimed to examine the quality of CoI disclosures in arrhythmia and devices presentations. METHODS: Recorded presentations from the Arrhythmia & Devices section of the ESC Annual Congress 2016-2020 were assessed. The number of words, conflicts, and time displayed was documented for CoI declarations. Meta-data including sponsorship by an industry partner, presenter sex, and institution were obtained. RESULTS: Of 1153 presentations assessed, 999 were suitable for inclusion. CoI statements were missing from 7.2% of presentations, and 58% reported ≥1 conflict. Those with conflicts spent less time-per-word on their disclosures (median 150 ms, interquartile range [IQR] 83-273 ms) compared with those without conflicts (median 250 ms, IQR 125-375 ms). One-in-eight presentations were sponsored (12.8%, n = 128). CoI statements were more likely to be missing in sponsored presentations (14.8%, n = 19) compared with non-sponsored presentations (6.1%, n = 53), P = 0.0003. Sponsored presentations contained a greater median number of CoIs (10, IQR 6-18) compared with non-sponsored sessions (1, IQR 0-5), P < 0.0001. Time-per-word spent on COI disclosures was 50% lower in sponsored sessions (125 ms, IQR 75-231 ms) compared with non-sponsored sessions (250 ms, IQR 125-375 ms), P < 0.0001. CONCLUSION: The majority of those presenting arrhythmia and devices research have CoIs to declare. Declarations were often missing or displayed for short periods of time. Presenters in sponsored sessions, while being more conflicted, had a lower standard of declaration suggesting a higher risk of potential bias which viewers had insufficient opportunity to assess.

Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation.

Cardiac resynchronisation therapy (CRT) is an effective treatment for patients with congestive heart failure and a wide QRS complex. However, up to 30% of patients are non-responders to therapy in terms of exercise capacity or left ventricular reverse remodelling. A number of controversies still remain surrounding patient selection, targeted lead implantation and optimisation of this important treatment. The development of biophysical models to predict the response to CRT represents a potential strategy to address these issues. In this article, we present how the personalisation of an electromechanical model of the myocardium can predict the acute haemodynamic changes associated with CRT. In order to introduce such an approach as a clinical application, we needed to design models that can be individualised from images and electrophysiological mapping of the left ventricle. In this paper the personalisation of the anatomy, the electrophysiology, the kinematics and the mechanics are described. The acute effects of pacing on pressure development were predicted with the in silico model for several pacing conditions on two patients, achieving good agreement with invasive haemodynamic measurements: the mean error on dP/dt(max) is 47.5±35mmHgs(-1), less than 5% error. These promising results demonstrate the potential of physiological models personalised from images and electrophysiology signals to improve patient selection and plan CRT.

Male gender and chronic obstructive pulmonary disease predict a poor clinical response in patients undergoing cardiac resynchronisation therapy.

AIMS: Current guidelines advocate cardiac resynchronisation therapy (CRT) in patients with class III/IV New York Heart Association (NYHA) heart failure, depressed left ventricular function and a broad QRS. However, a significant proportion of patients do not derive any benefit from CRT. The aim of this study was to identify clinical, electrocardiographic and echocardiographic predictors of response to CRT. METHODS: A retrospective analysis of patients undergoing CRT in our institution was performed. A favourable clinical response to CRT was defined as an improvement in NYHA Heart failure class of ≥ 1 and lack of hospitalisation with heart failure. Comparisons were made between responders and non-responders in terms of baseline characteristics and potential predictors of CRT response (QRS width, presence of left bundle branch block, atrial fibrillation, evidence of mechanical dyssynchrony on echocardiography and LV lead position). RESULTS: A total of 164 patients had full follow-up data. The mean follow-up was 293 days. Of patients undergoing CRT, 90 (58.9%) had a favourable clinical response to CRT. Predictors of a lack of clinical response to CRT were male gender (p = 0.012) and chronic obstructive pulmonary disease (COPD) (0.008). Pre-implant echocardiographic dyssynchrony assessment appeared not to predict response to CRT (p = 0.87); however, there was a trend towards a positive response in those patients with significant dyssynchrony (p = 0.09) defined as interventricular delay > 40 ms or maximal LV delay of > 80 ms. CONCLUSION: Male gender and coexisting COPD were shown to be independent predictors of non-response to CRT in this cohort of patients fulfilling current criteria for CRT.

A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures.

We describe a system for respiratory motion correction of MRI-derived roadmaps for use in X-ray guided cardiac catheterisation procedures. The technique uses a subject-specific affine motion model that is quickly constructed from a short pre-procedure MRI scan. We test a dynamic MRI sequence that acquires a small number of high resolution slices, rather than a single low resolution volume. Additionally, we use prior knowledge of the nature of cardiac respiratory motion by constraining the model to use only the dominant modes of motion. During the procedure the motion of the diaphragm is tracked in X-ray fluoroscopy images, allowing the roadmap to be updated using the motion model. X-ray image acquisition is cardiac gated. Validation is performed on four volunteer datasets and three patient datasets. The accuracy of the model in 3D was within 5mm in 97.6% of volunteer validations. For the patients, 2D accuracy was improved from 5 to 13mm before applying the model to 2-4mm afterwards. For the dynamic MRI sequence comparison, the highest errors were found when using the low resolution volume sequence with an unconstrained model.