Using electronic health records and continuous intra-cardiac electrogram monitoring to understand cardiovascular outcomes in patients with pacemakers and defibrillators

Background: After pacemaker implant, atrial fibrillation (AF) and heart failure (HF) are common and require prompt treatment. For implantable cardioverter-defibrillator (ICD) recipients, most do not benefit, nor can we identify who receives recurrent shocks for ventricular arrhythmia (VA). Devices continuously record intra-cardiac electrogram waveforms (EGMs), and their potential as biomarkers of arrhythmogenesis and myocardial remodelling has not yet been exploited. Aim: Identify clinically useful biomarkers for progressive cardiac disease in device recipients, applying machine learning methods to routinely acquired data. Methods: I will adopt a unique approach leveraging national electrophysiology audit data (n=310,974) and device-based physiological sensor measurements from the largest UK device population (n=19,864, >250,000 EGMs). To inform better ICD implant selection, I will evaluate competing arrhythmia and non-arrhythmia mortality, and identify contemporary long-term mortality trends. Detailed rule-based and machine learning analysis will derive EGM biomarkers, validated against surface electrocardiograms. I will identify features predictive of mortality, VA, and precursors to AF, externally validating models with two centres. Using EGM surrogates of mechanoelectrical feedback, I will perform serial assessments to characterise structural and electrophysiological remodelling in pacing-induced HF. Outputs: Refining ICD implant criteria. Biomarkers harnessing ‘wearables’-type data from remote monitoring to reduce VA, stroke and HF morbidity.