Sudden cardiac death. Research Program on Artificial Intelligence Applied to Cardiac Rhythm Disorders.
The identification of individuals at
long-term risk of sudden cardiac death (SCD) is a difficult task. Our research
group has been focusing on pre-emptive risk stratification and prevention of
SCD by identifying sentinel events which frequently precede SCD, a task
facilitated by recent advances in communication and remote transmission
technologies. We hypothesise that it is possible to identify a high-risk group
of subjects at risk of SCD and thus apply immediate preventive therapies.
Application of artificial intelligence to continuous device monitoring may help
identify specific patterns prior to the occurrence of a fatal event. The DAI-PP
program (centralised at HEGP) has been initiated with a 2-year enrolment and
5-year follow-up of more than 5000 patients with implantable
cardioverter-defibrillators and remote continuous monitoring. Furthermore,
systematic collection of digital electrocardiograms is part of the prospective
Paris-Sudden Death Expertise Center registry, which collects all SCD since May
2011. An improved understanding of electrical signals and their dynamics prior
to the occurrence of a fatal arrhythmic event may allow a better triage and
identification of patients at high-risk of events.
My portfolio includes a number of high-impact publications in the field of SCD prediction in the heart failure setting, most of which focusing on patients with clinical indication for implantable cardioverter-defibrillators or cardiac resynchronization therapy devices. Most of this work was developed while I was working at the Royal Papworth Hospital NHS Trust, in Cambridge, UK, and done in collaboration with Professors Eloi Marijon and Serge Boveda.