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Principal Investigator: John Barnard
Organization: CLEVELAND CLINIC LERNER COM-CWRU
Fiscal Year: 2024
Award: $716,119
Funding agency: National Heart Lung and Blood Institute
PROJECT SUMMARY
Although ablation to isolate pulmonary vein (PV) triggers has revolutionized atrial fibrillation (AF) management,
performing effective AF ablation remains challenging. The procedure remains limited by targeting of ill-defined
substrates, a 2-6% risk of major complications and limited success (single procedure 5-year success as low as
17-56%; 63-81% after the last ablation). A major recommendation of a recent NHLBI-sponsored report on the
research needs and priorities for AF catheter ablation was to study how cardiac structure affects AF ablation
success. There is a clear unmet need for non-invasive imaging tools to aid in improved patient selection,
anatomic targeting and personalization of ablation or medical therapies. Our team has developed novel
computational imaging (radiomics) methods to analyze cardiac computed tomography (CT) scans that were
shown to predict the risk of recurrent AF post-ablation (AUC=0.84, N=167). These approaches included novel
morphologic, fractal and atlas based features that teased out differences between PVs and the left atrial
appendage (LAA), solely from analyses of CT scans. We propose to build upon our preliminary data using
radiomic (computer extracted) features from radiographic images to use supervised and unsupervised machine
learning methods that can analyze digitized radiographic and electro-anatomic images from the left atrium (LA)
and PVs in over 2000 patients from two large AF ablation centers (Cleveland Clinic, Vanderbilt). Our project
will focus on tackling the following main objectives: 1) Identify, evaluate and validate radiomic features and
imaging-clinical nomograms predictive of recurrent AF after ablation; 2) Identify and validate regional radiomic
sites predictive of post-ablation AF recurrence with the goal of identifying personalized targets for patients
undergoing AF ablation; and 3) Identify biological correlates of radiomic features to understand the
arrhythmogenic mechanisms underlying anatomic susceptibility to recurrent AF, using genomic analyses. Our
3 aims will test the following hypotheses: 1) Radiographic imaging can detect anatomic features that predict AF
recurrence after ablation; 2) Regional radiomic features can predict sites that can be considered for additional
ablation; and 3) Radiomic morphologic features are correlated with electroanatomic features and genomic
variants associated with AF susceptibility. Tools developed will enable integration of radiographic and clinical
data that may lead to improved patient selection, anatomic targeting and personalization of ablation or medical
therapies. Successful project completion will yield a novel artificial intelligence-based imaging platform that can
be tested for personalized targeting of AF ablation, as well as insights into the biologic basis of AF.
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