期刊
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE
卷 10, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JTEHM.2021.3134160
关键词
Cardiology; electrophysiology; biomedical imaging; machine learning; biomarkers
资金
- National Cancer Institute of the National Institutes of Health [U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1]
- National Heart Lung and Blood Institute of the National Institutes of Health [R01HL111314]
- National Center for Research Resources [C06RR12463-01]
- U.S. Department of Defense (DOD) Prostate Cancer Idea Development Award
- DOD Lung Cancer Idea Development Award
- DOD Peer Reviewed Cancer Research Program [W81XWH-16-1-0329]
- Ohio Third Frontier Technology Validation Fund
- Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering
- American Heart Association Atrial Fibrillation Strategically Focused Research Network [18SFRN34110067, 18SFRN34170013]
- National Center for Advancing Translational Sciences of the National Institutes of Health, Clinical and Translational Science Collaborative of Cleveland [UL1TR002548, UL1RR024989]
- Cleveland Clinic Department of Cardiovascular Medicine Philanthropy Research Funds
- Tomsich Atrial Fibrillation Research Fund
- Cleveland Clinic Center of Excellence in Cardiovascular Translational Functional Genomics
This study aimed to identify radiomic and clinical features associated with post-ablation recurrence of atrial fibrillation (AF). Cardiac CT scans were analyzed to find pulmonary vein morphology that is associated with increased likelihood of AF recurrence within 1 year of catheter ablation. Predictive models based on radiomic and clinical features may help identify candidates with the greatest likelihood of successful outcome in AF ablation.
Objective: To identify radiomic and clinical features associated with post-ablation recurrence of AF, given that cardiac morphologic changes are associated with persistent atrial fibrillation (AF), and initiating triggers of AF often arise from the pulmonary veins which are targeted in ablation. Methods: Subjects with pre-ablation contrast CT scans prior to first-time catheter ablation for AF between 2014-2016 were retrospectively identified. A training dataset (D-1) was constructed from left atrial and pulmonary vein morphometric features extracted from equal numbers of consecutively included subjects with and without AF recurrence determined at 1 year. The top-performing combination of feature selection and classifier methods based on C-statistic was evaluated on a validation dataset (D-2), composed of subjects retrospectively identified between 2005-2010. Clinical models (M-C) were similarly evaluated and compared to radiomic (M-R) and radiomic-clinical models (M-RC), each independently validated on D-2. Results: Of 150 subjects in D-1, 108 received radiofrequency ablation and 42 received cryoballoon. Radiomic features of recurrence included greater right carina angle, reduced anterior-posterior atrial diameter, greater atrial volume normalized to height, and steeper right inferior pulmonary vein angle. Clinical features predicting recurrence included older age, greater BMI, hypertension, and warfarin use; apixaban use was associated with reduced recurrence. AF recurrence was predicted with radio-frequency ablation models on D2 subjects with C-statistics of 0.68, 0.63, and 0.70 for radiomic, clinical, and combined feature models, though these were not prognostic in patients treated with cryoballoon. Conclusions: Pulmonary vein morphology associated with increased likelihood of AF recurrence within 1 year of catheter ablation was identified on cardiac CT. Significance: Radiomic and clinical features-based predictive models may assist in identifying atrial fibrillation ablation candidates with greatest likelihood of successful outcome.
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