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Left Atrial Wall Stress and the Long-Term Outcome of Catheter Ablation of Atrial Fibrillation: An Artificial Intelligence-Based Prediction of Atrial Wall Stress

 Jae-Hyuk Lee  ;  Oh-Seok Kwon  ;  Jaemin Shim  ;  Jisu Lee  ;  Hee-Jin Han  ;  Hee Tae Yu  ;  Tae-Hoon Kim  ;  Jae-Sun Uhm  ;  Boyoung Joung  ;  Moon-Hyoung Lee  ;  Young-Hoon Kim  ;  Hui-Nam Pak 
 FRONTIERS IN PHYSIOLOGY, Vol.12 : 686507, 2021-07 
Journal Title
Issue Date
artificial intelliegnce ; atrial fibrillation ; atrial wall stress ; catheter ablation ; rhythm outcome
Atrial stretch may contribute to the mechanism of atrial fibrillation (AF) recurrence after atrial fibrillation catheter ablation (AFCA). We tested whether the left atrial (LA) wall stress (LAW-stress[measured]) could be predicted by artificial intelligence (AI) using non-invasive parameters (LAW-stress[AI]) and whether rhythm outcome after AFCA could be predicted by LAW-stress[AI] in an independent cohort. Cohort 1 included 2223 patients, and cohort 2 included 658 patients who underwent AFCA. LAW-stress[measured] was calculated using the Law of Laplace using LA diameter by echocardiography, peak LA pressure measured during procedure, and LA wall thickness measured by customized software (AMBER) using computed tomography. The highest quartile (Q4) LAW-stress[measured] was predicted and validated by AI using non-invasive clinical parameters, including non-paroxysmal type of AF, age, presence of hypertension, diabetes, vascular disease, and heart failure, left ventricular ejection fraction, and the ratio of the peak mitral flow velocity of the early rapid filling to the early diastolic velocity of the mitral annulus (E/Em). We tested the AF/atrial tachycardia recurrence 3 months after the blanking period after AFCA using the LAW-stress[measured] and LAW-stress[AI] in cohort 1 and LAW-stress[AI] in cohort 2. LAW-stress[measured] was independently associated with non-paroxysmal AF (p < 0.001), diabetes (p = 0.012), vascular disease (p = 0.002), body mass index (p < 0.001), E/Em (p < 0.001), and mean LA voltage measured by electrogram voltage mapping (p < 0.001). The best-performing AI model had acceptable prediction power for predicting Q4-LAW-stress[measured] (area under the receiver operating characteristic curve 0.734). During 26.0 (12.0-52.0) months of follow-up, AF recurrence was significantly higher in the Q4-LAW-stress[measured] group [log-rank p = 0.001, hazard ratio 2.43 (1.21-4.90), p = 0.013] and Q4-LAW-stress[AI] group (log-rank p = 0.039) in cohort 1. In cohort 2, the Q4-LAW-stress[AI] group consistently showed worse rhythm outcomes (log-rank p < 0.001). A higher LAW-stress was associated with poorer rhythm outcomes after AFCA. AI was able to predict this complex but useful prognostic parameter using non-invasive parameters with moderate accuracy.
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1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kwon, Oh-Seok(권오석)
Kim, Tae-Hoon(김태훈) ORCID logo https://orcid.org/0000-0003-4200-3456
Pak, Hui Nam(박희남) ORCID logo https://orcid.org/0000-0002-3256-3620
Uhm, Jae Sun(엄재선) ORCID logo https://orcid.org/0000-0002-1611-8172
Yu, Hee Tae(유희태) ORCID logo https://orcid.org/0000-0002-6835-4759
Lee, Moon-Hyoung(이문형) ORCID logo https://orcid.org/0000-0002-7268-0741
Lee, Jae Hyuk(이재혁)
Joung, Bo Young(정보영) ORCID logo https://orcid.org/0000-0001-9036-7225
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