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  • Joung, Boyoung  (4)
  • Kim, Young-Hoon  (4)
  • 1
    Online Resource
    Online Resource
    Clinical Usefulness of Virtual Ablation Guided Catheter Ablation of Atrial Fibrillation Targeting Restitution Parameter-Guided Catheter Ablation: CUVIA-REGAB Prospective Randomized Study
    XMLink ; 2022
    In:  Korean Circulation Journal Vol. 52, No. 9 ( 2022), p. 699-
    Details
    In: Korean Circulation Journal, XMLink, Vol. 52, No. 9 ( 2022), p. 699-
    Type of Medium: Online Resource
    ISSN: 1738-5520 , 1738-5555
    URL: Article
    DOI: 10.4070/kcj.2022.0113
    Language: English
    Publisher: XMLink
    Publication Date: 2022
    detail.hit.zdb_id: 2528698-5
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  • 2
    Online Resource
    Online Resource
    Image_3.tif
    Frontiers Media SA ; 2022
    In:  Frontiers in Cardiovascular Medicine Vol. 9 ( 2022-2-16)
    Details
    In: Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 9 ( 2022-2-16)
    Abstract: We developed a prediction model for atrial fibrillation (AF) progression and tested whether machine learning (ML) could reproduce the prediction power in an independent cohort using pre-procedural non-invasive variables alone. Methods Cohort 1 included 1,214 patients and cohort 2, 658, and all underwent AF catheter ablation (AFCA). AF progression to permanent AF was defined as sustained AF despite repeat AFCA or cardioversion under antiarrhythmic drugs. We developed a risk stratification model for AF progression (STAAR score) and stratified cohort 1 into three groups. We also developed an ML-prediction model to classify three STAAR risk groups without invasive parameters and validated the risk score in cohort 2. Results The STAAR score consisted of a stroke (2 points, p = 0.003), persistent AF (1 point, p = 0.049), left atrial (LA) dimension ≥43 mm (1 point, p = 0.010), LA voltage & lt;1.109 mV (2 points, p = 0.004), and PR interval ≥196 ms (1 point, p = 0.001), based on multivariate Cox analyses, and it had a good discriminative power for progression to permanent AF [area under curve (AUC) 0.796, 95% confidence interval (CI): 0.753–0.838]. The ML prediction model calculated the risk for AF progression without invasive variables and achieved excellent risk stratification: AUC 0.935 for low-risk groups (score = 0), AUC 0.855 for intermediate-risk groups (score 1–3), and AUC 0.965 for high-risk groups (score ≥ 4) in cohort 1. The ML model successfully predicted the high-risk group for AF progression in cohort 2 (log-rank p & lt; 0.001). Conclusions The ML-prediction model successfully classified the high-risk patients who will progress to permanent AF after AFCA without invasive variables but has a limited discrimination power for the intermediate-risk group.
    Type of Medium: Online Resource
    ISSN: 2297-055X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fcvm.2022.813914
    DOI: 10.3389/fcvm.2022.813914.s001
    DOI: 10.3389/fcvm.2022.813914.s002
    DOI: 10.3389/fcvm.2022.813914.s003
    DOI: 10.3389/fcvm.2022.813914.s004
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2781496-8
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  • 3
    Online Resource
    Online Resource
    Data_Sheet_1.docx
    Frontiers Media SA ; 2021
    In:  Frontiers in Physiology Vol. 12 ( 2021-7-2)
    Details
    In: Frontiers in Physiology, Frontiers Media SA, Vol. 12 ( 2021-7-2)
    Abstract: 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 & lt; 0.001), diabetes ( p = 0.012), vascular disease ( p = 0.002), body mass index ( p & lt; 0.001), E/Em ( p & lt; 0.001), and mean LA voltage measured by electrogram voltage mapping ( p & lt; 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 & lt; 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.
    Type of Medium: Online Resource
    ISSN: 1664-042X
    URL: Article
    URL: Article
    DOI: 10.3389/fphys.2021.686507
    DOI: 10.3389/fphys.2021.686507.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2564217-0
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  • 4
    Online Resource
    Online Resource
    Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence
    BMJ ; 2022
    In:  Open Heart Vol. 9, No. 1 ( 2022-01), p. e001898-
    Details
    In: Open Heart, BMJ, Vol. 9, No. 1 ( 2022-01), p. e001898-
    Abstract: We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN). Methods This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed and repeated five times after data shuffling. Results The CNN-GWAS predictive power for AF had an area under the curve (AUC) of 0.78±0.01 based on the Japanese GWAS, AUC of 0.79±0.01 based on the European GWAS, and AUC of 0.82±0.01 based on the multiethnic GWAS, respectively. Gradient-weighted class activation mapping assigned high saliency scores for AF associated SNPs, and the PITX2 obtained the highest saliency score. The CNN-GWAS did not show AF prediction power by SNPs with non-significant p value subset (AUC 0.56±0.01) despite larger numbers of SNPs. The CNN-GWAS had no prediction power for odd–even registration numbers (AUC 0.51±0.01). Conclusions AF can be predicted by genetic information alone with moderate accuracy. The CNN-GWAS can be a robust and useful tool for detecting polygenic diseases by capturing the cumulative effects and genetic interactions of moderately associated but statistically significant SNPs. Trial registration number NCT02138695 .
    Type of Medium: Online Resource
    ISSN: 2053-3624
    URL: Article
    DOI: 10.1136/openhrt-2021-001898
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 2747269-3
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