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Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation
FUNDING ACKNOWLEDGEMENTS: Type of funding sources: None. INTRODUCTION: Left atrial cardiomyopathy (ACM) is invasively diagnosed by presence of low voltage substrate (LVS) in electro-anatomical mapping. ACM is associated with high (50%) AF recurrence rates after PVI, but also with increased risk for...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207173/ http://dx.doi.org/10.1093/europace/euad122.531 |
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author | Jadidi, A Pilia, N Huang, T Mueller-Edenborn, B Allgeier, J Nairn, D Loewe, A Westermann, D Arentz, T |
author_facet | Jadidi, A Pilia, N Huang, T Mueller-Edenborn, B Allgeier, J Nairn, D Loewe, A Westermann, D Arentz, T |
author_sort | Jadidi, A |
collection | PubMed |
description | FUNDING ACKNOWLEDGEMENTS: Type of funding sources: None. INTRODUCTION: Left atrial cardiomyopathy (ACM) is invasively diagnosed by presence of low voltage substrate (LVS) in electro-anatomical mapping. ACM is associated with high (50%) AF recurrence rates after PVI, but also with increased risk for de-novo AF and ischemic stroke. AIMS: We establish and compare ACM-diagnosis based on 12-lead-ECG analysis, using (A) digital amplified p-wave analysis during SR versus (B) a neural network trained to diagnose ACM using automatically determined sinus-p-wave features (duration, morphology) in 12-lead-ECG as inputs. METHODS: Left atrial (LA) voltage mapping was acquired during SR in 270 AF (50% paroxysmal; 43% female; age: 64+/-11years) prior to PVI. ACM was defined as presence of left atrial LVS<0.5mV at >2cm2 during SR, and was detected in 95/270 (35.2%) of patients. P-wave-analysis was assessed for outcome prediction in a prospective cohort of persistent AF patients undergoing rhythm monitoring using 7-day Holter at 6 and 12 months post PVI-only approach. RESULTS: The duration of amplified sinus-p-wave (APWD) >151ms (left top panel) enabled to diagnose ACM with an AUC 0.87 (sensitivity: 78% and specificity: 76%, left bottom panel). The accuracy of the AI-neural-network-derived ECG-analysis (using automatically determined APWD and p-morphology criteria) for ACM diagnosis was: AUC 0.85, sensitivity 74%, specificity 78%, accuracy: 77% (see right panel top and bottom). Application of AI-derived ACM-diagnosis on a prospective cohort of persistent AF patients undergoing PVI-only approach (n=58) enabled arrhythmia outcome prediction: Patients with vs. without ACM (based on AI-derived p-wave-analysis) had significantly higher arrhythmia recurrences at 12 months following PVI (46% vs. 23%, p=0.017). CONCLUSION: Both the measurement of the APWD and the automatic neural network-based p-wave-analysis enable diagnosis of individuals with left atrial cardiomyopathy with high accuracy in a large cohort of patients with paroxysmal and persistent AF. Diagnosis of ACM based on automatic neural-network-based P-wave analysis enables identification of patients at high risk for arrhythmia recurrence post PVI-only ablation approach. [Figure: see text] |
format | Online Article Text |
id | pubmed-10207173 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102071732023-05-25 Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation Jadidi, A Pilia, N Huang, T Mueller-Edenborn, B Allgeier, J Nairn, D Loewe, A Westermann, D Arentz, T Europace 38.3 - Artificial Intelligence (Machine Learning, Deep Learning) FUNDING ACKNOWLEDGEMENTS: Type of funding sources: None. INTRODUCTION: Left atrial cardiomyopathy (ACM) is invasively diagnosed by presence of low voltage substrate (LVS) in electro-anatomical mapping. ACM is associated with high (50%) AF recurrence rates after PVI, but also with increased risk for de-novo AF and ischemic stroke. AIMS: We establish and compare ACM-diagnosis based on 12-lead-ECG analysis, using (A) digital amplified p-wave analysis during SR versus (B) a neural network trained to diagnose ACM using automatically determined sinus-p-wave features (duration, morphology) in 12-lead-ECG as inputs. METHODS: Left atrial (LA) voltage mapping was acquired during SR in 270 AF (50% paroxysmal; 43% female; age: 64+/-11years) prior to PVI. ACM was defined as presence of left atrial LVS<0.5mV at >2cm2 during SR, and was detected in 95/270 (35.2%) of patients. P-wave-analysis was assessed for outcome prediction in a prospective cohort of persistent AF patients undergoing rhythm monitoring using 7-day Holter at 6 and 12 months post PVI-only approach. RESULTS: The duration of amplified sinus-p-wave (APWD) >151ms (left top panel) enabled to diagnose ACM with an AUC 0.87 (sensitivity: 78% and specificity: 76%, left bottom panel). The accuracy of the AI-neural-network-derived ECG-analysis (using automatically determined APWD and p-morphology criteria) for ACM diagnosis was: AUC 0.85, sensitivity 74%, specificity 78%, accuracy: 77% (see right panel top and bottom). Application of AI-derived ACM-diagnosis on a prospective cohort of persistent AF patients undergoing PVI-only approach (n=58) enabled arrhythmia outcome prediction: Patients with vs. without ACM (based on AI-derived p-wave-analysis) had significantly higher arrhythmia recurrences at 12 months following PVI (46% vs. 23%, p=0.017). CONCLUSION: Both the measurement of the APWD and the automatic neural network-based p-wave-analysis enable diagnosis of individuals with left atrial cardiomyopathy with high accuracy in a large cohort of patients with paroxysmal and persistent AF. Diagnosis of ACM based on automatic neural-network-based P-wave analysis enables identification of patients at high risk for arrhythmia recurrence post PVI-only ablation approach. [Figure: see text] Oxford University Press 2023-05-24 /pmc/articles/PMC10207173/ http://dx.doi.org/10.1093/europace/euad122.531 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | 38.3 - Artificial Intelligence (Machine Learning, Deep Learning) Jadidi, A Pilia, N Huang, T Mueller-Edenborn, B Allgeier, J Nairn, D Loewe, A Westermann, D Arentz, T Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation |
title | Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation |
title_full | Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation |
title_fullStr | Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation |
title_full_unstemmed | Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation |
title_short | Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation |
title_sort | comparison of amplified p-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation |
topic | 38.3 - Artificial Intelligence (Machine Learning, Deep Learning) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207173/ http://dx.doi.org/10.1093/europace/euad122.531 |
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