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Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety
BACKGROUND: During slow pathway modification for atrioventricular nodal reentrant tachycardia, heart block may occur if ablation cannot be stopped in time in response to high risk electrogram features (HREF). OBJECTIVES: To develop an automatic algorithm to monitor HREF and terminate ablation earlie...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352454/ https://www.ncbi.nlm.nih.gov/pubmed/37469484 http://dx.doi.org/10.3389/fcvm.2023.1212837 |
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author | Tam, Tsz Kin Lai, Angel Chan, Joseph Y. S. Au, Alex C. K. Chan, Chin Pang Cheng, Yuet Wong Yan, Bryan P. |
author_facet | Tam, Tsz Kin Lai, Angel Chan, Joseph Y. S. Au, Alex C. K. Chan, Chin Pang Cheng, Yuet Wong Yan, Bryan P. |
author_sort | Tam, Tsz Kin |
collection | PubMed |
description | BACKGROUND: During slow pathway modification for atrioventricular nodal reentrant tachycardia, heart block may occur if ablation cannot be stopped in time in response to high risk electrogram features (HREF). OBJECTIVES: To develop an automatic algorithm to monitor HREF and terminate ablation earlier than human reaction. METHODS: Digital electrogram data from 332 ablation runs from February 2020 to June 2022 were included. They were divided into training and validation sets which contained 126 and 206 ablation runs respectively. HREF in training set was measured. Then a program was developed with cutoff values decided from training set to capture all these HREF. Simulation ablation videos were rendered using validation set electrogram data. The videos were played to three independent electrophysiologists who each determined when to stop ablation. Timing of ablation termination, sensitivity, and specificity were compared between human and program. RESULTS: Reasons for ablation termination in the training set include short AA time, short VV time, AV block and VA block. Cutoffs for the program were set to maximize program sensitivity. Sensitivity and specificity for the program in the validation set were 95.2% and 91.1% respectively, which were comparable to that of human performance at 93.5% and 95.4%. If HREF were recognized by both human and program, ablations were terminated earlier by the program 90.2% of times, by a median of 574 ms (interquartile range 412–807 ms, p < 0.001). CONCLUSION: Algorithmic-driven monitoring of slow pathway modification can supplement human judgement to improve ablation safety. |
format | Online Article Text |
id | pubmed-10352454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103524542023-07-19 Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety Tam, Tsz Kin Lai, Angel Chan, Joseph Y. S. Au, Alex C. K. Chan, Chin Pang Cheng, Yuet Wong Yan, Bryan P. Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: During slow pathway modification for atrioventricular nodal reentrant tachycardia, heart block may occur if ablation cannot be stopped in time in response to high risk electrogram features (HREF). OBJECTIVES: To develop an automatic algorithm to monitor HREF and terminate ablation earlier than human reaction. METHODS: Digital electrogram data from 332 ablation runs from February 2020 to June 2022 were included. They were divided into training and validation sets which contained 126 and 206 ablation runs respectively. HREF in training set was measured. Then a program was developed with cutoff values decided from training set to capture all these HREF. Simulation ablation videos were rendered using validation set electrogram data. The videos were played to three independent electrophysiologists who each determined when to stop ablation. Timing of ablation termination, sensitivity, and specificity were compared between human and program. RESULTS: Reasons for ablation termination in the training set include short AA time, short VV time, AV block and VA block. Cutoffs for the program were set to maximize program sensitivity. Sensitivity and specificity for the program in the validation set were 95.2% and 91.1% respectively, which were comparable to that of human performance at 93.5% and 95.4%. If HREF were recognized by both human and program, ablations were terminated earlier by the program 90.2% of times, by a median of 574 ms (interquartile range 412–807 ms, p < 0.001). CONCLUSION: Algorithmic-driven monitoring of slow pathway modification can supplement human judgement to improve ablation safety. Frontiers Media S.A. 2023-07-03 /pmc/articles/PMC10352454/ /pubmed/37469484 http://dx.doi.org/10.3389/fcvm.2023.1212837 Text en © 2023 Tam, Lai, Chan, Au, Chan, Cheng and Yan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Tam, Tsz Kin Lai, Angel Chan, Joseph Y. S. Au, Alex C. K. Chan, Chin Pang Cheng, Yuet Wong Yan, Bryan P. Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety |
title | Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety |
title_full | Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety |
title_fullStr | Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety |
title_full_unstemmed | Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety |
title_short | Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety |
title_sort | automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352454/ https://www.ncbi.nlm.nih.gov/pubmed/37469484 http://dx.doi.org/10.3389/fcvm.2023.1212837 |
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