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An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction
(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applicati...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623357/ https://www.ncbi.nlm.nih.gov/pubmed/34834501 http://dx.doi.org/10.3390/jpm11111149 |
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author | Liu, Wen-Cheng Lin, Chin Lin, Chin-Sheng Tsai, Min-Chien Chen, Sy-Jou Tsai, Shih-Hung Lin, Wei-Shiang Lee, Chia-Cheng Tsao, Tien-Ping Cheng, Cheng-Chung |
author_facet | Liu, Wen-Cheng Lin, Chin Lin, Chin-Sheng Tsai, Min-Chien Chen, Sy-Jou Tsai, Shih-Hung Lin, Wei-Shiang Lee, Chia-Cheng Tsao, Tien-Ping Cheng, Cheng-Chung |
author_sort | Liu, Wen-Cheng |
collection | PubMed |
description | (1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0–8.0 min) to 4.0 min (IQR, 3.0–5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0–82.0 min) to 61 min (IQR, 56.8–73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance. |
format | Online Article Text |
id | pubmed-8623357 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86233572021-11-27 An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction Liu, Wen-Cheng Lin, Chin Lin, Chin-Sheng Tsai, Min-Chien Chen, Sy-Jou Tsai, Shih-Hung Lin, Wei-Shiang Lee, Chia-Cheng Tsao, Tien-Ping Cheng, Cheng-Chung J Pers Med Article (1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0–8.0 min) to 4.0 min (IQR, 3.0–5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0–82.0 min) to 61 min (IQR, 56.8–73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance. MDPI 2021-11-04 /pmc/articles/PMC8623357/ /pubmed/34834501 http://dx.doi.org/10.3390/jpm11111149 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Wen-Cheng Lin, Chin Lin, Chin-Sheng Tsai, Min-Chien Chen, Sy-Jou Tsai, Shih-Hung Lin, Wei-Shiang Lee, Chia-Cheng Tsao, Tien-Ping Cheng, Cheng-Chung An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction |
title | An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction |
title_full | An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction |
title_fullStr | An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction |
title_full_unstemmed | An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction |
title_short | An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction |
title_sort | artificial intelligence-based alarm strategy facilitates management of acute myocardial infarction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623357/ https://www.ncbi.nlm.nih.gov/pubmed/34834501 http://dx.doi.org/10.3390/jpm11111149 |
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