Cargando…
Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm
FUNDING ACKNOWLEDGEMENTS: Type of funding sources: None. INTRODUCTION: Electrocardiography (ECG) can be easily obtained at a low cost and includes voltage and time interval representing heart conditions. We hypothesized that artificial intelligence (AI) detects a subtle abnormality in 12-lead ECG an...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207619/ http://dx.doi.org/10.1093/europace/euad122.291 |
_version_ | 1785046497668104192 |
---|---|
author | Park, J W Kwon, O S Kim, D H Yu, H T Kim, T H Uhm, J S Joung, B Y Lee, M H Hwang, C Pak, H N |
author_facet | Park, J W Kwon, O S Kim, D H Yu, H T Kim, T H Uhm, J S Joung, B Y Lee, M H Hwang, C Pak, H N |
author_sort | Park, J W |
collection | PubMed |
description | FUNDING ACKNOWLEDGEMENTS: Type of funding sources: None. INTRODUCTION: Electrocardiography (ECG) can be easily obtained at a low cost and includes voltage and time interval representing heart conditions. We hypothesized that artificial intelligence (AI) detects a subtle abnormality in 12-lead ECG and may predict individual mortality. METHODS: Among 502,411 population in UK Biobank, 42,096 individuals had 12-lead ECG from 2013 to 2022. Among population with available ECG, 4,512 individuals were enrolled in this study adjusting the following inclusion criteria; age under 60 years, sinus rhythm, PR interval 120~200ms, QTc interval 350~460ms, and QRS duration 70~100ms. We developed and tested convolutional neural network (CNN) model to predict all cause death, cardiovascular (CV) death, or sudden cardiac arrest (SCA). The study population were divided into train (80%), validation (10%), and test (20%) set. RESULTS: Among 4,512 patients with median 3.7 years [IQR; 2.7-5.1] of follow-up, the rate of all-cause mortality was 11.6% (524). In overall study population, median age was 55.5 years and proportion of male sex was 42.2%. The patients with all-cause death were older (p<0.001) and had more comorbidities (p<0.001). In the train set, CNN model showed 0.93 in AUC for predicting all-cause death. In the test set, CNN model showed consistent good performance power (AUC 0.90) for all-cause death. In subgroup analysis, 102 of 4153 (2.46%) and 57 of 4065 (1.40%) patients experienced CV death and SCA, respectively. The performance power in test set were 0.90 in AUC for CV death and 0.87 in AUC for SCA. CONCLUSIONS: AI detects and predicts future all-cause death, CV death, and SCA in median of 2.6 years by analyzing standard 12-lead ECG in generally looking normal sinus rhythm. [Figure: see text] |
format | Online Article Text |
id | pubmed-10207619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102076192023-05-25 Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm Park, J W Kwon, O S Kim, D H Yu, H T Kim, T H Uhm, J S Joung, B Y Lee, M H Hwang, C Pak, H N Europace 13.3 - Diagnostic Methods FUNDING ACKNOWLEDGEMENTS: Type of funding sources: None. INTRODUCTION: Electrocardiography (ECG) can be easily obtained at a low cost and includes voltage and time interval representing heart conditions. We hypothesized that artificial intelligence (AI) detects a subtle abnormality in 12-lead ECG and may predict individual mortality. METHODS: Among 502,411 population in UK Biobank, 42,096 individuals had 12-lead ECG from 2013 to 2022. Among population with available ECG, 4,512 individuals were enrolled in this study adjusting the following inclusion criteria; age under 60 years, sinus rhythm, PR interval 120~200ms, QTc interval 350~460ms, and QRS duration 70~100ms. We developed and tested convolutional neural network (CNN) model to predict all cause death, cardiovascular (CV) death, or sudden cardiac arrest (SCA). The study population were divided into train (80%), validation (10%), and test (20%) set. RESULTS: Among 4,512 patients with median 3.7 years [IQR; 2.7-5.1] of follow-up, the rate of all-cause mortality was 11.6% (524). In overall study population, median age was 55.5 years and proportion of male sex was 42.2%. The patients with all-cause death were older (p<0.001) and had more comorbidities (p<0.001). In the train set, CNN model showed 0.93 in AUC for predicting all-cause death. In the test set, CNN model showed consistent good performance power (AUC 0.90) for all-cause death. In subgroup analysis, 102 of 4153 (2.46%) and 57 of 4065 (1.40%) patients experienced CV death and SCA, respectively. The performance power in test set were 0.90 in AUC for CV death and 0.87 in AUC for SCA. CONCLUSIONS: AI detects and predicts future all-cause death, CV death, and SCA in median of 2.6 years by analyzing standard 12-lead ECG in generally looking normal sinus rhythm. [Figure: see text] Oxford University Press 2023-05-24 /pmc/articles/PMC10207619/ http://dx.doi.org/10.1093/europace/euad122.291 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 | 13.3 - Diagnostic Methods Park, J W Kwon, O S Kim, D H Yu, H T Kim, T H Uhm, J S Joung, B Y Lee, M H Hwang, C Pak, H N Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm |
title | Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm |
title_full | Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm |
title_fullStr | Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm |
title_full_unstemmed | Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm |
title_short | Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm |
title_sort | artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm |
topic | 13.3 - Diagnostic Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207619/ http://dx.doi.org/10.1093/europace/euad122.291 |
work_keys_str_mv | AT parkjw artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT kwonos artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT kimdh artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT yuht artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT kimth artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT uhmjs artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT joungby artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT leemh artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT hwangc artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm AT pakhn artificialintelligencepredictsallcauseandcardiovascularmortalitiesusing12leadelectrocardiographyinsinusrhythm |