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Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism
AIMS: Although overt hyperthyroidism adversely affects a patient’s prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and th...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707932/ https://www.ncbi.nlm.nih.gov/pubmed/36713007 http://dx.doi.org/10.1093/ehjdh/ztac013 |
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author | Choi, Byungjin Jang, Jong Hwan Son, Minkook Lee, Min Sung Jo, Yong Yeon Jeon, Ja Young Jin, Uram Soh, Moonseung Park, Rae Woong Kwon, Joon myoung |
author_facet | Choi, Byungjin Jang, Jong Hwan Son, Minkook Lee, Min Sung Jo, Yong Yeon Jeon, Ja Young Jin, Uram Soh, Moonseung Park, Rae Woong Kwon, Joon myoung |
author_sort | Choi, Byungjin |
collection | PubMed |
description | AIMS: Although overt hyperthyroidism adversely affects a patient’s prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and the association between thyroid function and ECG is well known. However, it is difficult for clinicians to detect hyperthyroidism through subtle ECG changes. For early detection of hyperthyroidism, we aimed to develop and validate an electrocardiographic biomarker based on a deep learning model (DLM) for detecting hyperthyroidism. METHODS AND RESULTS: This multicentre retrospective cohort study included patients who underwent ECG and TFTs within 24 h. For model development and internal validation, we obtained 174 331 ECGs from 113 194 patients. We extracted 48 648 ECGs from 33 478 patients from another hospital for external validation. Using 500 Hz raw ECG, we developed a DLM with 12-lead, 6-lead (limb leads, precordial leads), and single-lead (lead I) ECGs to detect overt hyperthyroidism. We calculated the model’s performance on the internal and external validation sets using the area under the receiver operating characteristic curve (AUC). The AUC of the DLM using a 12-lead ECG was 0.926 (0.913–0.94) for internal validation and 0.883(0.855–0.911) for external validation. The AUC of DLMs using six and a single-lead were in the range of 0.889–0.906 for internal validation and 0.847–0.882 for external validation. CONCLUSION: We developed a DLM using ECG for non-invasive screening of overt hyperthyroidism. We expect this model to contribute to the early diagnosis of diseases and improve patient prognosis. |
format | Online Article Text |
id | pubmed-9707932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97079322023-01-27 Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism Choi, Byungjin Jang, Jong Hwan Son, Minkook Lee, Min Sung Jo, Yong Yeon Jeon, Ja Young Jin, Uram Soh, Moonseung Park, Rae Woong Kwon, Joon myoung Eur Heart J Digit Health Original Article AIMS: Although overt hyperthyroidism adversely affects a patient’s prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and the association between thyroid function and ECG is well known. However, it is difficult for clinicians to detect hyperthyroidism through subtle ECG changes. For early detection of hyperthyroidism, we aimed to develop and validate an electrocardiographic biomarker based on a deep learning model (DLM) for detecting hyperthyroidism. METHODS AND RESULTS: This multicentre retrospective cohort study included patients who underwent ECG and TFTs within 24 h. For model development and internal validation, we obtained 174 331 ECGs from 113 194 patients. We extracted 48 648 ECGs from 33 478 patients from another hospital for external validation. Using 500 Hz raw ECG, we developed a DLM with 12-lead, 6-lead (limb leads, precordial leads), and single-lead (lead I) ECGs to detect overt hyperthyroidism. We calculated the model’s performance on the internal and external validation sets using the area under the receiver operating characteristic curve (AUC). The AUC of the DLM using a 12-lead ECG was 0.926 (0.913–0.94) for internal validation and 0.883(0.855–0.911) for external validation. The AUC of DLMs using six and a single-lead were in the range of 0.889–0.906 for internal validation and 0.847–0.882 for external validation. CONCLUSION: We developed a DLM using ECG for non-invasive screening of overt hyperthyroidism. We expect this model to contribute to the early diagnosis of diseases and improve patient prognosis. Oxford University Press 2022-04-20 /pmc/articles/PMC9707932/ /pubmed/36713007 http://dx.doi.org/10.1093/ehjdh/ztac013 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Article Choi, Byungjin Jang, Jong Hwan Son, Minkook Lee, Min Sung Jo, Yong Yeon Jeon, Ja Young Jin, Uram Soh, Moonseung Park, Rae Woong Kwon, Joon myoung Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism |
title | Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism |
title_full | Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism |
title_fullStr | Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism |
title_full_unstemmed | Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism |
title_short | Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism |
title_sort | electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707932/ https://www.ncbi.nlm.nih.gov/pubmed/36713007 http://dx.doi.org/10.1093/ehjdh/ztac013 |
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