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Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study

BACKGROUND: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. OBJECTIVE: The aim of this study was to predict incident arrhythmia prospectively wit...

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Autores principales: Zhang, Yaqi, Han, Yongxia, Gao, Peng, Mo, Yifu, Hao, Shiying, Huang, Jia, Ye, Fangfan, Li, Zhen, Zheng, Le, Yao, Xiaoming, Li, Xiaodong, Wang, Xiaofang, Huang, Chao-Jung, Jin, Bo, Zhang, Yani, Yang, Gabriel, Alfreds, Shaun T, Kanov, Laura, Sylvester, Karl G, Widen, Eric, Li, Licheng, Ling, Xuefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929752/
https://www.ncbi.nlm.nih.gov/pubmed/33595452
http://dx.doi.org/10.2196/23606
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author Zhang, Yaqi
Han, Yongxia
Gao, Peng
Mo, Yifu
Hao, Shiying
Huang, Jia
Ye, Fangfan
Li, Zhen
Zheng, Le
Yao, Xiaoming
Li, Zhen
Li, Xiaodong
Wang, Xiaofang
Huang, Chao-Jung
Jin, Bo
Zhang, Yani
Yang, Gabriel
Alfreds, Shaun T
Kanov, Laura
Sylvester, Karl G
Widen, Eric
Li, Licheng
Ling, Xuefeng
author_facet Zhang, Yaqi
Han, Yongxia
Gao, Peng
Mo, Yifu
Hao, Shiying
Huang, Jia
Ye, Fangfan
Li, Zhen
Zheng, Le
Yao, Xiaoming
Li, Zhen
Li, Xiaodong
Wang, Xiaofang
Huang, Chao-Jung
Jin, Bo
Zhang, Yani
Yang, Gabriel
Alfreds, Shaun T
Kanov, Laura
Sylvester, Karl G
Widen, Eric
Li, Licheng
Ling, Xuefeng
author_sort Zhang, Yaqi
collection PubMed
description BACKGROUND: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. OBJECTIVE: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. METHODS: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. CONCLUSIONS: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.
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spelling pubmed-79297522021-03-05 Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study Zhang, Yaqi Han, Yongxia Gao, Peng Mo, Yifu Hao, Shiying Huang, Jia Ye, Fangfan Li, Zhen Zheng, Le Yao, Xiaoming Li, Zhen Li, Xiaodong Wang, Xiaofang Huang, Chao-Jung Jin, Bo Zhang, Yani Yang, Gabriel Alfreds, Shaun T Kanov, Laura Sylvester, Karl G Widen, Eric Li, Licheng Ling, Xuefeng JMIR Med Inform Original Paper BACKGROUND: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. OBJECTIVE: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. METHODS: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. CONCLUSIONS: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care. JMIR Publications 2021-02-17 /pmc/articles/PMC7929752/ /pubmed/33595452 http://dx.doi.org/10.2196/23606 Text en ©Yaqi Zhang, Yongxia Han, Peng Gao, Yifu Mo, Shiying Hao, Jia Huang, Fangfan Ye, Zhen Li, Le Zheng, Xiaoming Yao, Zhen Li, Xiaodong Li, Xiaofang Wang, Chao-Jung Huang, Bo Jin, Yani Zhang, Gabriel Yang, Shaun T Alfreds, Laura Kanov, Karl G Sylvester, Eric Widen, Licheng Li, Xuefeng Ling. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 17.02.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Zhang, Yaqi
Han, Yongxia
Gao, Peng
Mo, Yifu
Hao, Shiying
Huang, Jia
Ye, Fangfan
Li, Zhen
Zheng, Le
Yao, Xiaoming
Li, Zhen
Li, Xiaodong
Wang, Xiaofang
Huang, Chao-Jung
Jin, Bo
Zhang, Yani
Yang, Gabriel
Alfreds, Shaun T
Kanov, Laura
Sylvester, Karl G
Widen, Eric
Li, Licheng
Ling, Xuefeng
Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study
title Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study
title_full Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study
title_fullStr Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study
title_full_unstemmed Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study
title_short Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study
title_sort electronic health record–based prediction of 1-year risk of incident cardiac dysrhythmia: prospective case-finding algorithm development and validation study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929752/
https://www.ncbi.nlm.nih.gov/pubmed/33595452
http://dx.doi.org/10.2196/23606
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