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Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction

BACKGROUND: Previous studies indicate that decreased heart-rate variability (HRV) is related to the risk of death in patients after acute myocardial infarction (AMI). However, the conventional indices of HRV have poor predictive value for mortality. Our aim was to develop novel predictive models bas...

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Autores principales: Song, Tao, Qu, Xiu Fen, Zhang, Ying Tao, Cao, Wei, Han, Bai He, Li, Yang, Piao, Jing Yan, Yin, Lei Lei, Da Cheng, Heng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023175/
https://www.ncbi.nlm.nih.gov/pubmed/24886422
http://dx.doi.org/10.1186/1471-2261-14-59
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author Song, Tao
Qu, Xiu Fen
Zhang, Ying Tao
Cao, Wei
Han, Bai He
Li, Yang
Piao, Jing Yan
Yin, Lei Lei
Da Cheng, Heng
author_facet Song, Tao
Qu, Xiu Fen
Zhang, Ying Tao
Cao, Wei
Han, Bai He
Li, Yang
Piao, Jing Yan
Yin, Lei Lei
Da Cheng, Heng
author_sort Song, Tao
collection PubMed
description BACKGROUND: Previous studies indicate that decreased heart-rate variability (HRV) is related to the risk of death in patients after acute myocardial infarction (AMI). However, the conventional indices of HRV have poor predictive value for mortality. Our aim was to develop novel predictive models based on support vector machine (SVM) to study the integrated features of HRV for improving risk stratification after AMI. METHODS: A series of heart-rate dynamic parameters from 208 patients were analyzed after a mean follow-up time of 28 months. Patient electrocardiographic data were classified as either survivals or cardiac deaths. SVM models were established based on different combinations of heart-rate dynamic variables and compared to left ventricular ejection fraction (LVEF), standard deviation of normal-to-normal intervals (SDNN) and deceleration capacity (DC) of heart rate. We tested the accuracy of predictors by assessing the area under the receiver-operator characteristics curve (AUC). RESULTS: We evaluated a SVM algorithm that integrated various electrocardiographic features based on three models: (A) HRV complex; (B) 6 dimension vector; and (C) 8 dimension vector. Mean AUC of HRV complex was 0.8902, 0.8880 for 6 dimension vector and 0.8579 for 8 dimension vector, compared with 0.7424 for LVEF, 0.7932 for SDNN and 0.7399 for DC. CONCLUSIONS: HRV complex yielded the largest AUC and is the best classifier for predicting cardiac death after AMI.
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spelling pubmed-40231752014-05-17 Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction Song, Tao Qu, Xiu Fen Zhang, Ying Tao Cao, Wei Han, Bai He Li, Yang Piao, Jing Yan Yin, Lei Lei Da Cheng, Heng BMC Cardiovasc Disord Research Article BACKGROUND: Previous studies indicate that decreased heart-rate variability (HRV) is related to the risk of death in patients after acute myocardial infarction (AMI). However, the conventional indices of HRV have poor predictive value for mortality. Our aim was to develop novel predictive models based on support vector machine (SVM) to study the integrated features of HRV for improving risk stratification after AMI. METHODS: A series of heart-rate dynamic parameters from 208 patients were analyzed after a mean follow-up time of 28 months. Patient electrocardiographic data were classified as either survivals or cardiac deaths. SVM models were established based on different combinations of heart-rate dynamic variables and compared to left ventricular ejection fraction (LVEF), standard deviation of normal-to-normal intervals (SDNN) and deceleration capacity (DC) of heart rate. We tested the accuracy of predictors by assessing the area under the receiver-operator characteristics curve (AUC). RESULTS: We evaluated a SVM algorithm that integrated various electrocardiographic features based on three models: (A) HRV complex; (B) 6 dimension vector; and (C) 8 dimension vector. Mean AUC of HRV complex was 0.8902, 0.8880 for 6 dimension vector and 0.8579 for 8 dimension vector, compared with 0.7424 for LVEF, 0.7932 for SDNN and 0.7399 for DC. CONCLUSIONS: HRV complex yielded the largest AUC and is the best classifier for predicting cardiac death after AMI. BioMed Central 2014-05-01 /pmc/articles/PMC4023175/ /pubmed/24886422 http://dx.doi.org/10.1186/1471-2261-14-59 Text en Copyright © 2014 Song et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Song, Tao
Qu, Xiu Fen
Zhang, Ying Tao
Cao, Wei
Han, Bai He
Li, Yang
Piao, Jing Yan
Yin, Lei Lei
Da Cheng, Heng
Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction
title Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction
title_full Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction
title_fullStr Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction
title_full_unstemmed Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction
title_short Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction
title_sort usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023175/
https://www.ncbi.nlm.nih.gov/pubmed/24886422
http://dx.doi.org/10.1186/1471-2261-14-59
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