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Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure

AIMS: Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF...

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Autores principales: Luongo, Giorgio, Rees, Felix, Nairn, Deborah, Rivolta, Massimo W., Dössel, Olaf, Sassi, Roberto, Ahlgrim, Christoph, Mayer, Louisa, Neumann, Franz-Josef, Arentz, Thomas, Jadidi, Amir, Loewe, Axel, Müller-Edenborn, Björn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918925/
https://www.ncbi.nlm.nih.gov/pubmed/35295255
http://dx.doi.org/10.3389/fcvm.2022.812719
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author Luongo, Giorgio
Rees, Felix
Nairn, Deborah
Rivolta, Massimo W.
Dössel, Olaf
Sassi, Roberto
Ahlgrim, Christoph
Mayer, Louisa
Neumann, Franz-Josef
Arentz, Thomas
Jadidi, Amir
Loewe, Axel
Müller-Edenborn, Björn
author_facet Luongo, Giorgio
Rees, Felix
Nairn, Deborah
Rivolta, Massimo W.
Dössel, Olaf
Sassi, Roberto
Ahlgrim, Christoph
Mayer, Louisa
Neumann, Franz-Josef
Arentz, Thomas
Jadidi, Amir
Loewe, Axel
Müller-Edenborn, Björn
author_sort Luongo, Giorgio
collection PubMed
description AIMS: Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF. METHODS: A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments. RESULTS: The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified. CONCLUSION: Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up.
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spelling pubmed-89189252022-03-15 Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure Luongo, Giorgio Rees, Felix Nairn, Deborah Rivolta, Massimo W. Dössel, Olaf Sassi, Roberto Ahlgrim, Christoph Mayer, Louisa Neumann, Franz-Josef Arentz, Thomas Jadidi, Amir Loewe, Axel Müller-Edenborn, Björn Front Cardiovasc Med Cardiovascular Medicine AIMS: Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF. METHODS: A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments. RESULTS: The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified. CONCLUSION: Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up. Frontiers Media S.A. 2022-02-28 /pmc/articles/PMC8918925/ /pubmed/35295255 http://dx.doi.org/10.3389/fcvm.2022.812719 Text en Copyright © 2022 Luongo, Rees, Nairn, Rivolta, Dössel, Sassi, Ahlgrim, Mayer, Neumann, Arentz, Jadidi, Loewe and Müller-Edenborn. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Luongo, Giorgio
Rees, Felix
Nairn, Deborah
Rivolta, Massimo W.
Dössel, Olaf
Sassi, Roberto
Ahlgrim, Christoph
Mayer, Louisa
Neumann, Franz-Josef
Arentz, Thomas
Jadidi, Amir
Loewe, Axel
Müller-Edenborn, Björn
Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure
title Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure
title_full Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure
title_fullStr Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure
title_full_unstemmed Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure
title_short Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure
title_sort machine learning using a single-lead ecg to identify patients with atrial fibrillation-induced heart failure
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918925/
https://www.ncbi.nlm.nih.gov/pubmed/35295255
http://dx.doi.org/10.3389/fcvm.2022.812719
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