Cargando…

Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke

BACKGROUND AND PURPOSE: The early identification of cardioembolic stroke is critical for the early initiation of anticoagulant treatment. However, it can be challenging to identify the major cardiac source, particularly since the predominant source, paroxysmal atrial fibrillation (AF), may not be pr...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Chao-Hui, Lee, Meng, Weng, Hsu-Huei, Lee, Jiann-Der, Yang, Jen-Tsung, Tsai, Yuan-Hsiung, Huang, Yen-Chu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513827/
https://www.ncbi.nlm.nih.gov/pubmed/36176550
http://dx.doi.org/10.3389/fneur.2022.952462
_version_ 1784798152693383168
author Chen, Chao-Hui
Lee, Meng
Weng, Hsu-Huei
Lee, Jiann-Der
Yang, Jen-Tsung
Tsai, Yuan-Hsiung
Huang, Yen-Chu
author_facet Chen, Chao-Hui
Lee, Meng
Weng, Hsu-Huei
Lee, Jiann-Der
Yang, Jen-Tsung
Tsai, Yuan-Hsiung
Huang, Yen-Chu
author_sort Chen, Chao-Hui
collection PubMed
description BACKGROUND AND PURPOSE: The early identification of cardioembolic stroke is critical for the early initiation of anticoagulant treatment. However, it can be challenging to identify the major cardiac source, particularly since the predominant source, paroxysmal atrial fibrillation (AF), may not be present at the time of stroke. In this study, we aimed to evaluate imaging predictors for unrecognized AF in patients with acute ischemic stroke. METHODS: We performed a cross-sectional analysis of data and magnetic resonance imaging (MRI) scans from two prospective cohorts of patients who underwent serial 12-lead electrocardiography and 24-h Holter monitoring to detect unrecognized AF. The imaging patterns in diffusion-weighted imaging and imaging characteristics were assessed and classified. A logistic regression model was used to identify predictive factors for newly detected AF in patients with acute ischemic stroke. RESULTS: A total of 734 patients were recruited for analysis, with a median age of 72 (interquartile range: 65–79) years and a median National Institutes of Health Stroke Scale score of 4 (interquartile range: 2–6). Of these patients, 64 (8.7%) had newly detected AF during the follow-up period. Stepwise multivariate logistic regression revealed that age ≥75 years [adjusted odds ratio (aOR) 5.66, 95% confidence interval (CI) 2.98–10.75], receiving recombinant tissue plasminogen activator treatment (aOR 4.36, 95% CI 1.65–11.54), congestive heart failure (aOR 6.73, 95% CI 1.85–24.48), early hemorrhage in MRI (aOR 3.62, 95% CI 1.52–8.61), single cortical infarct (aOR 6.49, 95% CI 2.35–17.92), and territorial infarcts (aOR 3.54, 95% CI 1.06–11.75) were associated with newly detected AF. The C-statistic of the prediction model for newly detected AF was 0.764. CONCLUSION: Initial MRI at the time of stroke may be useful to predict which patients have cardioembolic stroke caused by unrecognized AF. Further studies are warranted to verify these findings and their application to high-risk patients.
format Online
Article
Text
id pubmed-9513827
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95138272022-09-28 Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke Chen, Chao-Hui Lee, Meng Weng, Hsu-Huei Lee, Jiann-Der Yang, Jen-Tsung Tsai, Yuan-Hsiung Huang, Yen-Chu Front Neurol Neurology BACKGROUND AND PURPOSE: The early identification of cardioembolic stroke is critical for the early initiation of anticoagulant treatment. However, it can be challenging to identify the major cardiac source, particularly since the predominant source, paroxysmal atrial fibrillation (AF), may not be present at the time of stroke. In this study, we aimed to evaluate imaging predictors for unrecognized AF in patients with acute ischemic stroke. METHODS: We performed a cross-sectional analysis of data and magnetic resonance imaging (MRI) scans from two prospective cohorts of patients who underwent serial 12-lead electrocardiography and 24-h Holter monitoring to detect unrecognized AF. The imaging patterns in diffusion-weighted imaging and imaging characteristics were assessed and classified. A logistic regression model was used to identify predictive factors for newly detected AF in patients with acute ischemic stroke. RESULTS: A total of 734 patients were recruited for analysis, with a median age of 72 (interquartile range: 65–79) years and a median National Institutes of Health Stroke Scale score of 4 (interquartile range: 2–6). Of these patients, 64 (8.7%) had newly detected AF during the follow-up period. Stepwise multivariate logistic regression revealed that age ≥75 years [adjusted odds ratio (aOR) 5.66, 95% confidence interval (CI) 2.98–10.75], receiving recombinant tissue plasminogen activator treatment (aOR 4.36, 95% CI 1.65–11.54), congestive heart failure (aOR 6.73, 95% CI 1.85–24.48), early hemorrhage in MRI (aOR 3.62, 95% CI 1.52–8.61), single cortical infarct (aOR 6.49, 95% CI 2.35–17.92), and territorial infarcts (aOR 3.54, 95% CI 1.06–11.75) were associated with newly detected AF. The C-statistic of the prediction model for newly detected AF was 0.764. CONCLUSION: Initial MRI at the time of stroke may be useful to predict which patients have cardioembolic stroke caused by unrecognized AF. Further studies are warranted to verify these findings and their application to high-risk patients. Frontiers Media S.A. 2022-09-13 /pmc/articles/PMC9513827/ /pubmed/36176550 http://dx.doi.org/10.3389/fneur.2022.952462 Text en Copyright © 2022 Chen, Lee, Weng, Lee, Yang, Tsai and Huang. 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 Neurology
Chen, Chao-Hui
Lee, Meng
Weng, Hsu-Huei
Lee, Jiann-Der
Yang, Jen-Tsung
Tsai, Yuan-Hsiung
Huang, Yen-Chu
Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
title Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
title_full Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
title_fullStr Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
title_full_unstemmed Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
title_short Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
title_sort identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513827/
https://www.ncbi.nlm.nih.gov/pubmed/36176550
http://dx.doi.org/10.3389/fneur.2022.952462
work_keys_str_mv AT chenchaohui identificationofmagneticresonanceimagingfeaturesforthepredictionofunrecognizedatrialfibrillationinacuteischemicstroke
AT leemeng identificationofmagneticresonanceimagingfeaturesforthepredictionofunrecognizedatrialfibrillationinacuteischemicstroke
AT wenghsuhuei identificationofmagneticresonanceimagingfeaturesforthepredictionofunrecognizedatrialfibrillationinacuteischemicstroke
AT leejiannder identificationofmagneticresonanceimagingfeaturesforthepredictionofunrecognizedatrialfibrillationinacuteischemicstroke
AT yangjentsung identificationofmagneticresonanceimagingfeaturesforthepredictionofunrecognizedatrialfibrillationinacuteischemicstroke
AT tsaiyuanhsiung identificationofmagneticresonanceimagingfeaturesforthepredictionofunrecognizedatrialfibrillationinacuteischemicstroke
AT huangyenchu identificationofmagneticresonanceimagingfeaturesforthepredictionofunrecognizedatrialfibrillationinacuteischemicstroke