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Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke
Background and purposeThe 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 pres...
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Published in: | Frontiers in neurology 2022-09, Vol.13, p.952462-952462 |
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description | Background and purposeThe 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. MethodsWe 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. ResultsA 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. ConclusionInitial 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. |
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fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_0d9565ab4130473c8c020396107ddafd</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_0d9565ab4130473c8c020396107ddafd</doaj_id><sourcerecordid>2720427261</sourcerecordid><originalsourceid>FETCH-LOGICAL-c442t-d7de17dc3237c35cefa9ffd9bbd8f3c0412a82ba36b9fe7f57cbf69514a992d3</originalsourceid><addsrcrecordid>eNpVkk1v3CAQhq0qlRol-QG9ccxlN3wZzKVSFbXNSpFyyR1hGLwkXtgCrtTe-7_LxmnVcAA0zDzDvHq77iPBW8YGdeMjLHlLMaVb1VMu6LvunAjBN5Sq_uy_-4fuqpQn3BZTigl23v3eOYg1-GBNDSmi5NHBTBFqsChDSdFECyi0WIgT8mDq0sLIp4zqHtAxgwv2b-USM9g0xfALHDI1BzMjH8Yc5nmlh4iMXWoDFruHQ-tRak7PcNm992YucPV6XnSPX7883t5t7h--7W4_328s57RunHRApLOMMmlZb8Eb5b1T4-gGzyzmhJqBjoaJUXmQvpd29EL1hBulqGMX3W7FumSe9DG3sfJPnUzQL4GUJ21ym3wGjZ3qRW9GThjmktnBYtpEEwRL54w_sT6trOMyHsDZpmI28xvo25cY9npKP3T7DhuobIDrV0BO3xcoVR-aKtCkipCWoqmkmLdNkJZK1lSbUykZ_L82BOuTA_SLA_TJAXp1APsDOsKqfg</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2720427261</pqid></control><display><type>article</type><title>Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke</title><source>PubMed Central(OpenAccess)</source><creator>Chen, Chao-Hui ; Lee, Meng ; Weng, Hsu-Huei ; Lee, Jiann-Der ; Yang, Jen-Tsung ; Tsai, Yuan-Hsiung ; Huang, Yen-Chu</creator><creatorcontrib>Chen, Chao-Hui ; Lee, Meng ; Weng, Hsu-Huei ; Lee, Jiann-Der ; Yang, Jen-Tsung ; Tsai, Yuan-Hsiung ; Huang, Yen-Chu</creatorcontrib><description>Background and purposeThe 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. MethodsWe 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. ResultsA 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. ConclusionInitial 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.</description><identifier>ISSN: 1664-2295</identifier><identifier>EISSN: 1664-2295</identifier><identifier>DOI: 10.3389/fneur.2022.952462</identifier><language>eng</language><publisher>Frontiers Media S.A</publisher><subject>atrial fibrillation ; cardioembolic stroke ; cryptogenic stroke ; ischemic stroke ; MRI ; Neurology</subject><ispartof>Frontiers in neurology, 2022-09, Vol.13, p.952462-952462</ispartof><rights>Copyright © 2022 Chen, Lee, Weng, Lee, Yang, Tsai and Huang. 2022 Chen, Lee, Weng, Lee, Yang, Tsai and Huang</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-d7de17dc3237c35cefa9ffd9bbd8f3c0412a82ba36b9fe7f57cbf69514a992d3</citedby><cites>FETCH-LOGICAL-c442t-d7de17dc3237c35cefa9ffd9bbd8f3c0412a82ba36b9fe7f57cbf69514a992d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513827/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513827/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids></links><search><creatorcontrib>Chen, Chao-Hui</creatorcontrib><creatorcontrib>Lee, Meng</creatorcontrib><creatorcontrib>Weng, Hsu-Huei</creatorcontrib><creatorcontrib>Lee, Jiann-Der</creatorcontrib><creatorcontrib>Yang, Jen-Tsung</creatorcontrib><creatorcontrib>Tsai, Yuan-Hsiung</creatorcontrib><creatorcontrib>Huang, Yen-Chu</creatorcontrib><title>Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke</title><title>Frontiers in neurology</title><description>Background and purposeThe 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. MethodsWe 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. ResultsA 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. ConclusionInitial 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.</description><subject>atrial fibrillation</subject><subject>cardioembolic stroke</subject><subject>cryptogenic stroke</subject><subject>ischemic stroke</subject><subject>MRI</subject><subject>Neurology</subject><issn>1664-2295</issn><issn>1664-2295</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVkk1v3CAQhq0qlRol-QG9ccxlN3wZzKVSFbXNSpFyyR1hGLwkXtgCrtTe-7_LxmnVcAA0zDzDvHq77iPBW8YGdeMjLHlLMaVb1VMu6LvunAjBN5Sq_uy_-4fuqpQn3BZTigl23v3eOYg1-GBNDSmi5NHBTBFqsChDSdFECyi0WIgT8mDq0sLIp4zqHtAxgwv2b-USM9g0xfALHDI1BzMjH8Yc5nmlh4iMXWoDFruHQ-tRak7PcNm992YucPV6XnSPX7883t5t7h--7W4_328s57RunHRApLOMMmlZb8Eb5b1T4-gGzyzmhJqBjoaJUXmQvpd29EL1hBulqGMX3W7FumSe9DG3sfJPnUzQL4GUJ21ym3wGjZ3qRW9GThjmktnBYtpEEwRL54w_sT6trOMyHsDZpmI28xvo25cY9npKP3T7DhuobIDrV0BO3xcoVR-aKtCkipCWoqmkmLdNkJZK1lSbUykZ_L82BOuTA_SLA_TJAXp1APsDOsKqfg</recordid><startdate>20220913</startdate><enddate>20220913</enddate><creator>Chen, Chao-Hui</creator><creator>Lee, Meng</creator><creator>Weng, Hsu-Huei</creator><creator>Lee, Jiann-Der</creator><creator>Yang, Jen-Tsung</creator><creator>Tsai, Yuan-Hsiung</creator><creator>Huang, Yen-Chu</creator><general>Frontiers Media S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20220913</creationdate><title>Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke</title><author>Chen, Chao-Hui ; Lee, Meng ; Weng, Hsu-Huei ; Lee, Jiann-Der ; Yang, Jen-Tsung ; Tsai, Yuan-Hsiung ; Huang, Yen-Chu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-d7de17dc3237c35cefa9ffd9bbd8f3c0412a82ba36b9fe7f57cbf69514a992d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>atrial fibrillation</topic><topic>cardioembolic stroke</topic><topic>cryptogenic stroke</topic><topic>ischemic stroke</topic><topic>MRI</topic><topic>Neurology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Chao-Hui</creatorcontrib><creatorcontrib>Lee, Meng</creatorcontrib><creatorcontrib>Weng, Hsu-Huei</creatorcontrib><creatorcontrib>Lee, Jiann-Der</creatorcontrib><creatorcontrib>Yang, Jen-Tsung</creatorcontrib><creatorcontrib>Tsai, Yuan-Hsiung</creatorcontrib><creatorcontrib>Huang, Yen-Chu</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>Frontiers in neurology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Chao-Hui</au><au>Lee, Meng</au><au>Weng, Hsu-Huei</au><au>Lee, Jiann-Der</au><au>Yang, Jen-Tsung</au><au>Tsai, Yuan-Hsiung</au><au>Huang, Yen-Chu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke</atitle><jtitle>Frontiers in neurology</jtitle><date>2022-09-13</date><risdate>2022</risdate><volume>13</volume><spage>952462</spage><epage>952462</epage><pages>952462-952462</pages><issn>1664-2295</issn><eissn>1664-2295</eissn><abstract>Background and purposeThe 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. MethodsWe 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. ResultsA 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. ConclusionInitial 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.</abstract><pub>Frontiers Media S.A</pub><doi>10.3389/fneur.2022.952462</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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title | Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke |
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