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<meta name="DC.Title" content="Optimal Prediction of Carotid Intraplaque Hemorrhage Using Clinical and Lumen Imaging Markers" />
<meta name="DC.Identifier" content="10.3174/ajnr.A4454" />
<meta name="DC.Date" content="2015-12-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2015 by American Journal of Neuroradiology. Indicates open access to non-subscribers at www.ajnr.org" />
<meta name="DC.AccessRights" content="open-access" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: MR imaging detects intraplaque hemorrhage with high accuracy by using the magnetization-prepared rapid acquisition of gradient echo sequence. Still, MR imaging is not readily available for all patients, and many undergo CTA instead. Our goal was to determine essential clinical and lumen imaging predictors of intraplaque hemorrhage, as indicators of its presence and clues to its pathogenesis.

MATERIALS AND METHODS: In this retrospective cross-sectional study, patients undergoing stroke work-up with MR imaging/MRA underwent carotid intraplaque hemorrhage imaging. We analyzed 726 carotid plaques, excluding vessels with non-carotid stroke sources ( n = 420), occlusions ( n = 7), or near-occlusions ( n = 3). Potential carotid imaging predictors of intraplaque hemorrhage included percentage diameter and millimeter stenosis, plaque thickness, ulceration, and intraluminal thrombus. Clinical predictors were recorded, and a multivariable logistic regression model was fitted. Backward elimination was used to determine essential intraplaque hemorrhage predictors with a thresholded 2-sided P &lt; .10. Receiver operating characteristic analysis was also performed.

RESULTS: Predictors of carotid intraplaque hemorrhage included plaque thickness (OR = 2.20, P &lt; .001), millimeter stenosis (OR = 0.46, P &lt; .001), ulceration (OR = 4.25, P = .020), age (OR = 1.11, P = .001), and male sex (OR = 3.23, P = .077). The final model discriminatory value was excellent (area under the curve = 0.932). This was significantly higher than models using only plaque thickness (area under the curve = 0.881), millimeter stenosis (area under the curve = 0.830), or ulceration (area under the curve= 0.715, P &lt; .001).

CONCLUSIONS: Optimal discrimination of carotid intraplaque hemorrhage requires information on plaque thickness, millimeter stenosis, ulceration, age, and male sex. These factors predict intraplaque hemorrhage with high discriminatory power and may provide clues to the pathogenesis of intraplaque hemorrhage. This model could be used to predict the presence of intraplaque hemorrhage when MR imaging is contraindicated.

IPH
:   intraplaque hemorrhage
AUC
:   area under the curve" />
<meta name="DC.Contributor" content="M.S. McLaughlin" />
<meta name="DC.Contributor" content="P.J. Hinckley" />
<meta name="DC.Contributor" content="S.M. Treiman" />
<meta name="DC.Contributor" content="S.-E. Kim" />
<meta name="DC.Contributor" content="G.J. Stoddard" />
<meta name="DC.Contributor" content="D.L. Parker" />
<meta name="DC.Contributor" content="G.S. Treiman" />
<meta name="DC.Contributor" content="J.S. McNally" />
<meta name="article:published_time" content="2015-12-01" />
<meta name="article:section" content="EXTRACRANIAL VASCULAR" />
<meta name="citation_title" content="Optimal Prediction of Carotid Intraplaque Hemorrhage Using Clinical and Lumen Imaging Markers" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;MR imaging detects intraplaque hemorrhage with high accuracy by using the magnetization-prepared rapid acquisition of gradient echo sequence. Still, MR imaging is not readily available for all patients, and many undergo CTA instead. Our goal was to determine essential clinical and lumen imaging predictors of intraplaque hemorrhage, as indicators of its presence and clues to its pathogenesis.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;In this retrospective cross-sectional study, patients undergoing stroke work-up with MR imaging/MRA underwent carotid intraplaque hemorrhage imaging. We analyzed 726 carotid plaques, excluding vessels with non-carotid stroke sources (&lt;i&gt;n&lt;/i&gt; = 420), occlusions (&lt;i&gt;n&lt;/i&gt; = 7), or near-occlusions (&lt;i&gt;n&lt;/i&gt; = 3). Potential carotid imaging predictors of intraplaque hemorrhage included percentage diameter and millimeter stenosis, plaque thickness, ulceration, and intraluminal thrombus. Clinical predictors were recorded, and a multivariable logistic regression model was fitted. Backward elimination was used to determine essential intraplaque hemorrhage predictors with a thresholded 2-sided &lt;i&gt;P&lt;/i&gt; &amp;lt; .10. Receiver operating characteristic analysis was also performed.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Predictors of carotid intraplaque hemorrhage included plaque thickness (OR = 2.20, &lt;i&gt;P&lt;/i&gt; &amp;lt; .001), millimeter stenosis (OR = 0.46, &lt;i&gt;P&lt;/i&gt; &amp;lt; .001), ulceration (OR = 4.25, &lt;i&gt;P&lt;/i&gt; = .020), age (OR = 1.11, &lt;i&gt;P&lt;/i&gt; = .001), and male sex (OR = 3.23, &lt;i&gt;P&lt;/i&gt; = .077). The final model discriminatory value was excellent (area under the curve = 0.932). This was significantly higher than models using only plaque thickness (area under the curve = 0.881), millimeter stenosis (area under the curve = 0.830), or ulceration (area under the curve= 0.715, &lt;i&gt;P&lt;/i&gt; &amp;lt; .001).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Optimal discrimination of carotid intraplaque hemorrhage requires information on plaque thickness, millimeter stenosis, ulceration, age, and male sex. These factors predict intraplaque hemorrhage with high discriminatory power and may provide clues to the pathogenesis of intraplaque hemorrhage. This model could be used to predict the presence of intraplaque hemorrhage when MR imaging is contraindicated.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
<meta name="citation_publisher" content="American Journal of Neuroradiology" />
<meta name="citation_publication_date" content="2015/12/01" />
<meta name="citation_mjid" content="ajnr;36/12/2360" />
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<meta name="citation_issn" content="0195-6108" />
<meta name="citation_issn" content="1936-959X" />
<meta name="citation_doi" content="10.3174/ajnr.A4454" />
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<meta name="citation_volume" content="36" />
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<meta name="citation_article_type" content="Research Article" />
<meta name="citation_section" content="EXTRACRANIAL VASCULAR" />
<meta name="citation_firstpage" content="2360" />
<meta name="citation_lastpage" content="2366" />
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<meta name="citation_author" content="M.S. McLaughlin" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research" />
<meta name="citation_author_orcid" content="http://orcid.org/0000-0002-3733-9847" />
<meta name="citation_author" content="P.J. Hinckley" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research" />
<meta name="citation_author" content="S.M. Treiman" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research" />
<meta name="citation_author" content="S.-E. Kim" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research" />
<meta name="citation_author" content="G.J. Stoddard" />
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<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research" />
<meta name="citation_author" content="G.S. Treiman" />
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<meta name="citation_author_institution" content="cDepartment of Surgery (G.S.T.), University of Utah, Salt Lake City, Utah" />
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<meta name="twitter:title" content="Optimal Prediction of Carotid Intraplaque Hemorrhage Using Clinical and Lumen Imaging Markers" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: MR imaging detects intraplaque hemorrhage with high accuracy by using the magnetization-prepared rapid acquisition of gradient echo sequence. Still, MR imaging is not readily available for all patients, and many undergo CTA instead. Our goal was to determine essential clinical and lumen imaging predictors of intraplaque hemorrhage, as indicators of its presence and clues to its pathogenesis.

MATERIALS AND METHODS: In this retrospective cross-sectional study, patients undergoing stroke work-up with MR imaging/MRA underwent carotid intraplaque hemorrhage imaging. We analyzed 726 carotid plaques, excluding vessels with non-carotid stroke sources ( n = 420), occlusions ( n = 7), or near-occlusions ( n = 3). Potential carotid imaging predictors of intraplaque hemorrhage included percentage diameter and millimeter stenosis, plaque thickness, ulceration, and intraluminal thrombus. Clinical predictors were recorded, and a multivariable logistic regression model was fitted. Backward elimination was used to determine essential intraplaque hemorrhage predictors with a thresholded 2-sided P &lt; .10. Receiver operating characteristic analysis was also performed.

RESULTS: Predictors of carotid intraplaque hemorrhage included plaque thickness (OR = 2.20, P &lt; .001), millimeter stenosis (OR = 0.46, P &lt; .001), ulceration (OR = 4.25, P = .020), age (OR = 1.11, P = .001), and male sex (OR = 3.23, P = .077). The final model discriminatory value was excellent (area under the curve = 0.932). This was significantly higher than models using only plaque thickness (area under the curve = 0.881), millimeter stenosis (area under the curve = 0.830), or ulceration (area under the curve= 0.715, P &lt; .001).

CONCLUSIONS: Optimal discrimination of carotid intraplaque hemorrhage requires information on plaque thickness, millimeter stenosis, ulceration, age, and male sex. These factors predict intraplaque hemorrhage with high discriminatory power and may provide clues to the pathogenesis of intraplaque hemorrhage. This model could be used to predict the presence of intraplaque hemorrhage when MR imaging is contraindicated.

IPH
:   intraplaque hemorrhage
AUC
:   area under the curve" />
<meta name="og-title" property="og:title" content="Optimal Prediction of Carotid Intraplaque Hemorrhage Using Clinical and Lumen Imaging Markers" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/36/12/2360" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: MR imaging detects intraplaque hemorrhage with high accuracy by using the magnetization-prepared rapid acquisition of gradient echo sequence. Still, MR imaging is not readily available for all patients, and many undergo CTA instead. Our goal was to determine essential clinical and lumen imaging predictors of intraplaque hemorrhage, as indicators of its presence and clues to its pathogenesis.

MATERIALS AND METHODS: In this retrospective cross-sectional study, patients undergoing stroke work-up with MR imaging/MRA underwent carotid intraplaque hemorrhage imaging. We analyzed 726 carotid plaques, excluding vessels with non-carotid stroke sources ( n = 420), occlusions ( n = 7), or near-occlusions ( n = 3). Potential carotid imaging predictors of intraplaque hemorrhage included percentage diameter and millimeter stenosis, plaque thickness, ulceration, and intraluminal thrombus. Clinical predictors were recorded, and a multivariable logistic regression model was fitted. Backward elimination was used to determine essential intraplaque hemorrhage predictors with a thresholded 2-sided P &lt; .10. Receiver operating characteristic analysis was also performed.

RESULTS: Predictors of carotid intraplaque hemorrhage included plaque thickness (OR = 2.20, P &lt; .001), millimeter stenosis (OR = 0.46, P &lt; .001), ulceration (OR = 4.25, P = .020), age (OR = 1.11, P = .001), and male sex (OR = 3.23, P = .077). The final model discriminatory value was excellent (area under the curve = 0.932). This was significantly higher than models using only plaque thickness (area under the curve = 0.881), millimeter stenosis (area under the curve = 0.830), or ulceration (area under the curve= 0.715, P &lt; .001).

CONCLUSIONS: Optimal discrimination of carotid intraplaque hemorrhage requires information on plaque thickness, millimeter stenosis, ulceration, age, and male sex. These factors predict intraplaque hemorrhage with high discriminatory power and may provide clues to the pathogenesis of intraplaque hemorrhage. This model could be used to predict the presence of intraplaque hemorrhage when MR imaging is contraindicated.

IPH
:   intraplaque hemorrhage
AUC
:   area under the curve" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node14315--5" data-node-nid="14315" id="top-node-14315--61648320042" data-pisa="ajnr;36/12/2360" data-pisa-master="ajnr;ajnr.A4454" data-apath="/ajnr/36/12/2360.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">EXTRACRANIAL VASCULAR</span></span></div>
  
      <div class="highwire-cite-access"><span class="highwire-citation-access"><i class="highwire-access-icon highwire-access-icon-open-access open-access hw-icon-open-access" title="Open Access" aria-hidden="true"></i><span  class="element-invisible highwire-access-icon highwire-access-icon-open-access" style="">Open Access</span></span></div>
  
      <h1 class="highwire-cite-title" id="page-title">Optimal Prediction of Carotid Intraplaque Hemorrhage Using Clinical and Lumen Imaging Markers</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">M.S. McLaughlin</span>, <span class="highwire-citation-author" data-delta="1">P.J. Hinckley</span>, <span class="highwire-citation-author" data-delta="2">S.M. Treiman</span>, <span class="highwire-citation-author" data-delta="3">S.-E. Kim</span>, <span class="highwire-citation-author" data-delta="4">G.J. Stoddard</span>, <span class="highwire-citation-author" data-delta="5">D.L. Parker</span>, <span class="highwire-citation-author" data-delta="6">G.S. Treiman</span> and <span class="highwire-citation-author" data-delta="7">J.S. McNally</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">December 2015,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2360-2366; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A4454 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-14315--61648320042" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">M.S. McLaughlin </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=M.S.%2BMcLaughlin%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=McLaughlin%20M&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AM.S.%2BMcLaughlin%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="http://orcid.org/0000-0002-3733-9847" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M.S. McLaughlin</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">P.J. Hinckley </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=P.J.%2BHinckley%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Hinckley%20P&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AP.J.%2BHinckley%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">S.M. Treiman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=S.M.%2BTreiman%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Treiman%20S&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AS.M.%2BTreiman%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">S.-E. Kim </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=S.-E.%2BKim%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Kim%20S&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AS.-E.%2BKim%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">G.J. Stoddard </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Orthopedics (G.J.S.), Study Design and Biostatistics Center</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=G.J.%2BStoddard%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Stoddard%20G&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AG.J.%2BStoddard%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">D.L. Parker </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=D.L.%2BParker%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Parker%20D&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AD.L.%2BParker%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">G.S. Treiman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Surgery (G.S.T.), University of Utah, Salt Lake City, Utah</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Department of Surgery (G.S.T.), VA Salt Lake City Health Care System, Salt Lake City, Utah.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=G.S.%2BTreiman%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Treiman%20G&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AG.S.%2BTreiman%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">J.S. McNally </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=J.S.%2BMcNally%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=McNally%20J&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AJ.S.%2BMcNally%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2>Abstract</h2><div id="sec-1" class="subsection"><p id="p-2"><strong>BACKGROUND AND PURPOSE:</strong> MR imaging detects intraplaque hemorrhage with high accuracy by using the magnetization-prepared rapid acquisition of gradient echo sequence. Still, MR imaging is not readily available for all patients, and many undergo CTA instead. Our goal was to determine essential clinical and lumen imaging predictors of intraplaque hemorrhage, as indicators of its presence and clues to its pathogenesis.</p></div><div id="sec-2" class="subsection"><p id="p-3"><strong>MATERIALS AND METHODS:</strong> In this retrospective cross-sectional study, patients undergoing stroke work-up with MR imaging/MRA underwent carotid intraplaque hemorrhage imaging. We analyzed 726 carotid plaques, excluding vessels with non-carotid stroke sources (<em>n</em> = 420), occlusions (<em>n</em> = 7), or near-occlusions (<em>n</em> = 3). Potential carotid imaging predictors of intraplaque hemorrhage included percentage diameter and millimeter stenosis, plaque thickness, ulceration, and intraluminal thrombus. Clinical predictors were recorded, and a multivariable logistic regression model was fitted. Backward elimination was used to determine essential intraplaque hemorrhage predictors with a thresholded 2-sided <em>P</em> &lt; .10. Receiver operating characteristic analysis was also performed.</p></div><div id="sec-3" class="subsection"><p id="p-4"><strong>RESULTS:</strong> Predictors of carotid intraplaque hemorrhage included plaque thickness (OR = 2.20, <em>P</em> &lt; .001), millimeter stenosis (OR = 0.46, <em>P</em> &lt; .001), ulceration (OR = 4.25, <em>P</em> = .020), age (OR = 1.11, <em>P</em> = .001), and male sex (OR = 3.23, <em>P</em> = .077). The final model discriminatory value was excellent (area under the curve = 0.932). This was significantly higher than models using only plaque thickness (area under the curve = 0.881), millimeter stenosis (area under the curve = 0.830), or ulceration (area under the curve= 0.715, <em>P</em> &lt; .001).</p></div><div id="sec-4" class="subsection"><p id="p-5"><strong>CONCLUSIONS:</strong> Optimal discrimination of carotid intraplaque hemorrhage requires information on plaque thickness, millimeter stenosis, ulceration, age, and male sex. These factors predict intraplaque hemorrhage with high discriminatory power and may provide clues to the pathogenesis of intraplaque hemorrhage. This model could be used to predict the presence of intraplaque hemorrhage when MR imaging is contraindicated.</p></div></div><div class="section glossary" id="glossary-1"><h2 class="">ABBREVIATIONS:</h2><dl id="def-list-1"><dt id="def-item-1">IPH<span class="def-item-dt-sep"></span></dt><dd>intraplaque hemorrhage<span class="def-item-dd-sep"></span></dd><dt id="def-item-2">AUC<span class="def-item-dt-sep"></span></dt><dd>area under the curve</dd></dl></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2015 by American Journal of Neuroradiology</li></ul><div class="license" id="license-1"><p id="p-1">Indicates open access to non-subscribers at <a href="http://www.ajnr.org" rel="license">www.ajnr.org</a></p></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/36/12/2360.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div>  </div>

  
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 36</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 12 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Dec 2015 </div></div>
      
      
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    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2015, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2360-2366; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A4454 </span></div>
  
  
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">M.S.</span> <span class="nlm-surname">McLaughlin</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">P.J.</span> <span class="nlm-surname">Hinckley</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">S.M.</span> <span class="nlm-surname">Treiman</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">S.-E.</span> <span class="nlm-surname">Kim</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">G.J.</span> <span class="nlm-surname">Stoddard</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">D.L.</span> <span class="nlm-surname">Parker</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">G.S.</span> <span class="nlm-surname">Treiman</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">J.S.</span> <span class="nlm-surname">McNally</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2015, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(12) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2360-2366; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A4454 </span></div>
  
  
  
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