Plaque characteristics and hemodynamics contribute to neurological impairment in patients with ischemic stroke and transient ischemic attack View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2020-09-30

AUTHORS

Song Liu, Ruowei Tang, Weiwei Xie, Shengting Chai, Qingqing Zhang, Yu Luo, Yu Guo, Chao Chai, Lixiang Huang, Meizhu Zheng, Jinxia Zhu, Binge Chang, Qi Yang, Song Jin, Zhaoyang Fan, Shuang Xia

ABSTRACT

ObjectivesWe aimed to investigate differential characteristics of plaque in the middle cerebral artery (MCA) and hemodynamics in patients with ischemic stroke and transient ischemic attack (TIA), and to develop a predictive model for the presence of ischemic stroke and neurological impairment.MethodsSixty-seven patients with acute ischemic events in MCA territory who underwent high-resolution vessel wall imaging between September 2016 and August 2018 were reviewed retrospectively. Patients were assigned to either the stroke group or TIA group, according to diffusion-weighted imaging and neurological examination. Plaque characteristics and anterograde score (AnS) were calculated. Tmax > 6.0-s volume was acquired by RApid Processing of perfusIon and Diffusion software. Multivariate logistic regression analysis and multiple linear regression analysis were performed to establish a predictive model for irreversible infarction occurrence and clinical severity.ResultsForty-five patients were assigned to the stroke group, and 22 were assigned to the TIA group. Plaque length, intraplaque hemorrhage (IPH), enhancement, AnS, and Tmax > 6.0-s volumes were significantly different between the two groups (p < 0.05). IPH and AnS were independent predictors for patients with stroke (p = 0.020 and 0.034, respectively). Tmax > 6.0-s volume, IPH, hypertension, and AnS were associated with high National Institutes of Health Stroke Scale (NIHSS) scores (all p < 0.05, R = 0.725, and adjusted R2 = 0.494).ConclusionsIPH and AnS are useful in predicting stroke occurrence. Tmax > 6.0-s volume, IPH, hypertension, and AnS are associated with neurological impairment of the patients.Key Points• Ischemic stroke and TIA patients have different plaque characteristics and hemodynamics.• Intraplaque hemorrhage and anterograde score have high diagnostic efficiency for ischemic stroke.• The combination of Tmax > 6.0-s volume, intraplaque hemorrhage, hypertension, and anterograde score can predict the National Institutes of Health Stroke Scale scores of patients. More... »

PAGES

2062-2072

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-020-07327-1

DOI

http://dx.doi.org/10.1007/s00330-020-07327-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1131328251

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32997174


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Brain Ischemia", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Hemodynamics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ischemic Attack, Transient", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ischemic Stroke", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Magnetic Resonance Imaging", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Plaque, Atherosclerotic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Retrospective Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stroke", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Song", 
        "id": "sg:person.013024177765.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013024177765.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tang", 
        "givenName": "Ruowei", 
        "id": "sg:person.07435744344.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07435744344.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xie", 
        "givenName": "Weiwei", 
        "id": "sg:person.016607462365.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016607462365.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chai", 
        "givenName": "Shengting", 
        "id": "sg:person.010104411165.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010104411165.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Qingqing", 
        "id": "sg:person.011454444615.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011454444615.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People\u2019s Hospital Affiliated to Tongji University School of Medicine, 200081, Shanghai, China", 
          "id": "http://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "Department of Radiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People\u2019s Hospital Affiliated to Tongji University School of Medicine, 200081, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Luo", 
        "givenName": "Yu", 
        "id": "sg:person.01166423053.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01166423053.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Guo", 
        "givenName": "Yu", 
        "id": "sg:person.01114220035.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01114220035.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chai", 
        "givenName": "Chao", 
        "id": "sg:person.01341033735.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01341033735.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Huang", 
        "givenName": "Lixiang", 
        "id": "sg:person.01116723555.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01116723555.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin Third Central Hospital, 300170, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417032.3", 
          "name": [
            "Department of Radiology, Tianjin Third Central Hospital, 300170, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zheng", 
        "givenName": "Meizhu", 
        "id": "sg:person.011100762235.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011100762235.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MR Collaboration, Siemens Healthcare Ltd., 100102, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.452598.7", 
          "name": [
            "MR Collaboration, Siemens Healthcare Ltd., 100102, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhu", 
        "givenName": "Jinxia", 
        "id": "sg:person.011444626063.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011444626063.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Neurosurgery, Tianjin First Central Hospital, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Neurosurgery, Tianjin First Central Hospital, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chang", 
        "givenName": "Binge", 
        "id": "sg:person.0765643437.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0765643437.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Beijing Xuanwu Hospital, Capital Medical University, 100053, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Xuanwu Hospital, Capital Medical University, 100053, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Qi", 
        "id": "sg:person.01353064311.62", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353064311.62"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin Huanhu Hospital, 300350, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.413605.5", 
          "name": [
            "Department of Radiology, Tianjin Huanhu Hospital, 300350, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jin", 
        "givenName": "Song", 
        "id": "sg:person.010241260205.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010241260205.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fan", 
        "givenName": "Zhaoyang", 
        "id": "sg:person.01136172260.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China", 
          "id": "http://www.grid.ac/institutes/grid.417024.4", 
          "name": [
            "Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xia", 
        "givenName": "Shuang", 
        "id": "sg:person.0755674257.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755674257.40"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00330-015-4008-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009298470", 
          "https://doi.org/10.1007/s00330-015-4008-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2020-09-30", 
    "datePublishedReg": "2020-09-30", 
    "description": "ObjectivesWe aimed to investigate differential characteristics of plaque in the middle cerebral artery (MCA) and hemodynamics in patients with ischemic stroke and transient ischemic attack (TIA), and to develop a predictive model for the presence of ischemic stroke and neurological impairment.MethodsSixty-seven patients with acute ischemic events in MCA territory who underwent high-resolution vessel wall imaging between September 2016 and August 2018 were reviewed retrospectively. Patients were assigned to either the stroke group or TIA group, according to diffusion-weighted imaging and neurological examination. Plaque characteristics and anterograde score (AnS) were calculated. Tmax > 6.0-s volume was acquired by RApid Processing of perfusIon and Diffusion software. Multivariate logistic regression analysis and multiple linear regression analysis were performed to establish a predictive model for irreversible infarction occurrence and clinical severity.ResultsForty-five patients were assigned to the stroke group, and 22 were assigned to the TIA group. Plaque length, intraplaque hemorrhage (IPH), enhancement, AnS, and Tmax > 6.0-s volumes were significantly different between the two groups (p < 0.05). IPH and AnS were independent predictors for patients with stroke (p = 0.020 and 0.034, respectively). Tmax > 6.0-s volume, IPH, hypertension, and AnS were associated with high National Institutes of Health Stroke Scale (NIHSS) scores (all p < 0.05, R = 0.725, and adjusted R2 = 0.494).ConclusionsIPH and AnS are useful in predicting stroke occurrence. Tmax > 6.0-s volume, IPH, hypertension, and AnS are associated with neurological impairment of the patients.Key Points\u2022 Ischemic stroke and TIA patients have different plaque characteristics and hemodynamics.\u2022 Intraplaque hemorrhage and anterograde score have high diagnostic efficiency for ischemic stroke.\u2022 The combination of Tmax > 6.0-s volume, intraplaque hemorrhage, hypertension, and anterograde score can predict the National Institutes of Health Stroke Scale scores of patients.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00330-020-07327-1", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8877875", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8882794", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1289120", 
        "issn": [
          "0938-7994", 
          "1432-1084"
        ], 
        "name": "European Radiology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "31"
      }
    ], 
    "keywords": [
      "transient ischemic attack", 
      "Health Stroke Scale score", 
      "Stroke Scale score", 
      "middle cerebral artery", 
      "ischemic stroke", 
      "intraplaque hemorrhage", 
      "plaque characteristics", 
      "neurological impairment", 
      "ischemic attack", 
      "TIA group", 
      "stroke group", 
      "Scale score", 
      "multivariate logistic regression analysis", 
      "ResultsForty-five patients", 
      "higher National Institutes", 
      "MethodsSixty-seven patients", 
      "acute ischemic events", 
      "high-resolution vessel wall", 
      "National Institute", 
      "logistic regression analysis", 
      "regression analysis", 
      "different plaque characteristics", 
      "diffusion-weighted imaging", 
      "TIA patients", 
      "ischemic events", 
      "MCA territory", 
      "neurological examination", 
      "cerebral artery", 
      "independent predictors", 
      "stroke occurrence", 
      "clinical severity", 
      "plaque length", 
      "high diagnostic efficiency", 
      "multiple linear regression analysis", 
      "patients", 
      "linear regression analysis", 
      "stroke", 
      "hypertension", 
      "hemorrhage", 
      "hemodynamics", 
      "diagnostic efficiency", 
      "vessel wall", 
      "impairment", 
      "scores", 
      "group", 
      "Tmax", 
      "artery", 
      "diffusion software", 
      "perfusion", 
      "plaques", 
      "predictive model", 
      "severity", 
      "An", 
      "volume", 
      "predictors", 
      "ObjectivesWe", 
      "examination", 
      "Institute", 
      "occurrence", 
      "differential characteristics", 
      "imaging", 
      "characteristics", 
      "events", 
      "analysis", 
      "presence", 
      "combination", 
      "combination of Tmax", 
      "attacks", 
      "wall", 
      "length", 
      "model", 
      "rapid processing", 
      "enhancement", 
      "territory", 
      "processing", 
      "software", 
      "efficiency"
    ], 
    "name": "Plaque characteristics and hemodynamics contribute to neurological impairment in patients with ischemic stroke and transient ischemic attack", 
    "pagination": "2062-2072", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1131328251"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00330-020-07327-1"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "32997174"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00330-020-07327-1", 
      "https://app.dimensions.ai/details/publication/pub.1131328251"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:46", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_838.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00330-020-07327-1"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00330-020-07327-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00330-020-07327-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-020-07327-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-020-07327-1'


 

This table displays all metadata directly associated to this object as RDF triples.

307 TRIPLES      21 PREDICATES      112 URIs      103 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00330-020-07327-1 schema:about N057d054abd234abb9d536cf2057ddb9d
2 N0b09e32bcd484ba6a5a2f180e6fa555e
3 N133ea5edbc2045769c5db77781a1a94e
4 N8c5383fc7270474c9fa1c085e9beb457
5 N90c9a13be7f04a14b2f00f7781543fa3
6 Na15f9c51968b466c9f9d6f9c7cec6f8f
7 Nc081b942c1c34abf9a64311418945fdd
8 Ne4cecda1d31b41278e18401148964857
9 Nea5952f14453482aa309f6767f1e6a48
10 anzsrc-for:11
11 anzsrc-for:1109
12 schema:author N7957a9f199fe4ea1a4ffed5cb4e185bd
13 schema:citation sg:pub.10.1007/s00330-015-4008-5
14 schema:datePublished 2020-09-30
15 schema:datePublishedReg 2020-09-30
16 schema:description ObjectivesWe aimed to investigate differential characteristics of plaque in the middle cerebral artery (MCA) and hemodynamics in patients with ischemic stroke and transient ischemic attack (TIA), and to develop a predictive model for the presence of ischemic stroke and neurological impairment.MethodsSixty-seven patients with acute ischemic events in MCA territory who underwent high-resolution vessel wall imaging between September 2016 and August 2018 were reviewed retrospectively. Patients were assigned to either the stroke group or TIA group, according to diffusion-weighted imaging and neurological examination. Plaque characteristics and anterograde score (AnS) were calculated. Tmax > 6.0-s volume was acquired by RApid Processing of perfusIon and Diffusion software. Multivariate logistic regression analysis and multiple linear regression analysis were performed to establish a predictive model for irreversible infarction occurrence and clinical severity.ResultsForty-five patients were assigned to the stroke group, and 22 were assigned to the TIA group. Plaque length, intraplaque hemorrhage (IPH), enhancement, AnS, and Tmax > 6.0-s volumes were significantly different between the two groups (p < 0.05). IPH and AnS were independent predictors for patients with stroke (p = 0.020 and 0.034, respectively). Tmax > 6.0-s volume, IPH, hypertension, and AnS were associated with high National Institutes of Health Stroke Scale (NIHSS) scores (all p < 0.05, R = 0.725, and adjusted R2 = 0.494).ConclusionsIPH and AnS are useful in predicting stroke occurrence. Tmax > 6.0-s volume, IPH, hypertension, and AnS are associated with neurological impairment of the patients.Key Points• Ischemic stroke and TIA patients have different plaque characteristics and hemodynamics.• Intraplaque hemorrhage and anterograde score have high diagnostic efficiency for ischemic stroke.• The combination of Tmax > 6.0-s volume, intraplaque hemorrhage, hypertension, and anterograde score can predict the National Institutes of Health Stroke Scale scores of patients.
17 schema:genre article
18 schema:isAccessibleForFree false
19 schema:isPartOf N2142cdfcbfe84abd8489a425a3c45925
20 N38054718830441899c4bca6aab4259e2
21 sg:journal.1289120
22 schema:keywords An
23 Health Stroke Scale score
24 Institute
25 MCA territory
26 MethodsSixty-seven patients
27 National Institute
28 ObjectivesWe
29 ResultsForty-five patients
30 Scale score
31 Stroke Scale score
32 TIA group
33 TIA patients
34 Tmax
35 acute ischemic events
36 analysis
37 artery
38 attacks
39 cerebral artery
40 characteristics
41 clinical severity
42 combination
43 combination of Tmax
44 diagnostic efficiency
45 different plaque characteristics
46 differential characteristics
47 diffusion software
48 diffusion-weighted imaging
49 efficiency
50 enhancement
51 events
52 examination
53 group
54 hemodynamics
55 hemorrhage
56 high diagnostic efficiency
57 high-resolution vessel wall
58 higher National Institutes
59 hypertension
60 imaging
61 impairment
62 independent predictors
63 intraplaque hemorrhage
64 ischemic attack
65 ischemic events
66 ischemic stroke
67 length
68 linear regression analysis
69 logistic regression analysis
70 middle cerebral artery
71 model
72 multiple linear regression analysis
73 multivariate logistic regression analysis
74 neurological examination
75 neurological impairment
76 occurrence
77 patients
78 perfusion
79 plaque characteristics
80 plaque length
81 plaques
82 predictive model
83 predictors
84 presence
85 processing
86 rapid processing
87 regression analysis
88 scores
89 severity
90 software
91 stroke
92 stroke group
93 stroke occurrence
94 territory
95 transient ischemic attack
96 vessel wall
97 volume
98 wall
99 schema:name Plaque characteristics and hemodynamics contribute to neurological impairment in patients with ischemic stroke and transient ischemic attack
100 schema:pagination 2062-2072
101 schema:productId N81879037b84f4e39b77b591bb8e5c24d
102 N9f84b9031fba40219bcf9c253041fdaf
103 Nd672ecea247d49ff88f8b7fc199d2ed7
104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131328251
105 https://doi.org/10.1007/s00330-020-07327-1
106 schema:sdDatePublished 2022-10-01T06:46
107 schema:sdLicense https://scigraph.springernature.com/explorer/license/
108 schema:sdPublisher N72135a54dbfe460a89ef0523051942a1
109 schema:url https://doi.org/10.1007/s00330-020-07327-1
110 sgo:license sg:explorer/license/
111 sgo:sdDataset articles
112 rdf:type schema:ScholarlyArticle
113 N057d054abd234abb9d536cf2057ddb9d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Retrospective Studies
115 rdf:type schema:DefinedTerm
116 N0b09e32bcd484ba6a5a2f180e6fa555e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Brain Ischemia
118 rdf:type schema:DefinedTerm
119 N133ea5edbc2045769c5db77781a1a94e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Ischemic Stroke
121 rdf:type schema:DefinedTerm
122 N1504ef6275624c79817710aa2ba87621 rdf:first sg:person.010104411165.57
123 rdf:rest N2887c3fb6c704fd190e3e2db1ac774bd
124 N1bf8c3f59ea34e28a5244515f8fde8ad rdf:first sg:person.07435744344.39
125 rdf:rest N79c2569686c84748929129d897df59bc
126 N207abffe45ac44dc82c30903ae0566f2 rdf:first sg:person.01114220035.09
127 rdf:rest N9e187a10a1234b1e906651e8a99755ef
128 N2142cdfcbfe84abd8489a425a3c45925 schema:issueNumber 4
129 rdf:type schema:PublicationIssue
130 N2887c3fb6c704fd190e3e2db1ac774bd rdf:first sg:person.011454444615.00
131 rdf:rest Nc660889829154cda87fb9589a029864d
132 N301164f87a1c4633900bbb22d0e28f37 rdf:first sg:person.01116723555.39
133 rdf:rest N8f1dfbf9e97a4af2bd5bf32f182c2576
134 N38054718830441899c4bca6aab4259e2 schema:volumeNumber 31
135 rdf:type schema:PublicationVolume
136 N5cdebc8dd2c946e0b52b9ba1ce8a2cec rdf:first sg:person.01353064311.62
137 rdf:rest Nf34fef380ad84e0698c573b150d52fd1
138 N643969246ce24c8eb821677138a17632 rdf:first sg:person.0755674257.40
139 rdf:rest rdf:nil
140 N6692f54345e74e68a8e83918f8458318 rdf:first sg:person.0765643437.00
141 rdf:rest N5cdebc8dd2c946e0b52b9ba1ce8a2cec
142 N72135a54dbfe460a89ef0523051942a1 schema:name Springer Nature - SN SciGraph project
143 rdf:type schema:Organization
144 N7957a9f199fe4ea1a4ffed5cb4e185bd rdf:first sg:person.013024177765.48
145 rdf:rest N1bf8c3f59ea34e28a5244515f8fde8ad
146 N79c2569686c84748929129d897df59bc rdf:first sg:person.016607462365.57
147 rdf:rest N1504ef6275624c79817710aa2ba87621
148 N81879037b84f4e39b77b591bb8e5c24d schema:name pubmed_id
149 schema:value 32997174
150 rdf:type schema:PropertyValue
151 N8c5383fc7270474c9fa1c085e9beb457 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Magnetic Resonance Imaging
153 rdf:type schema:DefinedTerm
154 N8f1dfbf9e97a4af2bd5bf32f182c2576 rdf:first sg:person.011100762235.19
155 rdf:rest Nc87e4e2cc82348848c104c8cc50e98bb
156 N90c9a13be7f04a14b2f00f7781543fa3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Plaque, Atherosclerotic
158 rdf:type schema:DefinedTerm
159 N92f6dba148c645c59f9923ff480af746 rdf:first sg:person.01136172260.58
160 rdf:rest N643969246ce24c8eb821677138a17632
161 N9e187a10a1234b1e906651e8a99755ef rdf:first sg:person.01341033735.17
162 rdf:rest N301164f87a1c4633900bbb22d0e28f37
163 N9f84b9031fba40219bcf9c253041fdaf schema:name doi
164 schema:value 10.1007/s00330-020-07327-1
165 rdf:type schema:PropertyValue
166 Na15f9c51968b466c9f9d6f9c7cec6f8f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Hemodynamics
168 rdf:type schema:DefinedTerm
169 Nc081b942c1c34abf9a64311418945fdd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
170 schema:name Ischemic Attack, Transient
171 rdf:type schema:DefinedTerm
172 Nc660889829154cda87fb9589a029864d rdf:first sg:person.01166423053.59
173 rdf:rest N207abffe45ac44dc82c30903ae0566f2
174 Nc87e4e2cc82348848c104c8cc50e98bb rdf:first sg:person.011444626063.14
175 rdf:rest N6692f54345e74e68a8e83918f8458318
176 Nd672ecea247d49ff88f8b7fc199d2ed7 schema:name dimensions_id
177 schema:value pub.1131328251
178 rdf:type schema:PropertyValue
179 Ne4cecda1d31b41278e18401148964857 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
180 schema:name Stroke
181 rdf:type schema:DefinedTerm
182 Nea5952f14453482aa309f6767f1e6a48 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
183 schema:name Humans
184 rdf:type schema:DefinedTerm
185 Nf34fef380ad84e0698c573b150d52fd1 rdf:first sg:person.010241260205.32
186 rdf:rest N92f6dba148c645c59f9923ff480af746
187 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
188 schema:name Medical and Health Sciences
189 rdf:type schema:DefinedTerm
190 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
191 schema:name Neurosciences
192 rdf:type schema:DefinedTerm
193 sg:grant.8877875 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-020-07327-1
194 rdf:type schema:MonetaryGrant
195 sg:grant.8882794 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-020-07327-1
196 rdf:type schema:MonetaryGrant
197 sg:journal.1289120 schema:issn 0938-7994
198 1432-1084
199 schema:name European Radiology
200 schema:publisher Springer Nature
201 rdf:type schema:Periodical
202 sg:person.010104411165.57 schema:affiliation grid-institutes:grid.417024.4
203 schema:familyName Chai
204 schema:givenName Shengting
205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010104411165.57
206 rdf:type schema:Person
207 sg:person.010241260205.32 schema:affiliation grid-institutes:grid.413605.5
208 schema:familyName Jin
209 schema:givenName Song
210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010241260205.32
211 rdf:type schema:Person
212 sg:person.011100762235.19 schema:affiliation grid-institutes:grid.417032.3
213 schema:familyName Zheng
214 schema:givenName Meizhu
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011100762235.19
216 rdf:type schema:Person
217 sg:person.01114220035.09 schema:affiliation grid-institutes:grid.417024.4
218 schema:familyName Guo
219 schema:givenName Yu
220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01114220035.09
221 rdf:type schema:Person
222 sg:person.01116723555.39 schema:affiliation grid-institutes:grid.417024.4
223 schema:familyName Huang
224 schema:givenName Lixiang
225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01116723555.39
226 rdf:type schema:Person
227 sg:person.01136172260.58 schema:affiliation grid-institutes:grid.50956.3f
228 schema:familyName Fan
229 schema:givenName Zhaoyang
230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58
231 rdf:type schema:Person
232 sg:person.011444626063.14 schema:affiliation grid-institutes:grid.452598.7
233 schema:familyName Zhu
234 schema:givenName Jinxia
235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011444626063.14
236 rdf:type schema:Person
237 sg:person.011454444615.00 schema:affiliation grid-institutes:grid.417024.4
238 schema:familyName Zhang
239 schema:givenName Qingqing
240 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011454444615.00
241 rdf:type schema:Person
242 sg:person.01166423053.59 schema:affiliation grid-institutes:grid.24516.34
243 schema:familyName Luo
244 schema:givenName Yu
245 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01166423053.59
246 rdf:type schema:Person
247 sg:person.013024177765.48 schema:affiliation grid-institutes:grid.417024.4
248 schema:familyName Liu
249 schema:givenName Song
250 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013024177765.48
251 rdf:type schema:Person
252 sg:person.01341033735.17 schema:affiliation grid-institutes:grid.417024.4
253 schema:familyName Chai
254 schema:givenName Chao
255 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01341033735.17
256 rdf:type schema:Person
257 sg:person.01353064311.62 schema:affiliation grid-institutes:grid.24696.3f
258 schema:familyName Yang
259 schema:givenName Qi
260 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353064311.62
261 rdf:type schema:Person
262 sg:person.016607462365.57 schema:affiliation grid-institutes:grid.417024.4
263 schema:familyName Xie
264 schema:givenName Weiwei
265 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016607462365.57
266 rdf:type schema:Person
267 sg:person.07435744344.39 schema:affiliation grid-institutes:grid.417024.4
268 schema:familyName Tang
269 schema:givenName Ruowei
270 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07435744344.39
271 rdf:type schema:Person
272 sg:person.0755674257.40 schema:affiliation grid-institutes:grid.417024.4
273 schema:familyName Xia
274 schema:givenName Shuang
275 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755674257.40
276 rdf:type schema:Person
277 sg:person.0765643437.00 schema:affiliation grid-institutes:grid.417024.4
278 schema:familyName Chang
279 schema:givenName Binge
280 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0765643437.00
281 rdf:type schema:Person
282 sg:pub.10.1007/s00330-015-4008-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009298470
283 https://doi.org/10.1007/s00330-015-4008-5
284 rdf:type schema:CreativeWork
285 grid-institutes:grid.24516.34 schema:alternateName Department of Radiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, 200081, Shanghai, China
286 schema:name Department of Radiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, 200081, Shanghai, China
287 rdf:type schema:Organization
288 grid-institutes:grid.24696.3f schema:alternateName Department of Radiology, Beijing Xuanwu Hospital, Capital Medical University, 100053, Beijing, China
289 schema:name Department of Radiology, Beijing Xuanwu Hospital, Capital Medical University, 100053, Beijing, China
290 rdf:type schema:Organization
291 grid-institutes:grid.413605.5 schema:alternateName Department of Radiology, Tianjin Huanhu Hospital, 300350, Tianjin, China
292 schema:name Department of Radiology, Tianjin Huanhu Hospital, 300350, Tianjin, China
293 rdf:type schema:Organization
294 grid-institutes:grid.417024.4 schema:alternateName Department of Neurosurgery, Tianjin First Central Hospital, 300192, Tianjin, China
295 Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China
296 schema:name Department of Neurosurgery, Tianjin First Central Hospital, 300192, Tianjin, China
297 Department of Radiology, Tianjin First Central Hospital, No.24 Fukang Road, Nankai District, 300192, Tianjin, China
298 rdf:type schema:Organization
299 grid-institutes:grid.417032.3 schema:alternateName Department of Radiology, Tianjin Third Central Hospital, 300170, Tianjin, China
300 schema:name Department of Radiology, Tianjin Third Central Hospital, 300170, Tianjin, China
301 rdf:type schema:Organization
302 grid-institutes:grid.452598.7 schema:alternateName MR Collaboration, Siemens Healthcare Ltd., 100102, Beijing, China
303 schema:name MR Collaboration, Siemens Healthcare Ltd., 100102, Beijing, China
304 rdf:type schema:Organization
305 grid-institutes:grid.50956.3f schema:alternateName Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA
306 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA
307 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...