Quantification of clot spatial heterogeneity and its impact on thrombectomy Article Swipe
Yang Liu
,
Waleed Brinjikji
,
Mehdi Abbasi
,
Daying Dai
,
Jorge Arturo Larco
,
Sarosh Irfan Madhani
,
Adnan Hussain Shahid
,
Oana Madalina Mereuta
,
Raul G. Nogueira
,
Peter Kvamme
,
Kennith F. Layton
,
Josser E Delgado Almandoz
,
Ricardó A. Hanel
,
Vítor Mendes Pereira
,
Mohammed Almekhlafi
,
Albert J. Yoo
,
Babak S. Jahromi
,
Matthew J. Gounis
,
Biraj M. Patel
,
Seán Fitzgerald
,
Karen Doyle
,
Diogo C Haussen
,
Alhamza R Al‐Bayati
,
Mahmoud Mohammaden
,
Leonardo Pisani
,
Gabriel Martins Rodrigues
,
Ike Thacker
,
Yasha Kayan
,
Alexander Copelan
,
Amin Aghaebrahim
,
Eric Sauvageau
,
Andrew M. Demchuk
,
Parita Bhuva
,
Jazba Soomro
,
Pouya Nazari
,
Donald R. Cantrell
,
Ajit S Puri
,
John W. Entwistle
,
Ramanathan Kadirvel
,
Harry J. Cloft
,
David F. Kallmes
,
Luis Savastano
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1136/neurintsurg-2021-018183
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1136/neurintsurg-2021-018183
Background Compositional and structural features of retrieved clots by thrombectomy can provide insight into improving the endovascular treatment of ischemic stroke. Currently, histological analysis is limited to quantification of compositions and qualitative description of the clot structure. We hypothesized that heterogeneous clots would be prone to poorer recanalization rates and performed a quantitative analysis to test this hypothesis. Methods We collected and did histology on clots retrieved by mechanical thrombectomy from 157 stroke cases (107 achieved first-pass effect (FPE) and 50 did not). Using an in-house algorithm, the scanned images were divided into grids (with sizes of 0.2, 0.3, 0.4, 0.5, and 0.6 mm) and the extent of non-uniformity of RBC distribution was computed using the proposed spatial heterogeneity index (SHI). Finally, we validated the clinical significance of clot heterogeneity using the Mann–Whitney test and an artificial neural network (ANN) model. Results For cases with FPE, SHI values were smaller (0.033 vs 0.039 for grid size of 0.4 mm, P=0.028) compared with those without. In comparison, the clot composition was not statistically different between those two groups. From the ANN model, clot heterogeneity was the most important factor, followed by fibrin content, thrombectomy techniques, red blood cell content, clot area, platelet content, etiology, and admission of intravenous tissue plasminogen activator (IV-tPA). No statistical difference of clot heterogeneity was found for different etiologies, thrombectomy techniques, and IV-tPA administration. Conclusions Clot heterogeneity can affect the clot response to thrombectomy devices and is associated with lower FPE. SHI can be a useful metric to quantify clot heterogeneity.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/neurintsurg-2021-018183
- OA Status
- green
- Cited By
- 23
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200067996
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200067996Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1136/neurintsurg-2021-018183Digital Object Identifier
- Title
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Quantification of clot spatial heterogeneity and its impact on thrombectomyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-15Full publication date if available
- Authors
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Yang Liu, Waleed Brinjikji, Mehdi Abbasi, Daying Dai, Jorge Arturo Larco, Sarosh Irfan Madhani, Adnan Hussain Shahid, Oana Madalina Mereuta, Raul G. Nogueira, Peter Kvamme, Kennith F. Layton, Josser E Delgado Almandoz, Ricardó A. Hanel, Vítor Mendes Pereira, Mohammed Almekhlafi, Albert J. Yoo, Babak S. Jahromi, Matthew J. Gounis, Biraj M. Patel, Seán Fitzgerald, Karen Doyle, Diogo C Haussen, Alhamza R Al‐Bayati, Mahmoud Mohammaden, Leonardo Pisani, Gabriel Martins Rodrigues, Ike Thacker, Yasha Kayan, Alexander Copelan, Amin Aghaebrahim, Eric Sauvageau, Andrew M. Demchuk, Parita Bhuva, Jazba Soomro, Pouya Nazari, Donald R. Cantrell, Ajit S Puri, John W. Entwistle, Ramanathan Kadirvel, Harry J. Cloft, David F. Kallmes, Luis SavastanoList of authors in order
- Landing page
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https://doi.org/10.1136/neurintsurg-2021-018183Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11178127Direct OA link when available
- Concepts
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Medicine, Stroke (engine), Thrombolysis, Fibrin, Internal medicine, Cardiology, Myocardial infarction, Engineering, Mechanical engineering, ImmunologyTop concepts (fields/topics) attached by OpenAlex
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23Total citation count in OpenAlex
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2025: 4, 2024: 6, 2023: 8, 2022: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 2, 30, 49, 61, 79, 101, 104, 134, 203, 224, 238 |
| abstract_inverted_index.can | 10, 230, 245 |
| abstract_inverted_index.did | 62, 81 |
| abstract_inverted_index.for | 153, 219 |
| abstract_inverted_index.mm) | 103 |
| abstract_inverted_index.mm, | 158 |
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| abstract_inverted_index.red | 194 |
| abstract_inverted_index.the | 15, 34, 87, 105, 115, 124, 131, 166, 178, 184, 232 |
| abstract_inverted_index.two | 175 |
| abstract_inverted_index.was | 112, 169, 183, 217 |
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| abstract_inverted_index.0.3, | 98 |
| abstract_inverted_index.0.4, | 99 |
| abstract_inverted_index.0.5, | 100 |
| abstract_inverted_index.Clot | 228 |
| abstract_inverted_index.FPE, | 145 |
| abstract_inverted_index.FPE. | 243 |
| abstract_inverted_index.From | 177 |
| abstract_inverted_index.cell | 196 |
| abstract_inverted_index.clot | 35, 128, 167, 181, 198, 215, 233, 252 |
| abstract_inverted_index.from | 70 |
| abstract_inverted_index.grid | 154 |
| abstract_inverted_index.into | 13, 92 |
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| abstract_inverted_index.size | 155 |
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| abstract_inverted_index.this | 56 |
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| abstract_inverted_index.with | 144, 161, 241 |
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| abstract_inverted_index.(FPE) | 78 |
| abstract_inverted_index.(with | 94 |
| abstract_inverted_index.0.039 | 152 |
| abstract_inverted_index.Using | 83 |
| abstract_inverted_index.area, | 199 |
| abstract_inverted_index.blood | 195 |
| abstract_inverted_index.cases | 73, 143 |
| abstract_inverted_index.clots | 7, 41, 65 |
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| abstract_inverted_index.grids | 93 |
| abstract_inverted_index.index | 119 |
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| abstract_inverted_index.not). | 82 |
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| abstract_inverted_index.would | 42 |
| abstract_inverted_index.(0.033 | 150 |
| abstract_inverted_index.(SHI). | 120 |
| abstract_inverted_index.IV-tPA | 225 |
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| abstract_inverted_index.effect | 77 |
| abstract_inverted_index.extent | 106 |
| abstract_inverted_index.fibrin | 190 |
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| abstract_inverted_index.model. | 140 |
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| abstract_inverted_index.stroke | 72 |
| abstract_inverted_index.tissue | 207 |
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| abstract_inverted_index.values | 147 |
| abstract_inverted_index.Methods | 58 |
| abstract_inverted_index.Results | 141 |
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| abstract_inverted_index.devices | 237 |
| abstract_inverted_index.divided | 91 |
| abstract_inverted_index.factor, | 187 |
| abstract_inverted_index.groups. | 176 |
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| abstract_inverted_index.limited | 25 |
| abstract_inverted_index.network | 138 |
| abstract_inverted_index.provide | 11 |
| abstract_inverted_index.scanned | 88 |
| abstract_inverted_index.smaller | 149 |
| abstract_inverted_index.spatial | 117 |
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| abstract_inverted_index.analysis | 23, 53 |
| abstract_inverted_index.clinical | 125 |
| abstract_inverted_index.compared | 160 |
| abstract_inverted_index.computed | 113 |
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| abstract_inverted_index.features | 4 |
| abstract_inverted_index.followed | 188 |
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| abstract_inverted_index.ischemic | 19 |
| abstract_inverted_index.platelet | 200 |
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| abstract_inverted_index.quantify | 251 |
| abstract_inverted_index.response | 234 |
| abstract_inverted_index.without. | 163 |
| abstract_inverted_index.(IV-tPA). | 210 |
| abstract_inverted_index.activator | 209 |
| abstract_inverted_index.admission | 204 |
| abstract_inverted_index.collected | 60 |
| abstract_inverted_index.different | 172, 220 |
| abstract_inverted_index.etiology, | 202 |
| abstract_inverted_index.histology | 63 |
| abstract_inverted_index.important | 186 |
| abstract_inverted_index.improving | 14 |
| abstract_inverted_index.performed | 50 |
| abstract_inverted_index.retrieved | 6, 66 |
| abstract_inverted_index.treatment | 17 |
| abstract_inverted_index.validated | 123 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Currently, | 21 |
| abstract_inverted_index.algorithm, | 86 |
| abstract_inverted_index.artificial | 136 |
| abstract_inverted_index.associated | 240 |
| abstract_inverted_index.difference | 213 |
| abstract_inverted_index.first-pass | 76 |
| abstract_inverted_index.mechanical | 68 |
| abstract_inverted_index.structural | 3 |
| abstract_inverted_index.structure. | 36 |
| abstract_inverted_index.Conclusions | 227 |
| abstract_inverted_index.comparison, | 165 |
| abstract_inverted_index.composition | 168 |
| abstract_inverted_index.description | 32 |
| abstract_inverted_index.etiologies, | 221 |
| abstract_inverted_index.hypothesis. | 57 |
| abstract_inverted_index.intravenous | 206 |
| abstract_inverted_index.plasminogen | 208 |
| abstract_inverted_index.qualitative | 31 |
| abstract_inverted_index.statistical | 212 |
| abstract_inverted_index.techniques, | 193, 223 |
| abstract_inverted_index.compositions | 29 |
| abstract_inverted_index.distribution | 111 |
| abstract_inverted_index.endovascular | 16 |
| abstract_inverted_index.histological | 22 |
| abstract_inverted_index.hypothesized | 38 |
| abstract_inverted_index.quantitative | 52 |
| abstract_inverted_index.significance | 126 |
| abstract_inverted_index.thrombectomy | 9, 69, 192, 222, 236 |
| abstract_inverted_index.Compositional | 1 |
| abstract_inverted_index.heterogeneity | 118, 129, 182, 216, 229 |
| abstract_inverted_index.heterogeneous | 40 |
| abstract_inverted_index.statistically | 171 |
| abstract_inverted_index.Mann–Whitney | 132 |
| abstract_inverted_index.heterogeneity. | 253 |
| abstract_inverted_index.non-uniformity | 108 |
| abstract_inverted_index.quantification | 27 |
| abstract_inverted_index.recanalization | 47 |
| abstract_inverted_index.administration. | 226 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5031364712 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 42 |
| corresponding_institution_ids | https://openalex.org/I1330342723 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.91884866 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |