The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume Article Swipe
YOU?
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· 2021
· Open Access
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· DOI: https://doi.org/10.1177/17474930211050749
Background Among prognostic imaging variables, the hematoma volume on admission computed tomography (CT) has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage. Aims To examine whether different features of hematoma shape are associated with functional outcome in deep intracerebral hemorrhage. Methods We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearman’s Rho to assess the correlation between shape features and three-month modified Rankin scale (mRS) score, and the area under the receiver operating characteristic curve (AUC) to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS > 3. Results Among 14 shape features, the maximum intracerebral hemorrhage diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >∼3.5 cm indicating higher three-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 versus 0.35 ( p = 0.006), an AUC [mRS>2] of 0.71 versus 0.68 ( p = 0.004), and an AUC [mRS>3] of 0.71 versus 0.69 ( p = 0.029). In multiple regression analysis adjusted for known outcome predictors, the maximum coronal diameter was independently associated with three-month mRS (p < 0.001). Conclusions A coronal-plane maximum diameter measurement offers greater prognostic value in deep intracerebral hemorrhage than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials, and may substitute volume in prognostic intracerebral hemorrhage scoring systems.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/17474930211050749
- OA Status
- green
- Cited By
- 9
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3203163810
Raw OpenAlex JSON
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https://openalex.org/W3203163810Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/17474930211050749Digital Object Identifier
- Title
-
The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volumeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-27Full publication date if available
- Authors
-
Stefan P. Haider, Adnan I. Qureshi, Abhi Jain, Hishan Tharmaseelan, Elisa R. Berson, Shahram Majidi, Christopher G. Filippi, Adrian Mak, David J. Werring, Julián Acosta, Ajay Malhotra, Jennifer A. Kim, Lauren Sansing, Guido J. Falcone, Kevin N. Sheth, Seyedmehdi PayabvashList of authors in order
- Landing page
-
https://doi.org/10.1177/17474930211050749Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/9005571Direct OA link when available
- Concepts
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Coronal plane, Medicine, Intracerebral hemorrhage, Modified Rankin Scale, Hematoma, Receiver operating characteristic, Nuclear medicine, Radiology, Coronal suture, Internal medicine, Surgery, Glasgow Coma Scale, Ischemia, Ischemic strokeTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 64, 88 |
| abstract_inverted_index.790 | 48 |
| abstract_inverted_index.AUC | 159, 171 |
| abstract_inverted_index.Rho | 63, 149 |
| abstract_inverted_index.The | 140 |
| abstract_inverted_index.and | 22, 54, 71, 78, 95, 169, 243 |
| abstract_inverted_index.are | 36 |
| abstract_inverted_index.for | 186, 235 |
| abstract_inverted_index.has | 13 |
| abstract_inverted_index.mRS | 104, 138, 199 |
| abstract_inverted_index.may | 224, 244 |
| abstract_inverted_index.the | 5, 17, 51, 66, 79, 82, 90, 112, 118, 122, 190 |
| abstract_inverted_index.was | 121, 194 |
| abstract_inverted_index.> | 105 |
| abstract_inverted_index.< | 201 |
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| abstract_inverted_index.0.35 | 153 |
| abstract_inverted_index.0.40 | 151 |
| abstract_inverted_index.0.68 | 164 |
| abstract_inverted_index.0.69 | 176 |
| abstract_inverted_index.0.71 | 162, 174 |
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| abstract_inverted_index.size | 237 |
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| abstract_inverted_index.well | 102 |
| abstract_inverted_index.were | 58 |
| abstract_inverted_index.with | 38, 128, 197 |
| abstract_inverted_index.(AUC) | 87 |
| abstract_inverted_index.(mRS) | 76 |
| abstract_inverted_index.Among | 1, 108 |
| abstract_inverted_index.curve | 86 |
| abstract_inverted_index.known | 187 |
| abstract_inverted_index.offer | 231 |
| abstract_inverted_index.plane | 120 |
| abstract_inverted_index.scale | 75 |
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| abstract_inverted_index.value | 212 |
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| abstract_inverted_index.assess | 65 |
| abstract_inverted_index.higher | 136 |
| abstract_inverted_index.metric | 223 |
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| abstract_inverted_index.score, | 77 |
| abstract_inverted_index.simple | 221 |
| abstract_inverted_index.trial, | 53 |
| abstract_inverted_index.versus | 144, 152, 163, 175 |
| abstract_inverted_index.volume | 7, 146, 246 |
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| abstract_inverted_index.0.004), | 168 |
| abstract_inverted_index.0.006), | 157 |
| abstract_inverted_index.0.029). | 180 |
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| abstract_inverted_index.coronal | 119, 131, 142, 192 |
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| abstract_inverted_index.outcome | 21, 40, 97, 188 |
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| abstract_inverted_index.scoring | 251 |
| abstract_inverted_index.trials, | 242 |
| abstract_inverted_index.volume. | 219 |
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| abstract_inverted_index.adjusted | 185 |
| abstract_inverted_index.analysis | 184 |
| abstract_inverted_index.analyzed | 47 |
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| abstract_inverted_index.criteria | 239 |
| abstract_inverted_index.diameter | 116, 132, 143, 193, 207 |
| abstract_inverted_index.expedite | 225 |
| abstract_inverted_index.features | 32, 57, 70, 94 |
| abstract_inverted_index.hematoma | 6, 34, 145, 218, 236 |
| abstract_inverted_index.mRS>2 | 100 |
| abstract_inverted_index.modified | 73 |
| abstract_inverted_index.multiple | 182 |
| abstract_inverted_index.outcome, | 127 |
| abstract_inverted_index.patients | 49 |
| abstract_inverted_index.quantify | 89 |
| abstract_inverted_index.receiver | 83 |
| abstract_inverted_index.systems. | 252 |
| abstract_inverted_index.admission | 9, 228 |
| abstract_inverted_index.biomarker | 234 |
| abstract_inverted_index.different | 31 |
| abstract_inverted_index.features, | 111 |
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| abstract_inverted_index.operating | 84 |
| abstract_inverted_index.potential | 233 |
| abstract_inverted_index.predictor | 19, 124 |
| abstract_inverted_index.strongest | 18, 123 |
| abstract_inverted_index.>∼3.5 | 133 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.[mRS>2] | 160 |
| abstract_inverted_index.[mRS>3] | 172 |
| abstract_inverted_index.assessment | 226 |
| abstract_inverted_index.associated | 37, 196 |
| abstract_inverted_index.calculated | 61 |
| abstract_inverted_index.considered | 16 |
| abstract_inverted_index.functional | 39, 126 |
| abstract_inverted_index.hemorrhage | 115, 216, 250 |
| abstract_inverted_index.indicating | 135 |
| abstract_inverted_index.prognostic | 2, 211, 248 |
| abstract_inverted_index.regression | 183 |
| abstract_inverted_index.substitute | 245 |
| abstract_inverted_index.tomography | 11 |
| abstract_inverted_index.variables, | 4 |
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| abstract_inverted_index.association | 91 |
| abstract_inverted_index.correlation | 67 |
| abstract_inverted_index.eligibility | 238 |
| abstract_inverted_index.hemorrhage. | 26, 44 |
| abstract_inverted_index.measurement | 208 |
| abstract_inverted_index.predictors, | 189 |
| abstract_inverted_index.quantified. | 59 |
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| abstract_inverted_index.Spearman’s | 62 |
| abstract_inverted_index.coronal-plane | 205 |
| abstract_inverted_index.independently | 195 |
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| abstract_inverted_index.characteristic | 85 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 16 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.72434069 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |