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Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage
BACKGROUND: Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth. METHODS: We used data from the...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634162/ https://www.ncbi.nlm.nih.gov/pubmed/36341120 http://dx.doi.org/10.3389/fneur.2022.999223 |
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author | Zhao, Mingpei Huang, Wei Huang, Shuna Lin, Fuxin He, Qiu Zheng, Yan Gao, Zhuyu Cai, Lveming Ye, Gengzhao Chen, Renlong Wu, Siying Fang, Wenhua Wang, Dengliang Lin, Yuanxiang Kang, Dezhi Yu, Lianghong |
author_facet | Zhao, Mingpei Huang, Wei Huang, Shuna Lin, Fuxin He, Qiu Zheng, Yan Gao, Zhuyu Cai, Lveming Ye, Gengzhao Chen, Renlong Wu, Siying Fang, Wenhua Wang, Dengliang Lin, Yuanxiang Kang, Dezhi Yu, Lianghong |
author_sort | Zhao, Mingpei |
collection | PubMed |
description | BACKGROUND: Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth. METHODS: We used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT. RESULTS: A total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %. CONCLUSION: The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently. |
format | Online Article Text |
id | pubmed-9634162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96341622022-11-05 Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage Zhao, Mingpei Huang, Wei Huang, Shuna Lin, Fuxin He, Qiu Zheng, Yan Gao, Zhuyu Cai, Lveming Ye, Gengzhao Chen, Renlong Wu, Siying Fang, Wenhua Wang, Dengliang Lin, Yuanxiang Kang, Dezhi Yu, Lianghong Front Neurol Neurology BACKGROUND: Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth. METHODS: We used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT. RESULTS: A total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %. CONCLUSION: The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently. Frontiers Media S.A. 2022-10-21 /pmc/articles/PMC9634162/ /pubmed/36341120 http://dx.doi.org/10.3389/fneur.2022.999223 Text en Copyright © 2022 Zhao, Huang, Huang, Lin, He, Zheng, Gao, Cai, Ye, Chen, Wu, Fang, Wang, Lin, Kang and Yu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Zhao, Mingpei Huang, Wei Huang, Shuna Lin, Fuxin He, Qiu Zheng, Yan Gao, Zhuyu Cai, Lveming Ye, Gengzhao Chen, Renlong Wu, Siying Fang, Wenhua Wang, Dengliang Lin, Yuanxiang Kang, Dezhi Yu, Lianghong Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage |
title | Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage |
title_full | Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage |
title_fullStr | Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage |
title_full_unstemmed | Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage |
title_short | Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage |
title_sort | quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634162/ https://www.ncbi.nlm.nih.gov/pubmed/36341120 http://dx.doi.org/10.3389/fneur.2022.999223 |
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