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Background <p>Understanding the risk factors for hematoma expansion (HE) in different regions of intracerebral hemorrhage (ICH) can help in the development of more accurate HE prediction tools and in implementing more effective clinical treatment interventions. This study aims to investigate the risk factors for HE in patients with lobar and deep ICH.</p> Methods <p>A retrospective analysis was conducted on 558 cases of primary supratentorial ICH from Tongji Hospital Affiliated to Tongji University. Patients were categorized into lobar ICH and deep ICH groups. Differential analysis of ICH characteristics at different locations was performed, followed by subgroup analysis based on HE occurrence. Binary logistic regression was used to identify independent risk factors for HE in each group.</p> Results <p>Among the 404 patients with ICH who underwent follow-up noncontrast computed tomography (NCCT) scans, the proportion with HE was similar in the deep ICH group (23.2%) and the lobar ICH group (22.7%). Binary logistic regression analysis revealed that fluid level (odds ratio [OR] 4.77, 95% confidence interval [CI] 1.74–13.06), admission Glasgow Coma Scale score (OR 0.87, 95% CI 0.80–0.96), and time from onset to NCCT examination (OR 0.84, 95% CI 0.75–0.94) were independently associated with HE in the&#xa0;deep ICH&#xa0;group. In the&#xa0;lobar ICH&#xa0;group, irregular shape (OR 4.96, 95% CI 1.37–18.01) and fibrinogen level (OR 0.42, 95% CI 0.21–0.86) were significant risk factors.</p> Conclusions <p>Fluid level, low admission Glasgow Coma Scale score, and shorter time from onset to NCCT are independent predictors of HE in deep ICH, whereas irregular shape and low fibrinogen levels are independent predictors of HE in lobar ICH. These findings are of great significance for elucidating the mechanisms underlying HE in different locations of ICH and for developing precise predictive models of HE.</p>

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Differential Risk Factors for Hematoma Expansion in Deep and Lobar Intracerebral Hemorrhage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月4日

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  • Kangwei Zhang,
  • Baoqing Yang,
  • Lai Wei,
  • Xiang Zhou,
  • Fushi Han,
  • Jinxi Meng,
  • Xingyu Zhao,
  • Bo Zhang,
  • Daxiao Chen,
  • Peijun Wang

摘要

Background

Understanding the risk factors for hematoma expansion (HE) in different regions of intracerebral hemorrhage (ICH) can help in the development of more accurate HE prediction tools and in implementing more effective clinical treatment interventions. This study aims to investigate the risk factors for HE in patients with lobar and deep ICH.

Methods

A retrospective analysis was conducted on 558 cases of primary supratentorial ICH from Tongji Hospital Affiliated to Tongji University. Patients were categorized into lobar ICH and deep ICH groups. Differential analysis of ICH characteristics at different locations was performed, followed by subgroup analysis based on HE occurrence. Binary logistic regression was used to identify independent risk factors for HE in each group.

Results

Among the 404 patients with ICH who underwent follow-up noncontrast computed tomography (NCCT) scans, the proportion with HE was similar in the deep ICH group (23.2%) and the lobar ICH group (22.7%). Binary logistic regression analysis revealed that fluid level (odds ratio [OR] 4.77, 95% confidence interval [CI] 1.74–13.06), admission Glasgow Coma Scale score (OR 0.87, 95% CI 0.80–0.96), and time from onset to NCCT examination (OR 0.84, 95% CI 0.75–0.94) were independently associated with HE in the deep ICH group. In the lobar ICH group, irregular shape (OR 4.96, 95% CI 1.37–18.01) and fibrinogen level (OR 0.42, 95% CI 0.21–0.86) were significant risk factors.

Conclusions

Fluid level, low admission Glasgow Coma Scale score, and shorter time from onset to NCCT are independent predictors of HE in deep ICH, whereas irregular shape and low fibrinogen levels are independent predictors of HE in lobar ICH. These findings are of great significance for elucidating the mechanisms underlying HE in different locations of ICH and for developing precise predictive models of HE.