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Updated: May 3, 2026

A Low Mortality Rat Model to Assess Delayed Cerebral Vasospasm After Experimental Subarachnoid Hemorrhage
Published on: January 17, 2013
A comprehensive prediction model predicts perihematomal edema growth in the acute stage after intracerebral
Shu-Qiang Zhang1, Yan-Ling Zhang2, Liang Yuan3
1Department of Radiology, Chongqing University FuLing Hospital, Chongqing 408000, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Background:
Perihematomal edema (PHE) is regarded as a potential intervention indicator of secondary injury following intracerebral hemorrhage (ICH). But it still lacks a comprehensive prediction model for early PHE formation.
Methods:
The included ICH patients have received an initial Computed Tomography scan within 6 hours of symptom onset. Hematoma volume and PHE volume were computed using semiautomated computer-assisted software. The volume of the hematoma, edema around the hematoma, and surface area of the hematoma were calculated. The platelet-to-lymphocyte ratio (PLR) was calculated by dividing the platelet count by the lymphocyte cell count. All analyses were 2-tailed, and the significance level was determined by P <0.05.
Results:
A total of 226 patients were included in the final analysis. The optimal cut-off values for PHE volume increase to predict poor outcomes were determined as 5.5 mL. For clinical applicability, we identified a value of 5.5 mL as the optimal threshold for early PHE growth. In the multivariate logistic regression analyses, we finally found that baseline hematoma surface area (p < 0.001), expansion-prone hematoma (p < 0.001), and PLR (p = 0.033) could independently predict PHE growth. The comprehensive prediction model demonstrated good performance in predicting PHE growth, with an area under the curve of 0.841, sensitivity of 0.807, and specificity of 0.732.
Conclusion:
In this study, we found that baseline hematoma surface area, expansion-prone hematoma, and PLR were independently associated with PHE growth. Additionally, a risk nomogram model was established to predict the PHE growth in patients with ICH.
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