The association between cerebral small vessel disease and unfavorable hematoma morphology in primary intracerebral hemorrhage
Objective To study the association between cerebral small vessel diseases (CSVD) and unfavorable hematoma morphology in primary intracerebral hemorrhage (ICH).Methods Patients with primary ICH who were admitted to West China Hospital of Sichuan University from March 2012 to January 2021 were consecu...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Annals of Medicine |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2530226 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Objective To study the association between cerebral small vessel diseases (CSVD) and unfavorable hematoma morphology in primary intracerebral hemorrhage (ICH).Methods Patients with primary ICH who were admitted to West China Hospital of Sichuan University from March 2012 to January 2021 were consecutively included. The unfavorable hematoma morphology included any hypodensity, any irregularity, black hole, blend sign, Barras shape score ≥3, Barras density score ≥3, immature hematoma and combined Barras total score (CBTS) ≥4. The combined hematoma morphology score (CHMS) was evaluated by allocating 1 point for the presence of each of the mentioned unfavorable hematoma morphology. Multivariable binary logistic and ordinal regressions, together with unsupervised machine learning, were used to determine the association between CSVD and unfavorable hematoma morphology features.Results Univariable analysis showed that older age and hypertension were associated with white matter hyperintensities (WMH) presence. Regarding hematoma morphology, Barras density score ≥3, CBTS ≥4 and higher CHMS were associated with WMH absence (all p < 0.05). Multivariable regression indicated that lower WMH presence were significantly associated with both CBTS ≥4 and higher CHMS after correcting for confounders. Futhermore, we employed unsupervised machine learning using K-means algorithm to cluster patients into different groups according to CSVD burden, and the results showed that cluster with higher CSVD burden was less likely to be associated with unfavorable hematoma morphology such as black hole and higher CHMS after correcting for confounders.Conclusions Lower CSVD burden might be associated with higher incidence of unfavorable hematoma morphology features, such as CTBS ≥4 and higher CHMS. |
|---|---|
| ISSN: | 0785-3890 1365-2060 |