Cerebral small vessel disease imaging markers predict hematoma expansion in patients with spontaneous intracerebral hemorrhage
10.3760/cma.j.issn.1673-4165.2021.08.007
- VernacularTitle:脑小血管病影像学标志预测自发性脑出血患者的血肿增大
- Author:
Di GAO
1
;
Lijun LIU
;
Yanhong YANG
;
Hong LI
;
Lanjing WANG
;
Min CHU
;
Jijun TENG
Author Information
1. 青岛大学附属医院神经内科，青岛 266003
- Keywords:
Cerebral hemorrhage;
Cerebral small vessel diseases;
Hematoma;
Magnetic resonance imaging;
Tomography, X-ray computed
- From:
International Journal of Cerebrovascular Diseases
2021;29(8):594-601
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the correlation between the imaging markers of cerebral small vessel disease (CSVD) and early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH).Methods:Patients with sICH admitted to the Department of Neurology, the Affiliated Hospital of Qingdao University between January 1, 2015 and December 31, 2019 were enrolled retrospectively. All patients received noncontrast CT (NCCT) within 6 h after onset. Within 24 h after the initial NCCT examination, they were reexamed to determine whether HE occurred, and brain MRI examination was completed within 48 h after onset. HE was defined as the increase of hematoma volume on NCCT reexamination by >33% or >6 ml compared with the baseline. NCCT was used to evaluate the abnormal morphology and density signs, including blend sign, swirl sign, black hole sign, island sign, and satellite sign. MRI was used to evaluate CSVD imaging markers, including lacunar infarcts (LIs), enlarged perivascular space (EPVS), white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and cortical superficial siderosis (CSS). Multivariate logistic regression analysis was used to determine independent risk factors for HE. The receiver operator characteristic (ROC) curve was used to evaluate the predictive ability of imaging markers for HE in patients with sICH. Results:A total of 216 patients with sICH were included. Their age was 57±15 years, 113 (61.6%) were male, 88 (40.7%) had HE, 123 (56.9%) had NCCT signs, 122 (56.5%) had CMBs, 143 (66.2%) had WMHs, 44 (20.4%) had CSS, 25 (11.6%) had LIs, and 31 (14.4%) had EPVS. The baseline hematoma volume, blood calcium, the modified Rankin Scale score and the National Institutes of Health Stroke Scale score at admission, and detection rates of NCCT signs, CMBs, WMHs and CSS in the HE group were significantly higher than those in the non-HE group (all P<0.05). Multivariate logistic regression analysis showed that the blood calcium (odds ratio [ OR] 0.040, 95% confidence interval [ CI] 0.004-0.238; P=0.001), any NCCT signs ( OR 3.275, 95% CI 1.492-7.188; P=0.003), CMBs grade 4 ( OR 3.591, 95% CI 1.146-11.250; P=0.028), CSS ( OR 3.008, 95% CI 1.214-7.452; P=0.017), NCCT signs+ CMBs grade 3 ( OR 3.390, 95% CI 1.035-11.102; P=0.044), NCCT signs+ CMBs grade 4 ( OR 5.473, 95% CI 1.352-22.161; P=0.017), and NCCT signs+ CSS ( OR 3.544, 95% CI 1.215-10.336; P=0.021) were the independent risk factors for HE in patients with sICH. ROC curve analysis showed that the sensitivity of NCCT signs, CMBs and CSS for predicting HE were 81.8%, 64.8% and 34.1%, respectively, and the specificity were 60.2%, 60.9% and 89.1%, respectively. The predictive sensitivity of NCCT signs+ CMBs and NCCT signs+ CSS (59.1% and 30.7%, respectively) was lower than that of single imaging marker, while the specificity (78.1% and 93.7%, respectively) was higher than that of single imaging marker. Conclusions:The imaging markers of CSVD are closely associated with the risk of HE in patients with sICH. Severe CMBs and CSS are the independent risk factors for HE in patients with sICH. The specificity of NCCT signs combined with CSVD imaging markers for predicting HE is increased but the sensitivity decreased.
