The correlation between total magnetic resonance imaging burden and middle cerebral artery pulsatility index in elderly patients with cerebral small vessel diseases
10.3760/cma.j.cn113694-20210527-00368
- VernacularTitle:老年脑小血管病患者磁共振成像总体负担与大脑中动脉搏动指数的相关性
- Author:
Sibo LI
1
;
Yanqiu JIA
;
Shicong ZHAO
;
Hengli CHEN
;
Peiyuan LYU
;
Wei JIN
Author Information
1. 河北省人民医院神经内科，石家庄 050051
- Keywords:
Aged;
Cerebrovascular disorders;
Magnetic resonance imaging;
Ultrasonography, Doppler;
Middle cerebral artery;
Pulsatility index
- From:
Chinese Journal of Neurology
2022;55(2):96-101
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the correlation between middle cerebral artery (MCA) pulsatility index (PI) and total magnetic resonance imaging (MRI) burden in elderly patients with cerebral small vessel diseases (CSVD).Methods:A total of 203 CSVD inpatients aged 60 years and above who were hospitalized in the Department of Neurology of Hebei General Hospital from March 2017 to December 2020 were enrolled. The clinical data, transcranial Doppler ultrasound parameters and brain MRI data were collected. According to the total burden score, the patients were divided into low burden group (0-1 point) and high burden group (2-4 points). Univariate and multivariate Logistic regression analysis was used to analyze the correlation between MCA PI and total MRI burden in the elderly patients with CSVD. Subsequently, the receiver operating characteristic curve was used to evaluate the value of MCA PI for predicting the high MRI burden of CSVD in the elderly.Results:Hypertension ( OR=2.569, 95% CI 1.068-6.182, P=0.035), systolic blood pressure ( OR=1.033, 95% CI 1.006-1.061, P=0.016), creatinine ( OR=1.044, 95% CI 1.009-1.079, P=0.013) and MCA PI ( OR=1.125, 95% CI 1.087-1.166, P<0.001) were independently correlated with the increasing total MRI burden in the elderly patients with CSVD. Spearman rank correlation analysis revealed that there was strong and positive correlation between MCA PI and high MRI burden in the elderly patients with CSVD ( r=0.65, P<0.001). The analysis showed that when the cut-off for MCA PI was 1.11, it could identify high MRI burden of CSVD in the elderly. The area under the curve was 0.908 (95% CI 0.864-0.953, P<0.001). The sensitivity and specificity were 0.852 and 0.880, respectively. The positive predictive value was 92.38%, and the negative predictive value was 77.70%. Conclusion:The MCA PI is positively correlated with total MRI burden in the elderly patients with CSVD, and has a higher value in predicting the total MRI burden in the elderly CSVD patients, which probably bring brighter prospects for its clinical application.
