摘要目的:探讨老年脑小血管病（CSVD）患者磁共振成像（MRI）总体负担与大脑中动脉（MCA）搏动指数的相关性。方法:选择2017年3月至2020年12月在河北省人民医院神经内科住院的60岁及以上的老年CSVD患者203例，收集其临床资料、经颅多普勒超声（TCD）参数和头颅MRI数据。依据MRI总体负担评分将患者分为低负担组（0~1分）和高负担组（2~4分），采用单因素分析及多因素Logistic回归分析老年CSVD患者MRI总体负担与MCA 搏动指数的相关性。建立受试者工作特征曲线以评估MCA搏动指数对老年人CSVD患者MRI高负担的预测价值。结果:高血压（ OR=2.569，95% CI 1.068~6.182， P=0.035）、收缩压（ OR=1.033，95% CI 1.006~1.061， P=0.016）、肌酐（ OR=1.044，95% CI 1.009~1.079， P=0.013）和MCA 搏动指数（ OR=1.125，95% CI 1.087~1.166， P<0.001）与老年CSVD患者MRI总体负担增加独立相关。MCA 搏动指数与老年CSVD患者MRI总体负担呈强正相关（ r=0.65， P<0.001），其预测高负担的最佳临界值为1.11，曲线下面积为0.908（95% CI 0.864~0.953， P<0.001），敏感度和特异度分别为0.852和0.880，阳性预测值为92.38%，阴性预测值为77.70%。 结论:TCD参数MCA 搏动指数与老年CSVD患者MRI总体负担呈正相关，对老年CSVD患者MRI总体负担的预测价值较高，具有一定的临床应用前景。

abstractsObjective: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.