Correlation Analysis Between Intracranial Artery Pulsatility And Total Magnetic Resonance Imaging Burden In Patients With Cerebral Small Vessel Disease

Background and PurposeCerebral small vessel disease(CSVD)is the main cause of vascular dementia and ischemic stroke.As people live longer,the burden of CSVD disease is increasing,posing a huge challenge to global health care.The pathogenesis of CSVD is not completely clear.Studies have found that the MRI manifestations of CSVD are related to intracranial hemodynamic changes,and the increase of arterial pulsatility aggravates the severity of white matter hyperintensity,lacunae and cerebral microbleeds.In recent years,more and more studies have shown that arterial pulsatility is the driving force of fluid flow in the perivascular spaces(PVS).Increased arterial pulsatility reduces its net flow and inhifies the circulation of cerebrospinal fluid and interstitial fluid,which is closely related to the enlargement of PVS.Middle cerebral artery(MCA)is one of the main branches of intracranial blood vessels,and its Pulsatility Index(PI)can be used to evaluate arterial pulsatility.Therefore,we evaluated the MRI characteristics of CSVD,analyzed the relationship between MRI total burden score and MCA PI,and built a prediction model based on MCA PI for patients with severe CSVD.This study provides theoretical support for the role of intracranial hemodynamics in the pathogenesis of CSVD and provides reference for the economical and effective screening of CSVD.MethodsWe analysed 118 patients from December 2018 to October 2020,who were admitted to the Department of Neurology of The Second Affiliated Hospital of Zhengzhou University.We collected general information and serological indicators of patients.The patient’s MCA systolic peak blood flow velocity,average blood flow velocity,and pulsatility index PI were obtained by transcranial Doppler ultrasound(TCD)examination,and the average PI value of bilateral MCA was calculated.According to MRI examination,the total burden score of MRI markers(4 items including lacune,white matter hyperintensity,perivascular space,and cerebral micrbleed)was calculated,0-4 points.0-2 points were divided into the low-load group and 3-4 points into the high-load group.We analyzed the differences in the general information of the two groups,conducted a multivariate logistics regression analysis to explore the risk factors of CSVD,analyzed the correlation between MCA PI and CSVD,and used MCA PI to build a predictive model for severe CSVD.Results(1)We sorted out the distribution of imaging markers of patients.According to the total burden score,8 cases were divided into 0 point group,23 cases were divided into 1 point group,38 cases were divided into 2 points groups,33 cases were divided into 3 points groups,and 16 cases were divided into 4 points groups.(2)We analyzed and compared the differences in demographic data,serological indicators,and TCD parameters between the low-load group and the high-load group.As a result,there were significant differences in age and hypertension.The proportion of age and hypertension in the high burden group was significantly higher than that in the low burden group(P <0.05).There was no statistical difference in serum indicators between the two groups.Among the TCD indicators,average blood flow velocity,left MCA PI,right MCA PI,and average MCA PI deviation were statistically different(P <0.05).The high burden group’s peak systolic blood flow velocity and average blood flow velocity were both lower than the low burden group.(3)Indicators with statistical significance for the difference between the two groups were included in the Logstic regression analysis.After adjusting for risk factors the average MCA PI(or: 1.404 95%CI: 1.136-1.736,P=0.002)was independently associated with high CSVD burden.(4)Spearman correlation analysis was performed between the MCA PI value and the severity of CSVD.They were positively correlated.The correlation between the severity of CSVD and the average bilateral MCA PI(rs=0.489,P<0.001)was slightly stronger than that of unilateral.(5)We used the average MCA PI to predict the CSVD of the high burden group and draw the ROC curve.The best cut-off point was 0.92,the area under the curve was 0.727(95% CI: 0.634-0.821 P <0.01),the sensitivity was 79.6%,and the specificity was 65.2%.Conclusion(1)Changes in intracranial hemodynamics are related to the occurrence mechanism of CSVD.The increase in MCA PI are independent risk factors for the increase of CSVD burden.(2)MCA PI value is positively correlated with the severity of CSVD,which can be used to judge the severity of CSVD.(3)The TCD parameter MCA PI has a certain predictive effect on the high burden of CSVD.And TCD may become a supplementary tool for screening CSVD.