Total Cerebral Small Vessel Disease Burden Predicts the Outcome of Acute Stroke Patients after Intra-Arterial

Mengqi Yang1, Jiahui Liang2, Baohui Weng1

  • 1Department of Neurology and Stroke Center, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.

Insights

A combined measure of cerebral small vessel disease (cSVD) burden predicts outcomes in acute ischemic stroke (AIS) patients undergoing intra-arterial thrombectomy (IAT). Higher cSVD burden correlates with poorer neurological function after IAT.

Area of Science:

  • Neurology
  • Radiology
  • Cardiovascular Research

Background:

  • Cerebral small vessel diseases (cSVD) markers often coexist and impact neurological outcomes.
  • The combined effect of multiple cSVD markers on treatment efficacy is not fully understood.
  • Intra-arterial thrombectomy (IAT) is a critical treatment for acute ischemic stroke (AIS).

Purpose of the Study:

  • To develop and validate a model assessing the total cSVD burden.
  • To investigate the predictive value of this total cSVD burden for AIS patient outcomes after IAT.
  • To determine if combined cSVD markers improve outcome prediction compared to individual markers.

Main Methods:

  • Retrospective analysis of 271 AIS patients treated with IAT (October 2018 - March 2021).
  • Calculation of multiple cSVD markers using magnetic resonance imaging.
  • Assessment of modified Rankin Scale (mRS) scores at 90 days and logistic regression analysis.

Main Results:

  • Higher total cSVD burden was significantly associated with poor neurological outcomes (mRS 3-5).
  • A predictive model incorporating total cSVD burden, age, onset-to-reperfusion time, ASPECTS, NIHSS, and mTICI achieved high predictive accuracy (AUC 0.90).
  • Excluding total cSVD burden from the model significantly reduced its predictive capability (AUC 0.82, p=0.045).

Conclusions:

  • Total cSVD burden is an independent predictor of clinical outcomes in AIS patients following IAT.
  • Integrating total cSVD burden into predictive models enhances the accuracy of forecasting post-IAT outcomes.
  • This comprehensive assessment of cSVD may guide treatment decisions and prognostication for AIS patients.
Abstract