Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning.

ABSTRACT:

Aims

Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS).

Methods

We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis.

Results

In the feature evaluation of SVO-AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and peri

SUBMITTER: Wang X 

PROVIDER: S-EPMC10018092 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature