Prediction of stroke severity: systematic evaluation of lesion representations.

ABSTRACT:

Objective

To systematically evaluate which lesion-based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets.

Methods

We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N₁ = 109, N₂ = 638, N₃ = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes.

Results

We found that prediction models trained on small single-center datasets could perform well using within-dataset cross-validation, but results did not generalize to independent datasets (median R² _N1

SUBMITTER: Bonkhoff AK 

PROVIDER: S-EPMC11651206 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature