Comparison of outcome prediction models post-stroke for a population-based registry with clinical variables collected at admission vs. discharge.

ABSTRACT:

Aim

The ability to predict outcomes can help clinicians to better triage and treat stroke patients. We aimed to build prediction models using clinical data at admission and discharge to assess predictors highly relevant to stroke outcomes.

Methods

A total of 37,094 patients from the Taiwan Stroke Registry (TSR) were enrolled to ascertain clinical variables and predict their mRS outcomes at 90 days. The performances (i.e., the area under the curves (AUCs)) of these independent predictors identified by logistic regression (LR) based on clinical variables were compared.

Results

Several outcome prediction models based on different patient subgroups were evaluated, and their AUCs based on all clinical variables at admission and discharge were 0.85-0.88 and 0.92-0.96, respe

SUBMITTER: Hsu KC 

PROVIDER: S-EPMC8963213 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature