Artifical intelligence-powered delta-NIHSS-based model for predicting recurrence, disability and mortality after acute ischaemic strokes (DISCO): a modelling study.

ABSTRACT:

Background

Stroke is a leading cause of mortality and disability globally, with high rates of adverse outcomes. We aimed to develop and test a clinically accessible and explainable predictive model for post-stroke composite outcomes that could identify patients at high-risk for targeted interventions.

Methods

This modelling study used artificial intelligence to develop and validate a prognostic predictive model for post-stroke outcomes at 3 months and over 5 years, using comprehensive data from the third China National Stroke Registry (CNSR-III; 2015-2018). The development cohort comprised 11,313 patients from CNSR-III. Validation cohort 1 (n = 2627 from CNSR-III; internal validation) were selected at random on the basis of hospital assignment. Validation cohort 2 (external

SUBMITTER: Cheng S 

PROVIDER: S-EPMC12753274 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature