Artifical intelligence-powered delta-NIHSS-based model for predicting recurrence, disability and mortality after...

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.

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 validation) included 5158 patients with a history of ischaemic stroke, excluding those diagnosed with TIA, from the CHANCE-2 trial (2019-2021) done at 202 centres in China. Validation cohort 3 (external validation: disability and mortality) involved 3855 patients with a history of acute ischaemic stroke or TIA from a single centre in the Clinical Research Center for Stroke-Fifth Division (CRCS-5) cohort (2011-2024) in South Korea. We evaluated 309 hospitalisation variables, including baseline characteristics, medical history, hospitalisation data, biomarkers, geographical factors, National Institutes of Health Stroke Scale (NIHSS) scores, modified Rankin Scale (mRS), and stroke polygenic risk scores, using an extreme gradient boosting tree model. Feature importance was assessed via Shapley values. Primary outcomes were 3-month stroke recurrence, disability (mRS >2), and mortality. Secondary outcomes were assessed at six additional time points over 5 years. A nested cross-validation scheme was employed for feature selection and internal validation in 80% of patients (n = 11,313) from CNSR-III cohort. Testing was performed in the remaining 20% of patients (n = 2627) from CNSR-III (validation cohort 1). External validation was performed in validation cohorts 2 and 3.

Global and domain-specific delta-NIHSS_{(admission-discharge)} emerged as the strongest predictor of stroke recurrence, disability, and mortality. The Delta-NIHSS-based Predictor for Post-Stroke Composite Outcomes (DISCO) model, integrating 16 delta-NIHSS_{(admission-to-discharge)} measures, age, gender, TOAST subtypes, history of TIA, stroke, diabetes and mRS at discharge, achieved the area under the receiver operating curve (AUCs) of 0.805 and 0.815 for recurrence and mortality and of 0.852 for disability at 3 months in validation cohort 2. The 1% patients with highest-risk exhibited a 19.27-fold relative risk (RR) for recurrence, 36.59-fold RR for disability, and 225.5-fold RR for mortality at 3 months. The DISCO model is available at https://www.discosysu.cn.

The DISCO model, incorporating 23 clinical variables, demonstrates high accuracy, robustness, clinical accessibility, and explainability in predicting post-stroke outcomes. The predictive strength of delta-NIHSS_{(admission-discharge)} provides mechanistic insights into stroke outcomes and could potentially inform future acute stroke treatment and rehabilitation strategies. Future studies, with external validation across diverse ancestries and mechanistic studies elucidating the predictive power of delta-NIHSS in stroke recovery, are warranted.

National Key Research and Development Program of China, National Natural Science Foundation of China, Young Elite Scientists Sponsorship Program by the China Association for Science and Technology, and Young Talents Supporting Program of Capital Medical University.