Artificial intelligence to predict modified rankin score after acute stroke symptoms using wrist-worn triaxial accelerometry data

Abstract

Background

The modified Rankin Scale (mRS) is the most common measure of post-stroke functional outcome. However, its impact on post-stroke care is limited by its subjectivity, impracticality in vulnerable populations, and susceptibility to cultural and language barriers. Artificial intelligence (AI) applied to triaxial wrist-worn accelerometry data may offer an objective way to characterize post-stroke functional status and related changes.

Methods

We analyzed data from the REACH Stroke-Sleep study at Columbia University Irving Medical Center. Using single-day epochs averaged over the first 30 days of recording, we trained logistic repression (LR), random forest (RF) and long short-term memory (LSTM) models to predict 1-month and 6-month mRS scores, as well as 1- to-6 month mRS changes. 5-fold cross validation was used, and model performance was evaluated using area under receiver-operating curve (AUROC) for binary exact-match predictions.

Results

We identified 362 patients, of whom 302 (83.4 %) had 1-month and 251 (69.3 %) had 6-month mRS scores. RF models (1-month AUROC 0.65, 95 %CI 0.57-0.73; 6-month 0.59, 95 %CI 0.51-0.66; ΔmRS 0.62, 95 %CI 0.53-0.71) outperformed LSTM (0.55, 95 %CI 0.48-0.63; 0.53, 95 %CI 0.45-0.60, 0.49; 95 %CI 0.39-0.59) and LR (0.54, 95 %CI 0.47-0.61; 0.58, 95 %CI 0.50-0.66; 0.50, 95 %CI 0.40- 0.61) models.

Conclusions

AI applied to wearable accelerometry predicted short-term mRS score and functional change after stroke with accuracy above chance. Although predictive performance was modest, these results provide proof-of-concept that AI applied to passive wearable data can capture meaningful post-stroke functional variation and could inform future real-time monitoring frameworks. Integrating multimodal wearable and clinical data may further improve prediction of post-stroke functional outcomes.