Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage

This is the authors' abstract. We don't add key points for this paper.

Objective: Delirium is associated with worse outcomes in patients with stroke and neurocritical illness, but delirium detection in these patients can be challenging with existing screening tools. To address this gap, we aimed to develop and evaluate machine learning models that detect episodes of post-stroke delirium based on data from wearable activity monitors in conjunction with stroke-related clinical features. Design: Prospective observational cohort study. Setting: Neurocritical Care and Stroke Units at an academic medical center. Patients: We recruited 39 patients with moderate-to-severe acute intracerebral hemorrhage (ICH) and hemiparesis over a 1-year period [mean (SD) age 71.3 (12.20), 54% male, median (IQR) initial NIH Stroke Scale 14.5 (6), median (IQR) ICH score 2 (1)]. Measurements and main Among actigraphy features, night-time actigraph data were especially relevant for classification accuracy. Conclusions: We found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.