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

ABSTRACT:

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)

SUBMITTER: Ahmed A 

PROVIDER: S-EPMC10288850 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature