Derivation and Validation of an Algorithm to Detect Stroke Using Arm Accelerometry Data

BACKGROUND: Early diagnosis is essential for effective stroke therapy. Strokes in hospitalized patients are associated with worse outcomes compared with strokes in the community. We derived and validated an algorithm to identify strokes by monitoring upper limb movements in hospitalized patients. METHODS AND RESULTS: A prospective case–control study in hospitalized patients evaluated bilateral arm accelerometry from patients with acute stroke with lateralized weakness and controls without stroke. We derived a stroke classifier algorithm from 123 controls and 77 acute stroke cases and then validated the performance in a separate cohort of 167 controls and 33 acute strokes, measuring false alarm rates in nonstroke controls and time to detection in stroke cases. Faster detection time was associated with more false alarms. With a median false alarm rate among nonstroke controls of 3.6 (interquartile range [IQR], 2.1–5.0) alarms per patient per day, the median time to detection was 15.0 (IQR, 8.0–73.5) minutes. A median false alarm rate of 1.1 (IQR. 0–2.2) per patient per day was associated with a median time to stroke detection of 29.0 (IQR, 11.0–58.0) minutes. There were no differences in algorithm performance for subgroups dichotomized by age, sex, race, handedness, nondominant hemisphere involvement, intensive care unit versus ward, or daytime versus nighttime. CONCLUSIONS: Arm movement data can be used to detect asymmetry indicative of stroke in hospitalized patients with a low false alarm rate. Additional studies are needed to demonstrate clinical usefulness.