Objective: We sought to test the hypothesis that technology could predict the risk of falls in Parkinson's disease (PD) patients with orthostatic hypotension (OH) with greater accuracy than in-clinic assessment.
Methods: Twenty-six consecutive PD patients with OH underwent clinical (including home-like assessments of activities of daily living) and kinematic evaluations of balance and gait as well as beat-to-beat blood pressure (BP) monitoring to estimate their association with the risk of falls. Fall frequency was captured by a diary collected prospectively over 6 months. When applicable, the sensitivity, specificity, and diagnostic accuracy were measured using the area under the receiver operating characteristics curve (AUC). Additional in-clinic assessments included the OH Symptom Assessment (OHSA), the OH Daily Activity Score (OHDAS), and the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).
Results: The prevalence of falls was 53.8% over six months. There was no association between the risk of falls and test of gait and postural stability (p ≥ 0.22) or home-like activities of daily living (p > 0.08). Conversely, kinematic data (waist sway during time-up-and-go, jerkiness, and centroidal frequency during postural sway with eyes-opened) predicted the risk of falls with high sensitivity and specificity (> 80%; AUC ≥ 0.81). There was a trend for higher risk of falls in patients with orthostatic mean arterial pressure ≤ 75 mmHg.
Conclusions: Kinematic but not clinical measures predicted falls in PD patients with OH. Orthostatic mean arterial pressure ≤ 75 mmHg may represent a hemodynamic threshold below which falls become more prevalent, supporting the aggressive deployment of corrective measures.
Keywords: Falls; Orthostatic hypotension; Parkinson's disease; Wearable sensors.