Session Information
Date: Monday, June 20, 2016
Session Title: Surgical Therapy
Session Time: 12:30pm-2:00pm
Objective: Pilot study to determine the impact of remote monitoring using objective, wearable sensors on the advanced therapy referral rate in patients with advanced Parkinson’s disease (PD) and if algorithms could automatically screen patients for referral.
Background: Advanced therapy, such as deep brain stimulation (DBS) or levodopa-carbidopa intestinal gel (LCIG), can significantly improve quality of life in PD. However, determining which individuals with PD should be referred for advanced therapy is a challenging problem. Challenges exist both in accessibility for patients who could benefit from advanced therapy but do not have access to movement disorder experts and in excluding patients not appropriate for advanced therapy to minimize risk and healthcare costs.
Methods: Forty individuals with advanced PD were followed for one year with 20 receiving standard care alone and 20 using motion sensor-based remote monitoring once per month in conjunction with standard care. Baseline therapy parameters, clinical recommendations, and changes to therapy were captured during the study. Using clinician referral as the gold standard, objective motor features representing symptom severity, dyskinesia severity, and fluctuations were used as inputs to classification algorithms developed post-hoc to predict which patients should be referred for advanced therapy.
Results: When clinicians had access to remote monitoring reports for patients, the referral rate for advanced therapies was significantly higher compared to that for patients receiving standard care alone (63.6% versus 11.8%, p<0.005). Using leave-one-out cross-validation, the advanced therapy referral algorithm had an overall accuracy of 75% and area under the ROC curve of 0.80 when using four days of home monitoring.
Conclusions: Remote monitoring technologies and algorithms to screen patients for advanced therapy referral may improve access to advanced therapies and have a significant impact on improving quality of life for patients with advanced Parkinson’s disease.
An earlier version of this abstract was submitted to the 2016 American Academy of Neurology Annual Meeting.
To cite this abstract in AMA style:
D.A. Heldman, J.P. Giuffrida, E. Cubo. Wearable sensors and decision algorithms for advanced therapy referral in Parkinson’s disease [abstract]. Mov Disord. 2016; 31 (suppl 2). https://www.mdsabstracts.org/abstract/wearable-sensors-and-decision-algorithms-for-advanced-therapy-referral-in-parkinsons-disease/. Accessed October 23, 2026.« Back to 2016 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/wearable-sensors-and-decision-algorithms-for-advanced-therapy-referral-in-parkinsons-disease/