Objective: To review the literature on digital biomarkers’ role in DBS candidacy assessment and treatment response in people with Parkinson Disease (PwP).

Background: Visit frequency may be burdensome for some PwP undergoing DBS candidacy evaluation, and the data collected may not accurately reflect their daily functioning. These, among other factors, may explain why whereas around 20% of PwP may be DBS eligible, only around 1% are implanted [1]. Remote monitoring technology may provide supplemental data points to inform the candidacy evaluation, as well as to track surgical outcomes.

Method: We performed a PubMed search using “Parkinson disease” AND (DBS OR deep brain stimulation[Title]) AND (biometric feedback OR wearable[Title]). Only English-language randomized controlled trials and observational studies on the use of digital biomarkers for patient selection and DBS programming response were included. Publications on digital biomarker use in experimental settings were excluded.

Results: Eleven publications met our inclusion criteria: five longitudinal cohorts, five case-controlled studies and one randomized controlled trial, pooling 1,056 PwP. The mean follow up for subjects was 17.34 weeks. Ambulatory monitoring devices included inertial measurement units, electromyography, accelerometry and gyroscopes. Most studies used wrist-worn sensors (7), followed by hand worn/held (two) and chest implanted devices (one). Two did not specify the bodily attachment site.

Seven of the 11 studies (64%) proposed a predictive model for anticipated DBS response: 6 of them comparing their findings to the UPDRS, among which three were comparable to the UDPRS’ predictive value. Symptomatic improvements were reported in motor fluctuations/dyskinesias, psychosocial symptoms, tremor, and postural instability. The remaining 4/11 papers explored the use of wearable data to guide post-DBS adjustment of either oral or continuously-infused intestinal levodopa gel dosage.

Conclusion: Wearable biometric feedback devices may enhance ambulatory patient surveillance. Although currently underutilized, these technologies may provide supplemental data to better inform DBS candidacy, as well as to guide medication adjustments post-surgically.

References: Khodakarami, H., Farzanehfar, P., & Horne, M. (2019). The use of data from the parkinson’s kinetigraph to identify potential candidates for device assisted therapies. Sensors, 19(10), 2241. https://doi.org/10.3390/s19102241

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MDS Abstracts - https://www.mdsabstracts.org/abstract/the-use-of-biometric-feedback-devices-for-stratification-of-deep-brain-stimulation-dbs-eligibility-and-treatment-response-in-parkinson-disease/