Save
Novel multi-sensor algorithm captures subtle progression over one year in multiple sclerosis
ECTRIMS Online Library. Krysko K. Oct 12, 2018; 232030
Kristen M Krysko
Kristen M Krysko
Login now to access Regular content available to all registered users.

You may also access this content "anytime, anywhere" with the Free MULTILEARNING App for iOS and Android
Abstract
Discussion Forum (0)
Rate & Comment (0)

Abstract: 277

Type: Scientific Session

Abstract Category: Clinical aspects of MS - Clinical assessment tools

Introduction: Conventional disability metrics for MS such as EDSS are insensitive to short-term change, making these suboptimal for evaluating progression in clinical trials. New inexpensive outcome measures with greater sensitivity to change in disability are needed.
Objective: To determine whether longitudinal evaluation with a wearable multi-sensor device (MYO) leveraged from the computer control and gaming industry can capture progression in MS.
Methods: Consecutive patients with MS were followed prospectively at routine clinic visits approximately every 6 months for up to 2 years. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope and surface electromyogram sensors. A single scalar metric of within-patient limb progression was created by combining the change in signal waveform features over time. These upper (UE) and lower extremity (LE) metrics were compared between progressive and relapsing MS with logistic regression models with calculation of area under (AU) ROC. Pearson correlations were used to compare these metrics with baseline and change in EDSS.
Results: 68 were included (53 RR, 9 PP, 6 SP) with 72% female, mean age 48 years, median disease duration 11 years, median EDSS 2.5 (range 0-7), and median follow-up 10 months. Test-retest reliability was excellent (ICC 0.80-0.87). The unit-less summary metrics differentiated RR from progressive MS (LE: RR mean 823; progressive mean 1126, p< 0.001; similar difference for UE observed), with AUROC 0.83 for LE and 0.74 for UE. For every 1 standard deviation (SD) unit increase in the LE metric, there was 2.7 times the odds (95% CI 1.4-5.3) of progressive MS. For every 1 SD unit increase in the UE metric, there was 2.8 times the odds (95% CI 1.4-5.3) of progressive MS. These associations remained after adjustment for age, disease duration and sex. Higher baseline EDSS was associated with greater value of the scalar progression metric (LE r=0.41, p< 0.001; UE r=0.43, p< 0.001). The scalar metrics captured worsening in function not detected by the EDSS over short follow-up. In a subset with stable EDSS, the metric suggested subclinical progression.
Conclusions: The MYO device distinguished progressive from relapsing MS and detected short-term change in function not captured by EDSS. This device, readily adaptable to clinical practice, holds great promise for rapid assessment of subtle progression.
Disclosure: UCSF CTSI pilot grants program, Investigator initiated grant from Genentech
Dr. Kristen Krysko is supported by the Sylvia Lawry award from the National Multiple Sclerosis Society.
Dr. Alireza Akhbardeh has no disclosures.
Jennifer Arjona has no disclosures.
Dr. Bardia Nourbakhsh has current grant support from PCORI and the NMSS.
Dr. Emmanuelle Waubant has participated in multicenter clinical trials funded by Genentech and Novartis. She has current support from the NMSS, PCORI, and Race to Erase MS.
Dr. Pierre Antoine Gourraud is supported by the ATIP-Avenir INSERM program and the Region Pays de Loire ConnecTalent, ARSEP Foundation (France), and the Nantes University Foundation.
Dr. Graves has received recent grant and clinical trial support from the National MS Society, Race to Erase MS, UCSF CTSI RAP program, Biogen, and Genentech. She has received honoraria from Biogen and Genzyme for non-promotional trainee education events.

Code of conduct/disclaimer available in General Terms & Conditions
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.


Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.



Google Analytics is used for user behavior tracking/reporting. Google Analytics works in parallel and independently from MLG’s features. Google Analytics relies on cookies and these cookies can be used by Google to track users across different platforms/services.


Save Settings