Help Build an A.I. Model to Predict Myasthenia Gravis Symptom Patterns and Flares

NCT04590716 | Completed

• 1 other identifier: DOC-005-2020
• Study Type: observational
• Target: 113
• Locations: 1 country
• Sites: 1

Brief Summary

There are limited objective measurements of MG symptoms as well as a dearth of data at a granular level of MG (myasthenia gravis) symptoms and triggers occurring longitudinally. This study is designed to use the strengths of mobile smartphones which enable participant-driven real time capture of data manually and through augmented sensors such as video and audio, in order to better characterize MG symptoms and flares. The study aims to enroll approximately 200 participants for approximately 9 months until analyzable data is available from at least 100 participants. Participants will complete in-app surveys for 3 months with, audiovisual recording of symptoms. This will take approximately 35 minutes per week after the initial survey.

Conditions

Myasthenia Gravis

Outcome Measures

Primary Outcomes (1)

• Audiovisual recording of voice exercises to detect patterns and changes in voice and facial symptoms

  participants to complete the audio and visual data modules designed to capture patient MG symptoms (especially ocular and voice). e.g * Vocal e.g.: * Say "papapapa" for 4 seconds * Say "tatatatata" for 4 seconds * Say "kakakaka" 4 seconds * Say "mamamama" 4 seconds * Say "papapapa" 4 seconds * Say "buttercup, buttercup, buttercup" 4 seconds * Say "aaaahhh" and hold it as long as you can * Counting e.g.: * Look straight at the camera for 4 seconds * Count as precisely as possible from 1 to 25 while looking up * Look straight at the camera for 4 seconds The recordings will be used to detect change from baseline and any patterns that may occur. This will be used to analyze where and if different features are linked to see if a single or combined effect of the features is connected to flare frequency and/or severity.

  After enrollment, 3 months with in-app twice a week audiovisual recording of symptoms.

Secondary Outcomes (1)

• Completion of MG-Quality of Life assessment

  Approximately 10 minutes each week for 3 months.

Interventions

Data Collection OTHER

This is a non-interventional study conducted on the participant's smartphones to record MG related symptoms and conditions.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with myasthenia gravis (MG) who meet the inclusion criteria will be invited to join this digital health trial. There will be a web pre-screening link where potential participants will self-screen to see if they meet the basic eligibility criteria for this study. The recruitment tool for this trial is developed for diversity, fairness, and inclusion. With the aim to ensure diversity in the demographics of the trial to better understand the health needs of different populations. So, while some interested potential participants do qualify, they may not be invited into the trial due to these diversity requirements.

You may qualify if:

• Must have a documented diagnosis of Myasthenia Gravis
• Must have ocular (eye drooping) and/or bulbar (speech) symptoms
• Must be over the age of 18
• Must reside in the US for the duration of the study
• Must be able to read, understand, and write in English
• Must have a smartphone supported by the doc.ai research app (iOS and Android)

You may not qualify if:

• None

Sponsors & Collaborators

• doc.ai inc: lead
• UCB Biopharma SRL: collaborator

Study Sites (1)

Doc.Ai Mobile Based

Palo Alto, California, 94301, United States

Location

Related Publications (11)

• Kent RD, Kent JF, Rosenbek JC. Maximum performance tests of speech production. J Speech Hear Disord. 1987 Nov;52(4):367-87. doi: 10.1044/jshd.5204.367.

  PMID: 3312817 RESULT

• Konopka BM, Lwow F, Owczarz M, Laczmanski L. Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data. PLoS One. 2018 Aug 23;13(8):e0201950. doi: 10.1371/journal.pone.0201950. eCollection 2018.

  PMID: 30138442 RESULT

• Zhou ZR, Wang WW, Li Y, Jin KR, Wang XY, Wang ZW, Chen YS, Wang SJ, Hu J, Zhang HN, Huang P, Zhao GZ, Chen XX, Li B, Zhang TS. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med. 2019 Dec;7(23):796. doi: 10.21037/atm.2019.08.63.

  PMID: 32042812 RESULT

• Kang H. The prevention and handling of the missing data. Korean J Anesthesiol. 2013 May;64(5):402-6. doi: 10.4097/kjae.2013.64.5.402. Epub 2013 May 24.

  PMID: 23741561 RESULT

• Borza D, Darabant AS, Danescu R. Real-Time Detection and Measurement of Eye Features from Color Images. Sensors (Basel). 2016 Jul 16;16(7):1105. doi: 10.3390/s16071105.

  PMID: 27438838 RESULT

• Hegde S, Shetty S, Rai S, Dodderi T. A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. J Voice. 2019 Nov;33(6):947.e11-947.e33. doi: 10.1016/j.jvoice.2018.07.014. Epub 2018 Oct 11.

  PMID: 30316551 RESULT

• Duffy, JR: Motor Speech Disorders. Substrates, Differential Diagnosis and Management (1st ed). St. Louis, 1995, Mosby.

  RESULT

• Duffy, JR: Motor Speech Disorders. Substrates, Differential Diagnosis and Management (2nd ed). New York, 2005, Elsevier Health Sciences.

  RESULT

• T. Baltrusaitis, A. Zadeh, Y. C. Lim and L. Morency,

  RESULT

• Panayotov V., Chen G., Povey D., Khudanpur S. (2015). Librispeech: an ASR corpus based on public domain audio books, in Proceedings of the ICASSP (South Brisbane, QLD:), 5206-5210

  RESULT

• Steyaert S, Lootus M, Sarabu C, Framroze Z, Dickinson H, Lewis E, Steels JC, Rinaldo F. A decentralized, prospective, observational study to collect real-world data from patients with myasthenia gravis using smartphones. Front Neurol. 2023 Aug 1;14:1144183. doi: 10.3389/fneur.2023.1144183. eCollection 2023.

  PMID: 37588667 DERIVED

Related Links

• "Myasthenia gravis - Genetics Home Reference - NIH."
• Assessment Instruments for Patients with Myasthenia Gravis (MG)

MeSH Terms

Conditions

Myasthenia Gravis

Interventions

Data Collection

Condition Hierarchy (Ancestors)

Paraneoplastic Syndromes, Nervous System; Nervous System Neoplasms; Neoplasms by Site; Neoplasms; Paraneoplastic Syndromes; Autoimmune Diseases of the Nervous System; Nervous System Diseases; Neurodegenerative Diseases; Neuromuscular Junction Diseases; Neuromuscular Diseases; Autoimmune Diseases; Immune System Diseases

Intervention Hierarchy (Ancestors)

Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 2, 2020
• First Posted: October 19, 2020
• Study Start: October 2, 2020
• Primary Completion: July 26, 2021
• Study Completion: July 26, 2021
• Last Updated: July 29, 2021

Record last verified: 2021-07

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF, CSR
• Time Frame: At the end of the study, upon completion of the analysis.

Locations

US (1)