MS-DETECT: Early Detection of Multiple Sclerosis Progression with MSCopilot® Detect
- Conditions
- Multiple Sclerosis
- Interventions
- Device: MSCopilot® Detect mobile application
- Registration Number
- NCT05816122
- Lead Sponsor
- Ad scientiam
- Brief Summary
The study aims to evaluate MSCopilot® Detect, a smartphone application for at-home monitoring of patients with Multiple Sclerosis (MS).
The primary objective is to enhance and standardize remote monitoring of MS patients to accurately assess disease progression caused by either Relapse Activity Worsening (RAW) or Progression Independent of Relapses (PIRA). The study also aims to assess the safety, usability, and satisfaction of the solution.
A secondary objective is to determine MSCopilot® Detect's ability to provide early detection of disease changes and predict changes in Expanded Disability Status Scale (EDSS) scores in more patients.
Exploratory objectives include evaluating the relationship between MSCopilot® Detect composite and individual scores and other biomarkers such as MRI, soluble glial fibrillary acidic protein (sGFAP), and soluble neurofilament light chain (sNfL).
Patients will be able to download the free MSCopilot® Detect app. They will participate in 1 inclusion visit and 3 follow-up visits, scheduled at 6 months, 12 months, and 18 months (an additional visit at 24 months may be scheduled if necessary). Every 3 months, patients will complete validated questionnaires regarding MS symptoms and quality of life and participate in digital tests designed to monitor MS symptom progression.
The study will include 314 MS patients and will be conducted in the United States, Canada, Germany, Italy, Spain, Denmark and France
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 314
- Adult patients living with RRMS or SPMS, aged 30 to 65
- EDSS score between 2.5 to 6.5 points
- With a disease duration > 5 years
- Clinically stable during the last 3 months before inclusion (no relapses, no disability progression)
- No change in the DMT* during the last 3 months before inclusion
- Receiving optimal symptomatic treatments at baseline (clinician's judgment)
- Owns a personal smartphone which mobile operating system is above 14 for IOS (iPhone) and 8 for Android included with a good internet connexion
- Able to use a smartphone
- Able to read language in wich the mobile application is available and able to understand pictograms.
- Applicable to patients enrolled in France only: affiliated to a social security system.
- Medically unstable conditions that may hinder patient's ability to comply with the study procedures
- Inability to use a smartphone or MSCopilot® Detect application
- Patient with corrected visual acuity less than 4/10 (Monoyer) or 20/50 (Snellen at 20 feet) or +0,4 (LogMAR)
- Pregnancy and nursing women
- Persons under guardianship or curatorship
- Current drugs or/and alcohol abuse that could influence performance on the tests (clinician's judgment)
- Patients have participated in another clinical study within the previous 30 days of screening or are currently participating in another study that, in the opinion of the Investigator, might interfere with the patient's full participation in the study or confound the assessment of the patient or outcome of the study.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description MSCopilot® Detect MSCopilot® Detect mobile application Performance of digital tests and standard test in clinic at D0, M6, M12, M18 and M24 (if applicable) Use of MSCopilot® Detect at-home in between visits during 18 or 24 months (if applicable)
- Primary Outcome Measures
Name Time Method To evaluate MSCopilot® Detect individual scores and/or composite scores sensitivity to detect disability worsening based on revised-MSFC scores. Throughout the study at each hospital visit: Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable) The discriminatory ability of the MSCopilot® Detect application in detecting clinically meaningful worsening (disability worsening) based on the revised-MSFC will be evaluated using the Area Under the ROC Curve (AUC) method. Confidence intervals will also be estimated.
The minimum significant AUC value will be set at \>0.67, and Confidence Intervals will be calculated. Sensitivity and specificity will also be determined. To validate the estimated AUC values and cutoffs generated by the MSCopilot® Detect application, a train/test set will be utilised.
- Secondary Outcome Measures
Name Time Method To evaluate MSCopilot® Detect individual and/or composite scores ability to detect disease progression in absence of a relapsing activity (PIRA). Throughout the study at each hospital visit: Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable) Proportion of patients with PIRA (progression independent of relapses) based either on EDSS 24-week CDP compared to the proportion of patients with PIRA based on MSCopilot® Detect composite or individual scores progression status.
To assess reliability of MSCopilot® Detect individual scores and/or composite scores at-home over time. Day 0 versus Month 6, Month 6 versus Month 12, Month 12 versus Month 18, Month18 versus Month 24 (if applicable) AND Day 0 to Month 18 or Day 0 to Month 24 (if applicable) and Day 0 to Month 15 or Day 0 to Month 21 (if applicable) at home. Intraclass correlation coefficient (ICC) of MSCopilot® Detect individual scores and/or composite score at home with a maximum paired time point of 6 months.
ICC of MSCopilot® Detect individual scores and/or composite score at home will over time.To evaluate MSCopilot® Detect individual scores and/or composite scores sensitivity to detect disability worsening based on EDSS. Throughout the study at each hospital visit: Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable) The study will evaluate the sensitivity, specificity, concordance, and accuracy of the MSCopilot® Detect application in predicting confirmed disability worsening (CDW) at 24 weeks as also captured by the EDSS, using a contingency table.
We will also generate time series box plots of MSCopilot® Detect individual scores and/or composite scores based on the patient progression status (determined by EDSS), using both intra-patient and inter-patient visualisation.To evaluate MSCopilot® Detect individual scores and/or composite scores sensitivity to detect disability worsening earlier than the EDSS. Throughout the study at each hospital visit: Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable) The time to progression-free events based on EDSS and MSCopilot® Detect individual scores and/or composite scores will be estimated with a Kaplan-Meier method (Log-rank test for comparison). Univariate and multivariate survival models (e.g. Cox or Anderson-Gill survival model), will be used to estimate Hazards Rates (HRs), if relevant.
Proportions of disability worsening based on MSCopilot® Detect composite or individual scores and worsening based on the EDSS 24-week CDW will be compared for each timepoint.
Time series boxplot of MSCopilot® Detect individual scores and/or composite score according to the patient disability worsening (by EDSS) intra-patient and inter-patient.To assess the satisfaction and user experience with the MSCopilot® Detect web dashboard. Every 6 months for about 24 months Descriptive analysis over the course of the study of the answers to physician satisfaction \& user experience questionnaires relative to the use of the MSCopilot® Detect web dashboard.
To measure MSCopilot® Detect individual scores and/or composite scores association with the clinical scores. Throughout the study at each hospital visit: Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable) The association between digital and clinical scores will be studied with correlation coefficient calculated between:
* MSCopilot® Detect individual scores ((MWPT, MDT, MCT, MVT and Mobile Walking Endurance Test (MWET)) and/or composite score at home and EDSS score performed in clinic.
* MSCopilot® Detect individual scores (MWPT, MDT, MCT, MVT and MWET) and/or composite score at home and revised-MSFC-3 or Revised-MSFC3 + SLCLAT scores performed in clinic.To evaluate Patient QoL (Multiple Sclerosis Impact Scale: MSIS-29 and Modified Fatigue Impact Scale - 5-item version: MFIS-5) and their association with MSCopilot® Detect individual and composite scores. Throughout the study between in clinic visit, an average of 24 months Analysis of MSIS-29 and MFIS-5 questionnaires:
Descriptive analysis of questionnaire scores over time. Comparison of Patient QoL between patients with and without a confirmed disability worsening Correlation between MSCopilot® Detect individual and/or composite score at home and Questionnaires scores (MSIS29 and MFIS-5).
Sensitivity, specificity, concordance, and accuracy will be evaluated between clinically meaningful change of MSCopilot® Detect application (individual and composite scores) and questionnaires (MSIS29 and MFIS-5).
Proportions comparison of patients with a clinically meaningful change will be assessed between MSCopilot® Detect application (individual and composite scores) and questionnaires (MSIS29 and MFIS-5).
Clinically meaningful change for questionnaires:
MSIS-29: 8-point change compared to baseline. MFIS-5: 4-point change compared to baseline.To evaluate MSCopilot® Detect Adherence Throughout the study between in clinic visit, an average of 24 months Descriptive analysis of the mobile application's adherence data (number of completed questionnaires, number of performed tests, number of performed sessions, etc.)
To evaluate MSCopilot® Detect individual scores and/or composite scores ability to detect MSFC changes over time. Throughout the study at each hospital visit: Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable) Slopes parameters will be computed using a mix model to characterize the change from baseline in MSCopilot® Detect individual and/or composite scores and revised-MSFC scores over time.
The association between these slopes parameters will be assessed using correlation coefficient.To assess reproducibility of MSCopilot® Detect individual scores and/or composite scores between in-clinic and at-home digital tests. At home (Day 1, Month 6+1 day, Month 12 + 1 day, Month 18-1 day and Month 24-1 day (if applicable)) and in-clinic ((Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable)) Intraclass correlation coefficient (ICC) between MSCopilot® Detect individual scores and/or composite score at home and in-clinic.
To assess the satisfaction and user experience with the MSCopilot® Detect smartphone application and the MSCopilot® Detect web dashboard. Every 3 months for about 24 months Descriptive analysis over the course of the study of the answers to the patient satisfaction and user experience questionnaires related to the use of the MSCopilot® Detect smartphone application.
To describe the evolution of MSCopilot® Detect individual scores and/or composite scores at-home over time. Throughout the study between in clinic visit, an average of 24 months The time to progression-free events based on MSCopilot® Detect individual scores and/or composite scores will be estimated with a Kaplan-Meier method (Log-rank test for comparison). Univariate and multivariate survival models (e.g. Cox or Anderson-Gill survival model), will be used to estimate Hazards Rates (HRs), if relevant.
Cumulative proportions of disability worsening based on MSCopilot® Detect composite or individual scores for each timepoint.
Time series boxplot of MSCopilot® Detect individual scores and/or composite score according to the patient disability worsening (by MSCopilot® Detect) using both intra-patient and inter-patient visualisation.To evaluate the ability of MSCopilot® Detect individual scores and/or composite score to discriminate between a relapse and a momentary disability worsening (+1 point EDSS score for an initial EDSS <5,5; +0.5 point for an initial EDSS ≥5.5). Throughout the study at each hospital visit: Day 0, Month 6, Month 12, Month 18 and Month 24 (if applicable) Descriptive analysis:
MSCopilot® Detect individual and/or composite score profiles, between relapse and a momentary disability worsening patient.
Annualized Relapse Rate (ARR) calculation. Logistic regression to estimate Area under ROC Curve (i.e. discriminatory power).To evaluate MSCopilot® Detect Safety Throughout the study between in clinic visit, an average of 24 months Descriptive analysis of the number of Adverse Events related to MSCopilot® Detect will be reported.
Trial Locations
- Locations (36)
Robert Carruthers
🇨🇦Vancouver, Canada
University Hospital of Southern Denmark
🇩🇰Esbjerg, Denmark
Finn Sellebjerg
🇩🇰Glostrup, Denmark
Aaron Boster
🇺🇸Columbus, Ohio, United States
Gabriel Pardo
🇺🇸Oklahoma City, Oklahoma, United States
Vijayshree Yadav
🇺🇸Portland, Oregon, United States
Leorah Freeman
🇺🇸Austin, Texas, United States
Galina Vorobeychik
🇨🇦Burnaby, Canada
Mark Freedman
🇨🇦Ottawa, Canada
Sivagini Prakash
🇩🇰Viborg, Denmark
Hôpital Roger Salengro
🇫🇷Lille, France
Cécile Donzé
🇫🇷Lomme, France
Adil Maarouf
🇫🇷Marseille, France
Mikael Cohen
🇫🇷Nice, France
Bertrand Bourre
🇫🇷Rouen, France
Boris-Alexander Kallman
🇩🇪Bayreuth, Germany
Felix Bischof
🇩🇪Böblingen, Germany
Universitätsklinikum Carl Gustav Carus
🇩🇪Dresden, Germany
Rupert Knoblich
🇩🇪Erbach, Germany
Damiano Paolicelli
🇮🇹Bari, Italy
Emilio Portaccio
🇮🇹Florence, Italy
Emanuele D'Amico
🇮🇹Foggia, Italy
IRCCS Ospedale San Raffaele
🇮🇹Milan, Italy
Gary Álvarez Bravo
🇪🇸Girona, Spain
Enric Monreal
🇪🇸Madrid, Spain
University Hospital San Carlos
🇪🇸Madrid, Spain
Ana Alonso
🇪🇸Málaga, Spain
Miguel Llaneza
🇪🇸Oviedo, Spain
Jesùs Martin
🇪🇸Zaragoza, Spain
Craig E. Herrman
🇺🇸Indianapolis, Indiana, United States
Jennifer Graves
🇺🇸San Diego, California, United States
Joash Lazarus Sr.
🇺🇸Atlanta, Georgia, United States
Daniel Wynn
🇺🇸Northbrook, Illinois, United States
Jennifer Feng
🇺🇸New Orleans, Louisiana, United States
Robert Naismith
🇺🇸St Louis, Missouri, United States
St. Michael's Hospital
🇨🇦Toronto, Canada