Loading search results
CoMPaSS-NMD
Computational Models for New Patients Stratification Strategies of Neuromuscular Disorders
Sponsor study identifier
observational
500
2 countries
3
Study Start
May 1, 2024
Actual (reported)Primary Completion
March 31, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
April 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
The project "Computational Models for new Patients Stratification Strategies of Neuromuscular Disorders" (CoMPaSS-NMD) creates novel and universal tools for the diagnostic stratification of patients suffering from Hereditary Neuromuscular Diseases (HNMDs) aiming at personalised treatments. HNMDs often occur in young people, causing long-term disability and early death; these conditions bring lack of participation in society, need for permanent assistance and may require long-term institutionalisation. Multidimensional HNMD data - clinical, genetic, histopathological and MRI - will be provided by third-level clinical centers in Italy, France, Germany, Finland and the United Kingdom as part of the European Reference Network for Rare Neurological Diseases. Computational tools for high-dimensional clustering will be applied in an unsupervised learning approach using the internal structure of data to define groups of similar patients. Classification model averaging and integration techniques for federated learning-inspired model building and novel HNMD-specific descriptors of histopathological images will be implemented. The adoption of this multidimensional view has the potential to increment the diagnostic rate of HNMDs by 30% and foster effective actions by European national health systems. As main project outcome, the CoMPaSS-NMD Atlas Platform will be AI-based application providing precise clinical characterization of patients. The project will deliver recommendations and guidelines for stratification-based patient management to offer superior standard-of-care for diagnosis and prognosis and assist in planning clinical trials. It will follow a user-centred, co-design methodology with a strong stakeholder engagement and networking with other project consortia. The project engages partners with clinical, biotechnological, ICT, AI, ethical and legal, communication and exploitation competences: six clinical/academic centres, one academic, and four industrial partners.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
April 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Click on a node to explore related trials.
Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
May 1, 2024
Actual (reported)December 5, 2024
Actual (reported)December 16, 2024
Actual (reported)March 31, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
April 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Updated database of patients with HNMD that will be used to validate the algorithms and computational models developed in the unsupervised studies of existing genetic, histopathological, and MRI data
* number of newly diagnosed HNMD patients assigned to a particular supercluster-based phenotype category (+30%); * number of patients' superclusters and their multi-omics signatures achieved (at least 6); * digital representations of the data from total 500 patients included in the study.
36 months
Adults patients suffering HNDMs Blood samples, skin/biopsy sample, MRI data collection from adults to classify patient with HNMD through an AI system
Adults and kids patients suffering HNDMs Blood samples, skin/biopsy sample, MRI data collection from minors to classify patient with HNMD through an AI system
Adults patients suffering HNDMs Blood samples, skin/biopsy sample, MRI data collection from adults to classify patient with HNMD through an AI system
The intervention consist in firstly obtaining clinical, genetic, histopathological, MRI data from total 500 patients coming from the defined Cohorts. The adaptative AI-tool developed, based on data provided, will then identify multi-modal characteristics that will support patients' superclusters and their multi-omics signatures.
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
The study will include European population of patients with HNMD. The clinical referral centers UNIMORE and LMUM will recruit patients over 18 years old, while the FSM center will also recruit minors.
Ludwig-Maximilians-Universitaet Muenchen
Munich, Germany
Fondazione Stella Maris
San Miniato, Pisa, Italy
Azienda Ospedaliero-Universitaria di Modena
Modena, Italy
McCarthy JF, Marx KA, Hoffman PE, Gee AG, O'Neil P, Ujwal ML, Hotchkiss J. Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management. Ann N Y Acad Sci. 2004 May;1020:239-62. doi: 10.1196/annals.1310.020.
Savarese M, Di Fruscio G, Torella A, Fiorillo C, Magri F, Fanin M, Ruggiero L, Ricci G, Astrea G, Passamano L, Ruggieri A, Ronchi D, Tasca G, D'Amico A, Janssens S, Farina O, Mutarelli M, Marwah VS, Garofalo A, Giugliano T, Sampaolo S, Del Vecchio Blanco F, Esposito G, Piluso G, D'Ambrosio P, Petillo R, Musumeci O, Rodolico C, Messina S, Evila A, Hackman P, Filosto M, Di Iorio G, Siciliano G, Mora M, Maggi L, Minetti C, Sacconi S, Santoro L, Claes K, Vercelli L, Mongini T, Ricci E, Gualandi F, Tupler R, De Bleecker J, Udd B, Toscano A, Moggio M, Pegoraro E, Bertini E, Mercuri E, Angelini C, Santorelli FM, Politano L, Bruno C, Comi GP, Nigro V. The genetic basis of undiagnosed muscular dystrophies and myopathies: Results from 504 patients. Neurology. 2016 Jul 5;87(1):71-6. doi: 10.1212/WNL.0000000000002800. Epub 2016 Jun 8.
Blood samples, skin/biopsy sample, MRI data
ROSSELLA G TUPLER
University of Modena and Reggio Emilia
First Submitted
December 5, 2024
First Posted
December 16, 2024
Study Start
May 1, 2024
Actual (reported)Primary Completion
March 31, 2027
EstimatedContact the study team to confirm eligibility.
Cohen E, Bonne G, Rivier F, Hamroun D. The 2022 version of the gene table of neuromuscular disorders (nuclear genome). Neuromuscul Disord. 2021 Dec;31(12):1313-1357. doi: 10.1016/j.nmd.2021.11.004. No abstract available.
Muller KI, Ghelue MV, Lund I, Jonsrud C, Arntzen KA. The prevalence of hereditary neuromuscular disorders in Northern Norway. Brain Behav. 2021 Jan;11(1):e01948. doi: 10.1002/brb3.1948. Epub 2020 Nov 13.
Turner JA, Franklin G, Fulton-Kehoe D, Egan K, Wickizer TM, Lymp JF, Sheppard L, Kaufman JD. Prediction of chronic disability in work-related musculoskeletal disorders: a prospective, population-based study. BMC Musculoskelet Disord. 2004 May 24;5:14. doi: 10.1186/1471-2474-5-14.
Cylus J, Figueras J, Normand C, authors. Sagan A, Richardson E, North J, White C, editors. Will Population Ageing Spell the End of the Welfare State? A review of evidence and policy options [Internet]. Copenhagen (Denmark): European Observatory on Health Systems and Policies; 2019. Available from http://www.ncbi.nlm.nih.gov/books/NBK550573/
Bevan S. Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Pract Res Clin Rheumatol. 2015 Jun;29(3):356-73. doi: 10.1016/j.berh.2015.08.002. Epub 2015 Oct 24.
Planned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
April 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Last Updated
February 5, 2025
Record last verified: 2025-01
Download the complete ClinicalTrials.gov record imported by Syfrah, including fields not shown in this page. The registry may contain a newer revision.
Source: ClinicalTrials.gov.
JSON downloads include source attribution and retrieval details alongside the unchanged original record or the separately normalized values.
Source retrieved: