GenoMed4ALL: Improving SCD Classification and Prognosis by AI (GenoMed4ALL)
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ClinicalTrials.gov Identifier: NCT06019208 |
Recruitment Status :
Active, not recruiting
First Posted : August 31, 2023
Last Update Posted : April 12, 2024
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Tracking Information | |||||||||||||||||||||||||||||||
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First Submitted Date | June 15, 2023 | ||||||||||||||||||||||||||||||
First Posted Date | August 31, 2023 | ||||||||||||||||||||||||||||||
Last Update Posted Date | April 12, 2024 | ||||||||||||||||||||||||||||||
Actual Study Start Date | January 1, 2021 | ||||||||||||||||||||||||||||||
Actual Primary Completion Date | September 30, 2023 (Final data collection date for primary outcome measure) | ||||||||||||||||||||||||||||||
Current Primary Outcome Measures |
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Original Primary Outcome Measures | Same as current | ||||||||||||||||||||||||||||||
Change History | |||||||||||||||||||||||||||||||
Current Secondary Outcome Measures | Not Provided | ||||||||||||||||||||||||||||||
Original Secondary Outcome Measures | Not Provided | ||||||||||||||||||||||||||||||
Current Other Pre-specified Outcome Measures | Not Provided | ||||||||||||||||||||||||||||||
Original Other Pre-specified Outcome Measures | Not Provided | ||||||||||||||||||||||||||||||
Descriptive Information | |||||||||||||||||||||||||||||||
Brief Title | GenoMed4ALL: Improving SCD Classification and Prognosis by AI | ||||||||||||||||||||||||||||||
Official Title | Genomics and Personalized Medicine for All Though Artificial Intelligence in Haematological Diseases | ||||||||||||||||||||||||||||||
Brief Summary | GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in Haematological Diseases' aims to advance on individual SCD patients' disease characterisation and to improve the monitoring of patients' health status, optimise clinical therapy guidance and ultimately improved health outcomes by the identification of biomarkers and the development of individual (risk) models in SCD. Genomed4All supports the pooling of genomic, clinical data and other "-omics" health through a secure and privacy respectful data sharing platform based on the novel Federated Learning scheme, to advance research in personalised medicine in haematological diseases thanks to advanced Artificial Intelligence (AI) models and standardised interoperable sharing of cross-border data, without needing to directly share any sensitive clinical patients' data. The SCD Use case will gather multi-modal clinical and -OMICs data from 1,000 SCD patients in 4 EU-MS: France, Italy, Spain and The Netherlands. In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, GA101157011), GENOMED4ALL involves multiple clinical partners from the network, while leveraging on healthcare information and repositories that will be gathered incorporating interoperability standards as promoted by ERN-EuroBloodNet central registry, the European Rare Blood Disorders Platform. |
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Detailed Description | SCD is a chronic life-threatening multisystem disorder, autosomal recessively inherited, caused by the presence of abnormal hemoglobin S (HbS) resulting from the sickle mutation in the HBB gene. In spite of being a single gene mutation disorder, SCD presents extreme phenotypic variability that is incompletely understood. Several genetic and environmental factors are supposed to have an impact on disease phenotype, clinical manifestations, progression of organ damage and quality of life throughout the lifespan. Although significant progress has been made over the past few decades in the highly complex pathophysiology of SCD, the possibility of personalised medicine is still in its infancy. There is a lack of markers of disease severity, prognosis, and response to treatment. In particular, the heterogeneity of clinical expression of the disease along with long-term chronic complications due to the increased lifespan of patients should be addressed by innovative and personalised treatments. Furthermore, assessing the role of the novel treatments both in regards of long-term efficacy and safety but also of cost/efficacy ratio are required. The scarcity and fragmentation of SCD data prevent researchers from reaching the critical numbers needed for basic and clinical research. Research and data-driven solutions are therefore essential to improve the care of SCD patients and their quality of life. The availability of numerous treatment options as well as the high disease heterogeneity highlight the need to address patients' severity profiles and offer the best care for each affected individual. Developing the GENOMED4ALL AI algorithms for SCD will be of great importance for the in-depth characterization and prediction of the diverse complications of SCD. The primary endpoints of interest include:
RADeep will be used for standardization of existing clinical and laboratory data. A CRF was developed, including just over 250 data elements. The GenoMed4All CRF builds on previous work performed by RADeep and includes the "set of common data elements for rare disease registration", which was released in December 2017 as result of a dedicated working group facilitated by the Joint Research Centre (JRC). This approach will ensure interoperability with other similar initiatives in Europe and will also enable the collected data to be reused for future research studies. Genome-wide Association Studies (GWAS) extends the concept of association studies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) simultaneously and provide a cost-effective way to explore genetic variants across the whole genome. But despite considerable interest in identifying genetic modifiers in SCD, the majority of previous GWAS searched for genetic linkage and association with HbF levels, an established ameliorating factor of disease severity. Addintionally, the utilization of data science and artificial intelligence (AI) has been limited in SCD research. Therefore, the generation of GWAS data combined with the use of the most recent imputation panel for imputation offers an opportunity for the development of novel AI techniques and for novel discoveries in SCD. Silent Cerebral Infarcts (SCIs) are a significant cause of morbidity in SCD: they affect 25% of children by the age of 6 and 40% by the age of 18 with consequences on cognition, schooling, working capacity and quality of life. Hence, one of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics - quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI on MRIs, secondly, to correlate imaging data with other types of OMICS data in order to predict risk of occurrence and recurrence. The deformability of red blood cells (RBC) from individuals with SCD is markedly abnormal, regardless of genotype. Several studies reported some associations between the degree of impairment of RBC deformability measured at steady state in SCD patients and the presence of chronic complications, such as priapism, leg ulcers, glomerulopathy, etc. The recently developed technique of oxygen gradient ektacytometry allows for a more comprehensive functional characterization and rheological behavior of SCD RBCs over a range of oxygen tensions to test whether the rheological changes could reflect clinical severity/complications.Data on rheological characteristics of RBC on all patients in steady state is going to be obtained through Laser Optical Rotational Red Cell Analyzer (Lorrca) ektacytometer (RR Mechatronics). By combining a large amount standardized multimodal (clinical, multi-omics, and imaging) datasets, the investigators hypothesize that AI will allow to understand better SCD biology and classification, enhance prognostic/predictive capacity of currently available tools and apply treatments in a more targeted way, thus facilitating the implementation of personalized medicine program across EU. |
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Study Type | Observational | ||||||||||||||||||||||||||||||
Study Design | Observational Model: Cohort Time Perspective: Cross-Sectional |
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Target Follow-Up Duration | Not Provided | ||||||||||||||||||||||||||||||
Biospecimen | Not Provided | ||||||||||||||||||||||||||||||
Sampling Method | Non-Probability Sample | ||||||||||||||||||||||||||||||
Study Population | Patients affected by SCD | ||||||||||||||||||||||||||||||
Condition | Sickle Cell Disorders | ||||||||||||||||||||||||||||||
Intervention | Not Provided | ||||||||||||||||||||||||||||||
Study Groups/Cohorts | GENOMED4ALL - SCD patients
Non transplanted SCD patients aged over 1 year.
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Publications * |
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* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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Recruitment Information | |||||||||||||||||||||||||||||||
Recruitment Status | Active, not recruiting | ||||||||||||||||||||||||||||||
Estimated Enrollment |
1000 | ||||||||||||||||||||||||||||||
Original Estimated Enrollment | Same as current | ||||||||||||||||||||||||||||||
Estimated Study Completion Date | December 31, 2024 | ||||||||||||||||||||||||||||||
Actual Primary Completion Date | September 30, 2023 (Final data collection date for primary outcome measure) | ||||||||||||||||||||||||||||||
Eligibility Criteria | Inclusion Criteria:
Exclusion Criteria:
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Sex/Gender |
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Ages | 1 Year and older (Child, Adult, Older Adult) | ||||||||||||||||||||||||||||||
Accepts Healthy Volunteers | No | ||||||||||||||||||||||||||||||
Contacts | Contact information is only displayed when the study is recruiting subjects | ||||||||||||||||||||||||||||||
Listed Location Countries | France, Italy, Netherlands, Spain | ||||||||||||||||||||||||||||||
Removed Location Countries | |||||||||||||||||||||||||||||||
Administrative Information | |||||||||||||||||||||||||||||||
NCT Number | NCT06019208 | ||||||||||||||||||||||||||||||
Other Study ID Numbers | 928338 | ||||||||||||||||||||||||||||||
Has Data Monitoring Committee | No | ||||||||||||||||||||||||||||||
U.S. FDA-regulated Product |
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IPD Sharing Statement | Not Provided | ||||||||||||||||||||||||||||||
Current Responsible Party | Hospital Universitari Vall d'Hebron Research Institute | ||||||||||||||||||||||||||||||
Original Responsible Party | Same as current | ||||||||||||||||||||||||||||||
Current Study Sponsor | Hospital Universitari Vall d'Hebron Research Institute | ||||||||||||||||||||||||||||||
Original Study Sponsor | Same as current | ||||||||||||||||||||||||||||||
Collaborators | Not Provided | ||||||||||||||||||||||||||||||
Investigators |
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PRS Account | Hospital Universitari Vall d'Hebron Research Institute | ||||||||||||||||||||||||||||||
Verification Date | June 2023 |