Genome-based Management of Patients in Precision Medicine (Ge-Med) Towards a Genomic Health Program
GE-MED
1 other identifier
interventional
12,000
1 country
1
Brief Summary
The GE-MED APPROACH project will enroll patients (n = appr. 12.000) with unclear molecular cause of the disease, suspected genetic cause of the disease without detailed molecular analysis like Whole Exome Sequencing (WES). The novelty of this study is to integrate genomic health concepts into immediate clinical care. To achieve these goals, a novel structure for the Triple P (3P) concept of personalized medicine (Personalized, Predictive, Preventive) integrated into a well-established health care system and associated with novel decentralized Disease Analysing Task Forces (DATF) will be implemented. The overall goal of this study is to implement, for the first time, Whole Genome Sequencing (WGS) analysis as a first line diagnostic test for all clinical indications such as Rare Disease (RD )and familial cancer syndromes.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Jun 2021
Longer than P75 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
February 16, 2021
CompletedFirst Posted
Study publicly available on registry
February 18, 2021
CompletedStudy Start
First participant enrolled
June 1, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 1, 2026
ExpectedStudy Completion
Last participant's last visit for all outcomes
July 1, 2027
November 29, 2023
November 1, 2023
5.1 years
February 16, 2021
November 28, 2023
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Number of WGS analysis
WGS analysis as a first line diagnostic test for all clinical indications
Day 1
Study Arms (1)
WGS Diagnostic
EXPERIMENTALBoth underage and adult persons (male and female) with diagnostically unsolved rare diseases who have been or are included into diagnostic care at the University Hospital Tübingen, Germany (UKT) and who are suspected of having a genetic cause of the disease. Study related procedures: Blood sampling, anamnesis including pedigree, Next Generation Sequencing (NGS) analysis and other omics analysis (transcriptomics, proteomics, metabolomics).
Interventions
Blood sampling, short clinical characterization, WGS based sequencing, NGS analysis and other omics analysis (transcriptomics, proteomics, metabolomics).
Eligibility Criteria
You may qualify if:
- Unclear molecular cause of the disease
- Suspected genetic cause of the disease
You may not qualify if:
- Missing informed consent of the patient and if applicable the legal representative
- Previously performed WES or panel analysis
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
University Hospital Tübingen
Tübingen, 72076, Germany
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Olaf Rieß, Prof. Dr.
University Hospital Tübingen
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- NA
- Masking
- NONE
- Purpose
- BASIC SCIENCE
- Intervention Model
- SINGLE GROUP
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
February 16, 2021
First Posted
February 18, 2021
Study Start
June 1, 2021
Primary Completion (Estimated)
July 1, 2026
Study Completion (Estimated)
July 1, 2027
Last Updated
November 29, 2023
Record last verified: 2023-11
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- ANALYTIC CODE
- Time Frame
- Data will become available after analysis and unlimited.
- Access Criteria
- Authorized users within the participating organizations.
The GE-MED APPROACH study will provide data in a pseudonymized manner to national and international databases set up to increase the diagnostic yield through advanced analysis tools and matchmaking against other cohorts