Data Collection of Patients With Rare Bone Diseases
- Conditions
- Skeletal Dysplasia
- Registration Number
- NCT05247645
- Lead Sponsor
- Luca Sangiorgi
- Brief Summary
RD-DATA is a retrospective and prospective data collection, finalized for care and research purposes. It is articulated in main sections - strongly related and mutually dependent on each other - corresponding to different data domains: personal information, clinical data, genetic data, genealogical data, surgeries, etc.
This approach has been developed to corroborate and integrate data from different sources and evaluating several aspects of the diseases and to correlate genetic background and phenotypic outcomes, in order to better investigate diseases pathophysiology. Due to legal requirements, institutional directives and organizational issues, we are unable to include individuals residing outside Italy in the registry at this time. We are currently engaged in the preparation of a recruitment process for individuals residing outside Italy.
- Detailed Description
The traditional method of collecting patient information is frequently chaotic, inconvenient and sometimes even unsafe, particularly when dealing with rare diseases. In 2020, the need to simplify the diagnostic process and to overcome the difficulties of data storage and analysis, led to the suggestion of implementing the Data Collection of Patients With Rare Bone Diseases - RD-DATA.
The RD-DATA relies on an IT platform named Genotype-phenotype Data Integration platform (GeDI).This solution, realized by a collaboration among Rare Skeletal Disorders Department and a local software-house (Dilaxia Spa) is a General Data Protection Regulation (GDPR)-compliant, multi-client, web-accessible system and it has been designed according to current medical informatics standards (Orphanet code, ICD-10 (International Disease Classification), Human Genome Variants Society). GeDI is continuously implemented to improve management of persons with rare conditions with predominantly skeletal involvement and to help researchers in analysing collected information. RD-DATA is articulated in main sections:
Personal data: it comprises general information, birth details and residence data; Patient data: including the patients internal code, the hospital code and other details on patients; Diagnostic Process: the diagnosis, the status (affected, suspected, etc.), age at diagnosis, comorbidities, allergies, etc.; Genogram: a tool for designing family transmission of the disease, alongside information on the diseases status of all relatives included.
Clinical events: it records a long list of signs and symptoms as well as several additional items to describe the disease Genetic Analysis and Alteration: including technique, sample information, analysis duration, etc. In addition, this section comprises detailed information on detected pathological variants (gene, international reference, DNA change, Protein change, genomic position, etc.).
Visits: it includes the typology of the visit (genetic, orthopedic, rehabilitation, pediatric, etc.), the date of the visit, treatment, prescription, imaging, etc.
Treatments: this section comprises information of a wide range of treatments including pharmacological, devices, supplements, and other treatments such as psychological, nutritional, etc.; Documents: this repository allow us to store all types of documents (radiological reports, imaging, consents, clinical reports, etc.); Consents: this section provides a comprehensive overview of all consents collected, including the collection date; Samples: this section includes information on the samples, like the type, date of collection, etc.; PROs: this section collects information on patients reported outcomes such as the quality of life or ABC scale.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 1000
- All patients affected by rare diseases with predominantly skeletal involvement
- Any condition unrelated to rare diseases with predominantly skeletal involvement
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Natural History and Epidemiology in terms of clinical, genetic and functional evaluation Since the disease is rare, the timeframe is strictly related to patients enrolment and consequently to amount of collected data. A 10 years period will probably answer general issues. To maintain an established registry in order to assess epidemiology and natural history.
Collection of:
1. physical examinations data: assessment of severity of the disease
2. orthopedic and functional data: stature (cm), weight (kg), number and localization of sites affected by signs and symptoms, definition of deformities (localization and number), definition of limitations (localization and number)
3. surgical procedures: type, number and site of surgeries disease-related and age at surgeries
4. genetics background: target gene, type of mutation, type of variant detected, clinical significance
5. family history: inheritance in maternal or paternal line
6. treatment information: pharmacological, devices, supplements, and other treatments
Clinical, orthopedic and functional features are updated at each follow up. Clinical reports, medical charts and imaging are the primary sources of data.
- Secondary Outcome Measures
Name Time Method Genotype-Phenotype Correlation among clinical features and eventual molecular background 25 years The secondary outcome comprises the correlation between genotype and phenotype. This includes but is not limited to clinical features and genetic background. This will be pursued using the information collected during visits and follow-ups and the genetic information resulting from molecular investigations, when available.
Related Research Topics
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Trial Locations
- Locations (1)
Irccs Istituto Ortopedico Rizzoli
🇮🇹Bologna, Emilia-Romagna, Italy
Irccs Istituto Ortopedico Rizzoli🇮🇹Bologna, Emilia-Romagna, ItalyMarina Mordenti, PhDContact+39 051 6366062registri.malattierare@ior.itMarcella Lanza, PhDContact+39 051 6366169registri.malattierare@ior.itLuca Sangiorgi, PhDPrincipal Investigator
