Dataset Information

A guide to writing systematic reviews of rare disease treatments to generate FAIR-compliant datasets: building a Treatabolome.

ABSTRACT:

Background

Rare diseases are individually rare but globally affect around 6% of the population, and in over 70% of cases are genetically determined. Their rarity translates into a delayed diagnosis, with 25% of patients waiting 5 to 30 years for one. It is essential to raise awareness of patients and clinicians of existing gene and variant-specific therapeutics at the time of diagnosis to avoid that treatment delays add up to the diagnostic odyssey of rare diseases' patients and their families.

Aims

This paper aims to provide guidance and give detailed instructions on how to write homogeneous systematic reviews of rare diseases' treatments in a manner that allows the capture of the results in a computer-accessible form. The published results need to comply with the FAIR guiding principles for scientific data management and stewardship to facilitate the extraction of datasets that are easily transposable into machine-actionable information. The ultimate purpose is the creation of a database of rare disease treatments ("Treatabolome") at gene and variant levels as part of the H2020 research project Solve-RD.

Results

Each systematic review follows a written protocol to address one or more rare diseases in which the authors are experts. The bibliographic search strategy requires detailed documentation to allow its replication. Data capture forms should be built to facilitate the filling of a data capture spreadsheet and to record the application of the inclusion and exclusion criteria to each search result. A PRISMA flowchart is required to provide an overview of the processes of search and selection of papers. A separate table condenses the data collected during the Systematic Review, appraised according to their level of evidence.

Conclusions

This paper provides a template that includes the instructions for writing FAIR-compliant systematic reviews of rare diseases' treatments that enables the assembly of a Treatabolome database that complement existing diagnostic and management support tools with treatment awareness data.

SUBMITTER: Atalaia A

PROVIDER: S-EPMC7424983 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

A guide to writing systematic reviews of rare disease treatments to generate FAIR-compliant datasets: building a Treatabolome.

Atalaia Antonio A Thompson Rachel R Corvo Alberto A Carmody Leigh L Piscia Davide D Matalonga Leslie L Macaya Alfons A Lochmuller Angela A Fontaine Bertrand B Zurek Birte B Hernandez-Ferrer Carles C Reinhard Carola C Gómez-Andrés David D Desaphy Jean-François JF Schon Katherine K Lohmann Katja K Jennings Matthew J MJ Synofzik Matthis M Riess Olaf O Yaou Rabah Ben RB Evangelista Teresinha T Ratnaike Thiloka T Bros-Facer Virginie V Gumus Gulcin G Horvath Rita R Chinnery Patrick P Laurie Steven S Graessner Holm H Robinson Peter P Lochmuller Hanns H Beltran Sergi S Bonne Gisèle G

Orphanet journal of rare diseases 20200812 1

<h4>Background</h4>Rare diseases are individually rare but globally affect around 6% of the population, and in over 70% of cases are genetically determined. Their rarity translates into a delayed diagnosis, with 25% of patients waiting 5 to 30 years for one. It is essential to raise awareness of patients and clinicians of existing gene and variant-specific therapeutics at the time of diagnosis to avoid that treatment delays add up to the diagnostic odyssey of rare diseases' patients and their fa ...[more]

PMID: 32787960

Similar Datasets

Project description:BackgroundVariants in the LMNA gene, encoding lamins A/C, are responsible for a growing number of diseases, all of which complying with the definition of rare diseases. LMNA-related disorders have a varied phenotypic expression with more than 15 syndromes described, belonging to five phenotypic groups: Muscular Dystrophies, Neuropathies, Cardiomyopathies, Lipodystrophies and Progeroid Syndromes. Overlapping phenotypes are also reported. Linking gene and variants with phenotypic expression, disease mechanisms, and corresponding treatments is particularly challenging in laminopathies. Treatment recommendations are limited, and very few are variant-based.ObjectiveThe Treatabolome initiative aims to provide a shareable dataset of existing variant-specific treatment for rare diseases within the Solve-RD EU project. As part of this project, we gathered evidence of specific treatments for laminopathies via a systematic literature review adopting the FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines for scientific data production.MethodsTreatments for LMNA-related conditions were systematically collected from MEDLINE and Embase bibliographic databases and clinical trial registries (Cochrane Central Registry of Controlled Trials, clinicaltrial.gov and EudraCT). Two investigators extracted and analyzed the literature data independently. The included papers were assessed using the Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence.ResultsFrom the 4783 selected articles by a systematic approach, we identified 78 papers for our final analysis that corresponded to the profile of data defined in the inclusion and exclusion criteria. These papers include 2 guidelines/consensus papers, 4 meta-analyses, 14 single-arm trials, 15 case series, 13 cohort studies, 21 case reports, 8 expert reviews and 1 expert opinion. The treatments were summarized electronically according to significant phenome-genome associations. The specificity of treatments according to the different laminopathic phenotypical presentations is variable.ConclusionsWe have extracted Treatabolome-worthy treatment recommendations for patients with different forms of laminopathies based on significant phenome-genome parings. This dataset will be available on the Treatabolome website and, through interoperability, on genetic diagnosis and treatment support tools like the RD-Connect's Genome Phenome Analysis Platform.

Dataset Information

A guide to writing systematic reviews of rare disease treatments to generate FAIR-compliant datasets: building a Treatabolome.

Background

Aims

Results

Conclusions

Publications

A guide to writing systematic reviews of rare disease treatments to generate FAIR-compliant datasets: building a Treatabolome.

Similar Datasets

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