Evidence map›Paper›PMID 34301277›Full record

ArticleOrphanet journal of rare diseases2021

The LORIS MyeliNeuroGene rare disease database for natural history studies and clinical trial readiness.

Aaron Spahr, Zaliqa Rosli, Mélanie Legault, Luan T Tran, Simon Fournier, Helia Toutounchi, Lama Darbelli, Cécile Madjar, Cassandra Lucia, Marie-Lou St-Jean and 3 more

Abstract read
In one paragraph

Article in Orphanet journal of rare diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Journal of comparative effectiveness research · 2023
    Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Aaron SpahrDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
Zaliqa RosliMcGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Mélanie LegaultMcGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Luan T TranDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
Simon FournierDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
Helia ToutounchiDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
Lama DarbelliDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
Cécile MadjarMcGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Cassandra LuciaDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
Marie-Lou St-JeanDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
Samir DasMcGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Alan C EvansMcGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Geneviève BernardDepartment of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada. genevieve.bernard@mcgill.ca.ORCID 0000-0002-9634-2966

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRare diseases are estimated to affect 150-350 million people worldwide. With advances in next generation sequencing, the number of known disease-causing genes has increased significantly, opening the door for therapy development. Rare disease research has therefore pivoted from gene discovery to the exploration of potential therapies. With impending clinical trials on the horizon, researchers are in urgent need of natural history studies to help them identify surrogate markers, validate outcome measures, define historical control patients, and design therapeutic trials.

resultsWe customized a browser-accessible multi-modal (e.g. genetics, imaging, behavioral, patient-determined outcomes) database to increase cohort sizes, identify surrogate markers, and foster international collaborations. Ninety data entry forms were developed including family, perinatal, developmental history, clinical examinations, diagnostic investigations, neurological evaluations (i.e. spasticity, dystonia, ataxia, etc.), disability measures, parental stress, and quality of life. A customizable clinical letter generator was created to assist in continuity of patient care.

conclusionsSmall cohorts and underpowered studies are a major challenge for rare disease research. This online, rare disease database will be accessible from all over the world, making it easier to share and disseminate data. We have outlined the methodology to become Title 21 Code of Federal Regulations Part 11 Compliant, which is a requirement to use electronic records as historical controls in clinical trials in the United States. Food and Drug Administration compliant databases will be life-changing for patients and families when historical control data is used for emerging clinical trials. Future work will leverage these tools to delineate the natural history of several rare diseases and we are confident that this database will be used on a larger scale to improve care for patients affected with rare diseases.

Indexed as

Quality of LifeRare DiseasesDatabases, FactualFemaleHumansParentsPregnancyUnited StatesBiomarkersClinical trialsDatabasesInformation management systemsLeukodystrophyNatural historyOutcome measuresRare diseasesRegistry

Identifiers

PMID34301277
PMCPMC8299589

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.