Evidence map›Paper›PMID 37689674›Full record

ArticleOrphanet journal of rare diseases2023

Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States.

Amanda Wilson, Alexandra Chiorean, Mario Aguiar, Davorka Sekulic, Patrick Pavlick, Neha Shah, Lisa Sniderman King, Marie Génin, Mélissa Rollot, Margot Blanchon and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Orphanet journal of rare diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Gaucher disease, state of the art and perspectives.Journal of internal medicine · 2025
    Review
  7. Obstacles to Early Diagnosis of Gaucher Disease.Therapeutics and clinical risk management · 2025
    Review
  8. Article
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

14 authors at 3 institutions in 2 countries.

Amanda WilsonHealth Economics and Value Assessment, Sanofi, Cambridge, MA, USA.
Alexandra ChioreanQuinten Health, Paris, France.
Mario AguiarGlobal Medical Affairs, RD Hematology, Sanofi, Cambridge, MA, USA.
Davorka SekulicGlobal Medical Affairs, RD Hematology, Sanofi, Cambridge, MA, USA.
Patrick PavlickUS Rare Medical, Sanofi, Cambridge, MA, USA.
Neha ShahMedical Diagnostics, Sanofi, Cambridge, MA, USA.
Lisa Sniderman KingUS Rare Medical, Sanofi, Cambridge, MA, USA.
Marie GéninQuinten Health, Paris, France.
Mélissa RollotQuinten Health, Paris, France.
Margot BlanchonQuinten Health, Paris, France.
Simon GossetQuinten Health, Paris, France.
Martin MontmerleQuinten Health, Paris, France.
Cliona MolonyDigital Data Science, Sanofi, Cambridge, MA, USA.
Alexandra DumitriuGlobal Medical Affairs, Medical Evidence Generation, Sanofi, Cambridge, MA, USA. Alexandra.Dumitriu@sanofi.com.ORCID http://orcid.org/0000-0001-7887-0936
Sanofi (United States) · USAVEO Oncology (United States) · USSanofi (France) · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly diagnosis of Gaucher disease (GD) allows for disease-specific treatment before significant symptoms arise, preventing/delaying onset of complications. Yet, many endure years-long diagnostic odysseys. We report the development of a machine learning algorithm to identify patients with GD from electronic health records.

methodsWe utilized Optum's de-identified Integrated Claims-Clinical dataset (2007-2019) for feature engineering and algorithm training/testing, based on clinical characteristics of GD. Two algorithms were selected: one based on age of feature occurrence (age-based), and one based on occurrence of features (prevalence-based). Performance was compared with an adaptation of the available clinical diagnostic algorithm for identifying patients with diagnosed GD. Undiagnosed patients highly-ranked by the algorithms were compared with diagnosed GD patients.

resultsSplenomegaly was the most important predictor for diagnosed GD with both algorithms, followed by geographical location (northeast USA), thrombocytopenia, osteonecrosis, bone density disorders, and bone pain. Overall, 1204 and 2862 patients, respectively, would need to be assessed with the age- and prevalence-based algorithms, compared with 20,743 with the clinical diagnostic algorithm, to identify 28 patients with diagnosed GD in the integrated dataset. Undiagnosed patients highly-ranked by the algorithms had similar clinical manifestations as diagnosed GD patients.

conclusionsThe age-based algorithm identified younger patients, while the prevalence-based identified patients with advanced clinical manifestations. Their combined use better captures GD heterogeneity. The two algorithms were about 10-20-fold more efficient at identifying GD patients than the clinical diagnostic algorithm. Application of these algorithms could shorten diagnostic delay by identifying undiagnosed GD patients.

Indexed as

Bone DiseasesGaucher DiseaseAlgorithmsDelayed DiagnosisElectronic Health RecordsHumansRare DiseasesUnited StatesElectronic health recordsGaucher diseaseMachine learningPatient identificationReal-world evidence

Identifiers

PMID37689674
PMCPMC10492341
OpenAlexW4386572688

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.