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Open Access 01-12-2023 | Gaucher Disease | Research

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

Authors: Amanda Wilson, Alexandra Chiorean, Mario Aguiar, Davorka Sekulic, Patrick Pavlick, Neha Shah, Lisa Sniderman King, Marie Génin, Mélissa Rollot, Margot Blanchon, Simon Gosset, Martin Montmerle, Cliona Molony, Alexandra Dumitriu

Published in: Orphanet Journal of Rare Diseases | Issue 1/2023

Abstract

Background

Early 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.

Methods

We 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.

Results

Splenomegaly 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.

Conclusions

The 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.

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Title: Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States
Authors: Amanda Wilson
Alexandra Chiorean
Mario Aguiar
Davorka Sekulic
Patrick Pavlick
Neha Shah
Lisa Sniderman King
Marie Génin
Mélissa Rollot
Margot Blanchon
Simon Gosset
Martin Montmerle
Cliona Molony
Alexandra Dumitriu
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Gaucher Disease
Gaucher Disease
Published in: Orphanet Journal of Rare Diseases / Issue 1/2023
Electronic ISSN: 1750-1172
DOI: https://doi.org/10.1186/s13023-023-02868-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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