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Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies.
Faviez, Carole; Chen, Xiaoyi; Garcelon, Nicolas; Zaidan, Mohamad; Billot, Katy; Petzold, Friederike; Faour, Hassan; Douillet, Maxime; Rozet, Jean-Michel; Cormier-Daire, Valérie; Attié-Bitach, Tania; Lyonnet, Stanislas; Saunier, Sophie; Burgun, Anita.
Afiliação
  • Faviez C; Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France. carole.faviez@inserm.fr.
  • Chen X; HeKA, Inria Paris, Paris, F-75012, France. carole.faviez@inserm.fr.
  • Garcelon N; Universite Paris Cite, Paris, France. carole.faviez@inserm.fr.
  • Zaidan M; Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France.
  • Billot K; HeKA, Inria Paris, Paris, F-75012, France.
  • Petzold F; Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France.
  • Faour H; Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France.
  • Douillet M; HeKA, Inria Paris, Paris, F-75012, France.
  • Rozet JM; Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France.
  • Cormier-Daire V; Service de Néphrologie, Dialyse et Transplantation, Hôpital Universitaire Bicêtre, Assistance Publique-Hôpitaux de Paris (AP-HP), Kremlin Bicêtre, F-94270, France.
  • Attié-Bitach T; Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France.
  • Lyonnet S; Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France.
  • Saunier S; Division of Nephrology, University of Leipzig Medical Center, Leipzig, Germany.
  • Burgun A; Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France.
BMC Med Inform Decis Mak ; 24(1): 134, 2024 May 24.
Article em En | MEDLINE | ID: mdl-38789985
ABSTRACT

BACKGROUND:

There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies.

METHODS:

Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology.

RESULTS:

A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases.

CONCLUSION:

Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Raras / Registros Eletrônicos de Saúde / Ciliopatias Limite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Raras / Registros Eletrônicos de Saúde / Ciliopatias Limite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França