Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data.
Algorithm
Early diagnosis
Gaucher disease
Gaucher earlier diagnosis consensus scoring system
Real-world data
Journal
Orphanet journal of rare diseases
ISSN: 1750-1172
Titre abrégé: Orphanet J Rare Dis
Pays: England
ID NLM: 101266602
Informations de publication
Date de publication:
16 Feb 2024
16 Feb 2024
Historique:
received:
28
07
2023
accepted:
19
01
2024
medline:
17
2
2024
pubmed:
17
2
2024
entrez:
16
2
2024
Statut:
epublish
Résumé
Gaucher disease (GD) is a rare autosomal recessive condition associated with clinical features such as splenomegaly, hepatomegaly, anemia, thrombocytopenia, and bone abnormalities. Three clinical forms of GD have been defined based on the absence (type 1, GD1) or presence (types 2 and 3) of neurological signs. Early diagnosis can reduce the likelihood of severe, often irreversible complications. The aim of this study was to validate the ability of factors from the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system to discriminate between patients with GD1 and controls using real-world data from electronic patient medical records from Maccabi Healthcare Services, Israel's second-largest state-mandated healthcare provider. We applied the GED-C scoring system to 265 confirmed cases of GD and 3445 non-GD controls matched for year of birth, sex, and socioeconomic status identified from 1998 to 2022. The analyses were based on two databases: (1) all available data and (2) all data except free-text notes. Features from the GED-C scoring system applicable to GD1 were extracted for each individual. Patients and controls were compared for the proportion of the specific features and overall GED-C scores. Decision tree and random forest models were trained to identify the main features distinguishing GD from non-GD controls. The GED-C scoring distinguished individuals with GD from controls using both databases. Decision tree models for the databases showed good accuracy (0.96 [95% CI 0.95-0.97] for Database 1; 0.95 [95% CI 0.94-0.96] for Database 2), high specificity (0.99 [95% CI 0.99-1]) for Database 1; 1.0 [95% CI 0.99-1] for Database 2), but relatively low sensitivity (0.53 [95% CI 0.46-0.59] for Database 1; 0.32 [95% CI 0.25-0.38]) for Database 2). The clinical features of splenomegaly, thrombocytopenia (< 50 × 10 In this analysis of real-world patient data, certain individual features of the GED-C score discriminate more successfully between patients with GD and controls than the overall score. An enhanced diagnostic model may lead to earlier, reliable diagnoses of Gaucher disease, aiming to minimize the severe complications associated with this disease.
Sections du résumé
BACKGROUND
BACKGROUND
Gaucher disease (GD) is a rare autosomal recessive condition associated with clinical features such as splenomegaly, hepatomegaly, anemia, thrombocytopenia, and bone abnormalities. Three clinical forms of GD have been defined based on the absence (type 1, GD1) or presence (types 2 and 3) of neurological signs. Early diagnosis can reduce the likelihood of severe, often irreversible complications. The aim of this study was to validate the ability of factors from the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system to discriminate between patients with GD1 and controls using real-world data from electronic patient medical records from Maccabi Healthcare Services, Israel's second-largest state-mandated healthcare provider.
METHODS
METHODS
We applied the GED-C scoring system to 265 confirmed cases of GD and 3445 non-GD controls matched for year of birth, sex, and socioeconomic status identified from 1998 to 2022. The analyses were based on two databases: (1) all available data and (2) all data except free-text notes. Features from the GED-C scoring system applicable to GD1 were extracted for each individual. Patients and controls were compared for the proportion of the specific features and overall GED-C scores. Decision tree and random forest models were trained to identify the main features distinguishing GD from non-GD controls.
RESULTS
RESULTS
The GED-C scoring distinguished individuals with GD from controls using both databases. Decision tree models for the databases showed good accuracy (0.96 [95% CI 0.95-0.97] for Database 1; 0.95 [95% CI 0.94-0.96] for Database 2), high specificity (0.99 [95% CI 0.99-1]) for Database 1; 1.0 [95% CI 0.99-1] for Database 2), but relatively low sensitivity (0.53 [95% CI 0.46-0.59] for Database 1; 0.32 [95% CI 0.25-0.38]) for Database 2). The clinical features of splenomegaly, thrombocytopenia (< 50 × 10
CONCLUSION
CONCLUSIONS
In this analysis of real-world patient data, certain individual features of the GED-C score discriminate more successfully between patients with GD and controls than the overall score. An enhanced diagnostic model may lead to earlier, reliable diagnoses of Gaucher disease, aiming to minimize the severe complications associated with this disease.
Identifiants
pubmed: 38365689
doi: 10.1186/s13023-024-03042-y
pii: 10.1186/s13023-024-03042-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
71Informations de copyright
© 2024. The Author(s).
Références
Stirnemann J, et al. A review of Gaucher disease pathophysiology, clinical presentation and treatments. Int J Mol Sci. 2017;18:441.
doi: 10.3390/ijms18020441
pubmed: 28218669
pmcid: 5343975
Schwartz IVD, et al. Characteristics of 26 patients with type 3 Gaucher disease: a descriptive analysis from the Gaucher outcome survey. Mol Genet Metab Rep. 2018;14:73–9.
doi: 10.1016/j.ymgmr.2017.10.011
pubmed: 29326879
El-Beshlawy A, et al. Long-term hematological, visceral, and growth outcomes in children with Gaucher disease type 3 treated with imiglucerase in the international collaborative Gaucher group Gaucher registry. Mol Genet Metab. 2017;120:47–56.
doi: 10.1016/j.ymgme.2016.12.001
pubmed: 28040394
Castillon G, Chang SC, Moride Y. Global incidence and prevalence of Gaucher disease: a targeted literature review. J Clin Med. 2022;12:85.
doi: 10.3390/jcm12010085
pubmed: 36614898
pmcid: 9821068
Revel-Vilk S, Szer J, Zimran A. Gaucher disease and related lysosomal storage diseases. In: Williams Hematology. New York: McGraw-Hill Education; 2021. p. 1189–202.
Gonzalez DE, et al. Enzyme replacement therapy with velaglucerase alfa in Gaucher disease: results from a randomized, double-blind, multinational, Phase 3 study. Am J Hematol. 2013;88:166–71.
doi: 10.1002/ajh.23381
pubmed: 23386328
Hughes DA, et al. Velaglucerase alfa (VPRIV) enzyme replacement therapy in patients with Gaucher disease: long-term data from phase III clinical trials. Am J Hematol. 2015;90:584–91.
doi: 10.1002/ajh.24012
pubmed: 25801797
pmcid: 4654249
Mistry PK, et al. Timing of initiation of enzyme replacement therapy after diagnosis of type 1 Gaucher disease: effect on incidence of avascular necrosis. Br J Haematol. 2009;147:561–70.
doi: 10.1111/j.1365-2141.2009.07872.x
pubmed: 19732054
pmcid: 2774157
Mehta A, et al. Exploring the patient journey to diagnosis of Gaucher disease from the perspective of 212 patients with Gaucher disease and 16 Gaucher expert physicians. Mol Genet Metab. 2017;122:122–9.
doi: 10.1016/j.ymgme.2017.08.002
pubmed: 28847676
Mistry PK, et al. A reappraisal of Gaucher disease-diagnosis and disease management algorithms. Am J Hematol. 2011;86:110–5.
doi: 10.1002/ajh.21888
pubmed: 21080341
pmcid: 3058841
Mistry PK, Sadan S, Yang R, Yee J, Yang M. Consequences of diagnostic delays in type 1 Gaucher disease: the need for greater awareness among hematologists-oncologists and an opportunity for early diagnosis and intervention. Am J Hematol. 2007;82:697–701.
doi: 10.1002/ajh.20908
pubmed: 17492645
Mehta A, et al. Presenting signs and patient co-variables in Gaucher disease: outcome of the Gaucher earlier diagnosis consensus (GED-C) Delphi initiative. Intern Med J. 2019;49:578–91.
doi: 10.1111/imj.14156
pubmed: 30414226
pmcid: 6852187
Mehta A, et al. Scoring system to facilitate diagnosis of Gaucher disease. Intern Med J. 2020;50:1538–46.
doi: 10.1111/imj.14942
pubmed: 33174353
pmcid: 7839708
Savolainen MJ, et al. The Gaucher earlier diagnosis consensus point-scoring system (GED-C PSS): evaluation of a prototype in Finnish Gaucher disease patients and feasibility of screening retrospective electronic health record data for the recognition of potential undiagnosed patients in Finland. Mol Genet Metab Rep. 2021;27:100725.
doi: 10.1016/j.ymgmr.2021.100725
pubmed: 33604241
pmcid: 7875822
Revel-Vilk S, et al. Using the Gaucher earlier diagnosis consensus (GED-C) delphi score in a real-world dataset. Int J Transl Med. 2022;2:506–14.
Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol. 2022;75:25–36.
doi: 10.4097/kja.21209
pubmed: 35124947
pmcid: 8831439
Curovic RE, et al. Splenomegaly - diagnostic validity, work-up, and underlying causes. PLoS ONE. 2017;12:e0186674.
doi: 10.1371/journal.pone.0186674
Jamian L, Wheless L, Crofford LJ, Barnado A. Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record. Arthritis Res Ther. 2019;21:305.
doi: 10.1186/s13075-019-2092-7
pubmed: 31888720
pmcid: 6937803
Tang KL, Lucyk K, Quan H. Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study. CMAJ Open. 2017;5:E617-622.
doi: 10.9778/cmajo.20170036
pubmed: 28827414
pmcid: 5621953
Pehrsson M, et al. Screening for potential undiagnosed Gaucher disease patients: utilisation of the Gaucher earlier diagnosis consensus point-scoring system (GED-C PSS) in conjunction with electronic health record data, tissue specimens, and small nucleotide polymorphism (SNP) genotype data available in Finnish biobanks. Mol Genet Metab Rep. 2022;33:100911.
doi: 10.1016/j.ymgmr.2022.100911
pubmed: 36092251
pmcid: 9449642
Névéol A, Dalianis H, Velupillai S, Savova G, Zweigenbaum P. Clinical Natural Language Processing in languages other than English: opportunities and challenges. J Biomed Semant. 2018;9:12.
doi: 10.1186/s13326-018-0179-8
Hughes D, et al. Gaucher disease in bone: from pathophysiology to practice. J Bone Miner Res. 2019;34:996–1013.
doi: 10.1002/jbmr.3734
pubmed: 31233632
Mikosch P, et al. Patients with Gaucher disease living in England show a high prevalence of vitamin D insufficiency with correlation to osteodensitometry. Mol Genet Metab. 2009;96:113–20.
doi: 10.1016/j.ymgme.2008.12.001
pubmed: 19147383
Rite S, et al. Insulin-like growth factors in childhood-onset Gaucher disease. Pediatr Res. 2002;52:109–12.
doi: 10.1203/00006450-200207000-00020
pubmed: 12084856
Mistry PK, Taddei T, vom Dahl S, Rosenbloom BE. Gaucher disease and malignancy: a model for cancer pathogenesis in an inborn error of metabolism. Crit Rev Oncog. 2013;18:235–46.
doi: 10.1615/CritRevOncog.2013006145
pubmed: 23510066
pmcid: 4437216
Knevel R, Liao KP. From real-world electronic health record data to real-world results using artificial intelligence. Ann Rheum Dis. 2023;82:306–11.
doi: 10.1136/ard-2022-222626
pubmed: 36150748
Riskin D, et al. Using artificial intelligence to identify patients with migraine and associated symptoms and conditions within electronic health records. BMC Med Inform Decis Mak. 2023;23:121.
doi: 10.1186/s12911-023-02190-8
pubmed: 37452338
pmcid: 10349448
Ronicke S, et al. Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet J Rare Dis. 2019;14:69.
doi: 10.1186/s13023-019-1040-6
pubmed: 30898118
pmcid: 6427854
Gurovich Y, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60–4.
doi: 10.1038/s41591-018-0279-0
pubmed: 30617323
Wilson A, et al. Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States. Orphanet J Rare Dis. 2023;18:280.
doi: 10.1186/s13023-023-02868-2
pubmed: 37689674
pmcid: 10492341