Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data
Abstract 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...
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Orphanet Journal of Rare Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13023-024-03042-y |
| _version_ | 1797273252522885120 |
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| author | Shoshana Revel-Vilk Varda Shalev Aidan Gill Ora Paltiel Orly Manor Avraham Tenenbaum Liat Azani Gabriel Chodick |
| author_facet | Shoshana Revel-Vilk Varda Shalev Aidan Gill Ora Paltiel Orly Manor Avraham Tenenbaum Liat Azani Gabriel Chodick |
| author_sort | Shoshana Revel-Vilk |
| collection | DOAJ |
| description | Abstract 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 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 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 × 109/L), and hyperferritinemia (300–1000 ng/mL) were found to be the three most accurate classifiers of GD in both databases. Conclusion 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. |
| first_indexed | 2024-03-07T14:41:45Z |
| format | Article |
| id | doaj.art-77826b722b7b4492ac42c22598c306d1 |
| institution | Directory Open Access Journal |
| issn | 1750-1172 |
| language | English |
| last_indexed | 2024-03-07T14:41:45Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Orphanet Journal of Rare Diseases |
| spelling | doaj.art-77826b722b7b4492ac42c22598c306d12024-03-05T20:20:19ZengBMCOrphanet Journal of Rare Diseases1750-11722024-02-0119111010.1186/s13023-024-03042-yAssessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world dataShoshana Revel-Vilk0Varda Shalev1Aidan Gill2Ora Paltiel3Orly Manor4Avraham Tenenbaum5Liat Azani6Gabriel Chodick7Gaucher Unit, Shaare Zedek Medical CenterSackler School of Medicine, Tel Aviv UniversityTakeda Pharmaceuticals International AGFaculty of Medicine, Hebrew UniversityBraun School of Public Health and Community Medicine, Hebrew UniversitySackler School of Medicine, Tel Aviv University MaccabiTech, Maccabi Healthcare ServicesSackler School of Medicine, Tel Aviv UniversityAbstract 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 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 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 × 109/L), and hyperferritinemia (300–1000 ng/mL) were found to be the three most accurate classifiers of GD in both databases. Conclusion 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.https://doi.org/10.1186/s13023-024-03042-yGaucher diseaseAlgorithmEarly diagnosisReal-world dataGaucher earlier diagnosis consensus scoring system |
| spellingShingle | Shoshana Revel-Vilk Varda Shalev Aidan Gill Ora Paltiel Orly Manor Avraham Tenenbaum Liat Azani Gabriel Chodick Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data Orphanet Journal of Rare Diseases Gaucher disease Algorithm Early diagnosis Real-world data Gaucher earlier diagnosis consensus scoring system |
| title | Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data |
| title_full | Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data |
| title_fullStr | Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data |
| title_full_unstemmed | Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data |
| title_short | Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data |
| title_sort | assessing the diagnostic utility of the gaucher earlier diagnosis consensus ged c scoring system using real world data |
| topic | Gaucher disease Algorithm Early diagnosis Real-world data Gaucher earlier diagnosis consensus scoring system |
| url | https://doi.org/10.1186/s13023-024-03042-y |
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