Management Algorithms for Gaucher Disease Type 1 in Saudi Arabia: A Consensus Result from a National Meeting
Bibliographic record
Abstract
Background and Objectives Management and treatment of Gaucher disease are quite challenging because of its progressive nature and multisystem involvement. Gaucher disease is misdiagnosed or underdiagnosed in Saudi Arabia due to the high practice of consanguinity, especially among tribes. The prevalence is expected to be much higher than reported in the western countries. The diagnosed cases do not reflect the current prevalence in Saudi Arabia. Therefore, the aim of this consensus statement was to provide a comprehensive algorithmic guideline for the diagnosis and treatment of Gaucher disease for healthcare professionals, especially hematologists adapted to the local circumstances and possible diseases that may mimic Gaucher disease presenting clinical picture. Methods Adult and pediatric hematology or oncology experts from different healthcare sectors in Saudi Arabia reviewed the worldwide related peer-reviewed literature before the meeting on February 2nd, 2019 during the 17th Annual Meeting of Saudi Society of Hematology 2019. During this meeting, variable experiences of misdiagnosis patterns in Gaucher disease were discussed. Diagnosis and management algorithms of Gaucher disease were further discussed and adapted to match the situation in Saudi Arabia Results Splenomegaly is a cardinal sign for the diagnosis of Gaucher disease in adults. While the young aged patients with splenomegaly and gammopathies or multiple myeloma should be considered Gaucher disease after exclusion of the malignancies (Figure 1). While in patients with normal spleen and refractory immune thrombocytopenia (ITP) or associated with anemia, bone marrow biopsy is essential to roll out malignancy before going to enzymatic assay. In pediatric patients, splenomegaly and thrombocytopenia are alarming signs to refer the patients to a hematologist (Figure 2). The lack of adequate awareness among physicians and lack of easy diagnostic tests are the most challenging factors for Gaucher disease diagnosis. Hematological malignancies, thalassemia, ITP, and multiple myeloma are the most common differential diagnosis for Gaucher disease (Table 1). Regarding the short and long-term management goals, the authors added the hepatocellular carcinoma as one of the long-term complications of Gaucher disease and recommended MRI to test for bone mineral density.The authors agreed on the platelet count of <150 x 109/L is diagnostic for thrombocytopenia for Saudis. The authors recommend either MRI or DXA over the biomarkers are to ensure proper diagnosis and assessment of bone manifestations (Figure 3). Conclusion The compelling issue with Gaucher disease in Saudi Arabia is the late diagnosis. Therefore, identify the adult and pediatric milestones of clinical presentation and improve the access to enzyme assay and the gene sequencing could solve the issue of late diagnosis in Saudi Arabia. Besides, Gaucher disease registry with a screening program for thrombocytopenic patients with or without splenomegaly can provide an accurate estimation of the disease prevalence. Disclosures Elbagoury: Sanofi-Genzyme: Employment. Hussein:Sanofi-Genzyme: Employment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".