ArticleDiagnostics (Basel, Switzerland)2021
A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta Thalassemia Traits.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0.Frontiers in medicine · 2025Article
- A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.BMC medical informatics and decision making · 2024Article
- New logarithm-based discrimination formula for differentiating thalassemia trait from iron deficiency anemia in pregnancy.BMC pregnancy and childbirth · 2023Article
- Novel Decision Tool for More Severe α-Thalassemia Genotypes Screening with Functional Loss of Two or More α-Globin Genes: A Diagnostic Test Study.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
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Abstract
Thalassemia is one of the major inherited haematological disorders in the Southeast Asia region. This study explored the potential utility of red blood cell (RBC) parameters and reticulocyte cell population data (CPD) parameters in the differential diagnosis of α and β-thalassaemia traits as a rapid and cost-effective tool for screening of thalassemia traits. In this study, a total of 1597 subjects (1394 apparently healthy subjects, 155 subjects with α-thalassaemia trait, and 48 subjects with β-thalassaemia trait) were accrued. The parameters studied were the RBC parameters and reticulocyte CPD parameters derived from Unicel DxH800. A novel algorithm named αβ-algorithm was developed: (MN-LMALS-RET × RDW) - MCH) to discriminate α from β-thalassaemia trait with a cut-off value of 1742.5 [AUC = 0.966, sensitivity = 92%, specificity = 90%, 95% CI = 0.94-0.99]. Two prospective studies were carried: an in-house cohort to assess the specificity of this algorithm in 310 samples comprising various RBC disorders and in an interlaboratory cohort of 65 α-thalassemia trait, and 30 β-thalassaemia trait subjects to assess the reproducibility of the findings. We propose the αβ-algorithm to serve as a rapid, inexpensive surrogate evaluation tool of α and β-thalassaemia in the population screening of thalassemia traits in geographic regions with a high burden of these inherited blood disorders.
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