Critical appraisal of discriminant formulas for distinguishing thalassemia from iron deficiency in patients with microcytic anemia
Abstract
Background Many discriminant formulas have been reported for distinguishing thalassemia trait from iron deficiency in patients with microcytic anemia. Independent verification of several discriminant formulas is deficient or even lacking. Therefore, we have retrospectively investigated discriminant formulas in a large, well-characterized patient population. Methods The investigational population consisted of 2664 patients with microcytic anemia: 1259 had iron deficiency, 1196 'pure' thalassemia trait (877 β- and 319 α-thalassemia), 150 had thalassemia trait with concomitant iron deficiency or anemia of chronic disease, and 36 had other diseases. We investigated 25 discriminant formulas that only use hematologic parameters available on all analyzers; formulas with more advanced parameters were disregarded. The diagnostic performance was investigated using ROC analysis. Results The three best performing formulas were the Jayabose (RDW index), Janel (11T), and Green and King formulas. The differences between them were not statistically significant (p>0.333), but each of them had significantly higher area under the ROC curve than any other formula. The Jayabose and Green and King formulas had the highest sensitivities: 0.917 both. The highest specificity, 0.925, was found for the Janel formula, which is a composite score of 11 other formulas. All investigated formulas performed significantly better in distinguishing β- than α-thalassemia from iron deficiency. Conclusions In our p…
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The majority of existing ML models or rule-based formulas specifically differentiate one distinct thalassemia (α or β) from IDA.
“The majority of these formulas specifically differentiate one distinct thalassemia (α or β) from IDA (e.g., [14][15][16][17][18][19].”
From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [14]
The efficacy of the XGB model in distinguishing thalassemia from IDA was substantiated in a Spanish population.
“Moreover, the efficacy of the XGB model in distinguishing thalassemia from IDA was substantiated in a Spanish population, where it outperformed all other rule-based formulas, including Jayabose, Janel, Green and King, and Shine and Lal (14) (Supplemental Table 5).”
From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [14]
The XGB model outperformed all other rule-based formulas, including Jayabose, Janel, Green and King, and Shine and Lal, in distinguishing thalassemia from IDA in a Spanish population.
“Moreover, the efficacy of the XGB model in distinguishing thalassemia from IDA was substantiated in a Spanish population, where it outperformed all other rule-based formulas, including Jayabose, Janel, Green and King, and Shine and Lal (14) (Supplemental Table 5).”
From Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data (2024) · cites this paper as [14]
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Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data
Schipper, A., Rutten, M., van Gammeren, A., Harteveld, C. L., Urrechaga, E., Weerkamp, F., den Besten, G., Krabbe, J., Slomp, J., Schoonen, L., et al. (2024) · Clinical Chemistry · doi:10.1093/clinchem/hvae081 · Cited by 33 · see citing works
Self-citation
- Authors
- Urrechaga, E., & Hoffmann, J. J. M. L.
- Affiliations
- Hospital de Galdakao (ES)
- Abbott (Netherlands) (NL)
- Corresponding author
- Urrechaga, Eloísa
- Venue
- Clinical Chemistry and Laboratory Medicine (CCLM)
- Publisher
- Walter de Gruyter GmbH
- Published
- 2017-01-01
- Volume
- 55
- Issue
- 10
- Pages
- 1582-1591
- ISSN
- 1434-6621 (ISSN-L)
- 1437-4331
- OpenAlex
- W2587350956
- Cited by
- 47
- References
- 0
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 5 · 2022: 8 · 2021: 5 · 2020: 8 · 2019: 5 · 2018: 2 · 2017: 2
- FWCI
- 2.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Chemistry and Laboratory Medicine (CCLM) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Genetics › Hemoglobinopathies and Related Disorders
- Health Sciences › Medicine › Hematology › Iron Metabolism and Disorders
- Health Sciences › Medicine › Emergency Medicine › Hematological disorders and diagnostics
- Keywords
- thalassemia trait, iron deficiency anemia, microcytic anemia, differential diagnosis, beta-thalassemia, alpha-thalassemia, red cell distribution width, anemia of chronic disease, diagnostic accuracy
- MeSH terms
- Anemia, Hypochromic, C-Reactive Protein, Diagnosis, Differential, Humans, Iron, Retrospective Studies, ROC Curve, Sensitivity and Specificity, Thalassemia, alpha-Thalassemia, beta-Thalassemia, Anemia, Iron-Deficiency, Area Under Curve
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