Differentiating β-Thalassaemia From Iron Deficiency: Performance and Cut-Off Dependency of More Than 40 Red Cell Discrimination Indices in a Large German Cohort.

Authors

Rafid Al-Nabhan, Dani Hakimeh, Lena Oevermann

Published in

International journal of laboratory hematology. Sep 23, 2026. Epub Sep 23, 2026.

Abstract

Since the 1970s, more than 40 red blood cell-based formulas have been proposed to differentiate β-thalassaemia trait (BTT) from iron deficiency (ID) and iron deficiency anaemia (IDA), the main causes of microcytic anaemia. In Germany, where haemoglobinopathies are rare in the autochthonous population but increasingly encountered due to migration, rapid, low-cost screening tools are of growing importance. This study benchmarked more than 40 published discrimination indices (1973-2024) in a large laboratory dataset and explored new formulas.
In a retrospective analysis, 1316 of 5715 screened records met predefined criteria and were stratified into a BTT group (n = 953) and an ID/IDA group (n = 363). Diagnostic performance was assessed using sensitivity, specificity, Youden's index and ROC analysis.
No established index reliably separated BTT from ID/IDA, and performance was strongly cut-off-dependent: the best indices reached AUCs of approximately 0.80 with their published cut-offs (e.g., Wongprachum, 0.803) but up to 0.879 on cut-off-independent ROC analysis (Jayabose), indicating that published cut-offs are not directly transferable between populations. In the independent test dataset, three newly derived formulas performed comparably to the best published indices (AUCs 0.916-0.923 vs. up to 0.915); rather than outperforming existing models, their advantage lies in offering selectable cut-offs for sensitivity-, specificity- or balance-oriented use.
Local validation of discrimination indices and cut-offs is essential before clinical use. Simple CBC-based formulas may serve as low-cost preselection tools to identify individuals requiring further evaluation. As the new formulas were validated internally only, external validation is required before clinical application.

PMID:
42775929
Bibliographic data and abstract were imported from PubMed on 23 Sep 2026.

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