Study on the diagnostic and differential value of thalassemia through combined blood routine examination and...

This study aims to explore the diagnostic and differential values of thalassemia genotypes using combined complete blood count (CBC) and reticulocyte parameter analysis in neonates, considering physiological age-related hematological changes.

A retrospective study was conducted from October 2023 to March 2024 involving 315 neonates in the Huangshi area who underwent thalassemia gene detection, CBC, and reticulocyte analysis. Participants were categorized into a control group (n = 83), α-thalassemia group (n = 177), and β-thalassemia group (n = 55). Further subgrouping was based on genotype severity and age (≤28 days and >28 days). A comparative analysis of hematological parameters was performed, and logistic regression models were developed to predict and differentiate thalassemia types.

In both age strata, the α-thalassemia group exhibited significantly higher red blood cell (RBC) counts but lower mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC) compared to control and β-thalassemia groups (P < 0.05). The β-thalassemia group showed significantly higher red cell distribution width (RDW) than the other groups (P < 0.05). Reticulocyte parameters also showed distinct patterns: the α-thalassemia group had lower reticulocyte counts (RET#), while the β-thalassemia group had a higher immature reticulocyte fraction (IRF). A logistic regression model combining RBC, MCHC, RET#, and IRF to differentiate α- from β-thalassemia achieved an area under the curve (AUC) of 0.879, with a sensitivity of 72.7% and specificity of 89.2%. The combined models generally outperformed single-parameter analyses.

Hematological parameters like MCV, MCH, and RDW are effective initial screening indicators for neonatal thalassemia. Integrating reticulocyte analysis with routine CBC enhances screening accuracy and aids in the differential diagnosis of α- and β-thalassemia. This combined, age-stratified approach is valuable for improving early detection and management strategies.