DIAGNOSTIC PERFORMANCE OF HEMATOLOGICAL DISCRIMINATION INDICES FOR SCREENING Β-THALASSEMIA TRAIT IN PATIENTS WITH MICROCYTIC HYPOCHROMIC ANEMIA: A HOSPITAL-BASED OBSERVATIONAL STUDY

Background: Beta-thalassemia trait (BTT) and iron deficiency anemia (IDA) are the most common causes of microcytic hypochromic anemia and often present with overlapping hematological features. Hematological discrimination indices derived from routine complete blood count parameters have been proposed as cost-effective screening tools for differentiating these conditions. Aim: To evaluate the diagnostic performance of various hematological discrimination indices in differentiating β-thalassemia from iron deficiency anemia. Materials and Methods: This hospital-based observational study was conducted at the Department of Pathology, Dr. S.N. Medical College, Jodhpur, Rajasthan, India, between October 2025 and June 2026. A total of 550 patients with microcytic hypochromic anemia were enrolled. Complete blood count parameters, iron studies, and high-performance liquid chromatography (HPLC) were performed. Discrimination indices including Shine and Lal, England and Fraser, Green and King, Srivastava, Sirdah, Ehsani, Ricerca, Mean Density of Hemoglobin per Liter (MDHL), Mean Corpuscular Hemoglobin Density (MCHD), and Red Cell Distribution Width Index (RDWI) were calculated. Diagnostic performance was assessed using receiver operating characteristic analysis. Results: Among the participants, 435 (79.1%) had IDA and 115 (20.9%) had β-thalassemia. The Ehsani Index demonstrated the highest diagnostic accuracy (AUROC: 0.969), followed by the Shine and Lal Index (AUROC: 0.960) and Srivastava Index (AUROC: 0.949). The Shine and Lal Index showed the highest sensitivity (92.2%), whereas the Ehsani Index demonstrated the highest specificity (92.9%). Conclusion: The Ehsani, Shine and Lal, and Srivastava indices demonstrated excellent performance in differentiating β-thalassemia from iron deficiency anemia. These simple and inexpensive indices may serve as effective screening tools in resource-limited settings.