Building a Score to Discriminate Between Iron Deficiency Anemia and Beta Thalassemia Trait

Abstract

Objective:  Iron deficiency anemia (IDA) and thalassemia are the most common causes of anemia. Differentiating Beta thalassemia trait (βTT) from IDA can be challenging and often requires sophisticated procedures such as hemoglobin electrophoresis, High Performance Liquid Chromatography (HPLC), genetic and molecular studies, which are time and money consuming. However, many equations are made using the hemoglobin, MCV, RDW, MCH and R.B.Cs count to discriminate between IDA and βTT. An example of these equations are Metzer, Sirdah, Green and king, Shine and Lal, and Ehsani also many other equations are present, but no one equation is currently superior to the others. In our paper, we are aiming to build a scoring system to differentiate between IDA and βTT with higher sensitivity and specificity. Methodology: We used the five equations with high sensitivity and specificity and gave a point of either 1 or zero for each result, either IDA or βTT. If the final score is more than 3, it is most properly IDA, and if less than 3, it could be βTT. Results: We applied this method to 50 patients diagnosed with IDA or βTT and obtained a result with a good confidence interval (9.9 to 21) and high precision. Conclusion: We recommend utilizing this scoring system with the help of Artificial Intelligence (AI) as an easy, cheap, faster, more specific, and more sensitive tool to discriminate between IDA and βTT.