A novel discriminant algorithm for differential diagnosis of mild to moderate thalassemia and iron deficiency anemia

Author:

Pan Liqiu12,Li Linlin12,Qiu Yuling3,Ling Xiaoting12,Wang Chenghan12,Wu Zuhao4,Li Xiaoman12,Lin Faquan123,Huang Yifang123ORCID

Affiliation:

1. Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China

2. Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China

3. NHC Key Laboratory of Thalassemia Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China

4. School of Basic Medicine, Guangxi Medical University, Nanning, Guangxi, P. R. China.

Abstract

Background: Mild to moderate thalassemia trait (TT) and iron deficiency anemia (IDA) are the most common conditions of microcytic hypochromic anemia (MHA) and they exhibit highly similar clinical and laboratory features. It is sometimes difficult to make a differential diagnosis between TT and IDA in clinical practice. Therefore, a simple, effective, and reliable index is needed to discriminate between TT and IDA. Methods: Data of 598 patients (320 for TT and 278 for IDA) were enrolled and randomly assigned to training set (278 of 598, 70%) and validation set (320 of 598, 30%). Stepwise discriminant analysis was used to define the best diagnostic formula for the discrimination between TT and IDA in training set. The accuracy and diagnostic performance of formula was tested and verified by receiver operating characteristic (ROC) analysis in validation set and its diagnostic performance was compared with other published indices. Results: A novel formula, Thalassemia and IDA Discrimination Index (TIDI) = –13.932 + 0.434 × RBC + 0.033 × Hb + 0.025 ×MCHC + 53.593 × RET%, was developed to discriminate TT from IDA. TIDI showed a high discrimination performance in ROC analysis, with the Area Under the Curve (AUC) = 0.936, Youden’ s index = 78.7%, sensitivity = 89.5%, specificity = 89.2%, respectively. Furthermore, the formula index also obtained a good classification performance in distinguishing 5 common genotypes of TT from IDA (AUC from 0.854–0.987). Conclusion: The new, simple algorithm can be used as an effective and robust tool for the differential diagnosis of mild to moderate TT and IDA in Guangxi region, China.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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