文章基本信息

标题：The Evaluation of Results of Twenty Common Equations for Differentiation of Beta Thalassemia Trait from Iron Deficiency Anemia: A Cross-Sectional Study
本地全文：下载
作者：Hassan Ebrahimpour Sadagheyani ; Rahim Sharafkhani ; Shahriar Sakhaei 等
期刊名称：Iranian Journal of Public Health
印刷版ISSN：2251-6085
电子版ISSN：2251-6093
出版年度：2022
卷号：51
期号：4
DOI：10.18502/ijph.v51i4.9255
语种：English
出版社：Tehran University of Medical Sciences
摘要：Background: Beta Thalassemia Trait (BTT) and Iron Deficiency Anemia (IDA) were two common clinical problems with clinical hypochromic and microcytic manifestations, and their differentiation from each other was very important and needs innovative formulas and laboratory tests. Since the consideration of anemia as a pair with BTT leads to beta-thalassemia major birth in 25% of cases, offering prospective parents detailed information about the likelihood of their offspring developing BTT is essential. The present study aimed to investigate the performance of common equations in differentiation of BTT from IDA. Methods: In the present cross-sectional study, twenty common equations were selected in the differentiation of BTT from IDA. To evaluate the equations, the tests of 292 individuals (73 individuals with BTT and 219 individuals with IDA) were compared with the initial diagnosis of hypochromic and microcytic anemia using the formulas. Descriptive and value indices and Roc curve were utilized for all equations to analyze the results. Results: Among twenty differential equations, Bordbar, Kerman I, II and Srivastava equations had the highest area under Roc curve (AUC) of 0.841, 0.838, 0.836, and 0.830 respectively, but Kandhro I. equation had the lowest AUC (0.378). Conclusion: Given the importance of AUC and value indices of differential equations in clinical decision making, and results of evaluating common equations in differentiation of BTT from IDA. It is essential to improve the values of the equations by re-examining the parameters involved in them.
关键词：Measles ;Machine learning ;Time series ;Infection