Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

Abstract

Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA) to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA), and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC), hemoglobin (Hb), mean cell volume (MCV), mean cell hemoglobin (MCH), and RBC distribution width (RDW). The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia), only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

Decision‐tree‐based methods for differential diagnosis of β‐thalassemia trait from iron deficiency anemia

“…Six tree-based algorithms were applied to differentiate between βTT and IDA. These methods were implemented Urrechaga et al's (2013) study is the only method using MDA as a differential diagnosis function, which showed that 87.9% of β-thalassemia carriers were correctly classified, but our results exhibited a more accurate classification. They showed RBC, Hb, MCV, MCH, and RDW as predictor parameters for differential diagnosis.…”

“…However, there are a few studies that have implemented statistical methods for differential diagnosis. Urrechaga, Aguirre, and Izquierdo () suggested multivariable discriminant analysis for differential diagnosis of microcytic anemia. Multivariate discriminant analysis (MDA) is a parametric method, which relies on different assumptions.…”

Discriminant indices for distinguishing thalassemia and iron deficiency in patients with microcytic anemia: a meta-analysis

“…It is widely agreed that none of these indices is 100% sensitive or 100% specific. Even more complex approaches including combinations of different simple indices, multivariate discriminant analysis or artificial neural network computing are unable to reach absolute sensitivity and specificity [15][16][17][18][19][20][21][22][23]. It is somewhat surprising that comparative studies of these screening indices do not show a consistent picture: discriminant indices that are superior in one study may perform less well in another study.…”

Detection of Β Thalassemia Carriers by Red Cell Parameters Obtained From Automatic Counters Using Mathematical Formulas

“…The calculation for all the other formulas was based on the same cohort that the SVM formula was calculated. Urrechaga et al 30 used multiple discriminant analysis to differentiate IDA from β and α thalassemia carriers; they did not intend to separate thalassemia carriers from healthy subjects. Sargolzaie and Miri-Moghaddam 29 recently described the use of binary logistic regression analysis to find the best equation for a group of 100 β-thalassemia carriers and 77 IDA patients, in this study healthy subjects were not included.…”