Diagnostic performance of hematological discrimination indices to discriminate between βeta thalassemia trait and iron deficiency anemia and using cluster analysis: Introducing two new indices tested in Iranian population

A Novel Discriminating Tool for Microcytic Anemia in Childhood

Accurate and timely interpretation of microcytic anemia can be diagnostically challenging in the primary care setting. We sought to develop a novel model for distinguishing iron-deficiency anemia from thalassemia trait in the modern pediatric population. Demographic history and red blood cell indices were retrospectively characterized for 76 children referred to our pediatric hematology clinic for evaluation of microcytic anemia. Statistically significant variables were sequentially added into a logistic regression model to develop the final model. The final discriminating model incorporates red cell distribution width, mean corpuscular hemoglobin concentration, and red blood cell values. Favorable predictive performance is seen in the initial (sensitivity 89.2%, specificity 92.3%) and external validation cohort (sensitivity 84.4%, specificity 88.9%). This novel tool may aid in determining the cause of hypochromic, microcytic anemia in the primary care setting. Finally, the study cohort reflects an underrepresented group in the development of screening tools, and thus offers generalizability.

Distinguishing Iron Deficiency Anemia From Beta-Thalassemia Trait: Comparative Analysis of CRUISE Index and Other Traditional Diagnostic Indices

Introduction Iron deficiency anemia and beta-thalassemia trait are two common and important differentials of microcytic hypochromic anemia. Various discrimination indices using two or more common complete blood cell count (CBC) parameters have been used to distinguish between the two since 1973. Recently, a new discriminant index, the CRUISE index, was proposed in the year 2019. The efficacy of various older indices along with the CRUISE index was evaluated for patients in our geographical area. Materials and method Ours was a laboratory-based, cross-sectional study where 100 patients, based on inclusion and exclusion criteria, with microcytic hypochromic anemia were evaluated for CBC parameters along with serum ferritin and hemoglobin-high performance liquid chromatography (Hb HPLC). A total of eight discrimination indices namely, Mentzer, Srivastava, Shine & Lal, Green & King, RDWI, England & Fraser, Kerman I and CRUISE index were used and evaluated for their diagnostic efficacy using different statistical parameters. ROC curves were obtained and a new cut-off value was proposed for our population. Data was analysed using Microsoft Excel (Microsoft® Corp., Redmond, WA, USA) and SPSS v29.0.2.0 (20) (IBM Corp., Armonk, NY, USA). Results Out of the total 100 cases, 39 were beta-thalassemia trait and 61 were iron deficiency anemia cases. The average age was 36.7 (±12.7 SD) years. Among the 73 females, 43 were diagnosed as iron deficiency anemia (IDA) and 30 as beta-thalassemia trait (BTT) cases. Among the 27 males, 18 were diagnosed as IDA and nine as BTT cases. The mean values were significantly lower in IDA patients for mean corpuscular volume (MCV) (p=.008), mean corpuscular haemoglobin (MCH) (p=.003), and mean corpuscular haemoglobin concentration (MCHC) (p=.003) and significantly higher for red cell distribution width (RDW) (p=.020). The mean ferritin levels in cases of IDA were 7.61 (±3.75) mcg/L and in BTT were 87.09 (±66.77 SD) mcg/L. The mean HbA2 levels in IDA cases were 2.75% (±0.41% SD) and BTT cases were 5.57% (±0.73% SD). CRUISE index revealed the highest AUC (0.934), YI (76.21) and accuracy (90%) followed by the Mentzer index with a diagnostic accuracy of 81%. Shine & Lal index revealed the lowest AUC (0.710), YI (3.28) and accuracy (41%). Conclusion CRUISE index, which was recently proposed, was ranked 1st in terms of AUC, YI, and accuracy and was considered 2nd best in terms of sensitivity for differentially diagnosing the two conditions. Mentzer index, a commonly used index, also revealed a high diagnostic accuracy in our study for differentiating BTT from IDA. CRUISE index being a novel index, more research work needs to be carried out in various other geographical setups to evaluate the efficacy of this index.

Differentinating between non-transfusion dependant β-thalassemia and iron deficinecy anemia in children using ROC and logistic regression analysis: two novel discrimination indices designed for pediatric patients

Introduction

This cross-sectional study enrolled a group of 271 children with microcytic anemia in order to test the performance of 41 single and 2 composite formulas andindices in distinguishing between β-thalassemia (β-thal) and iron deficiency anemia (IDA) in the pediatric population.

Methods

Optimal pediatric cut-off values from the previously published formulas and indices were generated using ROC analysis. Logistic regression in R using generalized linear models (GLM) generated two new indices.

Results

Formulas and indices with optimal cut-offvalues in children with accuracy ≥90% were (in descending order): Matos & Carvalho index, MDHL(Telmissani) formula, England and Fraser formula, Pornprasert index, Sirachainan index, Telmissani (MCHD) formula, CRUISE index, Hameed index, Sargolzaie formula and Zaghloul II index. The CroThalDD-LM1 index has an accuracy of 93.36% (AUC 0.986, 95% CI 0.975-0.997), while the second CroThalDD-LM2 index utilizes absolute reticulocyte count alongside CBC variables, with an accuracy of 96.77% (AUC 0.985, 95% CI 0.988-0.999).

Discussion and conclusion

We recommend using aforementioned formulas and indices with corrected cut-off values and accuracy >90% alongside two new proposed indices. A comparison of both native and these new indices is encouraged. These are the first discrimination indices generated and designed precisely for the pediatric population, which includes preschool children.

Multi-criteria decision making to validate performance of RBC-based formulae to screen [Formula: see text]-thalassemia trait in heterogeneous haemoglobinopathies

BACKGROUND

India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies.

METHODS

We compared the performance of a recently developed formula SCS[Formula: see text] and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden's Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures.

RESULTS

MCDM methods revealed that the Shine & Lal and SCS[Formula: see text] were the best-performing formulae. Further, a modification of the SCS[Formula: see text] formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS[Formula: see text] along with the condition MCV[Formula: see text] 80 fl was recommended for a higher heterogeneous population set. It was found that SCS[Formula: see text] can classify all BTT samples with 100% sensitivity when MCV[Formula: see text] 80 fl.

CONCLUSIONS

We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS[Formula: see text] and its web application SUSOKA can provide 100% sensitivity when MCV[Formula: see text] 80 fl.

Logistic-Nomogram model based on red blood cell parameters to differentiate thalassemia trait and iron deficiency anemia in southern region of Fujian Province, China

BACKGROUND

Differentiation between thalassemia trait (TT) and iron deficiency anemia (IDA) is challenging and costly. This study aimed to construct and evaluate a model based on red blood cell (RBC) parameters to differentiate TT and IDA in the southern region of Fujian Province, China.

METHODS

RBC parameters of 364 TT patients and 316 IDA patients were reviewed. RBC parameter-based Logistic-Nomogram model to differentiate between TT and IDA was constructed by multivariate logistic regression analysis plus nomogram, and then compared with 22 previously reported differential indices.

RESULTS

The patients were randomly selected to a training cohort (nTT  = 248, nIDA  = 223) and a validation cohort (nTT  = 116, nIDA  = 93). In the training cohort, multivariate logistic regression analysis identified RBC count, mean corpuscular hemoglobin (MCH), and MCH concentration (MCHC) as independent parameters associated with TT susceptibility. A nomogram was plotted based on these parameters, and then the RBC parameter-based Logistic-Nomogram model g (μy ) = 1.92 × RBC count-0.51 × MCH + 0.14 × MCHC-39.2 was devised. The area under the curve (AUC) (95% CI) was 0.95 (0.93-0.97); sensitivity and specificity at the best cutoff score (120.24) were 0.93 and 0.89, respectively; the accuracy was 0.91. In the validation cohort, the RBC parameter-based Logistic-Nomogram model had AUC (95% CI) of 0.95 (0.91-0.98); sensitivity and specificity were 0.92 and 0.87, respectively; accuracy was 0.90. Moreover, compared with 22 reported differential indices, the RBC parameter-based Logistic-Nomogram model showed numerically higher AUC, net reclassification index, and integrated discrimination index (all p < 0.001).

CONCLUSION

The RBC parameter-based Logistic-Nomogram model shows high performance in differentiating patients with TT and IDA from the southern region of Fujian Province.

TT@MHA: A Machine Learning-based Webpage Tool for Discriminating Thalassemia Trait from Microcytic Hypochromic Anemia Patients

BACKGROUND

Iron deficiency anemia (IDA) and thalassemia trait (TT) are the most common causes of microcytic hypochromic anemia (MHA) and are endemic in lower resource settings and rural areas with poor medical infrastructure. Accurate discrimination between IDA and TT is an essential issue for MHA patients. Although various discriminant formulas have been reported, distinguishing between IDA and TT is still a challenging problem due to the diversity of anemic populations.

METHODS

We retrospectively collected laboratory data from 798 MHA patients. High proportions of α-TT (43.33%) and TT concomitant with IDA (TT&IDA) patients (14.04%) were found among TT patients. Five machine learning (ML) approaches, including Liner SVC (L-SVC), support vector machine learning (SVM), Extreme gradient boosting (XGB), Logistic Regression (LR), and Random Forest (RF), were applied to develop a discriminant model. Performance was assessed and compared with six existing discriminant formulas.

RESULTS

The RF model was chosen as the discriminant algorithm, namely TT@MHA. TT@MHA was tested in an interlaboratory cohort with a sensitivity, specificity, accuracy, and AUC of 91.91%, 91.00%, 91.53%, and 0.942, respectively. A webpage tool of TT@MHA (https://dxonline.deepwise.com/prediction/index.html?baseUrl=%2Fapi%2F&id=26408&topicName=undefined&from=share&platformType=wisdom) was developed to facilitate the healthcare providers in rural areas.

CONCLUSION

The ML-based TT@MHA algorithm, with high sensitivity and specificity, could help discriminate TT patients from MHA patients, especially in populations with high proportions of α-TT patients and TT&IDA patients. Moreover, a user-friendly webpage tool for TT@MHA could facilitate healthcare providers in rural areas where advanced technologies are not accessible.

Evaluation of the validity of the pre-marriage mean corpuscular volume value as a predictive test for b-thalassemia carrier status

Background

The national mandatory premarital screening test is based on mean corpuscular volume (MCV) > 80 fL value for the detection of β-thalassemia to provide acceptance for marriage. The objective of this study is to assess the efficacy of MCV as a screening test for β-thalassemia trait in the present population.

Methods

This study was conducted on 418 blood samples collected from adult individuals. The diagnosis of β-thalassemia carrier was given to those having HbA2 values equal to or above 3.5%. The diagnostic reliability of different RBC indices and formulas in discriminating cases of β-thalassemia trait were evaluated. Finally, a new index called "Momani" was determined based on MCV, RDW and RBC count.

Results

β-thalassemia trait was identified in 10% of the cases. The measured MCV value was significantly lower in β-thalassemia carrier group compared to non-carrier group (p = <0.001). MCV value and RBC count showed a higher diagnostic reliability than other RBC indices. We found that MCV ≤ 74.45 fL is more suitable cutoff value of MCV with 86.2% specificity, 71.4% sensitivity, 36.6% positive predictive value, and 96.4% negative predictive value. Finally, our index "Momani" was found to be useful in predicting carrier and paralleled the performance of Sirdah, Mentzer, and Ehsani indices.

Conclusions

MCV<80 is a useful but not a perfect cutoff point for the screening of β-thalassemia carriers from noncarriers. The diagnostic accuracy of MCV can be improved by selecting a new cutoff value. Moreover, "Momani" index shows good discrimination ability in diagnosing β-thalassemia carrier in our population.

Performance analysis of machine learning algorithms and screening formulae for β-thalassemia trait screening of Indian antenatal women

BACKGROUND

Currently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for β-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCS_BTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA[16].

METHODS

A diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden's Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS.

RESULTS

It was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden's index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCS_BTT. K-means clustering and the ranking from MCDM methods show that SCS_BTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies.

CONCLUSION

Comparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCS_BTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCS_BTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.

Advances in screening of thalassaemia

Thalassaemia is a common hereditary haemolytic anaemia. Mild cases of this disease may be asymptomatic, while patients with severe thalassaemias require high-dose blood transfusions and regular iron removal to maintain life or haematopoietic stem cell transplantation to be cured, imposing an enormous familial and social burden. Therefore, early, timely, and accurate screening of patients is of great importance. In recent years, with the continuous development of thalassaemia screening technologies, the accuracy of thalassaemia screening has also improved significantly. This article reviews the current research on thalassaemia screening.

Prognostic Potential of RDW in Discriminating Hemoglobinopathies among Patients reporting to Aga Khan Hospital, Kisumu

Background

Red cell distribution width (RDW) measures the extent of variation in red blood cell (RBC) volume in terms of coefficient of variation. It reflects the degree of variation in RBC’s sizes and shapes, characteristic of iron deficiency and anemias involving RBC destruction, especially hemoglobinopathies. Its values are often available as one of the RBC indices generated as complete blood cell count (CBC) using automated hematology analyzers. Hemoglobinopathies are highly prevalent in malaria-endemic geographical settings like the Sub-Saharan African which has over 200,000 currently documented annual major hemoglobinopathies with an alarming mortality rate of 50–90% by the age of 5 years usually undiagnosed. With a vast growing majority of hemoglobinopathy carriers, this public health problem is projected to escalate by the year 2050 due to unaffordable laboratory tests for screening of newborns and populations as recommended by World Health Organization in resource-limited settings. Therefore, innovative of a cost-effective diagnostic method would improve the survival of these children. The current study aimed to evaluate the overall ability of RDW in discriminating hemoglobinopathy and hemoglobinopathy-free cases within the Lake Victoria Economic Block region of Western Kenya served partly by the Aga Khan Hospital, Kisumu.

Objective

To determine the significance of RDW as a tool to differentiate between individuals with hemoglobinopathies and those without.

Method

This was a cross-sectional retrospective comparative hospital-based study that analyzed data from the hematology laboratory database for patients examined using high-performance liquid chromatography during the years 2015–2020. The study consisted of 488 participants (49.4%, n = 241 control; 50.6% n = 247 case, p = 0.786) aged between 1 month and 66 years selected conveniently through census. The relationship between RDW of the controls and cases was analyzed using Mann–Whitney U, Kruskal–Wallis tests among population groups and Dunn’s post hoc test within groups since the data were non-normally distributed.

Results

The RDW cutoff value was computed at 95% confidence interval (CI), and values greater than this indicated a diagnosis of hemoglobinopathy.

Conclusion

RDW at 95% CI was 19.9 [14.5 + (2.7 × 2 = 19.9)] cutoff point which proved to be an excellent screening tool for sickle cell disease phenotypes in Western Kenya but would generate many false positive and false negatives for pure Hb AS. RDW is a poor screening tool for, Hb AS + HbF, Hb AS + β thal and β-thalassemia since it could not differentiate diseased from non-diseases populations. Even though RDW proved to be a poor screening tool for beta thalassemia, other complete blood count (CBC) parameters such as MCV and red cell count can be used to identify thalassemia syndromes as well as iron deficiency anemia. Though out of the scope of this work, highlighting the significance of these parameters in addition to the RDW would improve its feasibility as a screening tool for all hemoglobinopathies. Normal reference range for children ≤ 5 years needs to be developed using prospective data for precise marking of disorders associated with red cell anisocytosis, and individuals ≥ 6 years can share RDW normal reference range regardless of their gender.

An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA2 cases

BACKGROUND

Since screening of α-thalassemia carriers by low HbA₂ has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA₂ patients.

METHODS

Laboratory data of 1213 patients with low HbA₂ used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.

RESULTS

The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA₂, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.

CONCLUSION

Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA₂ cases.

Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis

BACKGROUND

Several hematological indices have been already proposed to discriminate between iron deficiency anemia (IDA) and β-thalassemia trait (βTT). This study compared the diagnostic performance of different hematological discrimination indices with decision trees and support vector machines, so as to discriminate IDA from βTT using multidimensional scaling and cluster analysis. In addition, decision trees were used to determine the diagnostic classification scheme of patients.

METHODS

Consisting of 1178 patients with hypochromic microcytic anemia (708 patients with βTT and 470 patients with IDA), this cross-sectional study compared the diagnostic performance of 43 hematological discrimination indices with classification tree algorithms and support vector machines in order to discriminate IDA from βTT. Moreover, multidimensional scaling and cluster analysis were used to identify the homogeneous subgroups of discrimination methods with similar performance.

RESULTS

All the classification tree algorithms except the LOTUS tree algorithm showed acceptable accuracy measures for discrimination between IDA and βTT in comparison with other hematological discrimination indices. The results indicated that the CRUISE and C5.0 tree algorithms had better diagnostic performance and efficiency among other discrimination methods. Moreover, the AUC of CRUISE and C5.0 tree algorithms indicated more precise classification with values of 0.940 and 0.999, indicating excellent diagnostic accuracy of such models. Moreover, the CRUISE and C5.0 tree algorithms showed that mean corpuscular volume can be considered as the main variable in discrimination between IDA and βTT.

CONCLUSIONS

CRUISE and C5.0 tree algorithms as powerful methods in data mining techniques can be used to develop accurate differential methods along with other laboratory parameters for the discrimination of IDA and βTT. In addition, the multidimensional scaling method and cluster analysis can be considered as the most appropriate techniques to determine the discrimination indices with similar performance for future hematological studies.

Application of Bayesian Decision Tree in Hematology Research: Differential Diagnosis of β-Thalassemia Trait from Iron Deficiency Anemia

OBJECTIVE

Several discriminating techniques have been proposed to discriminate between β-thalassemia trait (βTT) and iron deficiency anemia (IDA). These discrimination techniques are essential clinically, but they are challenging and typically difficult. This study is the first application of the Bayesian tree-based method for differential diagnosis of βTT from IDA.

METHOD

This cross-sectional study included 907 patients with ages over 18 years old and a mean (±SD) age of 25 ± 16.1 with either βTT or IDA. Hematological parameters were measured using a Sysmex KX-21 automated hematology analyzer. Bayesian Logit Treed (BLTREED) and Classification and Regression Trees (CART) were implemented to discriminate βTT from IDA based on the hematological parameters.

RESULTS

This study proposes an automatic detection model of beta-thalassemia carriers based on a Bayesian tree-based method. The BLTREED model and CART showed that mean corpuscular volume (MCV) was the main predictor in diagnostic discrimination. According to the test dataset, CART indicated higher sensitivity and negative predictive value than BLTREED for differential diagnosis of βTT from IDA. However, the CART algorithm had a high false-positive rate. Overall, the BLTREED model showed better performance concerning the area under the curve (AUC).

CONCLUSIONS

The BLTREED model showed excellent diagnostic accuracy for differentiating βTT from IDA. In addition, understanding tree-based methods are easy and do not need statistical experience. Thus, it can help physicians in making the right clinical decision. So, the proposed model could support medical decisions in the differential diagnosis of βTT from IDA to avoid much more expensive, time-consuming laboratory tests, especially in countries with limited recourses or poor health services.

Verification of 20 Mathematical Formulas for Discriminating Between Iron Deficiency Anemia and Thalassemia Trait in Microcytic Anemia

BACKGROUND

Currently, more than 45 mathematical formulas based on simple red blood cell (RBC) parameters have been proposed for differentiating between iron deficiency and thalassemia in microcytic anemia, of which 20 are relatively new and have not been thoroughly independently verified. The study goal was to verify these 20 new formulas and to identify which RBC parameters have a decisive impact on the performance of those formulas.

METHODS

A database containing laboratory and diagnostic data from 2788 subject individuals with microcytic anemia was used for assessing performance by receiver operating characteristic (ROC) analysis.

RESULTS

The new Index26 had excellent performance, equivalent to the Green and King, Jayabose, and Janel formulas previously identified in the literature. The discriminant power of nearly all newer formulas was lower in our study than that claimed by the original authors. We discovered that a well-performing formula requires mean cell volume (MCV), RBC distribution width (RDW), and RBC measurements, whereas hemoglobin measurements appeared not to be essential.

CONCLUSIONS

Only the new Index26 performed at a level comparable to the very strongest established formulas. All other new formulas had lower performance than was claimed in the original publications, underscoring that independent verification of new formulas is indispensable.

Differential diagnosis of thalassemia and iron deficiency anemia in pregnant women using new formulas from multidimensional analysis of red blood cells

Background

Iron deficiency anemia (IDA) and thalassemia trait (TT) are the most common conditions of microcytic hypochromic anemia (MHA) in pregnant women. We used the BC-6800Plus analyzer to study the utility of erythrocyte and reticulocyte parameters for distinguishing TT from IDA in pregnant women.

Methods

A total of 454 anemic pregnant women, including 340 with IDA, 66 with β-thalassemia trait (β-TT) and 48 with α-thalassemia trait (α-TT), were included. Multiple comparisons among groups were performed, and diagnostic performance of parameters was determined using receiver operating characteristic (ROC) curve analysis, with P<0.05 indicating statistical significance.

Results

Reticulocyte production index (RPI) and the average volume of mature red blood cells (MCVm) in the IDA group were significantly higher than in the β-TT and α-TT groups. Red blood cell (RBC), reticulocyte percentage (Ret%), and RPI in the IDA group were significantly lower than in the α-TT and β-TT groups. We devised MHA 1=0.42× MCH -0.57× RPI -0.08× %MICROr -9.38 to distinguish IDA from α-TT. With a cut-off value of 0.61, the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were 0.868, 90.9%, and 68.5%, respectively. We devised MHA 2=0.04× %MICROr +0.12× MCVm -13.76× Ret# -6.29 to distinguish IDA from β-TT. With a cut-off value of 0.55, the AUC, sensitivity, and specificity were 0.878, 81.3%, and 80.3%, respectively.

Conclusions

Erythrocyte indices and formulas can be used as initial methods for the differential diagnosis of TT and IDA. MHA 1 and MHA 2 were the most useful indices in the differential diagnosis of α-TT from IDA and β-TT from IDA in pregnant women.

Role of RDW in mathematical formulas aiding the differential diagnosis of microcytic anemia

Many mathematical formulas containing simple red blood cell parameters have been proposed for differentiating between iron deficiency and thalassemia in patients with microcytic anemia. Approximately half of these formulas do include red cell distribution width (RDW), along with other red cell parameters. In the present study we investigated the role of RDW, expressed in relative or in absolute units in relation with the formulas' discriminant performance. We used a database containing over 2200 subjects with microcytic anemia, for whom a final diagnosis (iron-deficiency anemia, thalassemia, both or other) was available. Performance of the discriminant formulas was assessed by Receiver Operator Curve analysis. Substitution of relative by absolute RDW resulted in statistically significant performance increase (area under the ROC curve) in 16 out of 23 formulas, predominantly due to increased specificity. Relevant performance deterioration was seen in only three formulas that had low initial performance already with the original relative RDW. For optimal differential diagnostic performance, an RDW-based formula for distinguishing thalassemia from iron-deficiency anemia in microcytic anemia should contain 'absolute' instead of relative RDW.