Proxy indicators for identifying iron deficiency among anemic vegetarians in an area prevalent for thalassemia and hemoglobinopathies

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.

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.

New logarithm-based discrimination formula for differentiating thalassemia trait from iron deficiency anemia in pregnancy

BACKGROUND

Thalassemia trait (TT) and iron deficiency anemia (IDA) are the most common conditions of microcytic hypochromic anemia (MHA) in pregnant women. Accurate discrimination between TT and IDA is an important issue, and better methods are urgently needed. Although considerable RBC formulas and indices have been developed since 1973, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, we assessed the diagnostic function of 43 different differential formulas in patients with microcytic anemia by using accuracy measures and recommending a new log-based differential formula.

METHODS

The data of 430 pregnant women (229 with TT and 201 with IDA) were enrolled, and 44 formula performances were evaluated with receiver operating characteristic (ROC) analysis.

RESULTS

The newly introduced logarithm-based formula XS-1 performs better than the general discriminant index with sensitivity and specificity of 82.10 and 89.05, which are better than other formulas. In the pregnant population, the Shine and Lal and Roth..SVM. formulas have shown excellent performance, while other formulas showed poorer discriminative abilities in our study than in the original authors.

CONCLUSION

The logarithm-based formula XS-1 can be used to screen thalassemia and iron deficiency anemia during the first trimester. Considering the particularity of pregnancy, medical personnel in different regions should choose a screening formula similar to that of the local region and population when identifying thalassemia in pregnancy. Any formula should be independently verified locally before use. For the convenience of the health care team and experimental scientists, a web-based tool has been established at http://yyy.yiyiy.top/XS-1/ by which users can easily get their desired screening test result without going through the underlying mathematical and computational details.

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.

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.

Application of HbA2 levels and red cell indices-based new model in the differentiation of thalassemia traits from iron deficiency in hypochromic microcytic anemia Cases

INTRODUCTION

Thalassemia traits and iron deficiency anemia are the most common types of hypochromic microcytic anemia with similar clinical and laboratory features. It is vital to establish a new screening model based on HbA₂ levels and red cell indices for the differentiation of TT from IDA in hypochromic microcytic anemia cases.

METHOD

The data comprised of the red blood cell indices and HbA₂ prenatal diagnostic test results of 810 individuals who were identified to conform to the following criteria: MCV < 80 fl or MCH < 26 pg. We launched a new model consisting mainly of significative red cell indices and HbA₂ levels, as well as proposing cutoff values by using decision trees and logistic regression analyses. Next, we evaluated our new method by comparing the sensitivity, specificity, positive, and negative predictive values with those of the previous formulas.

RESULTS

We put forward a new model and compared it with 5 efficient formulas. The new model exhibited the highest accuracy (0.918), with its sensitivity and specificity calculated as 0.917 and 0.921, respectively. Our new model's Youden index was 0.838, which is higher than the other formulas' Youden indices.

CONCLUSIONS

The new screening model, based on HbA₂ levels and red cell indices, is suitable for the screening of thalassemia patients in the hypochromic microcytic anemia group and has the best efficiency in distinguishing TT and IDA.

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.

Factors associated with anaemia and iron deficiency among women of reproductive age in Northeast Thailand: a cross-sectional study

Background

Anaemia and iron deficiency (ID) affect women of reproductive age globally and considered to be a major public health problem in developing countries. This study determines the prevalence of anaemia and ID among women of reproductive age in urban northeast Thailand and examined the relative contribution of various risk factors to anaemia and ID in this population.

Methods

Three hundred ninety-nine non-pregnant women, aged 18–45 years, from three universities in northeast Thailand participated in this cross-sectional study. Selected socio-demographic, history of blood loss, usual consumption of red meat and tea/coffee, and anthropometric data were collected. Complete blood count including haemoglobin (Hb) concentration, serum ferritin (SF), C-reactive protein (CRP), and thalassemia were determined. Multiple logistic regressions were applied to identify the risk factors of anaemia and ID.

Results

Overall, 370 participants were included for data analyses after excluding women with severe/intermedia thalassemia diseases and/or those with positive serum CRP. The prevalence of anaemia, ID, and iron deficiency anaemia (IDA) were 28.4, 28.4, and 13.2%, respectively. Women with thalassemia had a higher prevalence of anaemia but a lower prevalence of ID than the women without thalassemia. By multiple regression analysis, ID [adjusted OR (AOR) = 4.9, 95% CI = 2.8–8.3], two α-gene defects (AOR = 8.0, 95% CI = 3.0–21.3) and homozygous Hb E (AOR = 8.5, 95% CI = 3.0–24.3) were identified as the potential risk factors of anaemia. Further, the odds of ID were significantly higher among women who donated blood within the past 3 months (AOR = 6.7, 95% CI = 2.8–16.3), and had moderate to a high amount of blood loss during menstruation (AOR = 2.2, 95% CI = 1.3–3.9).

Conclusion

This study found a relatively high but differential prevalence of anaemia and ID among women of reproductive age with or without thalassemia. Only homozygous Hb E and two α-gene defects of thalassemia types and ID were the main factors contributing to anaemia. Recent blood donation, and moderate to a high amount of blood loss during menstruation were potential risk factors of ID in this population.

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

Although the discrimination between β-thalassemia trait (βTT) and Iron deficiency anemia (IDA) is important clinically, but it is challenging and normally difficult; so if a patient with IDA is diagnosed as βTT, then it is deprived of iron therapy. This study purpose was to evaluate the 26 different discriminating indices diagnostic function in patients with microcytic anemia by using accuracy measures, and also recommending two distinct new discriminating indices as well. In this study, 907 patients were enrolled with the ages over 18-year-old with either βTT or IDA. Twenty-six discrimination indices diagnostic performance presented in earlier studies, and two new indices were introduced in this study (CRUISE index and index26) in order to evaluate the differential between βTT and IDA by using accuracy measures. 537 (59%) patients with βTT (299 (56%) women, and 238 (44%) men), and also 370 (41%) patients with IDA (293 (79%) women, and 77 (21%) men) were participated in this study for evaluating the 28 discrimination indices diagnostic performance. Two new introduced indices (CRUISE index and index26) have better performance than some discrimination indices. Indices with the amount of AUC higher than 0.8 had very appropriate diagnostic accuracy in discrimination between βTT and IDA, and also CRUISE index has good diagnostic accuracy, too. The present study was also the first cluster analysis application in order to identify the homogeneous subgroups of different indices with similar diagnostic function. In addition, new indices that offered in this study have presented a relatively closed diagnostic performance by using cluster analysis for the different indices described in earlier studies. Thus, we suggest the using of cluster analysis in order to determine differential indices with similar diagnostic performances.

ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia

Background

The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT.

Methods

We retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including k-nearest neighbor (k-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices.

Results

The data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool ‘ThalPred’ was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively.

Conclusion

ThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at http://codes.bio/thalpred/ by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.

Meat as a Pharmakon: An Exploration of the Biosocial Complexities of Meat Consumption

In contemporary dietary advice, meat is depicted as a pharmakon: it is believed to either heal or poison the human body (and mind). Often, it also serves as a scapegoat for a wide range of public health issues and other societal problems. Related attitudes, practices, and beliefs pertain to a demarcated mode of thinking or episteme that is characteristic for the so-called post-domestic or industrialized societies. The latter are not only typified by an abundant yet largely concealed production of meat, but increasingly also by moral crisis and confusion about its nutritional meaning. For an improved appreciation of the ambiguous position of meat in human health and disease, as well as the concomitant scattering into different subject positions (e.g., the omnivore, flexitarian, vegetarian, vegan, permaculturalist, and carnivore position), an interdisciplinary approach is required. To this end, the current study tentatively combines food research with a selection of (post-structuralist) concepts from the humanities. The aim is to outline a historical and biosocial need for meat (as well as its rejection) and to analyze how its transformative effects have contributed to a polarized discourse on diet and well-being in academia and society at large. Excessive categorization (for instance with respect to meat's alleged naturalness, normalness, necessity, and niceness) and Manichean thinking in binary opposites are among the key factors that lead to impassioned yet often sterile debates between the advocates and adversaries of meat eating in a post-truth context.

Critical appraisal of discriminant formulas for distinguishing thalassemia from iron deficiency in patients with microcytic anemia

BACKGROUND

Many discriminant formulas have been reported for distinguishing thalassemia trait from iron deficiency in patients with microcytic anemia. Independent verification of several discriminant formulas is deficient or even lacking. Therefore, we have retrospectively investigated discriminant formulas in a large, well-characterized patient population.

METHODS

The investigational population consisted of 2664 patients with microcytic anemia: 1259 had iron deficiency, 1196 'pure' thalassemia trait (877 β- and 319 α-thalassemia), 150 had thalassemia trait with concomitant iron deficiency or anemia of chronic disease, and 36 had other diseases. We investigated 25 discriminant formulas that only use hematologic parameters available on all analyzers; formulas with more advanced parameters were disregarded. The diagnostic performance was investigated using ROC analysis.

RESULTS

The three best performing formulas were the Jayabose (RDW index), Janel (11T), and Green and King formulas. The differences between them were not statistically significant (p>0.333), but each of them had significantly higher area under the ROC curve than any other formula. The Jayabose and Green and King formulas had the highest sensitivities: 0.917 both. The highest specificity, 0.925, was found for the Janel formula, which is a composite score of 11 other formulas. All investigated formulas performed significantly better in distinguishing β- than α-thalassemia from iron deficiency.

CONCLUSIONS

In our patient population, the Jayabose RDW index, the Green and King formula and the Janel 11T score are superior to all other formulas examined for distinguishing between thalassemia trait and iron deficiency anemia. We confirmed that all formulas perform much better in β- than in α-thalassemia carriers and also that they incorrectly classify approximately 30% of thalassemia carriers with concomitant other anemia as not having thalassemia. The diagnostic performance of even the best formulas is not high enough for making a final thalassemia diagnosis, but in countries with limited resources, they can be helpful in identifying those patients who need further examinations for genetic anemia.

Red Cell Indices and Formulas Used in Differentiation of β-Thalassemia Trait from Iron Deficiency in Thai Adults

β-Thalassemia (β-thal) and iron deficiency cause most microcytic anemias. Red cell indices and formulas have been established as simple, fast, and inexpensive in discrimination between these two hematological disorders in school children. However, whether these formulas could be applied to diagnose β-thal trait and iron deficiency in adult Thai subjects is unclear. The aim of this study was to examine the diagnostic accuracy of five red cell indices [red blood cell (RBC) counts, mean corpuscular volume (MCV), mean corpuscular hemoglobin (Hb) (MCH), mean corpuscular Hb concentration (MCHC), and red cell distribution width (RDW)] and nine formulas (RDW/RBC, RDW Index, Sirdah, Green and King, Mentzer, England and Fraser, Ehsani, Srivastava and Shine and Lal). Their sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV), efficiency, and Youden's Index were analyzed in 102 β-thal trait and 64 iron deficiency adult Thai subjects. The RDW/RBC formula proved to be the most reliable index as they had 100.0% specificity and PPV and the highest efficiency (94.58%) and Youden's Index (91.18%), as well as high sensitivity (91.18%) and NPV (87.67%). Therefore, this formula could be used in initial discrimination of β-thal trait from iron deficiency in adult Thai subjects.