Microcytic anemia is commonly the consequence of iron deficiency anemia (IDA), of thalassemia trait or a combination of these. IDA is a very frequent finding, not only in developing countries due to deficient nutritional status, but also in the western world, where women of childbearing age are often diagnosed with IDA due to intermittent blood loss in combination with insufficient iron intake . Thalassemia traditionally has a high prevalence in the Mediterranean area, countries in the Middle East, the Arabic peninsula and Southeast Asia, but nowadays population migration has spread thalassemia genes over nearly the entire globe. Differentiating mild or moderate IDA from thalassemia trait can be a diagnostic dilemma, as both conditions share many characteristics. Obviously a correct diagnosis in patients with microcytic anemia is important: it can provide an indication for supplementing iron to IDA patients, for avoiding unnecessary iron therapy in thalassemia carriers and of course also for preventing severe and lethal forms of thalassemia syndromes in the framework of premarital counseling in high-prevalence areas.
Apart from the basic complete blood count, laboratory tests like ferritin, hemoglobin analysis (HbA2 and abnormal Hb) and DNA analysis are the key diagnostic parameters for IDA, β- and α-thalassemia, respectively. However, areas where thalassemia is endemic often have low health care resources and these assays may not be generally available. Therefore, several simple screening indices have been developed for differentiating between thalassemia trait and IDA [2–8] and more recently these were supplemented with other supposedly better performing indices [9–14] (Table 1). 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 [15–23]. 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. The reasons for these discrepancies are not clear; possibly regional differences in thalassemia genotypes and analytical factors play a major role. Moreover, most published studies were comprised of small numbers of patients, up to a few hundred patients only, and this may also contribute to explaining the variable outcomes. In order to overcome these numerical limitations, we undertook a study using meta-analysis and composite ROC analysis for comparing the diagnostic performance of the various discriminant indices.
Materials and methods
For finding relevant literature we used PubMed (http://www.ncbi.nlm.nih.gov/pubmed), Scopus (www.scopus.com) and ProQuest (www.proquest.com), which together cover practically all biomedical journals and many other publications in the field. First, we used the combination search “microcytic and iron deficiency and/or thalassemia” and filtered using the terms “distinguish or differentiate or discriminant”. Second, we identified the original publications of all discriminant indices and searched for publications citing them. Finally, we perused the literature reference lists in the publications found above for references not yet covered. We did not restrict ourselves regarding language of the reports and occasionally used the help of a translator.
Studies that proposed a new discriminant index without validation in an independent patient cohort were disregarded. From studies that reported separate learning and validation sets, we only included the validation set in our analysis. Some studies had to be omitted because the results reported were insufficient for deriving sensitivity and specificity data. In order to obtain sufficient power in the statistical analysis, we included only discriminant indices that had been investigated by five or more studies.
The primary outcome in the analyses was the performance of different markers in the differential diagnosis of microcytic anemia as quantified in terms of true positive (TP), true negative (TN), false positive (FP) and false negative (FN) values. These values were retrieved from the publications found and entered into a database. In studies with sensitivities and specificities of 100% [i.e., studies with at least a zero in any of the cells (TP, TN, FP, FN)], 0.5 subjects were added to the all four cells, for the purpose of the analysis .
Pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) with their 95% confidence intervals (95% CI) were calculated using random effects models . Summarized sensitivities and specificities were also computed to assess the clinical effectiveness, even though both screening parameters are not considered appropriate for meta-analyses .
As an accuracy measure we calculated the diagnostic odds ratio (DOR), an indicator of test accuracy that comprises a combination of sensitivity and specificity and is independent of disease prevalence, making it very appropriate for comparing different studies [27, 28]. The higher the DOR value, the better discriminatory test performance is present.
Summary receiver operating characteristic (SROC) curves were used to summarize overall test performance and to calculate the area under the SROC curve (AUC), which is quite robust to heterogeneity . An AUC value <0.75 means that the test shows deficiencies in its diagnostic accuracy.
Regarding the assessment of heterogeneity, we used the inconsistency index (I2) , which quantifies the proportion of the total variation across studies caused by heterogeneity rather than chance, indicating heterogeneity at an I2 value >30%. We also conducted sensitivity analyses according to the region of origin, patient age (adults or children) and type of analyzer.
A bivariate generalized linear mixed-effects regression model  was used to test the robustness of these meta-analytical summaries and to compare these results among the different discriminant formulas evaluated. This bivariate approach accounts for potential between-study heterogeneity and incorporates the possible correlation between the sensitivity and the FP rate.
Publication bias was assessed visually by using a scatter plot of the inverse of the square root of the effective sample size (1/√ESS) versus the diagnostic log odds ratio, which would have a symmetric funnel shape when publication bias was absent. Formal testing for publication bias was conducted using a regression of the log DOR against 1/√ESS and weighting it according to the effective sample size, with p<0.10 indicating significant asymmetry .
All data were analyzed using the software packages Meta-DiSc (version 1.4)  and SAS System (9.4 release; SAS Institute, Cary, NC, USA).
We identified 147 reports in which at least one discriminant index had been evaluated. Twelve of these discriminant indices had been evaluated by five or more studies (Tables 1 and 2). In total, 99 such studies comprised 135,409 patient results, ranging from 3091 for the M/H ratio to 22,022 for the England and Fraser index (Table 2). Some 30 other discriminant formulas had been investigated in <5 studies and therefore were excluded from our current meta-analysis.
Out of these 99 studies, 36 (36%) were from Europe, 24 (24%) from the Mediterranean region, 20 (20%) from Southeast Asia, 14 (14%) were from North America and the few remaining studies were from Latin America and Australia. Forty-one studies (41%) investigated adults, 11 (11%) included only children, 13 (13%) were focused on mixed populations of adults and children and 35 (35%) did not provide the patients’ age. With regard to the hematology analyzers used, 32 (32%) conducted the analyses with Coulter, 20 (20%) with Bayer and 18 (18%) with Sysmex; the remaining 30% were performed with other analyzers or the analyzer type was not specified.
For each discriminant index, the DOR was calculated using the data from all applicable studies (Table 3). It appeared that the M/H ratio displayed the highest DOR, namely 100.8 (95% confidence interval 39.6–256.3); this DOR was higher than the DOR of all other discriminant indices. The RBC index gave the second highest DOR (47.0; 95% CI 29.5–74.9), closely followed by the Sirdah index (46.7; 95% CI 23.4–92.9) and the Ehsani index (44.7; CI 26.8–74.7), as shown in Table 3. There were four indices with a relatively low performance (DOR<16): the Bessmann (RDW), Shine and Lal, Srivastava and Ricerca indices (Table 3). There appeared to be qualitative evidence for DOR heterogeneity between studies (I2>70% in all indices; not shown). Considering the Bessman index (RDW) as a reference, the bivariate analysis showed results and comparisons to be very similar (see Table 4). In view of space constraints, the original data on TPs, FPs and FNs per study are not included; however, for interested readers they are available upon request.
The performance of each discriminant index is graphically illustrated in SROC plots, which also show the dispersion of the data from the individual studies reporting on that specific index (Figure 1 and Table 3). The M/H ratio showed the highest AUC value (0.956), again indicating the best diagnostic performance.
In a sub-analysis we sought to identify covariates that might be of influence on the diagnostic performance of the discriminant indices. Overall, the indices appeared to perform much better in adults than in children: DOR 33.6 (95% CI 27.7–40.7) for adults as compared with DOR 11.7 (95% CI 7.8–17.7) for children. Studies that had enrolled both children and adults, but did not report separate results, had intermediate diagnostic performance. Also the geographical region showed important differences: the highest overall DOR (53.1; 95% CI 41.1–68.6) was found in European populations, whereas the lowest DOR was obtained in studies from Southeast Asia (9.3; 95% CI 7.5–11.7), as illustrated in Table 5. The make of the hematology analyzer used had only minor influence: the older Coulter analyzers showed somewhat lower diagnostic performance, whereas the current Beckman-Coulter generation and the analyzers of all other manufacturers scored rather similar (not shown).
Finally, it appeared that potential publication bias was present (p<0.001), as judged by the degree of asymmetry in the funnel plots (not shown).
Differentiating IDA from thalassemia carrier status is a frequent issue in medical practice, in particular in subjects with mild or moderate IDA and in regions where thalassemia is common. It is not possible to distinguish both conditions using simple routine blood counts, as they are both associated with microcytic and hypochromic erythrocytes. However, in thalassemia RBC do tend to be more microcytic, whereas iron deficient RBC are often more hypochromic [59, 118]. These differences have been exploited by developing simple mathematical formulas for emphasizing the differences in RBC indices as a tool for distinguishing IDA from thalassemia trait [2–8]. However, the discriminative power of these simple indices never reached maximum diagnostic performance. The large number of discriminant indices described in the literature reflects that researchers were continuously stimulated devising new and supposedly better indices for applying in their local patient population. In the last decade, multiple studies have been published which compared different discriminant indices in the same patient cohort, aimed at identifying the index with the best overall performance. Yet, no single index emerged as the best and it became evident that even the performance ranking of the indices was different across the various investigations. Therefore we carried out a meta-analysis in order to find the discriminant indices with the highest overall performance. To our best knowledge this is the first time that this subject was investigated using a meta-analysis.
Any meta-analysis has inherent limitations and also in our study we faced various methodological issues. Most importantly, the designs of the studies investigated were far from homogeneous: there was huge variation in patient selection criteria, in types of thalassemia included, in geographical origin of the patients, in type of hematology analyzer used and in cut-off value for the respective discriminant indices. As each of these factors may play a role in the diagnostic utility of the indices, we will discuss them in more detail below.
Most discriminant indices were designed for distinguishing IDA and thalassemia in subjects with microcytic RBC. These two conditions explain the vast majority of microcytic RBC, but other diseases may be associated with microcytosis, too. For example, patients with anemia of chronic disease (ACD), although most often normocytic, may occasionally have microcytic anemia and many studies did not report whether ACD was an exclusion criterion for patient selection. However, some studies classified patients with ACD separately and the results of these studies indicate that ACD patients are more similar to IDA than to thalassemia carriers [56, 110]. Therefore, misclassification of ACD as thalassemia can be considered unlikely.
Overall, the indices performed better in adults than in children. However, when assessed in more detail it appeared that the older indices (England and Fraser, Mentzer, Green and King) evidently performed better in adults. In contrast, some newer discriminant indices (Jayabose, Sirdah and Ehsani) had a much better performance in children. The Srivastava and RBC indices appeared to be equally powerful in adults as in children. The M/H ratio has until now only been investigated in adult populations, so it remains to be seen how this discriminant index performs in children.
Virtually all studies included carriers of β-thalassemia and some studies recruited both α- and β-thalassemia carriers. Few studies comprised only carriers of α-thalassemia [78, 87, 123] or α- and δβ-thalassemia . The overall picture that emerges from these studies is that all discriminant indices perform better in β- than in α-thalassemia, even if only microcytic α-thalassemia carriers were included [35, 38, 61, 71, 98].
Some investigators included also subjects with other types of hemoglobinopathy, like HbE [116, 117, 125], HbO-Arab  and HbS, both sickle cell thalassemia and sickle cell disease [48, 94]. Unfortunately the numbers reported are too small for making a solid conclusion as to the utility of the discriminant indices in these conditions.
Thalassemia with concomitant iron deficiency
The presence of IDA in a thalassemia carrier is by no means a rare finding: many studies included such patients and they almost unanimously demonstrate that discriminant indices identify these patients as most likely having IDA . Although diagnostically incorrect, from a clinical perspective this is not problematic, as such patients need iron supplementation anyway. An indication for underlying thalassemia trait can only be obtained once the IDA component of the microcytic anemia is successfully resolved .
It has been reported that the RBC indices MCV, MCH and MCHC show remarkably small differences over the globe , enabling using them for internal quality control purposes. As many of the discriminant indices are based on these and other basic RBC parameters, one might expect that the indices would perform similarly in different areas of the world. However, our analysis surprisingly yielded indications for considerable differences between different geographical regions (Table 5). Overall, the indices performed best in European countries, but with notable differences: e.g., the Mentzer and Shine and Lal indices scored poorer than in other regions, while Green and King, Ricerca, Jayabose, Sirdah and Ehsani indices were superior in European studies. With the limitations of a meta-analysis explained above, one could roughly state that in a Mediterranean population the Mentzer, Shine and Lal and Sirdah indices would be preferred; in Southeast Asia the Srivastava index, whereas in Chinese populations the Ricerca index and Bessman index (RDW) can be expected to perform better. Anyway, our investigation has made clear that the performance of any index seems to depend on the regional population in which it is applied.
Type of hematology analyzer
As the reports investigated in our meta-analysis span a period of four decades, it was inevitable that a wide variety of hematology analyzers was used for performing the studies investigated here. Despite this, the influence of analyzer type on the diagnostic outcome was relatively small. Studies using older Coulter analyzers, which were abundant in clinical laboratories in the 1970s and 1980s, demonstrated somewhat lower performance than the analyzers in use during the last 20 years. The question arises whether this is due to the analyzer itself or to the lower degree of inter-laboratory standardization and quality control in those earlier years. Studies reported in the 21st century, where a high level of analytical standardization of basic RBC parameters is achieved, show a high degree of homogeneity and therefore we believe that the influence of analyzer type on the performance of the discriminant indices is limited, if not negligible. The only situation where one might expect more heterogeneity is for those discriminant indices that incorporate RDW, because this parameter is not well standardized and shows considerable differences between different analyzers [129, 130]. This factor may explain the moderate to low diagnostic performance of RDW-containing indices, as shown in Table 3.
One of the complications of our meta-analysis is that many authors have not used the original cut-off values of the discriminant indices, but applied an alternative cut-off. For example, Mentzer originally published his index with 13 as the cut-off value . Other authors, however, used values between 13 and 14 [20, 39, 48, 62, 70], between 14 and 15 [7, 34, 38, 43, 55, 58, 59, 65, 82, 87, 93, 94, 96, 101], 15.5 , 17 [44, 61, 92] or even as high as 20 , without proper validation. Therefore the effect of a modified cut-off value on an index’s performance is difficult to judge and may require further investigations. However, our present analysis has shown that cut-off values are not the most important contributors to the performance of a discriminant index.
This meta-analysis has demonstrated high variation in the performance of discriminant indices for distinguishing thalassemia trait from IDA. In general, the newer indices seem to be able to make this distinction better than the more traditional formulas. We have also shown that age (adult or child) and geographical region, but not the type of hematology analyzer, are important factors determining the diagnostic utility of the discriminant indices. We have objectively shown the superiority of the M/H ratio over other discriminant indices. Notwithstanding its high performance, even the M/H ratio cannot be used for making a final diagnosis of thalassemia trait. Its value lies in screening of microcytic individuals in order to select those in whom additional laboratory investigations are warranted for confirming the presence of thalassemia.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Financial support: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Srivastava PC. Differentiation of thalassaemia minor from iron deficiency. Lancet 1973;2:155–6.Google Scholar
Bessman JD, Feinstein DI. Quantitative anisocytosis as a discriminant between iron deficiency and thalassemia minor. Blood 1979;53:288–93.Google Scholar
Ricerca BM, Storti S, d’Onofrio G, Mancini S, Vittori M, Campisi S, et al. Differentiation of iron deficiency from thalassaemia trait: a new approach. Haematologica 1987;72:409–13.Google Scholar
Green R, King R. A new red cell discriminant incorporating volume dispersion for differentiating iron deficiency anemia from thalassemia minor. Blood Cells 1989;15:481–95.Google Scholar
Jayabose S, Giamelli J, Levondoglu-Tugal O, Sandoval C, Ozkaynak F, Visintainer P. Differentiating iron deficiency anemia from thalassemia minor by using an RDW-based index. J Ped Hematol Oncol 1999;21:314.CrossrefGoogle Scholar
Huber AR, Ottiger C, Risch L, Regenass S, Hergersberg M, Herklotz R. Thalassämie-syndrome: klinik und diagnose [Syndromes thalassémiques: clinique et diagnostic]. Schweiz Mediz Forum 2004;4:947–52.Google Scholar
Sirdah M, Tarazi I, Al Najjar E, Al Haddad R. Evaluation of the diagnostic reliability of different RBC indices and formulas in the differentiation of the beta-thalassaemia minor from iron deficiency in Palestinian population. Int J Lab Hematol 2008;30:324–30.CrossrefGoogle Scholar
Urrechaga E. Discriminant value of % microcytic/% hypochromic ratio in the differential diagnosis of microcytic anemia. Clin Chem Lab Med 2008;46:1752–8.Google Scholar
Ehsani MA, Shahgholi E, Rahiminejad MS, Seighali F, Rashidi A. A new index for discrimination between iron deficiency anemia and beta-thalassemia minor: results in 284 patients. Pakist J Biol Sci 2009;12:473–5.Google Scholar
Keikhaei B. A new valid formula in differentiating iron deficiency anemia from ß-thalassemia trait. Pakist J Med Sci 2010;26: 368–73.Google Scholar
Han P, Fung KP. Discriminant analysis of iron deficiency anaemia and heterozygous thalassaemia traits: a 3-dimensional selection of red cell indices. Clin Lab Haematol 1991;13:351–62.Google Scholar
Eldibany MM, Totonchi KF, Joseph NJ, Rhone D. Usefulness of certain red blood cell indices in diagnosing and differentiating thalassemia trait from iron-deficiency anemia. Am J Clin Pathol 1999;111:676–82.Google Scholar
Vicinanza P, Catalano L, Franco F, Vaccaro E, Cancellario S, Vicinanza M, et al. Two new HDW-based indexes to identify ß-thalassemia trait. Lab Hematol 2002;8:193–9.Google Scholar
Janel A, Roszyk L, Rapatel C, Mareynat G, Berger MG, Serre-Sapin AF. Proposal of a score combining red blood cell indices for early differentiation of beta-thalassemia minor from iron deficiency anemia. Hematology 2011;16:123–7.CrossrefGoogle Scholar
Schoorl M, Schoorl M, Linssen J, Villanueva MM, NoGuera JA, Martinez PH, et al. Efficacy of advanced discriminating algorithms for screening on iron-deficiency anemia and ß-thalassemia trait. Am J Clin Pathol 2012;138:300–4.CrossrefGoogle Scholar
Urrechaga E, Aguirre U, Izquierdo S. Multivariable discriminant analysis for the differential diagnosis of microcytic anemia. Anemia 2013;2013, 6 pages, Article ID 457834. http://dx.doi.org/10.1155/2013/457834.Crossref
Devillé WL, Buntinx F, Bouter LM, Montori VM, De Vet HC, Van Der Windt DA, et al. Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol 2002;2:1–13.Google Scholar
Honest H, Khan KS. Reporting of measures of accuracy in systematic reviews of diagnostic literature. BMC Health Serv Res 2002;2:1–4.Google Scholar
Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 2005;58:982–90.CrossrefGoogle Scholar
Dinnes J, Deeks J, Kirby J, Roderick P. A methodological review of how heterogeneity has been examined in systematic reviews of diagnostic test accuracy. Health Technol Assess 2005;9:1–113, iii.Google Scholar
Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 2005;58:882–93.CrossrefGoogle Scholar
Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol 2006;6:31.Google Scholar
Gimferrer E, Marigo G, Rutllant ML, Viñas J. Differentiation of iron deficiency from thalassaemia trait. Lancet 1975;305:114.Google Scholar
Klee GG, Fairbanks VF, Pierre RV, O’Sullivan MB. Routine erythrocyte measurements in diagnosis of iron deficiency anemia and thalassemia minor. Am J Clin Pathol 1976;66:870–7.Google Scholar
Hedge UM, White JM, Hart GH, Marsh GW. Diagnosis of a-thalassaemia trait from Coulter Counter ‘S’ indices. J Clin Pathol 1977;30:884–9.Google Scholar
Johnson CS, Tegos C, Beutler E. Thalassemia minor: routine erythrocyte measurements and differentiation from iron deficiency. Am J Clin Pathol 1983;80:31–6.Google Scholar
Chalevelakis G, Tsiroyannis K, Hatziioannou J, Arapakis G. Screening for thalassaemia and/or iron deficiency: evaluation of some discrimination functions. Scand J Clin Lab Invest 1984;44:1–6.CrossrefGoogle Scholar
Ghionni H, Miotti TC, Camandona V. Routine erytrocyte measurements and differentiation of thalassemia minor from iron deficiency. Minerva Med 1985;76:1143–8.Google Scholar
Juncà Piera J, Clotet Sala B, Millà Santos F, Ribas Mundó M. Indice de England en las microcitosis [England’s index in microcytosis]. Med Clín 1985;85:773.Google Scholar
Novak RW. Red blood cell distribution width in pediatric microcytic anemias. Pediatrics 1987;80:251–4.Google Scholar
Helleman PW, Bartels PC, van Waveren Hogervorst GD. Screening for thalassaemia using the width of the Technicon H6000/H601 erythrocyte size histograms. Scand J Clin Lab Invest 1988;48:697–704.Google Scholar
Bentley SA, Ayscue LH, Watson JM, Ross DW. The clinical utility of discriminant functions for the differential diagnosis of microcytic anemias. Blood Cells 1989;15:575–82.Google Scholar
Houwen B. The use of inference strategies in the differential diagnosis of microcytic anemia. Blood Cells 1989;15:509–32.Google Scholar
Makris PE. Utilization of a new index to distinguish heterozygous thalassemic syndromes: comparison of its specificity to five other discriminants. Blood Cells 1989;15:497–507.Google Scholar
Paterakis GS, Terzoglou G, Vasilioy E. The performance characteristics of an expert system for the ‘On-Line’ assessment of thalassemia trait and iron deficiency – Micro Hema Screen. Blood Cells 1989;15:541–61.Google Scholar
Qurtom HA, al-Saleh QA, Lubani MM, Hassanein A, Kaddoorah N, Qurtom MA, et al. The value of red cell distribution width in the diagnosis of anaemia in children. Eur J Pediatr 1989;148:745–8.CrossrefGoogle Scholar
Laso FJ, Mateos F, Ramos R, Herrero F, Pérez-Arellano JL, González-Buitrago JM. Amplitude of the distribution of erythrocyte size in the differential diagnosis of microcytic anemia. Med Clín 1990;94:1–4.Google Scholar
Juncà J, Flores A, Roy C, Alberti R, Milla F. Red cell distribution width, free erythrocyte protoporphyrin, and England-Fraser index in the differential diagnosis of microcytosis due to iron deficiency or beta-thalassemia trait. A study of 200 cases of microcytic anemia. Hematol Pathol 1991;5:33–6.Google Scholar
Ortega Carpio A, Pineda Alonso M, Pardo Alvarez J, Sánchez Ramos JL. The prognostic value of the hemogram in the differential diagnosis of microcytic anemia. Med Clín 1991;96:397.Google Scholar
Robertson EP, Pollock A, Yau KS, Chan LC. Use of Technicon H*1 technology in routine thalassaemia screening. Med Lab Sci 1992;49:259–64.Google Scholar
Jimenez CV. Iron deficiency anaemia and thalassaemia trait differentiated by simple haematological tests and serum iron concentrations. Clin Chem 1993;39:2271–5.Google Scholar
Sajjad Baqar M, Khurshid M, Molla A. Does red blood cell distribution width (RDW) improve evaluation of microcytic anaemias? J Pakist Med Ass 1993;43:149–51.Google Scholar
Das Gupta A, Hegde C, Mistri R. Red cell distribution width as a measure of severity of iron deficiency in iron deficiency anemia. Ind J Med Res 1994;100:177–83.Google Scholar
Burk M, Arenz J, Giagounidis AA, Schneider W. Erythrocyte indices as screening tests for the differentiation of microcytic anemias. Eur J Med Res 1995;1:33–7.Google Scholar
Erler BS, Vitagliano P, Lee S. Superiority of neural networks over discriminant functions for thalassemia minor screening of red blood cell microcytosis. Arch Pathol Lab Med 1995;119:350–4.Google Scholar
Yeo GS, Tan KH, Liu TC. The role of discriminant functions in screening for beta-thalassaemia traits during pregnancy. Singapore Med J 1995;36:615–8.Google Scholar
Lafferty JD, Crowther MA, Ali MA, Levine M. The evaluation of various mathematical RBC indices and their efficacy in discriminating between thalassemic and non-thalassemic microcytosis. Am J Clin Pathol 1996;106:201–5.Google Scholar
Mach-Pascual S, Darbellay R, Pilotto PA, Beris P. Investigation of microcytosis: a comprehensive approach. Eur J Haematol 1996;57:54–61.Google Scholar
Yuen SC, Brown RD, Forsyth CJ, Zarkos KB. A quick differentiation between iron deficiency and thalassemia syndrome. Aust J Med Sci 1996;17:106–9.Google Scholar
Liu TC, Seong PS, Lin TK. The erythrocyte cell hemoglobin distribution width segregates thalassemia traits from other nonthalassemic conditions with microcytosis. Am J Clin Pathol 1997;107:601–7.Google Scholar
Manglani M, Lokeshwar MR, Vani VG, Bhatia N, Mhaskar V. ‘Nestroft’ – An effective screening test for beta thalassemia trait. Ind Pediatr 1997;34:702–7.Google Scholar
Madan N, Sikka M, Sharma S, Rusia U, Kela K. Red cell indices and discriminant functions in the detection of beta-thalassaemia trait in a population with high prevalence of iron deficiency anaemia. Indian J Pathol Microbiol 1999;42:55–61.Google Scholar
Telmissani OA, Khalil S, George TR. Mean density of hemoglobin per liter of blood: a new hematologic parameter with an inherent discriminant function. Lab Hematol 1999;5:149–52.Google Scholar
Knznetsova YV, Kovrigina YS, Baidim LV, Bykova LP, Chernov VM, Tokarev YN, et al. Use of erythrocytic indexes and iron metabolism parameters in the differential diagnosis of microcytic anemias. Gematol Transfusiol 2000;45:46–8.Google Scholar
Melo MR, Purini MC, Cancado RD, Kooro F, Chiattone CS. The use of erythrocyte (RBC) indices in the differential diagnosis of microcytic anemias: is it an approach to be adopted? Rev Ass Med Brasil (1992) 2002;48:222–4.Google Scholar
Ghafouri M, Sefat LM, Sharifi L. Comparison of cell counter indices in differentiation of beta thalassemia trait and iron deficiency anemia. Sci J Iran Blood Transfus Org 2006;2:385–9.Google Scholar
AlFadhli SM, Al-Awadhi AM, AlKhaldi D. Validity assessment of nine discriminant functions used for the differentiation between iron deficiency anemia and thalassemia minor. J Trop Pediatr 2007;53:93–7.Google Scholar
Beyan C, Kaptan K, Ifran A. Predictive value of discrimination indices in differential diagnosis of iron deficiency anemia and beta-thalassemia trait. Eur J Haematol 2007;78:524–6.CrossrefGoogle Scholar
Ntaios G, Chatzinikolaou A, Saouli Z, Girtovitis F, Tsapanidou M, Kaiafa G, et al. Discrimination indices as screening tests for beta-thalassemic trait. Ann Hematol 2007;86:487–91.CrossrefGoogle Scholar
Rathod DA, Kaur A, Patel V, Patel K, Kabrawala R, Patel M, et al. Usefulness of cell counter-based parameters and formulas in detection of beta-thalassemia trait in areas of high prevalence. Am J Clin Pathol 2007;128:585–9.CrossrefGoogle Scholar
Okan V, Cigiloglu A, Cifci S, Yilmaz M, Pehlivan M. Red cell indices and functions differentiating patients with the β-thalassaemia trait from those with iron deficiency anaemia. J Int Med Res 2009;37:25–30.CrossrefGoogle Scholar
Rahim F, Keikhaei B. Better differential diagnosis of iron deficiency anemia from beta-thalassemia trait. Turk J Hematol 2009;26:138–45.Google Scholar
Ferrara M, Capozzi L, Russo R, Bertocco F, Ferrara D. Reliability of red blood cell indices and formulas to discriminate between β thalassemia trait and iron deficiency in children. Hematology 2010;15:112–5.CrossrefGoogle Scholar
Narchi H, Basak RB. Comparison of erythrocyte indices to differentiate between iron deficiency and alpha-thalassaemias in children with microcytosis and/or hypochromia. East Mediterr Health J 2010;16:966–71.Google Scholar
Niazi M, Tahir M, e Raziq F, Hameed A. Usefulness of red cell indices in differentiating microcytic hypochromic anemias. Gomal J Med Sci 2010;8:125–9.Google Scholar
Shen C, Jiang YM, Shi H, Liu JH, Zhou WJ, Dai QK, et al. Evaluation of indices in differentiation between iron deficiency anemia and β-thalassemia trait for Chinese children. J Ped Hematol Oncol 2010;32:e218–22.CrossrefGoogle Scholar
Mansukhani D, Sehgal K, Dadu T, Mankeshwar R, Fernandes S, Shaikh A, et al. Evaluation and comparison of CBC based indices for screening of beta thalassemia trait and its prevalence in a tertiary care hospital. Indian J Hematol Blood Transfus 2011;27:239–40.Google Scholar
Nesa A, Munir SF, Sultana T, Rahman MQ, Ahmed AN. Role of discrimination indices in differentiation of beta thalassaemia trait and iron deficiency anaemia. Mymensingh Med J 2011;20:110–4.Google Scholar
Trivedi DP, Shah HA. Discriminant functions in distinguishing beta-thalassemia trait and iron deficiency anemia: the value of the RDW-SD. Internet J Hematol 2011;7:1–13.Google Scholar
Urrechaga E, Borque L, Escanero JF. The role of automated measurement of red cell subpopulations on the Sysmex XE 5000 analyzer in the differential diagnosis of microcytic anemia. Int J Lab Hematol 2011;33:30–6.Google Scholar
Urrechaga E, Borque L, Escanero JF. The role of automated measurement of RBC subpopulations in differential diagnosis of microcytic anemia and ß-thalassemia screening. Am J Clin Pathol 2011;135:374–9.CrossrefGoogle Scholar
Williams C. The use of the automated parameter RDW-SD as a discriminator of both alpha and beta thalassaemia trait from iron deficiency. Aust J Med Sci 2011;32:95.Google Scholar
Batebi A, Pourreza A, Esmailian R. Discrimination of beta- thalassemia minor and iron deficiency anemia by screening test for red blood cell indices. Turk J Med Sci 2012;42:275–80.Google Scholar
Gibson F, Mason K, Serjeant B, Kulozik A, Happich M, Tolle G, et al. Screening for the beta-thalassaemia trait: hazards among populations of West African Ancestry. J Commun Genet 2012;3:13–8.CrossrefGoogle Scholar
Nalbantolu B, Güzel S, Büyükyalçin V, Donma MM, Güzel EC, Nalbantolu A, et al. Indices used in differentiation of thalassemia trait from iron deficiency anemia in pediatric population: are they reliable. Pediatr Hematol Oncol 2012;29:472–8.CrossrefGoogle Scholar
Nishad AA, Pathmeswaran A, Wickramasinghe AR, Premawardhena A. The Thal-index with the BTT prediction.exe to discriminate β-thalassaemia traits from other microcytic anaemias. Thalass Rep 2012;2:e1–2.Google Scholar
Dharmani P, Sehgal K, Dadu T, Mankeshwar R, Shaikh A, Khodaiji S. Developing a new index and its comparison with other CBC-based indices for screening of beta thalassemia trait in a tertiary care hospital. Int J Lab Hematol 2013;35:118.Google Scholar
Sahli CA, Bibi A, Ouali F, Hadj Fredj S, Dakhlaoui B, Othmani R, et al. Red cell indices: differentiation between β-thalassemia trait and iron deficiency anemia and application to sickle-cell disease and sickle-cell thalassemia. Clin Chem Lab Med 2013;51:2115–24.Google Scholar
Miri-Moghaddam E, Sargolzaie N. Cut off determination of discrimination indices in differential diagnosis between iron deficiency anemia and β-thalassemia minor. Int J Hematol Oncol Stem Cell Res 2014;8:1–6.Google Scholar
Ng EH, Leung JH, Lau YS, Ma ES. Evaluation of the new red cell parameters on Beckman Coulter DxH800 in distinguishing iron deficiency anaemia from thalassaemia trait. Int J Lab Hematol 2015;37:199–207.Google Scholar
Pornprasert S, Panya A, Punyamung M, Yanola J, Kongpan C. Red cell indices and formulas used in differentiation of β-thalassemia trait from iron deficiency in Thai school children. Hemoglobin 2014;38:258–61.CrossrefGoogle Scholar
Urrechaga E, Hoffmann JJ, Izquierdo S, Escanero JF. Differential diagnosis of microcytic anemia: the role of microcytic and hypochromic erythrocytes. Int J Lab Hematol 2015;37:334–40.CrossrefGoogle Scholar
Vehapoglu A, Ozgurhan G, Demir AD, Uzuner S, Nursoy MA, Turkmen S, et al. Hematological indices for differential diagnosis of beta thalassemia trait and iron deficiency anemia. Anemia 2014;2014:576738.Google Scholar
Verstraeten L, Biwer J, Didodo-Hila I, Groff P, Verstraeten-Pitiot A, Hendriks J-P. Tentative de diagnostic différentiel de la carence martiale vis-à-vis de la béta-thalassémie hétérozygote par I’automate Technicon H*3. Bull Soc Luxemb Biol Clin 1998;19:41–7.Google Scholar
Afroz M, Shamsi TS, Syed S. Predictive value of MCV/RBC count ratio to discriminate between iron deficiency anaemia and beta thalassaemia trait. J Pakist Med Ass 1998;48:18–9.Google Scholar
Kneifati-Hayek J, Fleischman W, Bernstein LH, Riccioli A, Bellevue R. A model for automated screening of thalassemia in hematology (Math study). Lab Hematol 2007;13:119–23.Google Scholar
Matos JF, Dusse LM, Stubbert RV, Ferreira MR, Coura-Vital W, Fernandes AP, et al. Comparison of discriminative indices for iron deficiency anemia and β thalassemia trait in a Brazilian population. Hematology 2013;18:169–74.CrossrefGoogle Scholar
Bessman JD, McClure S, Bates J. Distinction of microcytic disorders: comparison of expert, numerical-discriminant, and microcomputer analysis. Blood Cells 1989;15:533–40.Google Scholar
Flynn MM, Reppun TS, Bhagavan NV. Limitations of red blood cell distribution width (RDW) in evaluation of microcytosis. Am J Clin Pathol 1986;85:445–9.Google Scholar
Miguel A, Linares M, Miguel A, Miguel-Borja JM. Red blood cell distribution width analysis in differentiation between iron deficiency and thalassemia minor. Acta Haematol 1988;80:59.CrossrefGoogle Scholar
van Zeben D, Bieger R, van Wermeskerken RK, Castel A, Hermans J. Evaluation of microcytosis using serum ferritin and red blood cell distribution width. Eur J Haematol 1990;44:106–9.Google Scholar
Cesana BM, Maiolo AT, Gidiuli R, Damilano I, Massaro P, Polli EE. Relevance of red cell distribution width (RDW) in the differential diagnosis of microcytic anaemias. Clin Lab Haematol 1991;13:141–51.Google Scholar
Lima CS, Reis AR, Grotto HZ, Saad ST, Costa FF. Comparison of red cell distribution width and a red cell discriminant function incorporating volume dispersion for distinguishing iron deficiency from beta thalassemia trait in patients with microcytosis. São Paolo Med J 1996;114:1265–9.CrossrefGoogle Scholar
Kotwal J, Choudhry VP, Dwivedi SN, Bhargava M. Erythrocyte indices for discriminating thalassaemic and non-thalassaemic microcytosis in Indians. Nat Med J India 1999;12:266–7.Google Scholar
Buch AC, Karve PP, Panicker NK, Singru SA, Gupta SC. Role of red cell distribution width in classifying microcytic hypochromic anaemia. J Ind Med Ass 2011;109:297–9.Google Scholar
Nadarajan VS, Sthaneshwar P, Jayaranee S. RBC-Y/MCV as a discriminant function for differentiating carriers of thalassaemia and HbE from iron deficiency. Int J Lab Hematol 2010;32:215–21.CrossrefGoogle Scholar
Wongprachum K, Sanchaisuriya K, Sanchaisuriya P, Siridamrongvattana S, Manpeun S, Schlep FP. Proxy indicators for identifying iron deficiency among anemic vegetarians in an area prevalent for thalassemia and hemoglobinopathies. Acta Haematol 2012;127:250–5.CrossrefGoogle Scholar
d’Onofrio G, Zini G, Ricerca BM, Mancini S, Mango G. Automated measurement of red blood cell microcytosis and hypochromia in iron deficiency and beta-thalassemia trait. Arch Pathol Lab Med 1992;116:84–9.Google Scholar
Saleem M, Qureshi TZ, Anwar M, Ahmed S. Evaluation of M/H ratio for screening of beta thalassaemia trait. J Pakist Med Ass 1995;45:84–5.Google Scholar
Fossat C, Camoin-Jau L, Ma¡in V, Grob F, David B. Interprétations des anémies chez I’adulte: intérêt des paramètres érythrocytaires. Feuill Biol 1996;37:5–12.Google Scholar
Kremer A, Kutter D, Groff P. Iron deficiency or haemoglobinopathy? Differential diagnosis by red cell indices produced by the ADVIA® Bayer haematological automat. Clin Lab 1999;45: 547–51.Google Scholar
Yermiahu T, Ben-Shalom M, Porath A, Vardi H, Boantza A, Mazor D, et al. Quantitative determinations of microcytic-hypochromic red blood cell population and glycerol permeability in iron-deficiency anemia and beta thalassemia minor. Ann Hematol 1999;78:468–71.CrossrefGoogle Scholar
Lin CK, Yang ML, Jiang ML, Chien CC, Lin HH, Peng HW. Comparison of two screening methods, modified Hb H preparation and the osmotic fragility test, for alpha-thalassemic traits on the basis of gene mapping. J Clin Lab Anal 1991;5:392–5.CrossrefGoogle Scholar
Juncà J, Mañú-Pereira M, Radó-Trilla N, Vives-Corrons JL. Cell counter-based parameters and formulas in detection of beta-thalassemia trait. Am J Clin Pathol 2008;130:147–8; author reply 8.Google Scholar
Ittarat W, Ongcharoenjai S, Rayatong O, Pirat N. Correlation between some discrimination functions and hemoglobin E. J Med Ass Thailand 2000;83:259–65.Google Scholar
Agorasti A, Trivellas T, Papadopoulos V, Konstantinidou D. Innovative parameters RET-Y, sTfR, and sTfR-F index in patients with microcytic, hypochromic anemia – their special value for hemoglobinopathies. Lab Hematol 2007;13:63–8.CrossrefGoogle Scholar
Mosca A, Paleari R, Ivaldi G, Galanello R, Giordano PC. The role of haemoglobin A(2) testing in the diagnosis of thalassaemias and related haemoglobinopathies. J Clin Pathol 2009;62:13–7.CrossrefGoogle Scholar
Bull BS, Hay KL. Are red blood cell indexes international? Arch Pathol Lab Med 1985;109:604–6.Google Scholar
Lippi G, Pavesi F, Bardi M, Pipitone S. Lack of harmonization of red blood cell distribution width (RDW). Evaluation of four hematological analyzers. Clin Biochem 2014;47:1100–3.CrossrefGoogle Scholar
About the article
Johannes J.M.L. Hoffmann
Johannes (J.M.L.) Hoffmann has worked in clinical chemistry since 1976 when he became a trainee. Once certified as a Specialist in Laboratory Medicine he started working as the head of the hematology laboratory in a large tertiary-care teaching hospital in the Netherlands, where he was later also appointed director of the Department of Clinical Laboratories. In 1992 he obtained his PhD in Medical Sciences at Leiden University, the Netherlands, on a thesis in the field of fibrinolysis. Since 2008 he has been responsible for scientific affairs in hematology with Abbott Diagnostics in Europe. His scientific work comprises over 100 papers in peer-reviewed journals, mainly focused on general hematology, flow cytometry, coagulation and fibrinolysis. He is also author and co-author of several books on laboratory medicine and hematology. He is a member of the Editorial Board of Clinical Chemistry and Laboratory Medicine and acts as a reviewer for various other journals. In addition, he is a member of international committees and working groups on standardization in laboratory hematology.
Eloísa Urrechaga started her career in Clinical Chemistry in 1984 as a trainee in Hospital General de Asturias (Oviedo, Spain); she became head of laboratory in a community hospital, Hospital de Jarrio (Asturias, Spain) in 1990. From 2001 until present she has worked as a consultant for clinical chemistry in the Hospital Galdakao-Usansolo (Bizkaia, Spain), responsible for the Hematology Laboratory. In 2010 she obtained her PhD in Medical Sciences at Zaragoza University (Zaragoza, Spain) on a thesis in the field of erythropoiesis. She is an expert of the Spanish Science and Innovation Ministry and the Agency for Assessment of new technologies for research project validation. Member of the WHO Guideline Development Group – Ferritin International Micronutrient Malnutrition Prevention and Control, World Health Organization and a member of the Analytical Hematology and Trace Elements Commissions of the Spanish Society of Clinical Chemistry and Molecular Pathology. She is particularly involved in the development of new hematological parameters applied to the study of anemia and erythropoiesis, as well as glycohemoglobin. Recently, she published papers in the area of automation, biological parameters in functional iron deficiency, β-thalassemia, anemia of chronic diseases and other erythropoietic disorders. She serves as an editorial board member and scientific reviewer in different journals, including Clinical Chemistry and Laboratory Medicine.
Urko Aguirre is a mathematician and holds a Master in Computer Sciences at the University of the Basque Country (UPV/EHU). He is currently working at the Clinical Research Unit of the Hospital Galdakao-Usansolo (Bizkaia, Spain). His main research field is focused on predictive modeling.
Published Online: 2015-05-12
Published in Print: 2015-11-01