Abstract
Background: More than 40 mathematical indices have been proposed in the hematological literature for discriminating between iron deficiency anemia and thalassemia trait in subjects with microcytic red blood cells (RBCs). None of these discriminant indices is 100% sensitive and specific and also the ranking of the discriminant indices is not consistent. Therefore, we decided to conduct the first meta-analysis of the most frequently used discriminant indices.
Methods: An extensive literature search yielded 99 articles dealing with 12 indices that were investigated five or more times. For each discriminant index we calculated the diagnostic odds ratio (DOR) and summary ROC analysis was done for comparing the performance of the indices.
Results: The ratio of microcytic to hypochromic RBCs (M/H ratio) showed the best performance, DOR=100.8. This was significantly higher than that of all other indices investigated. The RBC index scored second (DOR=47.0), closely followed by the Sirdah index (DOR=46.7) and the Ehsani index (DOR=44.7). Subsequently, there was a group of four indices with intermediate and three with lower DOR. The lowest performance (DOR=6.8) was found for the RDW (Bessman index). Overall, the indices performed better for adults than for children.
Conclusions: The M/H ratio outperformed all other discriminant indices for discriminating between iron deficiency anemia and thalassemia trait. Although its sensitivity and specificity are not high enough for making a definitive diagnosis, it is certainly of value for identifying those subjects with microcytic RBC in whom diagnostic tests for confirming thalassemia are indicated.
Introduction
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 [1]. 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.
Discriminant indices for distinguishing thalassemia trait from iron deficiency anemia in patients with microcytic RBC (cut-off values transformed into generally used units: Hb in g/dL; RBC in 1012/L; MCV in fL; MCH in pg; RDW in %).
| Discriminant index | Calculation | Cut-off value | Reference |
|---|---|---|---|
| England and Fraser (E&F) | MCV – RBC – (5 Hb) – 3.4 | 0 | [2] |
| RBC | RBC | 5.0 | [2] |
| Mentzer | MCV / RBC | 13 | [3] |
| Srivastava | MCH / RBC | 3.8 | [4] |
| Shine and Lal (S&L) | MCV2×MCH | 1.53 | [5] |
| Bessman | RDW | 15 | [6] |
| Ricerca | RDW / RBC | 4.4 | [7] |
| Green and King (G&K) | MCV2×RDW / 100 Hb | 65 | [8] |
| Jayabose (RDW index) | MCV / (RBC×RDW) | 220 | [9] |
| Sirdah | MCV – RBC – (3 Hb) | 27.0 | [11] |
| M/H ratio | Microcytic RBC %/hypochromic RBC % | 3.7 | [12] |
| Ehsani | MCV – (10 RBC) | 15 | [13] |
Materials and methods
Literature search
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.
Statistical analysis
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 [24].
Pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) with their 95% confidence intervals (95% CI) were calculated using random effects models [25]. Summarized sensitivities and specificities were also computed to assess the clinical effectiveness, even though both screening parameters are not considered appropriate for meta-analyses [26].
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 [29]. 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) [30], 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 [28] 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 [31].
All data were analyzed using the software packages Meta-DiSc (version 1.4) [32] and SAS System (9.4 release; SAS Institute, Cary, NC, USA).
Results
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.
Twelve discriminant indices that were validated in five or more publications.
| Discriminant index | Studies evaluated | Patients included | References to studies |
|---|---|---|---|
| England and Fraser (E&F) | 74 | 22,022 | [11–14, 18, 20, 21, 33–100] |
| Mentzer | 70 | 21,331 | [7, 9, 11–14, 17, 18, 20, 21, 33, 34, 37–39, 41–46, 48, 53, 55–59, 61–68, 70–99, 101–104] |
| Shine and Lal (S&L) | 48 | 16,285 | [11, 12, 14, 18, 20, 37, 38, 41, 43–46, 48, 53, 58, 59, 64–68, 70–72, 74–76, 78–82, 84–86, 88–90, 92–95, 97–99, 104–106] |
| Bessman (RDW) | 48 | 12,039 | [7, 11, 14, 17, 20, 37, 39, 42–46, 48–50, 54–57, 59, 62, 68, 69, 71–78, 80, 81, 84, 87, 90, 94, 96, 97, 100, 107–115] |
| Green and King (G&K) | 47 | 14,439 | [11, 12, 14, 20, 21, 44–46, 53, 56, 57, 61, 62, 68, 69, 71–87, 89–91, 93–100, 104, 106, 112, 116, 117] |
| Srivastava | 45 | 14,884 | [7, 11–14, 18, 20, 34, 37, 43, 44, 53, 57, 58, 62, 63, 65, 67, 68, 71, 72, 74–76, 78–82, 84–87, 89–99, 104] |
| RBC | 38 | 8704 | [9, 13, 14, 20, 34, 36–39, 54, 56–58, 65, 67–69, 71–78, 80, 81, 83, 84, 87, 90, 94, 96, 97, 99, 100, 103, 107, 113] |
| Jayabose (RDWI) | 28 | 9847 | [11, 14, 20, 68, 71–73, 75–84, 87, 89, 90, 93–97, 99, 104, 117] |
| Ricerca | 25 | 9593 | [11, 12, 14, 20, 57, 71, 72, 74, 75, 78–82, 84–87, 89, 90, 93, 94, 96, 98, 99] |
| M/H ratio | 15 | 3091 | [12, 18, 21, 57, 61, 62, 85, 98, 100, 103, 118–122] |
| Ehsani | 14 | 6244 | [11, 13, 14, 20, 80, 85, 86, 91, 92, 94, 95, 97, 99, 104] |
| Sirdah | 12 | 5634 | [11, 14, 20, 80, 85, 86, 92, 94, 95, 97, 99, 104] |
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.
Diagnostic performance of the 12 discriminant indices, arranged in order of diagnostic odds ratio (DOR) with 95% confidence intervals (95% CI).
| Discriminant index | DOR (95% CI) | PLR (95% CI) | NLR (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) | AUC |
|---|---|---|---|---|---|---|
| M/H ratio | 100.8 (39.6–256.3) | 6.8 (4.8–9.8) | 0.07 (0.03–0.2) | 0.92 (0.87–0.98) | 0.86 (0.81–0.91) | 0.956 |
| RBC | 47.0 (29.5–74.9) | 8.1 (5.8–11.4) | 0.17 (0.13–0.22) | 0.85 (0.80–0.88) | 0.90 (0.86–0.93) | 0.923 |
| Sirdah | 46.7 (23.4–92.9) | 8.6 (4.8–15.5) | 0.18 (0.12–0.27) | 0.83 (0.75–0.89) | 0.90 (0.83–0.95) | 0.903 |
| Ehsani | 44.7 (26.8–74.7) | 5.1 (3.7–7.0) | 0.11 (0.10–0.18) | 0.91 (0.85–0.94) | 0.82 (0.76–0.87) | 0.925 |
| England and Fraser (E&F) | 34.7 (25.0–48.2) | 9.5 (7.2–12.6) | 0.27 (0.23–0.32) | 0.75 (0.70–0.79) | 0.92 (0.90–0.94) | 0.887 |
| Green and King (G&K) | 29.8 (18.5–47.8) | 7.2 (5.2–10.0) | 0.24 (0.2–0.3) | 0.79 (0.73–0.83) | 0.89 (0.85–0.92) | 0.898 |
| Jayabose (RDWI) | 28.6 (17.8–45.9) | 5.6 (4.4–7.1) | 0.20 (0.14–0.27) | 0.83 (0.78–0.88) | 0.85 (0.81–0.88) | 0.902 |
| Mentzer | 27.6 (20.7–36.6) | 5.6 (4.6–6.8) | 0.20 (0.17–0.24) | 0.82 (0.79–0.86) | 0.85 (0.82–0.88) | 0.896 |
| Shine and Lal (S&L) | 15.7 (8.8–28.0) | 1.6 (1.3–2.0) | 0.10 (0.07–0.16) | 0.96 (0.93–0.97) | 0.41 (0.27–0.56) | 0.885 |
| Ricerca | 15.6 (7.9–30.9) | 2.0 (1.4–2.7) | 0.12 (0.07–0.22) | 0.93 (0.88–0.97) | 0.52 (0.36–0.67) | 0.850 |
| Srivastava | 15.0 (10.9–20.6) | 4.1 (3.3–5.1) | 0.28 (0.23–0.34) | 0.78 (0.72–0.82) | 0.81 (0.77–0.85) | 0.850 |
| Bessman (RDW) | 6.8 (4.0–11.7) | 5.1 (4.2–6.2) | 0.21 (0.17–0.27) | 0.62 (0.61–0.63) | 0.66 (0.65–0.68) | 0.778 |
AUC, area under the ROC curve; NLR, negative likelihood ratio; PLR, positive likelihood ratio. The higher DOR values, the better discriminatory test performance is present. Positive and negative likelihood ratios >10 and <0.1 indicate that the test generates strong evidence to rule in or rule out a thalassemia diagnosis, respectively.
Bivariate generalized linear mixed model estimates and their 95% confidence intervals (CI).
| Sensitivity (95% CI) | p-Value | False positive rate (95% CI) | p-Value | |
|---|---|---|---|---|
| Intercept | 0.6 (0.2–0.9) | <0.001 | −1.2 (−1.5 to −0.80) | <0.001 |
| M/H ratio | 1.8 (1.1–2.5) | <0.001 | −0.6 (−1.3 to 0.1) | 0.10 |
| RBC | 1.1 (0.6–1.6) | <0.001 | −0.9 (−1.4 to −0.3) | <0.001 |
| Sirdah | 1.0 (0.3–1.8) | <0.001 | −1.0 (−1.8 to −0.2) | <0.001 |
| Ehsani | 1.7 (1.0–2.4) | <0.001 | −0.3 (−1.1 to 0.4) | 0.30 |
| England and Fraser (E&F) | 0.5 (0.1–0.9) | <0.001 | −1.1 (−1.6 to −0.7) | <0.001 |
| Green and King (G&K) | 0.7 (0.2–1.2) | <0.001 | −0.8 (−1.3 to −0.3) | <0.001 |
| Jayabose (RDWI) | 1.0 (0.5–1.5) | <0.001 | −0.5 (−1.1 to 0.0) | 0.10 |
| Mentzer | 1.0 (0.5–1.4) | <0.001 | −0.5 (−0.9 to −0.1) | <0.001 |
| Shine and Lal (S&L) | 2.2 (1.7–2.7) | <0.001 | 1.5 (1.0–2.0) | <0.001 |
| Ricerca | 1.8 (1.2–2.4) | <0.001 | 1.0 (0.5–1.6) | <0.001 |
| Srivastava | 0.6 (0.2–1.1) | <0.001 | −0.2 (−0.7 to 0.2) | 0.30 |
| Bessman (RDW) | Reference | Reference |
Reference: the Bessman (RDW) was considered as the reference group.
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.
Summary receiver operator characteristics (SROC) plots of the 12 discriminant indices.
In each plot, the dots represent the individual studies, with their sensitivity and specificity coordinates. The small circle is the summarized sensitivity and specificity, with the 95% confidence interval around it.
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).
Overall diagnostic performance of the discriminant indices by geographical region, arranged in order of diagnostic odds ratio (DOR) with 95% confidence intervals (95% CI).
| Region | DOR (95% CI) | PLR (95% CI) | NLR (95% CI) |
|---|---|---|---|
| Europe | 53.1 (41.1–68.6) | 6.6 (5.4–8.2) | 0.15 (0.12–0.19) |
| Australia | 26.2 (13.2–52.2) | 4.7 (3.3–6.7) | 0.22 (0.12–0.34) |
| China, Hong-Kong, Malaysia, Singapore | 19.6 (12.1–31.7) | 4.2 (2.0–8.5) | 0.23 (0.12–0.45) |
| Central and South America | 19.6 (9.9–38.5) | 5.8 (2.5–13.4) | 0.32 (0.24–0.42) |
| Eastern Mediterranean and Middle East | 17.7 (14.3–21.8) | 3.9 (3.2–4.7) | 0.26 (0.22–0.30) |
| North America | 12.9 (8.8–18.9) | 3.9 (3.0–5.1) | 0.35 (0.28–0.44) |
| Southeast Asia (India, Bangladesh, Sri Lanka, Thailand) | 9.3 (7.5–11.7) | 2.7 (2.3–3.2) | 0.32 (0.28–0.38) |
NLR, negative likelihood ratio; PLR, positive likelihood ratio.
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).
Discussion
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.
Patient selection
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.
Patient age
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.
Thalassemia types
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 [124]. 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 [126] 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 [118]. 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 [127].
Geographical origin
It has been reported that the RBC indices MCV, MCH and MCHC show remarkably small differences over the globe [128], 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.
Cut-off values
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 [3]. 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 [57], 17 [44, 61, 92] or even as high as 20 [102], 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.
Conclusions
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.
About the authors
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.
References
1. Milman N. Anemia – still a major health problem in many parts of the world! Ann Hematol 2011;90:369–77.10.1007/s00277-010-1144-5Search in Google Scholar
2. England JM, Fraser PM. Differentiation of iron deficiency from thalassaemia trait by routine blood-count. Lancet 1973;1: 449–52.Search in Google Scholar
3. Mentzer Jr WC. Differentiation of iron deficiency from thalassaemia trait. Lancet 1973;1:882.10.1016/S0140-6736(73)91446-3Search in Google Scholar
4. Srivastava PC. Differentiation of thalassaemia minor from iron deficiency. Lancet 1973;2:155–6.Search in Google Scholar
5. Shine I, Lal S. A strategy to detect β thalassaemia minor. Lancet 1977;1:692–4.10.1016/S0140-6736(77)92128-6Search in Google Scholar
6. Bessman JD, Feinstein DI. Quantitative anisocytosis as a discriminant between iron deficiency and thalassemia minor. Blood 1979;53:288–93.10.1182/blood.V53.2.288.288Search in Google Scholar
7. 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.Search in Google Scholar
8. 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.Search in Google Scholar
9. 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;21:314.10.1097/00043426-199907000-00040Search in Google Scholar
10. 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.Search in Google Scholar
11. 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;30:324–30.10.1111/j.1751-553X.2007.00966.xSearch in Google Scholar PubMed
12. Urrechaga E. Discriminant value of % microcytic/% hypochromic ratio in the differential diagnosis of microcytic anemia. Clin Chem Lab Med 2008;46:1752–8.10.1515/CCLM.2008.355Search in Google Scholar PubMed
13. 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;12:473–5.10.3923/pjbs.2009.473.475Search in Google Scholar PubMed
14. Keikhaei B. A new valid formula in differentiating iron deficiency anemia from ß-thalassemia trait. Pakist J Med Sci 2010;26: 368–73.Search in Google Scholar
15. 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;13:351–62.10.1111/j.1365-2257.1991.tb00299.xSearch in Google Scholar PubMed
16. Birndorf NI, Pentecost JO, Coakley JR, Spackman KA. An expert system to diagnose anemia and report results directly on hematology forms. Comp Biomed Res 1996;29:16–26.10.1006/cbmr.1996.0002Search in Google Scholar PubMed
17. 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;111:676–82.10.1093/ajcp/111.5.676Search in Google Scholar
18. 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.Search in Google Scholar
19. Yeh JS, Cheng CH. Using hierarchical soft computing method to discriminate microcyte anemia. Expert Syst Appl 2005;29:515–24.10.1016/j.eswa.2005.04.012Search in Google Scholar
20. 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;16:123–7.10.1179/102453311X12940641877849Search in Google Scholar
21. 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;138:300–4.10.1309/AJCP20UTTCAYKUDXSearch in Google Scholar
22. Barnhart-Magen G, Gotlib V, Marilus R, Einav Y. Differential diagnostics of thalassemia minor by artificial neural networks model. J Clin Lab Anal 2013;27:481–6.10.1002/jcla.21631Search in Google Scholar
23. 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/10.1155/2013/457834.10.1155/2013/457834Search in Google Scholar
24. 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;2:1–13.10.1186/1471-2288-2-9Search in Google Scholar
25. DerSimonian R, Laird N. Meta-analysis in clinical trials. Contr Clin Trails 1986;7:177–88.10.1016/0197-2456(86)90046-2Search in Google Scholar
26. Honest H, Khan KS. Reporting of measures of accuracy in systematic reviews of diagnostic literature. BMC Health Serv Res 2002;2:1–4.10.1186/1472-6963-2-4Search in Google Scholar
27. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003;56:1129–35.10.1016/S0895-4356(03)00177-XSearch in Google Scholar
28. 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;58:982–90.10.1016/j.jclinepi.2005.02.022Search in Google Scholar
29. Walter SD. Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Statist Med 2002;21:1237–56.10.1002/sim.1099Search in Google Scholar
30. 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.10.3310/hta9120Search in Google Scholar
31. 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;58:882–93.10.1016/j.jclinepi.2005.01.016Search in Google Scholar
32. 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;6:31.10.1186/1471-2288-6-31Search in Google Scholar
33. Gimferrer E, Marigo G, Rutllant ML, Viñas J. Differentiation of iron deficiency from thalassaemia trait. Lancet 1975;305:114.10.1016/S0140-6736(75)91132-0Search in Google Scholar
34. 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;66:870–7.10.1093/ajcp/66.5.870Search in Google Scholar
35. Hedge UM, White JM, Hart GH, Marsh GW. Diagnosis of a-thalassaemia trait from Coulter Counter ‘S’ indices. J Clin Pathol 1977;30:884–9.10.1136/jcp.30.9.884Search in Google Scholar
36. Walford DM, McPherson K, Deacon R. Discrimination between iron deficiency and heterozygous thalassaemia. Lancet 1979;313:323.10.1016/S0140-6736(79)90733-5Search in Google Scholar
37. Johnson CS, Tegos C, Beutler E. Thalassemia minor: routine erythrocyte measurements and differentiation from iron deficiency. Am J Clin Pathol 1983;80:31–6.10.1093/ajcp/80.1.31Search in Google Scholar PubMed
38. 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;44:1–6.10.3109/00365518409083779Search in Google Scholar PubMed
39. Ghionni H, Miotti TC, Camandona V. Routine erytrocyte measurements and differentiation of thalassemia minor from iron deficiency. Minerva Med 1985;76:1143–8.Search in Google Scholar
40. 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.Search in Google Scholar
41. Marti HR, Fischer S, Killer D, Burgi W. Can automated haematology analysers discriminate thalassaemia from iron deficiency? Acta Haematol 1987;78:180–3.10.1159/000205871Search in Google Scholar PubMed
42. Novak RW. Red blood cell distribution width in pediatric microcytic anemias. Pediatrics 1987;80:251–4.10.1542/peds.80.2.251Search in Google Scholar
43. 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;48:697–704.10.3109/00365518809085793Search in Google Scholar
44. 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.Search in Google Scholar
45. Houwen B. The use of inference strategies in the differential diagnosis of microcytic anemia. Blood Cells 1989;15:509–32.Search in Google Scholar
46. 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.Search in Google Scholar
47. 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.Search in Google Scholar
48. 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;148:745–8.10.1007/BF00443100Search in Google Scholar PubMed
49. 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.Search in Google Scholar
50. 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.Search in Google Scholar
51. 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.Search in Google Scholar
52. 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.Search in Google Scholar
53. Jimenez CV. Iron deficiency anaemia and thalassaemia trait differentiated by simple haematological tests and serum iron concentrations. Clin Chem 1993;39:2271–5.10.1093/clinchem/39.11.2271Search in Google Scholar
54. 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.Search in Google Scholar
55. 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.Search in Google Scholar
56. 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.Search in Google Scholar
57. 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.Search in Google Scholar
58. 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.Search in Google Scholar
59. 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;106:201–5.10.1093/ajcp/106.2.201Search in Google Scholar PubMed
60. Mach-Pascual S, Darbellay R, Pilotto PA, Beris P. Investigation of microcytosis: a comprehensive approach. Eur J Haematol 1996;57:54–61.10.1111/j.1600-0609.1996.tb00490.xSearch in Google Scholar PubMed
61. 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.Search in Google Scholar
62. 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;107:601–7.10.1093/ajcp/107.5.601Search in Google Scholar PubMed
63. 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.Search in Google Scholar
64. Sirdah M, Bilto YY, El Jabour S, Najjar K. Screening secondary school students in the Gaza Strip for β- thalassaemia trait. Clin Lab Haematol 1998;20:279–83.10.1046/j.1365-2257.1998.00037.xSearch in Google Scholar PubMed
65. 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.Search in Google Scholar
66. 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.Search in Google Scholar
67. 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.Search in Google Scholar
68. Demir A, Yarali N, Fisgin T, Duru F, Kara A. Most reliable indices in differentiation between thalassemia trait and iron deficiency anemia. Pediatr Intern 2002;44:612–6.10.1046/j.1442-200X.2002.01636.xSearch in Google Scholar PubMed
69. 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;48:222–4.10.1590/S0104-42302002000300034Search in Google Scholar
70. 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.Search in Google Scholar
71. 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;53:93–7.10.1093/tropej/fml070Search in Google Scholar PubMed
72. 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;78:524–6.10.1111/j.1600-0609.2007.00853.xSearch in Google Scholar PubMed
73. 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;86:487–91.10.1007/s00277-007-0302-xSearch in Google Scholar PubMed
74. 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;128:585–9.10.1309/R1YL4B4BT2WCQDGVSearch in Google Scholar PubMed
75. 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;37:25–30.10.1177/147323000903700103Search in Google Scholar PubMed
76. Rahim F, Keikhaei B. Better differential diagnosis of iron deficiency anemia from beta-thalassemia trait. Turk J Hematol 2009;26:138–45.Search in Google Scholar
77. 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;15:112–5.10.1179/102453310X12583347010098Search in Google Scholar PubMed
78. 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;16:966–71.10.26719/2010.16.9.966Search in Google Scholar
79. 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.Search in Google Scholar
80. 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:e218–22.10.1097/MPH.0b013e3181e5e26eSearch in Google Scholar PubMed
81. Tiwari AK, Chandola I, Ahuja A. Approach to blood donors with microcytosis. Transf Med 2010;20:88–94.10.1111/j.1365-3148.2009.00980.xSearch in Google Scholar PubMed
82. 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.Search in Google Scholar
83. 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.Search in Google Scholar
84. 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.Search in Google Scholar
85. 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;33:30–6.10.1111/j.1751-553X.2010.01237.xSearch in Google Scholar PubMed
86. 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;135:374–9.10.1309/AJCPJRH1I0XTNFGASearch in Google Scholar PubMed
87. 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.Search in Google Scholar
88. 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;42:275–80.10.3906/sag-0909-294Search in Google Scholar
89. 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;3:13–8.10.1007/s12687-011-0069-6Search in Google Scholar PubMed PubMed Central
90. 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;29:472–8.10.3109/08880018.2012.705230Search in Google Scholar PubMed
91. 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:e1–2.10.4081/thal.2012.e1Search in Google Scholar
92. Sudmann ÅA, Piehler A, Urdal P. Reticulocyte hemoglobin equivalent to detect thalassemia and thalassemic hemoglobin variants. Int J Lab Hematol 2012;34:605–13.10.1111/j.1751-553X.2012.01442.xSearch in Google Scholar PubMed PubMed Central
93. 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.Search in Google Scholar
94. 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;51:2115–24.10.1515/cclm-2013-0354Search in Google Scholar PubMed
95. 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.Search in Google Scholar
96. 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;37:199–207.10.1111/ijlh.12262Search in Google Scholar PubMed
97. 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;38:258–61.10.3109/03630269.2014.930044Search in Google Scholar PubMed
98. 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;37:334–40.10.1111/ijlh.12290Search in Google Scholar
99. 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;2014:576738.10.1155/2014/576738Search in Google Scholar
100. 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.Search in Google Scholar
101. 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.Search in Google Scholar
102. 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.Search in Google Scholar
103. Urrechaga E. Red blood cell microcytosis and hypochromia in the differential diagnosis of iron deficiency and ß-thalassaemia trait. Int J Lab Hematol 2009;31:528–34.10.1111/j.1751-553X.2008.01073.xSearch in Google Scholar
104. 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;18:169–74.10.1179/1607845412Y.0000000054Search in Google Scholar
105. Cappellini MD, Sampietro M, Fiorelli G, Vestroni E, Bianco I. Screening for thalassaemia. Lancet 1977;309:1100–1.10.1016/S0140-6736(77)92350-9Search in Google Scholar
106. Bessman JD, McClure S, Bates J. Distinction of microcytic disorders: comparison of expert, numerical-discriminant, and microcomputer analysis. Blood Cells 1989;15:533–40.Search in Google Scholar
107. Flynn MM, Reppun TS, Bhagavan NV. Limitations of red blood cell distribution width (RDW) in evaluation of microcytosis. Am J Clin Pathol 1986;85:445–9.10.1093/ajcp/85.4.445Search in Google Scholar PubMed
108. 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;80:59.10.1159/000205600Search in Google Scholar PubMed
109. Bhambhani K, Aronow R. Lead poisoning and thalassemia trait or iron deficiency. The value of the red blood cell distribution width. Am J Dis Child 1990;144:1231–3.10.1001/archpedi.1990.02150350063026Search in Google Scholar PubMed
110. 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;44:106–9.10.1111/j.1600-0609.1990.tb00359.xSearch in Google Scholar PubMed
111. 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.Search in Google Scholar
112. 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;114:1265–9.10.1590/S1516-31801996000500005Search in Google Scholar
113. 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.Search in Google Scholar
114. Harrington AM, Ward PC, Kroft SH. Iron deficiency anemia, beta-thalassemia minor, and anemia of chronic disease: a morphologic reappraisal. Am J Clin Pathol 2008;129:466–71.10.1309/LY7YLUPE7551JYBGSearch in Google Scholar PubMed
115. 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.Search in Google Scholar
116. 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;32:215–21.10.1111/j.1751-553X.2009.01174.xSearch in Google Scholar PubMed
117. 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;127:250–5.10.1159/000337032Search in Google Scholar PubMed
118. 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.Search in Google Scholar
119. 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.Search in Google Scholar
120. 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.Search in Google Scholar
121. 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.Search in Google Scholar
122. 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;78:468–71.10.1007/s002770050600Search in Google Scholar PubMed
123. 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;5:392–5.10.1002/jcla.1860050605Search in Google Scholar PubMed
124. 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.Search in Google Scholar
125. Ittarat W, Ongcharoenjai S, Rayatong O, Pirat N. Correlation between some discrimination functions and hemoglobin E. J Med Ass Thailand 2000;83:259–65.Search in Google Scholar
126. 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;13:63–8.10.1532/LH96.06044Search in Google Scholar PubMed
127. 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;62:13–7.10.1136/jcp.2008.056945Search in Google Scholar PubMed
128. Bull BS, Hay KL. Are red blood cell indexes international? Arch Pathol Lab Med 1985;109:604–6.Search in Google Scholar
129. Hoffmann JJ. Red cell distribution width and mortality risk. Clin Chim Acta 2012;413:824–5.10.1016/j.cca.2012.01.010Search in Google Scholar PubMed
130. 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;47:1100–3.10.1016/j.clinbiochem.2014.06.003Search in Google Scholar PubMed
©2015 by De Gruyter