A Local Equation for Differential Diagnosis of β-Thalassemia Trait and Iron Deficiency Anemia by Logistic Regression Analysis in Southeast Iran

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

Introduction

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

Methods

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

Results

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

Discussion and conclusion

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Should sex differences be considered when applying mathematical indices and formulas for discriminating β- thalassemia minor from iron deficiency?

Background

β-thalassemia minor (BTM) and iron deficiency (ID) are common disorders characterized by microcytosis and/or hypochromasia, leading to a challenge in their discrimination during mass-screening programs especially in developing countries where resources are limited. It has been shown with varying reliability that quick exclusion of either disorder could be achieved mathematically using RBC-based indices and formulas. However, none of these proposed indices and formulas considered the sex-based hematological differences. This comparative retrospective study examined the efficacy of using sex-based RBC indices in the mathematical discrimination BTM and ID in adult males and females.

Methods

The CBC of randomly selected eight hundred adults diagnosed with BTM or ID (200M & 200F BTM, and 200M & 200F ID) were used in the comparisons. The discrimination power, in terms of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Youden index were calculated for all subjects and separately for males and females for 20 mathematical indices and formulas.

Results

Data revealed significant differences in the RBC-based indices between males and females for both BTM and ID groups. Significant variation in reliability indicators for the different indices and formulas were discovered between males and females samples.

Conclusion

Sex-based indices and formulas are necessary to improve the reliability in mathematically discriminating between BTM and ID in mass screening programs. We also advocate for a large–scale multicenter study to establish the parameters of such indices and formulas with sex and age.

Detection of β-Thalassemia Carriers by Red Cell Parameters Obtained from Automatic Counters using Mathematical Formulas

Background

β-thalassemia major is a severe disease with high morbidity. The world prevalence of carriers is around 1.5–7%. The present study aimed to find a reliable formula for detecting β-thalassemia carriers using an extensive database of more than 22,000 samples obtained from a homogeneous population of childbearing age women with 3161 (13.6%) of β-thalassemia carriers and to check previously published formulas.

Methods

We applied a mathematical method based on the support vector machine (SVM) algorithm in the search for a reliable formula that can differentiate between thalassemia carriers and non-carriers, including normal counts or counts suspected to belong to iron-deficient women.

Results

Shine’s formula and our SVM formula showed >98% sensitivity and >99.77% negative predictive value (NPV). All other published formulas gave inferior results.

Conclusions

We found a reliable formula that can be incorporated into any automatic blood counter to alert health providers to the possibility of a woman being a β-thalassemia carrier. A further simple hemoglobin characterization by HPLC analysis should be performed to confirm the diagnosis, and subsequent family studies should be carried out. Our SVM formula is currently limited to women of fertility age until further analysis in other groups can be performed.