Detection of Β Thalassemia Carriers by Red Cell Parameters Obtained From Automatic Counters Using Mathematical Formulas

Roth, Idit Lachover; Lachover, Boaz; Koren, G.; Levin, Carina; Zalman, Lucia; Koren, Ariel

doi:10.4084/mjhid.2018.008

Cited by 28 publications

(29 citation statements)

References 29 publications

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“…While this research makes use of a dataset consisting of 40% of β-Thalassemia carrier patients which is quite suitable to get enough features of the positive class. Authors performed β-Thalassemia carrier detection by limiting their research on women of fertile age [12]. They completely ignored other age groups and gender in their study.…”

Section: Resultsmentioning

confidence: 99%

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Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

et al. 2021

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Thalassemia is viewed as a prevalent inherited blood disease that has gotten exorbitant consideration in the field of medical research around the world. Inherited diseases have a high risk that children will get these diseases from their parents. If both the parents are β-Thalassemia carriers then there are 25% chances that each child will have β-Thalassemia intermediate or β-Thalassemia major, which in most of its cases leads to death. Prenatal screening after counseling of couples is an effective way to control β-Thalassemia. Generally, identification of the Thalassemia carriers is performed by some quantifiable blood traits determined effectively by high-performance-liquid-chromatography (HPLC) test, which is costly, time-consuming, and requires specialized equipment. However, cost-effective and rapid screening techniques need to be devised for this problem. This study aims to detect β-Thalassemia carriers by evaluating red blood cell indices from the complete-blood-count test. The present study included Punjab Thalassemia Prevention Project Lab Reports dataset. The proposed SGR-VC is an ensemble of three machine learning algorithms: Support Vector Machine, Gradient Boosting Machine, and Random Forest. Comparative analysis proved that the proposed ensemble model using all indices of red blood cells is very effective in β-Thalassemia carrier screening with 93% accuracy.

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Section: Resultsmentioning

confidence: 99%

“…Some works used indices [8], [9] while other applied machine learning models to differentiate β-Thalassemia from Iron Deficiency Anemia (IDA) [10] [11]. In [12] authors detected β-Thalassemia carriers using Support Vector Machine (SVM). Aszhari et al [13] classified Thalassemia disease using Random Forest (RF).…”

Section: Introductionmentioning

confidence: 99%

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

et al. 2021

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“…In more recent studies, researchers have been exploring machine learning algorithmbased studies with advancements in data science [27,28]. In a study in Thailand, a webbased prediction tool for discrimination of thalassemia trait and IDA was developed using a machine learning algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…This study included a total of 64,586 subjects. Their SVM formula displayed high sensitivity(>98%) and >99.77% negative predictive value that is robust in distinguishing the β-thalassemia carrier from normal count subjects and iron-deficient women [28].…”

Section: Discussionmentioning

confidence: 99%

A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta Thalassemia Traits

Angeli

Sahibon²,

Yang³

et al. 2021

Diagnostics

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Thalassemia is one of the major inherited haematological disorders in the Southeast Asia region. This study explored the potential utility of red blood cell (RBC) parameters and reticulocyte cell population data (CPD) parameters in the differential diagnosis of α and β-thalassaemia traits as a rapid and cost-effective tool for screening of thalassemia traits. In this study, a total of 1597 subjects (1394 apparently healthy subjects, 155 subjects with α-thalassaemia trait, and 48 subjects with β-thalassaemia trait) were accrued. The parameters studied were the RBC parameters and reticulocyte CPD parameters derived from Unicel DxH800. A novel algorithm named αβ-algorithm was developed: (MN-LMALS-RET × RDW) − MCH) to discriminate α from β-thalassaemia trait with a cut-off value of 1742.5 [AUC = 0.966, sensitivity = 92%, specificity = 90%, 95% CI = 0.94–0.99]. Two prospective studies were carried: an in-house cohort to assess the specificity of this algorithm in 310 samples comprising various RBC disorders and in an interlaboratory cohort of 65 α-thalassemia trait, and 30 β-thalassaemia trait subjects to assess the reproducibility of the findings. We propose the αβ-algorithm to serve as a rapid, inexpensive surrogate evaluation tool of α and β-thalassaemia in the population screening of thalassemia traits in geographic regions with a high burden of these inherited blood disorders.

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“…To predict whether microcytic anemia is due to iron deficiency or β-thalassemia carrier, hematologic clinicians have applied to various erythrocyte parameters and algorithms. 3 There are over forty erythrocyte indices as described in a meta-analysis by Hoffman et al . (2015), among others Mentzer Index (MI; MCV/RBC), Srivastava Index (SI; MCH/RBC), Shine & Lal Index (SLI; MCV 2 xMCH/100), England & Fraser index (EF; MCV-RBC-(5-Hb)-5.19), Green & King index (GK; MCV 2 xRDW(Hbx100)), red cell distribution width index (RDWI) and many more.…”

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confidence: 99%

Thalassemia carrier detection among pregnant women

Sahiratmadja

Seu²,

Nainggolan

et al. 2020

Mediterr J Hematol Infect Dis

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Thalassemia major becomes the fifth catastrophic disease in Indonesia, however, national wide screening program is not mandatory. This study aimed to re-assess the validity of the various erythrocyte indices in determining ?-thalalssemia carrier among pregnant women in a low resource setting area. An analytic study was performed, comparing conventional cut-off various erythrocyte indices with new modified cut-offs by Kumar et al. The concordance analysis was calculated with Mentzer Index (MI) as reference. The validity of erythrocyte indices against hemoglobin (Hb) A2 were analysed, confirmed by molecular examination for ?- and common ?-Globin South East Asia population. Of 102 pregnant women, 34% was still anemic after completion of 90 iron pills. The concordance of conventional cut-off Shine & Lal index (<1530) was none to slight in agreement (kappa 0.097) to conventional cut-off MI (<13). The concordance of SLI increased significantly to substantial agreement when both used modified cut-offs (kappa 0.729). However, both SLI and MI had missed most of HbE carriers and ?-thalassemia carriers which seemed to be prevalent in this population as shown by DNA examination. In contrary, simple MCV<80fl and MCH<27pg had covered those carriers. This is the first study from Nusa Tenggara Timur, a low resource area in Indonesia in attempt to mass screen thalassemia carriers in this area where a simple MCV<80 fL and MCH<27pg have been used for preliminary screening rather than other indices. Since the population in eastern part of Indonesia has different genetic background compared to the west, DNA analysis is of great interest to map the spectrum of globin mutations.

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Detection of Β Thalassemia Carriers by Red Cell Parameters Obtained From Automatic Counters Using Mathematical Formulas

Cited by 28 publications

References 29 publications

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta Thalassemia Traits

Thalassemia carrier detection among pregnant women

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