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Clinical Classification, Diagnosis and Treatment for Thalassemia
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Clinical Classification, Diagnosis and Treatment for Thalassemia

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Hematology".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 4406

Special Issue Editor

Prof. Dr. Joan-Lluis Vives-Corrons

E-Mail Website
Guest Editor
Red Blood Cell and Hematopoietic Disorders Research Lab., Institute for Leukaemia Research Josep Carreras, Ctra de Can Ruti, Camí de les Escoles s/n. 08916-Badalona (Barcelona), Spain
Interests: Sickle-cell disease (SCD); thalassaemia; RBC enzymes deficiencies and hereditary membranopathies due to cytoskeleton abnormalities and ionic homeostasis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rare anaemias (RAs), ORPHA108997, include up to 132 rare and ultra-rare haematological conditions, representing a highly heterogeneous group of disorders. These are characterized by anaemia of variable degree, from mild forms to life-threatening chronic blood-transfusion dependence, and by complex and often unexplained genotype-phenotype correlations. More than 80% of RA are genetic disorders caused by mutations in more than 70 genes controlling red blood cell (RBC) production and structure. These mutations lead to alterations in haemoglobin (Hb) structure or synthesis, RBC maturation and differentiation, cell membrane structure, and enzyme deficiencies. The balance between haemolysis (mainly in the spleen) and erythropoiesis explains the severity of the anaemia and the patient’s ability to respond to treatment(s). In this context, differential diagnosis, prognosis and patient stratification are often difficult. Some studies have already demonstrated the usefulness of the targeted-NGS (t-NGS) approach in the investigation of specific subtypes of RA. However, the huge amount of data generated by NGS technology, and in particular the interpretation of variants of unknown (or uncertain) clinical significance (VUSs) which require additional validation, contribute to the low overall diagnostic rate of genetic strategies.

The inclusion of Lorrca osmoscan as a screening test in RBC membrane diagnostic workflow signifies an important advancement for the accurate diagnosis of hereditary spherocytosis (HS) patients, as well as for the identification of patients with hereditary elliptocytosis (HE) and dehydrated hereditary stomatocytosis (dHSt) or hereditary xerocytosis (HX).

Prof. Dr. Joan-Lluis Vives-Corrons
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sickle-cell disease (SCD)
  • thalassaemia
  • RBC enzymes deficiencies and hereditary membranopathies due to cytoskeleton abnormalities and ionic homeostasis

Published Papers (2 papers)

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Research

11 pages, 1457 KiB  
Article
DeepThal: A Deep Learning-Based Framework for the Large-Scale Prediction of the α+-Thalassemia Trait Using Red Blood Cell Parameters
by Krittaya Phirom, Phasit Charoenkwan, Watshara Shoombuatong, Pimlak Charoenkwan, Supatra Sirichotiyakul and Theera Tongsong
J. Clin. Med. 2022, 11(21), 6305; https://doi.org/10.3390/jcm11216305 - 26 Oct 2022
Cited by 3 | Viewed by 1617
Abstract
Objectives: To develop a machine learning (ML)-based framework using red blood cell (RBC) parameters for the prediction of the α+-thalassemia trait (α+-thal trait) and to compare the diagnostic performance with a conventional method using a single RBC parameter or [...] Read more.
Objectives: To develop a machine learning (ML)-based framework using red blood cell (RBC) parameters for the prediction of the α+-thalassemia trait (α+-thal trait) and to compare the diagnostic performance with a conventional method using a single RBC parameter or a combination of RBC parameters. Methods: A retrospective study was conducted on possible couples at risk for fetus with hemoglobin H (Hb H disease). Subjects with molecularly confirmed normal status (not thalassemia), α+-thal trait, and two-allele α-thalassemia mutation were included. Clinical parameters (age and gender) and RBC parameters (Hb, Hct, MCV, MCH, MCHC, RDW, and RBC count) obtained from their antenatal thalassemia screen were retrieved and analyzed using a machine learning (ML)-based framework and a conventional method. The performance of α+-thal trait prediction was evaluated. Results: In total, 594 cases (female/male: 330/264, mean age: 29.7 ± 6.6 years) were included in the analysis. There were 229 normal controls, 160 cases with the α+-thalassemia trait, and 205 cases in the two-allele α-thalassemia mutation category, respectively. The ML-derived model improved the diagnostic performance, giving a sensitivity of 80% and specificity of 81%. The experimental results indicated that DeepThal achieved a better performance compared with other ML-based methods in terms of the independent test dataset, with an accuracy of 80.77%, sensitivity of 70.59%, and the Matthews correlation coefficient (MCC) of 0.608. Of all the red blood cell parameters, MCH < 28.95 pg as a single parameter had the highest performance in predicting the α+-thal trait with the AUC of 0.857 and 95% CI of 0.816–0.899. The combination model derived from the binary logistic regression analysis exhibited improved performance with the AUC of 0.868 and 95% CI of 0.830–0.906, giving a sensitivity of 80.1% and specificity of 75.1%. Conclusions: The performance of DeepThal in terms of the independent test dataset is sufficient to demonstrate that DeepThal is capable of accurately predicting the α+-thal trait. It is anticipated that DeepThal will be a useful tool for the scientific community in the large-scale prediction of the α+-thal trait. Full article
(This article belongs to the Special Issue Clinical Classification, Diagnosis and Treatment for Thalassemia)
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12 pages, 432 KiB  
Article
Relationship between Serum Ferritin and Outcomes in β-Thalassemia: A Systematic Literature Review
by Farrukh Shah, Krystal Huey, Sohan Deshpande, Monica Turner, Madhura Chitnis, Emma Schiller, Aylin Yucel, Luciana Moro Bueno and Esther Natalie Oliva
J. Clin. Med. 2022, 11(15), 4448; https://doi.org/10.3390/jcm11154448 - 30 Jul 2022
Cited by 9 | Viewed by 2333
Abstract
Among the difficulties of living with β-thalassemia, patients frequently require blood transfusions and experience iron overload. As serum ferritin (SF) provides an indication of potential iron overload, we conducted a systematic literature review (SLR) to assess whether SF levels are associated with clinical [...] Read more.
Among the difficulties of living with β-thalassemia, patients frequently require blood transfusions and experience iron overload. As serum ferritin (SF) provides an indication of potential iron overload, we conducted a systematic literature review (SLR) to assess whether SF levels are associated with clinical and economic burden and patient-reported outcomes (PROs). The SLR was conducted on 23 April 2020 and followed by analysis of the literature. Dual-screening was performed at the title, abstract, and full-text levels using predefined inclusion and exclusion criteria. Ten studies identified by the SLR were eligible for inclusion in the analysis. Seven studies were conducted in Europe, and most were prospective or retrospective in design. The patient populations had a median age of 20.7–42.6 years, with a percentage of men of 38–80%. Sparse data were found on the correlation between SF levels and mortality, and hepatic, skeletal, and cardiac complications; however, in general, higher SF levels were associated with worsened outcomes. The bulk of the evidence reported on the significant association between higher SF levels and endocrine dysfunction in its many presentations, including a 14-fold increase in the risk of diabetes for patients with persistently elevated SF levels. No studies reporting data on PROs or economic burden were identified by the SLR. SF levels provide another option for prognostic assessment to predict a range of clinical outcomes in patients with β-thalassemia. Full article
(This article belongs to the Special Issue Clinical Classification, Diagnosis and Treatment for Thalassemia)
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