Prevalence of thalassemia in the Vietnamese population and building a clinical decision support system for prenatal screening for thalassemia
More details
Hide details
Center for Prenatal Diagnosis, National Hospital of Obstetrics and Gynecology, Hanoi, VIETNAM
Department of Obstetrics and Gynecology, Hanoi Medical University, Hanoi, VIETNAM
Department of Biomedical and genetics, Hanoi Medical University, Hanoi, VIETNAM
Department of Specialized Software, Academy of Military Science and Technology, Hanoi, VIETNAM
Department of Basic Sciences in Medicine and Pharmacy, University of Medicine and Pharmacy-Vietnam National University, Hanoi, VIETNAM
Department of Pediatrics, Hanoi Medical University, Hanoi, VIETNAM
Department of Pediatrics, Hanoi Medical University Hospital, Hanoi, VIETNAM
Clinical Genetics Center, Hanoi Medical University Hospital, Hanoi, VIETNAM
Online publication date: 2023-04-14
Publication date: 2023-07-01
Electron J Gen Med 2023;20(4):em501
The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems (CDSSs) for prenatal screening of thalassemia were created. A cross-sectional study was conducted on pregnant women and their husbands visiting from October 2020 to December 2021. A total of 10,112 medical records of first-time pregnant women and their husbands were collected. CDSS including two different types of systems for prenatal screening for thalassemia (expert system [ES] and four artificial intelligence [AI]-based CDSS) was built. 1,992 cases were used to train and test machine learning (ML) models while 1,555 cases were used for specialized ES evaluation. There were 10 key variables for AI-based CDSS for ML. The four most important features in thalassemia screening were identified. Accuracy of ES and AI-based CDSS was compared. The rate of patients with alpha thalassemia is 10.73% (1,085 patients), the rate of patients with beta-thalassemia is 2.24% (227 patients), and 0.29% (29 patients) of patients carry both alpha-thalassemia and beta-thalassemia gene mutations. ES showed an accuracy of 98.45%. Among AI-based CDSS developed, multilayer perceptron model was the most stable regardless of the training database (accuracy of 98.50% using all features and 97.00% using only the four most important features). AI-based CDSS showed satisfactory results. Further development of such systems is promising with a view to their introduction into clinical practice.
Sheth S, Thein SL. Thalassemia: A disorder of globin synthesis. In: Kaushansky K, Prchal JT, Burns LJ, Lichtman MA, Levi M, Linch DC, editors. Williams Hematology 10. New York, NY: McGraw-Hill Education; 2021. p. 12-3.
Gell DA. Structure and function of haemoglobins. Blood Cells Mol Dis. 2018;70:13-42. https://doi.org/10.1016/j.bcmd... Mid:29126700.
Viprakasit V, Ekwattanakit S. Clinical classification, screening and diagnosis for thalassemia. Hematol Oncol Clin North Am. 2018;32(2):193-211. https://doi.org/10.1016/j.hoc.... PMid:29458726.
Petrakos G, Andriopoulos P, Tsironi M. Pregnancy in women with thalassemia: Challenges and solutions. Int J Womens Health. 2016;8:441-51. https://doi.org/10.2147/IJWH.S... PMid:27660493 PMCid:PMC5019437.
Shang X, Xu X. Update in the genetics of thalassemia: what clinicians need to know. Best Pract Res Clin Obstet Gynaecol. 2017;39:3-15. https://doi.org/10.1016/j.bpob... PMid:27876354.
Farashi S, Harteveld CL. Molecular basis of α-thalassemia. Blood Cells Mol Dis. 2018;70:43-53. https://doi.org/10.1016/j.bcmd... PMid:29032940.
Shah FT, Sayani F, Trompeter S, Drasar E, Piga A. Challenges of blood transfusions in β-thalassemia. Blood Rev. 2019;37:100588. https://doi.org/10.1016/j.blre... PMid:31324412.
Bain BJ. Delta beta thalassemia. Am J Hematol. 2020;95(1):116. https://doi.org/10.1002/ajh.25... PMid:31444804.
Borgna-Pignatti C, Gamberini MR. Complications of thalassemia major and their treatment. Expert Rev Hematol. 2011;4(3):353-66. https://doi.org/10.1586/ehm.11... PMid:21668399.
Thalassaemia International Federation. Response to the proposal for the inclusion of whole blood and red blood cells on the WHO essential medicines lists (EML). Thalassaemia International Federation; 2013. Available at: https://issuu.com/internationa... (Accessed: 13 December 2022).
Fucharoen S, Winichagoon P. Haemoglobinopathies in Southeast Asia. Indian J Med Res. 2011;134:498-506.
Nguyen KHH. Thalassemia and a model of prevention in Vietnam. Sydney: The University of Sydney; 2005.
Dossanova A, Lozovoy V, Wood D, Lozovaya Y. Reducing the risk of postoperative genital complications in male adolescents. Int J Environ Sci Educ. 2016;11(13):5797-807.
Thalassaemia International Federation. Guidelines for the management of transfusion dependent thalassaemia. Thalassaemia International Federation; 2021. Available at: https://thalassaemia.org.cy/pu... (Accessed: 13 December 2022).
Dossanov B, Trofimchuk V, Lozovoy V, et al. Evaluating the results of long tubular bone distraction with an advanced rod monolateral external fixator for achondroplasia. Sci Rep. 2021;11(1):14727. https://doi.org/10.1038/s41598... PMid:34282216 PMCid:PMC8290032.
Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ. 2005;330:765. https://doi.org/10.1136/bmj.38... PMid:15767266 PMCid:PMC555881.
Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17. https://doi.org/10.1038/s41746... PMid:32047862 PMCid:PMC7005290.
Muhiyaddin R, Abd-Alrazaq AA, Househ M, Alam T, Shah Z. The impact of clinical decision support systems (CDSS) on physicians: A scoping review. Stud Health Technol Inform. 2020;470-3.
Amendolia SR, Brunetti A, Carta P, et al. A real-time classification system of thalassemic pathologies based on artificial neural networks. Med Decis Making. 2002;22(1):18-26. https://doi.org/10.1177/027298... PMid:11833662.
Masala GL, Golosio B, Cutzu R, Pola R. A two-layered classifier based on the radial basis function for the screening of thalassaemia. Comput Biol Med. 2013;43(11):1724-31. https://doi.org/10.1016/j.comp... PMid:24209918.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Int Res. 2020;16(1):321-57. https://doi.org/10.1613/jair.9....
Dang HTT. Screening for thalassemia in pregnant women who come for examination and treatment at the National Hospital of Obstetrics and Gynecology. Vietnam: National Hospital of Obstetrics and Gynecology; 2019.
Nguyen HHK. Research for alpha and beta thalassemia screening and diagnosis. Vietnam: Ho Chi Minh City University of Medicine and Pharmacy; 2013.
He S, Zhang Q, Li D, et al. Prevention and control of Hb barts disease in Guangxi Zhuang Autonomous Region, China. Eur J Obstet Gynecol. 2020:178:138-41. https://doi.org/10.1016/j.ejog... PMid:24792538.
Husna N, Handayani NSN. Molecular and haematological characteristics of alpha-thalassemia deletions in Yogyakarta special region, Indonesia. Rep Biochem Mol Biol. 2021;10(3):346-53. https://doi.org/10.52547/rbmb.... PMid:34981010 PMCid:PMC8718782.
Sarafidis M, Manta O, Kouris I, et al. Why a clinical decision support system is needed for tinnitus? Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:2075-8. https://doi.org/10.1109/EMBC46... PMid:34891697.
Klar R, Zaiß A. Medical expert systems: Design and applications in pulmonary medicine. Lung. 1990;168(1):1201-9. https://doi.org/10.1007/BF0271... PMid:2117122.
Rowe M. An introduction to machine learning for clinicians. Acad Med. 2019;94(10):1433-6. https://doi.org/10.1097/ACM.00... PMid:31094727.
Shaikh F, Dehmeshki J, Bisdas S, et al. Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics. Curr Probl Diagn Radiol. 2021;50(2):262-7. https://doi.org/10.1067/j.cpra... Mid:32591104.
AlAgha AS, Faris H, Hammo BH, Ala’M AZ. Identifying β-thalassemia carriers using a data mining approach: The case of the Gaza Strip, Palestine. Artif Intell Med. 2018;88:70-83. https://doi.org/10.1016/j.artm... PMid:29730048.
Egejuru NC, Olusanya SO, Asinobi AO, Adeyemi OJ, Adebayo VO, Idowu PA. Using data mining algorithms for thalassemia risk prediction. Int J Biomed Sci Eng. 2019;7(2):33. https://doi.org/10.11648/j.ijb....
Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digital Health. 2019;1(6):e271-97. https://doi.org/10.1016/S2589-... PMid:33323251.
Roshanov PS, Fernandes N, Wilczynski JM, et al. Features of effective computerised clinical decision support systems: metaregression of 162 randomised trials. BMJ. 2013;346:f657. https://doi.org/10.1136/bmj.f6... PMid:23412440.
Journals System - logo
Scroll to top