Skip to main content

Advertisement

Log in

Predicting Down syndrome and neural tube defects using basic risk factors

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Congenital anomalies are not only one of the main killers for infants but also one of the major causes of deaths under 5. Among congenital anomalies, Down syndrome or trisomy 21 (T-21) and neural tube defects (NTDs) are considered the most common. Expectant mothers in developing countries may not have access to or may not afford the advanced prenatal screening tests. To solve this issue, this paper explores the practicality of using only the basic risk factors for developing prediction models as a tool for initial risk assessment. The prediction models are based on logistic regression. The results show that the prediction models do not have a high balanced classification rate. However, these models can still be used as an effective tool for initial risk assessment for T-21 and NTDs by eliminating at least 50% of the cases with no or low risk.

Prenatal Risk Assessment of Trisomy-21 and Neural Tube Defects

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. (2016) Methods and data sources for child causes of death. World Health Organization

  2. (2017) Maternal Screening for Foetal Abnormality, Ministry of Health, Malaysia

  3. DNA laboratories Malaysia. http://www.dna-laboratories.com/

  4. Sonek JD, et al. (2012) Screening at 11-13 + 6 weeks’ gestation. Ceska Gynekol 77:92–104

    CAS  PubMed  Google Scholar 

  5. Khoshnood B, et al. (2000) Ethnic differences in the impact of advanced maternal age on birth prevalence of Down syndrome. Am J Public Health 90(11):1778–81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Deak KL, et al. (2008) Further evidence for a maternal genetic effect and a sex-influenced effect contributing to risk for human neural tube defects. Birth Defects Research. Part A. Clin Mol Teratol 82(10):662–9

    Article  CAS  Google Scholar 

  7. Shin M, et al. (2009) Prevalence of Down syndrome among children and adolescents in 10 regions of the United States. Pediatrics 124(6):1565–1571

    Article  PubMed  Google Scholar 

  8. Vogenberg FR (2009) Predictive and prognostic models: implications for healthcare decision-making in a modern recession. Am Health Drug Benefits 2(6):218–22

    PubMed  PubMed Central  Google Scholar 

  9. Steyerberg E (2009) Clinical prediction model - a practical approach to development, validation and updating. Springer, New York

    Google Scholar 

  10. Hallen S, et al. (2014) Physicians’ perceptions of the value of prognostic models: the benefits and risks of prognostic confidence. Health Expect 18:6

    Google Scholar 

  11. Merkatz IR, et al. (1984) An association between low maternal serum alpha-fetoprotein and fetal chromosomal abnormalities. Am J Obstetr Gynecol 148:886–894

    Article  CAS  Google Scholar 

  12. Wald NJ, et al. (1996) Prenatal screening for Down’s syndrome using inhibin-a as a serum marker. Prenat Diagn 53:16–23

    Google Scholar 

  13. Brizot ML, et al. (1995) Maternal serum hCG and fetal nuchal translucency thickness for the prediction of fetal trisomies in the first trimester of pregnancy. British J Obstetr Gynecol 32:102–127

    Google Scholar 

  14. Wald NJ, et al. (2003) First and second trimester antenatal screening for Down’s syndrome: the results of the serum, urine and ultrasound screening study (SURUSS). J Med Screen 10:56–66

    CAS  PubMed  Google Scholar 

  15. Cicero S, et al. (2001) Absence of nasal bone in fetuses with trisomy 21 at 11-14 weeks of gestation: an observational study. Lancet 358:1665–1673

    Article  CAS  PubMed  Google Scholar 

  16. Matias A, et al. (1998) Screening for chromosomal abnormalities at 11-14 weeks: the role of ductus venosus blood flow. Ultrasound Obstetr Gynecol 2:380–384

    Article  Google Scholar 

  17. Huggon IC, et al. (2003) Tricuspid regurgitation in the diagnosis of chromosomal anomalies in the fetus at 11-14 weeks of gestation. Heart 89:1071–1079

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Uzun O, et al. (2013) Prenatal risk assessment of trisomy 21 by probabilistic classifiers. In: Signal processing and commun. applications conf. Haspolat, pp 1–4

  19. Gersnoviez RJ (2011) Optimizing a first-trimester predictive model for trisomy 21. Proquest, Umi Dissertation Publishing

  20. Neocleous AC, et al. (2016) First trimester noninvasive prenatal diagnosis: a computational intelligence approach. IEEE J Biomed Health Inform 20(5):1427–1438

    Article  PubMed  Google Scholar 

  21. Neocleous AC, et al. (2016) Intelligent non-invasive diagnosis of aneuploidy: raw values and highly imbalanced dataset. IEEE J Biomed Health Inform 21(5):1271–1279

    Article  PubMed  Google Scholar 

  22. (2006). Adverse pregnancy outcome reporting system public data set. Illinois Department of Public Health October

  23. Tharwat A (2018) Classification assessment methods. Applied Computing and Informatics, 2018

  24. Kutner MH et al (2004) Applied linear regression models, 4th edn. McGraw-Hill Irwin

  25. Albasri A, et al. (2018) Hypertension referrals from community pharmacy to general practice: multivariate logistic regression analysis of 131 419 patients. British J General Pract 68(673):541–550

    Article  Google Scholar 

  26. Wulsin LR, et al. (2015) Autonomic imbalance as a predictor of metabolic risks, cardiovascular disease, diabetes, and mortality. J Clin Endocrinol Metabol 100:2443–2448

    Article  CAS  Google Scholar 

  27. Algamal ZY, et al. (2015) Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification. Comput Biol Med 67:136–145

    Article  CAS  PubMed  Google Scholar 

  28. Delacour H, et al. (2005) ROC (receiver operating characteristics) curve: principles and application in biology. Ann Biol Clin (Paris) 63(2):145–54

    CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the Universiti Teknologi Malaysia (UTM) for providing the facilities and environment for completion of this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Momina T. Khattak.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khattak, M.T., Supriyanto, E., Aman, M.N. et al. Predicting Down syndrome and neural tube defects using basic risk factors. Med Biol Eng Comput 57, 1417–1424 (2019). https://doi.org/10.1007/s11517-019-01969-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-019-01969-0

Keywords