A Systematic Review and Meta-Analysis of Non-Invasive Prenatal Diagnosis (NIPD) of Sickle Cell Disease (SCD)

Authors

  • Lauren Short Women’s Health Academic Centre, King’s College London, St Thomas’ Hospital, 10th Floor, North Wing, Westminster Bridge Road, SE1 7EH, UK
  • Winfred Baah Women’s Health Directorate, Guy’s and St Thomas’ Hospital NHS Foundation Trust, Westminster Bridge Road, SE1 7EH, UK
  • Eugene Oteng-Ntim Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, WC1 E7HT, UK

DOI:

https://doi.org/10.15379/2408-9877.2018.05.01.01

Keywords:

Non-invasive, Prenatal, Diagnosis, Testing, Sickle cell disease

Abstract

Introduction: Sickle cell disease (SCD) is a genetically inherited, recessive mutation of the haemoglobin ?S-gene. Each year, over 300,000 babies are born with SCD, which will have a significant impact on their quality of life and average life expectancy. Currently, for SCD to be tested prenatally, foetal DNA is extracted by amniocentesis, chorionic villus sampling or cordocentesis, and then analysed by polymerase chain reaction (PCR), for instance. These procedures increase the risk of foetal miscarriage by less than 0.5%. SCD may, however, be tested non-invasively using cell-free foetal DNA (cffDNA), which is extracted from maternal blood plasma. In this study, the current accuracy of using cffDNA testing for non-invasive prenatal diagnosis (NIPD) of SCD will be shown.

Methods: Using databases such as PubMed, Web of Science and Scopus, this study systematically reviewed existing studies pertaining to the use of cffDNA maternal blood samples for non-invasive prenatal testing (NIPT) or diagnosis (NIPD) for SCD in patients who were at risk of having a baby with SCD. The data collected from the systematic review of the studies was statistically analysed in the form of a meta-analysis, describing the proportion of correct diagnosis results for this method of prenatal testing.

Results: Of over 3,600 papers identified from the database searches, only five studies contained data pertaining to the use of cffDNA for prenatal testing of SCD and conformed to the inclusion criteria set out by this study. Collectively, these data showed an average of 81.30% accuracy of diagnosis when using cffDNA to test for SCD, with 18.70% of foetuses incorrectly diagnosed. These data were compiled as a Forest Plot meta-analysis.

Conclusion: CffDNA for non-invasive prenatal SCD diagnosis appears to have the potential to be an accurate technique for the testing of this genetic disease, despite not currently indicating a proportion of correct diagnosis results which would encourage the technique for clinical implementation. Whilst there are currently very limited data on the use of this technique for the specific testing of SCD, there is great opportunity for further research into the standardisation and clinical application of this procedure.

References

Rees DC, Williams TN and Gladwin MT. Sickle-cell disease. The Lancet 2010; 376(9757): 2018-2031. https://doi.org/10.1016/S0140-6736(10)61029-X

Barrett AN, Mcdonnell TC, Chan KC and Chitty LS. Digital PCR analysis of maternal plasma for noninvasive detection of sickle cell anemia. Clinical chemistry 2012; 58(6): 1026-1032. https://doi.org/10.1373/clinchem.2011.178939

Bender MA and Douthitt Seibel G. Sickle Cell Disease. In: RA Pagon, MP Adam, HH Ardinger, SE Wallace, A Amemiya, LJH. Bean, TD Bird, CT Fong, HC Mefford, RJH Smith and K Stephens, eds, Gene Reviews (R). Seattle (WA): University of Washington, Seattle 1993.

Piel FB, Hay SI, Gupta S, Weatherall DJ and Williams TN. Global burden of sickle cell anaemia in children under five, 2010–2050: modelling based on demographics, excess mortality, and interventions. PLoS Med 2013; 10(7): e1001484. https://doi.org/10.1371/journal.pmed.1001484

NHS CHOICES, 06/16, 2016-last update [Homepage of NHS Choices online], [Online]. Available: http://www.nhs.uk/ conditions/Sickle-cell-anaemia/Pages/Introduction.aspx Accessed 10/2/2016.

Hassell KL. Population estimates of sickle cell disease in the US. American Journal of Preventive Medicine 2010; 38(4): S512-S521. https://doi.org/10.1016/j.amepre.2009.12.022

Harvard Medical School, 5/4, 2002-last update [Homepage of Harvard University], [Online]. Available: http://sickle.bwh.harvard.edu/scd_prenatal.html Accessed: 30/1/2016.

Berry SM, Stone J, Norton ME, Johnson D, Berghella V and Society for Maternal-Fetal Medicine (SMFM. Fetal blood sampling. American Journal of Obstetrics and Gynecology 2013; 209(3): 70-180. https://doi.org/10.1016/j.ajog.2013.07.014

D'souza E, Nair S, Nadkarni A, Ghosh K and Colah RB. SRY sequence in maternal plasma: Implications for non-invasive prenatal diagnosis: First report from India. Indian journal of human genetics 2012; 18(1): 87-90. https://doi.org/10.4103/0971-6866.96661

Chen JJ, Tan JA, Chua KH, Tan PC and George E. Non-invasive prenatal diagnosis using fetal DNA in maternal plasma: a preliminary study for identification of paternally-inherited alleles using single nucleotide polymorphisms. BMJ open 2015; 5(7): e007648-2015-007648.

Wright CF, Wei Y, Higgins JP and Sagoo GS. Non-invasive prenatal diagnostic test accuracy for fetal sex using cell-free DNA a review and meta-analysis. BMC research notes 2012; 5: 476-0500-5-476.

Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Annals of Internal Medicine 2015; 162(11): 777-784. https://doi.org/10.7326/M14-2385

Sotiriadis A, Papatheodorou SI and Martins WP. Synthesizing Evidence from Diagnostic Accuracy TEsts: the SEDATE guideline. Ultrasound in Obstetrics &Gynecology 2016. https://doi.org/10.1002/uog.15762

Song JW and Chung KC. Observational studies: cohort and case-control studies. Plastic and Reconstructive Surgery 2010; 126(6): 2234-2242. https://doi.org/10.1097/PRS.0b013e3181f44abc

Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Jama 2000; 283(15): 2008-2012. https://doi.org/10.1001/jama.283.15.2008

University of York. Centre for Reviews and Dissemination. Systematic reviews: CRD's guidance for undertaking reviews in health care. University of York, Centre for Reviews & Dissemination; 2009.

NHLBI, 06/12, 2015-last update [Homepage of National Heart, Lung and Blood Institute], [Online]. Available: https://www.nhlbi.nih.gov/health/health-topics/topics/sca Accessed 5/3/2016.

Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al. Quadas-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine 2011; 155(8): 529-36. https://doi.org/10.7326/0003-4819-155-8-201110180-00009

Wells G. SBOD. The Newcastle–Ottawa Scale (NOS) for assessing the Quality of non-randomised Studies in Meta-analysis. In Proceedings or the Third Symposium on Systematic Reviews beyond the Basics 2000 Jul.

Cheung MC, Goldberg JD, Kan YW. Prenatal diagnosis of sickle cell anaemia and thalassaemia by analysis of fetal cells in maternal blood. Nature genetics 1996; 14(3): 264-8. https://doi.org/10.1038/ng1196-264

Phylipsen M, Yamsri S, Treffers EE, Jansen DT, Kanhai WA, Boon EM, et al. Non?invasive prenatal diagnosis of beta?thalassemia and sickle?cell disease using pyrophosphorolysis?activated polymerization and melting curve analysis. Prenatal diagnosis 2012; 32(6): 578-587. https://doi.org/10.1002/pd.3864

Fielding S, Mckay F, Barrett A, Jenkins L, Lench N, Chitty LS. Implementation of noninvasive prenatal diagnosis for single gene disorders into clinical practice - PCR-RED, dPCR or NGS? Presented as Paper Abstracts of the ISPD 17th International Conference on Prenatal Diagnosis and Therapy. Prenat Diagn 2013; 33(1): 1-26.

Yenilmez ED, Tuli A and Evrüke ?C. Noninvasive prenatal diagnosis experience in the Çukurova Region of Southern Turkey: detecting paternal mutations of sickle cell anemia and ??thalassemia in cell?free fetal DNA using high?resolution melting analysis. Prenatal diagnosis 2013; 33(11): 1054-1062. https://doi.org/10.1002/pd.4196

Crombie I and Davies H. 2009-last update [Homepage of Hayward Medical Communications], [Online]. Available: http://www.medicine.ox.ac.uk/bandolier/painres/download/whatis/meta-an.pdf Accessed: 15/4/ 2016.

Chandrasekharan S, Minear MA, Hung A and Allyse M. Noninvasive prenatal testing goes global. Science translational medicine 2014; 6(231): 231fs15. https://doi.org/10.1126/scitranslmed.3008704

Zamora J, Abraira V, Muriel A, Khan K and Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC medical research methodology 2006; 6(1): 1. https://doi.org/10.1186/1471-2288-6-31

Haidich A. Meta-analysis in medical research. Hippokratia 2011; 14(1): 9-37.

Kumar AA, Chunda-Liyoka C, Hennek JW, Mantina H, Lee SR, Patton MR et al. Evaluation of a density-based rapid diagnostic test for sickle cell disease in a clinical setting in Zambia. PloS one 2014; 9(12): e114540. https://doi.org/10.1371/journal.pone.0114540

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Published

2018-01-16

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