Antenatal screening for Down's syndrome: generic protocol

  • Major change
  • Protocol
  • Diagnostic

Authors

  • S Kate Alldred,

    Corresponding author
    1. The University of Liverpool, School of Reproductive and Developmental Medicine, Division of Perinatal and Reproductive Medicine, Liverpool, UK
    • S Kate Alldred, School of Reproductive and Developmental Medicine, Division of Perinatal and Reproductive Medicine, The University of Liverpool, First Floor, Liverpool Women's NHS Foundation Trust, Crown Street, Liverpool, L8 7SS, UK. k.alldred@liv.ac.uk.

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  • Jonathan J Deeks,

    1. University of Birmingham, Public Health, Epidemiology and Biostatistics, Birmingham, UK
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  • James P Neilson,

    1. The University of Liverpool, School of Reproductive and Developmental Medicine, Division of Perinatal and Reproductive Medicine, Liverpool, UK
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  • Zarko Alfirevic

    1. The University of Liverpool, School of Reproductive and Developmental Medicine, Division of Perinatal and Reproductive Medicine, Liverpool, UK
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Abstract

This is the protocol for a review and there is no abstract. The objectives are as follows:

The aim of this review was to estimate and compare the accuracy of screening tests for the detection of Down’s syndrome in the antenatal period, both as individual markers and as combinations of markers. Accuracy is described by the proportion of fetuses with Down’s syndrome detected by screening before birth (sensitivity or detection rate) and the proportion of women with a low risk (normal) screening test result who subsequently had a baby unaffected by Down's syndrome (specificity).

We investigated whether a uniform screening test is suitable for all women, or whether different screening methods are more applicable to different groups, defined by advanced maternal age, ethnic groups and aspects of the pregnancy and medical history such as multiple pregnancy, diabetes and family history of Down's syndrome. We also considered whether there existed evidence of overestimation of test accuracy in studies evaluating risk equations in the derivation sample rather than in a separate validation sample, and associated with differential verification bias.