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Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions.

ABSTRACT: Recent advances in massively parallel shotgun sequencing opened up new options for affordable non-invasive prenatal testing (NIPT) for fetus aneuploidy from DNA material extracted from maternal plasma. Tests typically compare chromosomal distributions of a tested sample with a control set of healthy samples with unaffected fetuses. Deviations above certain threshold levels are concluded as positive findings. The main problem with this approach is that the variance of the control set is dependent on the number of sequenced fragments. The higher the amount, the more precise the estimation of actual chromosomal proportions is. Testing a sample with a highly different number of sequenced reads as used in training may thus lead to over- or under-estimation of their variance, and so lead to false predictions. We propose the calculation of a variance for each tested sample adaptively, based on the actual number of its sequenced fragments. We demonstrate how it leads to more stable predictions, mainly in real-world diagnostics with the highly divergent inter-sample coverage.

SUBMITTER: Gazdarica J

PROVIDER: S-EPMC6678500 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions.

Gazdarica Juraj J Budis Jaroslav J Duris Frantisek F Turna Jan J Szemes Tomas T

International journal of molecular sciences 20190711 14

Recent advances in massively parallel shotgun sequencing opened up new options for affordable non-invasive prenatal testing (NIPT) for fetus aneuploidy from DNA material extracted from maternal plasma. Tests typically compare chromosomal distributions of a tested sample with a control set of healthy samples with unaffected fetuses. Deviations above certain threshold levels are concluded as positive findings. The main problem with this approach is that the variance of the control set is dependent ...[more]

PMID: 31336782

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Project description:ObjectiveNon-invasive prenatal testing (NIPT) is a front-line screening for fatal chromosomal aneuploidy. In pregnant women with a risk of having fetal congenital disorders, NIPT is anticipated to reduce the needs of invasive prenatal diagnostic test (IPD). The objective of this study was to understand the acceptance of NIPT and the utility of NIPT to mitigate concerns about IPD in the US high-risk pregnancy management.Design and settingThis was a retrospective observational research using healthcare records obtained from an academic healthcare system in the US. The study consisted of site-level longitudinal analysis and patient-level cross-sectional analysis.ParticipantA total of 5660 new high-risk pregnancies with age ≥35 years were identified for the longitudinal trend analysis. Cross-sectional utility assessment included 2057 pregnant women.Exposure and outcome measuresLongitudinal trends of NIPT order, IPD procedure and the number of patients diagnosed with high-risk pregnancy were descriptively summarised. In the cross-sectional assessment, we tested the association between the use of NIPT and IPD using multivariable regression.ResultsThe rate of increase in the NIPT use exceeded the changes in the number of high-risk pregnancies with age ≥35 years, while the number of annual IPD procedures has fluctuated without specific trends. There was no significant association between the numbers of NIPT and IPD with the adjusted ORs between 0.90 and 1.14 (p>0.1). The order of NIPT was not selected as an independent variable predicting the use of IPD. Clinical characteristics indicating low socioeconomic status and limited healthcare coverage are associated with less use of NIPT and lower clinical utility.ConclusionAlthough prenatal care accepted NIPT over the last decade, the utility of NIPT in mitigating concerns on IPD is unclear and needs further investigation. Limited clinical utility should be addressed in the context of disparity in prenatal care.

Dataset Information

Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions.

Publications

Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions.

Similar Datasets

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