Population-based screening for trisomies and atypical chromosomal abnormalities: Improving efficacy using the combined First Trimester Screening algorithm as well as individual risk parameters

I. Vogel, A. Tabor, C. Ekelund, S. Lou, J. Hyett, O.B. Petersen

Research output: Contribution to journalArticlepeer-review

Abstract

Aim: To examine the performance of the combined First Trimester Screening (cFTS) algorithm when outliers of 4 risk parameters (maternal age, nuchal translucency (NT) thickness, PAPP-A and β-hCG) were included in the classification of "high-risk". Methods: A retrospective analysis of singleton pregnancies undergoing cFTS between 2008 and 2011 in Denmark. Abnormal karyotypes were classified as trisomy 21 (T21), trisomy 13 (T13) and trisomy 18 (T18), sex chromosome aberrations and atypical abnormal karyotypes. Results: cFTS was completed in 193,638 pregnancies. In 10,205 (5.3%) cases, cytogenetic or molecular analysis was performed pre- or postnatally. An abnormal karyotype was seen in 1,122 (11.0%). The algorithm identified 87% of T21, 80% of T13, 75% of T18, 79% of sex chromosome aberrations and 35% of atypical abnormal karyotypes. Additional classification of a single risk parameter outlier (low PAPP-A or free β-hCG (< 0.2 MoMs), high β-hCG (≥5.0 MoMs), maternal age ≥45 years or NT ≥3.5 mm) as being at high-risk would have improved detection rates to 88, 80, 81, 81 and 37% respectively. The screen positive rate increased from 4.4 to 4.8%. Discussion: Addition of outliers of the 4 parameters used in cFTS algorithm will lead to a statistically significant increase in detection rates for chromosomal abnormality.
Original languageEnglish
Pages (from-to)424-429
Number of pages6
JournalFetal Diagnosis and Therapy
Volume45
Issue number6
DOIs
Publication statusPublished - 2019

Fingerprint

Dive into the research topics of 'Population-based screening for trisomies and atypical chromosomal abnormalities: Improving efficacy using the combined First Trimester Screening algorithm as well as individual risk parameters'. Together they form a unique fingerprint.

Cite this