Advanced first trimester screening (AFS): an improved test strategy for the individual risk assessment of fetal aneuploidies and malformations

Objective

First trimester risk assessment for fetal aneuploidies is computed on the base of a general background risk, which is depending on the maternal age. Thereby, the adjusted risk tends to rise with increasing age. Obversely, more unsuspicious fetal parameters [measurement of the nuchal translucency (NT) and biochemical parameters, free beta human chorionic gonadotropine (fß-Hcg) and pregnancy associated plasma protein A (Papp-A)] have to be observed to result in an unsuspicious test at higher age. It was the aim of this study to investigate the potential value of a novel risk assessment algorithm explicitly disregarding the maternal age.

Methods

This was an ultrasound cohort study of 1,463 singleton pregnancies at 11–14 weeks of gestation undergoing a first trimester screening for fetal aneuploidies by measuring the (NT), Papp-A and fß-hCG. In each case, the pregnancy outcome was obtained. Regarding either the detection of genetic affections or the combined detection of genetic or somatic anomalies, the test performance parameters (sensitivity, specificity, positive and negative predictive values) were calculated and compared with each other. For risk calculation the standard Fetal Medicine Foundation (FMF)-Software and an alternative software with a similar algorithm (JOY-Software) were utilized. Compared to this, the risk assessment had been modified by implementing a novel calculation algorithm (advanced first trimester screening algorithm, AFS) purposely disregarding the maternal age and again, the test performance parameters had been computed and were compared with the first ones.

Results

At the mere genetic analysis, all four test-strategies revealed to have identical sensitivity and negative predictive values. Compared to the standard FMF-Software, the JOY-Software showed a reduced false positive rate. In addition, in both softwares, the false positive rate is highly significant-reduced by implementing the AFS-algorithm. At combined genetic and somatic analysis, analogous results on different counts could be found.

Conclusion

In the effort to create an algorithm characterising somatic and fetal conditions of the fetus most properly, the inclusion of maternal age is not a helpful value and excluding the age from risk calculation leads to a high significant reduction of the false positive rate. Further, a comparable marked increase of both, specificity and positive predictive values, can be achieved for the FMF- and JOY-Software on the background of the generally more favourable JOY test performance.