Second‐trimester contingent screening for small‐for‐gestational‐age neonate

Abstract

Objectives First, to investigate the additive value of second‐trimester placental growth factor (PlGF) for the prediction of a small‐for‐gestational‐age (SGA) neonate. Second, to examine second‐trimester contingent screening strategies. Methods This was a prospective observational study in women with singleton pregnancy undergoing routine ultrasound examination at 19–24 weeks' gestation. We used the competing‐risks model for prediction of SGA. The parameters for the prior model and the likelihoods for estimated fetal weight (EFW) and uterine artery pulsatility index (UtA‐PI) were those presented in previous studies. A folded‐plane regression model was fitted in the dataset of this study to describe the likelihood of PlGF. We compared the prediction of screening by maternal risk factors against the prediction provided by a combination of maternal risk factors, EFW, UtA‐PI and PlGF. We also examined the additive value of PlGF in a policy that uses maternal risk factors, EFW and UtA‐PI. Results The study population included 40 241 singleton pregnancies. Overall, the prediction of SGA improved with increasing degree of prematurity, with increasing severity of smallness and in the presence of coexisting pre‐eclampsia. The combination of maternal risk factors, EFW, UtA‐PI and PlGF improved significantly the prediction provided by maternal risk factors alone for all the examined cut‐offs of birth weight and gestational age at delivery. Screening by a combination of maternal risk factors and serum PlGF improved the prediction of SGA when compared to screening by maternal risk factors alone. However, the incremental improvement in prediction was decreased when PlGF was added to screening by a combination of maternal risk factors, EFW and UtA‐PI. If first‐line screening for a SGA neonate with birth weight < 10th percentile delivered at < 37 weeks' gestation was by maternal risk factors and EFW, the same detection rate of 90%, at an overall false‐positive rate (FPR) of 50%, as that achieved by screening with maternal risk factors, EFW, UtA‐PI and PlGF in the whole population can be achieved by reserving measurements of UtA‐PI and PlGF for only 80% of the population. Similarly, in screening for a SGA neonate with birth weight < 10th percentile delivered at < 30 weeks, the same detection rate of 90%, at an overall FPR of 14%, as that achieved by screening with maternal risk factors, EFW, UtA‐PI and PlGF in the whole population can be achieved by reserving measurements of UtA‐PI and PlGF for only 70% of the population. The additive value of PlGF in reducing the FPR to about 10% with a simultaneous detection rate of 90% for a SGA neonate with birth weight < 3rd percentile born < 30 weeks, is gained by measuring PlGF in only 50% of the population when first‐line screening is by maternal factors, EFW and UtA‐PI. Conclusions The combination of maternal risk factors, EFW, UtA‐PI and PlGF provides effective second‐trimester prediction of SGA. Serum PlGF is useful for predicting a SGA neonate with birth weight < 3rd percenti...

Competing Risks Model for Prediction of Small for Gestational Age Neonates and the Role of Second Trimester Soluble Fms-like Tyrosine Kinase-1

“…Therefore, we did not examine the performance of sFlt-1 after 32 weeks. The sFlt-1/PlGF ratio likelihood had a similar structure to the one that was fitted for PlGF in a previous study [14]. Measurement of sFlt-1 did not improve the prediction of SGA (<10th percentile or <3rd percentile), with or without PE, and delivery at <32 weeks' gestation provided by maternal factors alone, at a fixed false positive rate of 10% (Table 3).…”

“…We have previously demonstrated that the model is stable and consistent by an internal validation process [7][8][9]12]. Additionally, applying the inferences for the model's parameters in datasets different than the one that had been used to obtain them has shown that our approach is effective when applied in a new case [14]. We acknowledge the need for external validation of our approach.…”

“…The recently developed competing risks approach for the prediction of SGA is based on the personalized joint distribution of Z BW and GA Delivery [7][8][9][10][11][12][13][14]. We combined the prior joint distribution of Z BW and GA Delivery with the likelihoods of the biochemical markers, according to Bayes' theorem, to obtain a pregnancy-specific joint posterior distribution that allows the calculation of risk for any chosen cut-off for Z BW and GA Delivery .…”

“…We converted PlGF and sFlt-1 to multiples of the median (MoM) values, as previously described [8][9][10][11][12][13][14]. We calculated the ratio sFlt-1 MoM to PlGF MoM, and we log 10 transformed it to approximate a Gaussian distribution.…”

“…Small for gestational age (SGA) fetuses/neonates are characterized by increased risk for stillbirth, morbidities and adverse outcomes that can be substantially reduced if the condition is identified antenatally [1][2][3][4][5][6]. We have recently presented a new approach in SGA prediction that considers SGA a spectrum condition that is reflected in two dimensions: gestational age at delivery (GA Delivery ) and Z score in birth weight for gestational age (Z BW ) [7][8][9][10][11][12][13][14]. This new method is a model for the joint distribution of GA Delivery and Z BW that uses the same traditional maternal factors and the already known biomarkers of impaired placentation but in a radically different new way.…”