Department of Obstetrics and Gynecology, GROW School of Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands,
Central Diagnostic Laboratory, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands.
Fetal Diagn Ther. 2019;46(4):274-284. doi: 10.1159/000499580. Epub 2019 May 8.
The aim of this study was to evaluate the value of adding fetal growth velocity and first trimester maternal biomarkers to baseline screening, for the prediction of small-for-gestational age (SGA) and adverse neonatal outcomes.
A retrospective cohort study was conducted of singleton pregnancies in the Maastricht University Medical Centre between 2012 and 2016. The biomarkers PAPP-A, β-hCG, PlGF, and sFlt-1 were measured at 11-13 weeks of gestational age (GA) and two fetal growth scans were performed (18-22 and 30-34 weeks of GA). Differences in biomarkers and growth velocities were compared between appropriate-for-gestational age (AGA; birth weight percentile 10-90) and SGA (birth weight percentile <10). Combinations of the biomarkers and fetal growth velocity were added to baseline screening for the prediction of SGA and adverse neonatal outcome.
We included 296 singleton pregnancies. Compared to AGA (n = 251), SGA neonates (n = 45) had significantly lower growth velocities in the abdominal circumference (mm/week): 10.1 ± 0.98 versus 10.8 ± 0.98, p = 0.001. Compared with AGA, the SGA neonates had higher sFlt-1 multiples of the median (MoM): 0.89 (0.55) versus 0.76 (0.44), p = 0.023, and a higher sFlt-1/PlGF MoM ratio: 1.09 (1.03) versus 0.90 (0.64), p = 0.027. For a 15% false-positive rate, the prediction of SGA neonates increased from 44.8% for the baseline screening model to 56.5% after the addition of fetal growth velocities, and to 73.9% after the further addition of maternal biomarkers (PPV 9.6%, NPV 82.4%). The corresponding AUC for the three models were 0.722, 0.804, and 0.839, respectively. In addition, AGA neonates with reduced fetal growth velocity had more adverse neonatal outcomes compared to the AGA reference group (12.4 vs. 3.9%, p = 0.013).
Combining fetal growth velocity with first trimester biomarkers resulted in a better prediction of SGA compared to baseline screening parameters alone. This approach could possibly result in reduced adverse neonatal outcomes in neonates, who are at a potential risk due to late mild placental dysfunction.
本研究旨在评估在基线筛查中加入胎儿生长速度和早孕期母源性生物标志物,对预测小于胎龄儿(SGA)和不良新生儿结局的价值。
对 2012 年至 2016 年期间在马斯特里赫特大学医学中心进行的单胎妊娠进行回顾性队列研究。在 11-13 孕周(GA)时测量 PAPP-A、β-hCG、PlGF 和 sFlt-1 等生物标志物,并进行两次胎儿生长扫描(18-22 周和 30-34 周 GA)。将适宜胎龄儿(AGA;出生体重百分位 10-90)与 SGA(出生体重百分位<10)的生物标志物和生长速度进行比较。将生物标志物和胎儿生长速度的组合添加到基线筛查中,以预测 SGA 和不良新生儿结局。
我们纳入了 296 例单胎妊娠。与 AGA 新生儿(n=251)相比,SGA 新生儿(n=45)的腹围生长速度明显较慢(mm/周):10.1±0.98 与 10.8±0.98,p=0.001。与 AGA 相比,SGA 新生儿的 sFlt-1 中位数倍数(MoM)更高:0.89(0.55)与 0.76(0.44),p=0.023,sFlt-1/PlGF MoM 比值更高:1.09(1.03)与 0.90(0.64),p=0.027。假阳性率为 15%时,SGA 新生儿的预测率从基线筛查模型的 44.8%增加到添加胎儿生长速度后的 56.5%,再增加到添加母源性生物标志物后的 73.9%(PPV 9.6%,NPV 82.4%)。三个模型的 AUC 分别为 0.722、0.804 和 0.839。此外,与 AGA 参考组相比,生长速度降低的 AGA 新生儿的不良新生儿结局更多(12.4%与 3.9%,p=0.013)。
与单独使用基线筛查参数相比,将胎儿生长速度与早孕期生物标志物相结合,可更好地预测 SGA。这种方法可能会减少由于晚期轻度胎盘功能障碍而处于潜在风险的新生儿的不良新生儿结局。