Objective
The objective of the study was to combine early, direct assessment of the placenta with indirect markers of placental development to identify pregnancies at greatest risk of delivering small-for-gestational age infants (SGA10).
Study Design
We prospectively collected 3-dimensional ultrasound volume sets, uterine artery pulsatility index, and maternal serum of singleton pregnancies at 11-14 weeks. Placental volume (PV), quotient (placental quotient [PQ] = PV/gestational age), mean placental diameter (MPD) and chorionic diameters, and the placental morphology index (PMI = MPD/PQ and adjusts the lateral placental dimensions for quotient) were measured offline. Maternal serum was assayed for placental growth factor and placental protein-13. These variables were evaluated as predictors of SGA10.
Results
Of the 578 pregnancies included in the study, 56 (9.7%) delivered SGA10. SGA10 pregnancies had a significantly smaller PV, PQ, MPD, and mean placental diameter and higher PMI compared with normal pregnancies ( P < .001 for each). Each placental measure remained significantly associated with SGA10 after adjusting for confounders and significantly improved the performance of the model using clinical variables alone ( P < .04 for each) with adjusted areas under the curve ranging from 0.71 to 0.74. Uterine artery pulsatility index did not remain significantly associated with SGA10 after adjusting for confounders ( P = .06). Placental growth factor was significantly lower in SGA10 pregnancies ( P = .02) and remained significant in adjusted models but failed to significantly improve the predictive performance of the models as measured by area under the curve ( P > .3). Placental protein-13 was not associated with SGA10 ( P = .99).
Conclusion
Direct assessment of placental size and shape with 3-dimensional ultrasound can serve as the foundation upon which to build a multivariable model for the early prediction of SGA.
Intrauterine growth restriction (IUGR) is a significant contributor to perinatal morbidity and mortality, including intrauterine fetal demise, newborn encephalopathy, and cerebral palsy and may have an adverse impact on long-term health outcomes such as cardiovascular disease. Several studies have indicated, however, that routine prenatal care fails to detect the vast majority of IUGR cases prior to delivery, preventing clinicians from instituting appropriate fetal surveillance aimed at improving outcomes. In addition, although there are no effective interventions shown to prevent IUGR, any candidate intervention would likely be more effective if implemented earlier in pregnancy to those at greatest risk.
The placenta serves as the key to the transfer of oxygen and nutrition to the fetus. In addition, placental size and shape at delivery are strongly correlated with newborn birthweight. Nevertheless, there are no standard, validated approaches to evaluating antenatal placental growth during pregnancy because the routine sonographic evaluation of the placenta focuses mainly on its location relative to the internal cervical os.
Advances in 3-dimensional (3D) ultrasound technology have allowed for noninvasive measurement of the placental volume. In fact, early placental volume has been shown to be significantly associated with IUGR and preeclampsia in several studies. Moreover, we have previously published pilot data that demonstrated how the relative contributions of both lateral placental growth and placental thickness to the placental volume may provide an enhanced assessment of early placental development and may even improve prediction of adverse pregnancy outcomes such as small-for-gestational-age (SGA). Therefore, we set out to further explore the ability of 3D ultrasonographic evaluation of the early placenta to identify pregnancies at greatest risk of IUGR.
In addition, although 3D ultrasound can be used to directly evaluate gross placental size and shape, there are elements of early placental development for which indirect markers may be better suited to evaluate. For example, uterine artery Doppler (UtAD) velocimetry measures the resistance to flow into the uterus, which has had a significant impact from effective trophoblastic invasion and remodeling of the maternal vasculature into a low-resistance system.
Investigational maternal serum markers may capture other critical components of early placental development such as placental angiogenesis and placental implantation. For example, placental growth factor (PlGF), a member of the vascular endothelial growth factor subfamily, is expressed by trophoblasts and exerts angiogenic effects on the developing placenta and its environment. Placental protein 13 (PP13), a galectin expressed by the placenta, binds to proteins in the extracellular matrix at the placenta-endometrium interface and assists in placental implantation and maternal artery remodeling. In fact, first-trimester serum concentrations of both of these serum markers are significantly decreased in pregnancies destined to develop complications such as preeclampsia.
The objective of this study was to develop a multivariable screening model combining direct and indirect markers of early placental development that can accurately identify pregnancies at increased risk of developing SGA in pregnancy.
Materials and Methods
In this prospective cohort study, women carrying singleton pregnancies who presented at 11-14 weeks’ gestation for nuchal translucency screening at the Hospital of the University of Pennsylvania were recruited and consented during their genetic counseling session according to an institutional review board-approved protocol (no. 811129). Singleton gestations with available 3D volume sets, maternal serum, and obstetric outcome data were included in this analysis. Exclusion criteria included multiple gestations, patients presenting after 14 weeks, and patients delivering outside of our institution.
Ultrasound techniques
Enrolled subjects had a 3D volume sweep of the placenta obtained transabdominally (4-8 MHz probe; GE Voluson Expert; GE Healthcare, Milwaukee, WI) during their nuchal translucency examination. Sonographers were instructed to maximize their sweep angle and sector width and use the Max sweep quality setting (ie, slower sweep speed) to ensure the sweep included the entire placental mass at high resolution. The volume dataset was stored on external hard drives for offline analysis. The fetal crown-rump length was also recorded to confirm the gestational age. Pregnancies without a known last menstrual period (LMP) date or whose LMP was 7 or more days discrepant from the ultrasound dating were redated to reflect the crown-rump length. Finally, bilateral UtAD velocimetry was performed by identifying the sagittal view of the cervix, gradually moving the transducer laterally to each side, identifying the uterine artery with color Doppler as it crossed the iliac vessels, and then interrogating the vessel to obtain the pulsatility index (PI) as a measure of downstream vascular resistance. The mean PI was used for the analyses.
Each of the sonographers taking part in this study were previously trained and certified in the performance of UtAD techniques as part of a prior multicentered cohort study (Preterm birth in nulliparous women: an understudied population at great risk-U10, Eunice Kennedy Shriver National Institute of Child Health and Human Development; ClinicalTrials.gov [no. NCT01322529 ]).
The stored placenta volume sets were manipulated offline using 4DVIEW (GE Healthcare, Vienna, Austria) by a single investigator (N.S.), who was blinded to pregnancy outcome and using previously described techniques. Briefly, placental volume (PV) was measured using virtual organ computer-aided analysis to trace the outline of the object of interest in successive planes obtained by rotating the object around the y-axis at 30° rotational intervals. The software then renders the structure and calculates the estimated volume ( Figure 1 , A). The placental quotient (PQ) was calculated to normalize the PV to gestational age (PQ = PV/days of gestation).
Next, to quantify the lateral placental dimensions, we obtained 4 measurements of the maternal placental surface evenly spaced around the circumference by: centering the placenta in all 3 orthogonal planes, measuring the traced length of the uterine-placental interface in the A and B planes to obtain yielding 2 orthogonal placental diameters, rotating the placenta 45° around the y-axis and repeating the 2 measurements ( Figure 1 , B). Thus, the mean placental diameter (MPD), the average of these 4 diameters, represents the lateral placental dimensions and approximates the gross surface area of the myometrial-placental interface.
We then calculated the placental morphology index (PMI = MPD/PQ), which quantifies the contribution of the lateral placental dimensions to the overall placental mass. Thus, the higher the PMI, the greater the relative contribution of the lateral placental dimensions compared with that of the placental thickness. On the other hand, a lower PMI signifies a more significant contribution of placental thickness to the overall placental mass.
Because there are data indicating the importance of the morphology and surface vasculature of the chorionic plate (the fetal surface of the placenta), we also obtained 4 evenly spaced measurements of the diameter of the fetal surface of the placenta to obtain a mean chorionic diameter (MCD) using the same rotational approach mentioned above ( Figure 1 ).
Serum markers
During the same patient encounter at 11-14 weeks’ gestation, 5 mL of maternal blood was drawn and centrifuged (1200 × g ) at room temperature for 10 minutes. The collected serum was stored at –80°C until analysis. Thawed serum was then assayed for 2 serum markers involved in early trophoblastic development, PlGF and PP13. Serum concentrations of PlGF and PP13 were measured in duplicate using commercially available ELISA kits (PlGF: R&D Systems, Inc, Minneapolis, MN; PP13: BlueGene Biotech, Shanghai, China) and analyzed as the multiple of the median for each gestational age week. Multiples of the median values for each serum biomarker were based on the median serum concentration from the study cohort.
Demographic variables and pregnancy outcomes
Demographic and outcome variables were extracted from the electronic medical record. The variables of interest included maternal age, ethnicity, prepregnancy body mass index (BMI), parity, medical comorbidities, gestational age at delivery, mode of delivery, birthweight, and birthweight percentile.
Statistical analysis
Placental ultrasound variables and serum markers were analyzed as potential predictive markers of adverse pregnancy outcome. The primary outcome of interest was birthweight of the 10th percentile or less (SGA10). SGA less than the fifth percentile (SGA5) served as a secondary outcome. Pregnancies were included in the appropriate for gestational age (AGA) group if the birthweight percentile was greater than the SGA outcome being analyzed.
The distributions of discrete variables were characterized by proportions and compared by Pearson χ 2 or exact methods, as appropriate. The Student t test (for normally distributed data) or Mann-Whitney U test (for ordinal or nonnormally distributed variables) were used to compare continuous variables. Receiver-operator characteristic curves were used for each significant variable and the area under the curve (AUC) served as a reflection of the overall ability of the variable to discriminate between pregnancies with an adverse outcome and those without. The AUC of individual measures as well as combinations of markers were compared using the z-statistic to test the equivalence of 2 AUCs derived from the same study subjects.
Finally, bootstrapping techniques with 1000 replications were performed to internally validate the performance of the models by estimating 95% confidence intervals for the AUCs. Data analysis was performed using STATA (version 12; StataCorp, College Station, TX).
Using previously published data involving our institution, we estimated the prevalence of SGA to be 14%. Thus, to be powered to a sensitivity of 70% (+/- 10%) and a type I error of 0.05, we needed to include 577 subjects in the analysis.
Results
Of the 578 pregnancies analyzed, 56 (9.7%) resulted in SGA10 and 28 (4.8%) SGA5. As seen in Table 1 , mean maternal age, BMI, and nulliparity were not significantly associated with SGA10, but the black and Asian races and the presence of chronic hypertension were significantly more represented in the SGA10 group compared with AGA pregnancies. In addition, there was a trend toward a higher prevalence of tobacco use among those with SGA10.
| Variable | Not SGA10 (n = 522) | SGA10 (n = 56) | P value | Not SGA5 (n = 550) | SGA5 (n = 28) | P value |
|---|---|---|---|---|---|---|
| Age, y, mean (SD) | 30.8 (5.8) | 29.6 (5.8) | .12 | 30.8 (5.8) | 29.6 (6.1) | .33 |
| Race, n, % | .016 | .086 | ||||
| Black | 208 (39.9) | 26 (46.4) | 218 (39.6) | 16 (57.1) | ||
| White/other | 264 (50.6) | 19 (33.9) | 275 (50) | 8 (28.6) | ||
| Asian | 50 (9.6) | 11 (19.6) | 57 (10.4) | 4 (14.3) | ||
| BMI, mean (SD) | 27.1 (6.8) | 26.2 (7.7) | .36 | 26.9 (6.8) | 28.5 (9.0) | .24 |
| Nulliparity, n, % | 97 (18.6) | 11 (19.6) | .85 | 102 (18.6) | 6 (21.4) | .7 |
| CHTN, n, % | 33 (6.3) | 9 (16.1) | .008 | 39 (7.1) | 3 (10.7) | .47 |
| Tobacco use, n, % | 48 (9.2) | 9 (16.7) | .1 | 50 (8.1) | 1 (25) | .006 |
Placental measures
Table 2 shows that PV, PQ, MPD, and MCD were all significantly smaller in SGA10 compared with AGA, indicating that a smaller placental mass is a risk factor for SGA. On the other hand, PMI was significantly larger in SGA10 cases, indicating that a relatively wider and flatter placenta was more closely associated with impaired growth compared with a relatively thicker placenta. These associations remained significant after adjusting for confounders ( Table 2 ).
| Variable | Not SGA10 (n = 522) | SGA10 (n = 56) | P value a | Unadjusted AUC (95% CI) | Adjusted AUC (95% CI) b | P value c |
|---|---|---|---|---|---|---|
| PV, mL | 69.8 (22) | 55.5 (17) | < .001 | 0.695 (0.625–0.766) | 0.743 (0.678–0.808) | .004 |
| PQ | 0.79 (0.2) | 0.63 (0.2) | < .001 | 0.697 (0.627–0.767) | 0.742 (0.677–0.807) | .004 |
| MPD, cm | 11.4 (1.4) | 10.6 (1.6) | < .001 | 0.632 (0.556–0.707) | 0.705 (0.634–0.777) | .04 |
| PMI | 15.2 (3.0) | 17.6 (3.4) | < .001 | 0.711 (0.644–0.779) | 0.740 (0.673–0.807) | .005 |
| MCD, cm | 8.3 (1.0) | 7.6 (0.8) | < .001 | 0.688 (0.621–0.754) | 0.736 (0.671–0.801) | .003 |
| Mean PI | 1.45 (0.5) | 1.64 (0.6) | .01 | 0.614 (0.532–0.696) | 0.6883 (0.619–0.758) | .064 |
b Adjusted for race, chronic hypertension, and tobacco
c P value comparing the AUC for clinical model (0.652) with the adjusted model using clinical factors plus the ultrasound variable
Receiver-operator characteristic analysis was used to examine the ability of our models and individual markers to discriminate pregnancies with SGA from pregnancies with appropriately sized infants. A clinical model including the clinical variables alone (ie, race, chronic hypertension, and tobacco use) yielded an AUC of 0.652 for predicting SGA10. Individual analyses of each placental measure yielded AUCs ranging from 0.63 for MPD to 0.71 for PMI ( Table 2 ). Importantly, the addition of any placental measure to the background clinical model significantly increased the AUC ( P ≤ .04 for each placental measure).
To compare the test characteristics for each sonographic measure, we identified the cutoff point for each variable that would yield a specificity of approximately 80% (ie, false-positive rate of approximately 20%). Table 3 shows the resulting relative risks and test characteristics for each sonographic measure using the chosen cutoff point. Overall, PMI yielded the highest relative risk (3.3; 95% confidence interval [CI], 2.0–5.3) and sensitivity (50.0%; 95% CI, 36.5–63.5%) for predicting SGA10.
| Characteristic | Cutoff point a | Relative risk (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) | PPV (95% CI) | NPV (95% CI) |
|---|---|---|---|---|---|---|
| PV, cc | ≤51.0 | 2.6 (1.6–4.3) | 42.9% (30.0–56.7) | 80.1% (76.3–83.4) | 18.8% (12.6–26.8) | 92.9% (90.0–95.0) |
| PQ | ≤0.59 | 2.6 (1.6–4.2) | 41.1% (28.4–55.0) | 80.8% (77.1–84.1) | 18.7% (12.5–26.9) | 92.7% (89.9–94.9) |
| MPD, cm | ≤10.0 | 1.8 (1.0–3.0) | 30.4% (19.2–44.3) | 81.2% (77.6–84.4) | 14.8% (9.1–22.9) | 91.6% (88.6–93.9) |
| PMI | ≥17.4 | 3.3 (2.0–5.3) | 50.0% (36.5–63.5) | 79.5% (75.7–82.8) | 20.7% (14.4–28.7) | 93.7% (90.9–95.7) |
| MCD, cm | ≤7.5 | 2.5 (1.6–4.2) | 42.9% (30.0–56.7) | 79.3% (75.5–82.7) | 18.2% (12.2–26.0) | 92.8% (89.9–95.0) |
| Mean PI | ≥1.88 | 1.9 (1.2–3.2) | 35.7% (23.7–49.7) | 80.0% (76.3–83.3) | 16.3% (10.4–24.2) | 92.0% (89.0–94.2) |
a Cutoff point refers to the value of the ultrasound parameter below or above which the screening test was deemed positive.
When examining SGA5, the only significant clinical variables were race and tobacco use ( Table 1 ). A logistic model with these 2 clinical factors yielded an AUC of 0.686. Once again, each of the placental measures was significantly associated with SGA5, even after adjusting for these confounders. Also, the addition of each placental measure to the clinical model yielded significantly higher AUCs compared with the clinical model alone ( P ≤ .04) with the highest adjusted AUC being for MCD (0.804) ( Table 4 ). Once again, PMI yielded the highest relative risk (3.7; 95% CI, 1.8–7.6) and sensitivity (50%; 95% CI, 31.1–68.9) at approximately 80% specificity, although MCD and PQ performed similarly ( Table 5 ).
| Variable | Not SGA (n = 550) | SGA (n = 28) | P value a | Unadjusted AUC (95% CI) | Adjusted AUC (95% CI) b | P value c |
|---|---|---|---|---|---|---|
| PV, mL | 69.3 (22) | 52.6 (16) | < .001 | 0.725 (0.636–0.814) | 0.793 (0.723–0.862) | .02 |
| PQ | 0.78 (0.2) | 0.6 (0.2) | < .001 | 0.733 (0.646–0.820) | 0.797 (0.728–0.866) | .02 |
| MPD, cm | 11.4 (1.6) | 10.2 (1.7) | .001 | 0.688 (0.589–0.784) | 0.784 (0.716–0.852) | .04 |
| PMI | 15.3 (3.0) | 17.9 (3.6) | < .001 | 0.724 (0.635–0.814) | 0.776 (0.698–0.853) | .03 |
| MCD, cm | 8.3 (1.0) | 7.4 (0.8) | < .001 | 0.734 (0.652–0.816) | 0.804 (0.734–0.874) | .003 |
| Mean PI | 1.46 (0.5) | 1.62 (0.6) | .14 | 0.598 (0.487–0.708) | 0.717 (0.628–0.807) | .28 |
b Adjusted for race and tobacco
c P value comparing the AUC for clinical model (0.686) with the adjusted model using clinical factors plus the ultrasound variable.
| Characteristic | Cutoff point a | Relative risk (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) | PPV (95% CI) | NPV (95% CI) |
|---|---|---|---|---|---|---|
| PV, mL | ≤51.0 | 3.0 (1.5–6.2) | 46.4% (28.0–65.8) | 79.1% (75.4–82.4) | 10.1% (5.7–17.1) | 96.7% (94.4–98.1) |
| PQ | ≤0.59 | 3.2 (1.6–6.6) | 46.4% (28.0–65.8) | 80.0% (76.4–83.2) | 10.6% (6.0–17.7) | 96.7% (94.5–98.1) |
| MPD, cm | ≤10.0 | 2.6 (1.3–5.4) | 39.3% (22.1–59.3) | 81.1% (77.5–84.2) | 9.6% (5.1–16.8) | 96.3% (94.1–97.8) |
| PMI | ≥17.7 | 3.7 (1.8–7.6) | 50.0% (31.1–68.9) | 80.2% (76.6–83.4) | 11.4% (6.6–18.7) | 96.9% (94.8–98.2) |
| MCD, cm | ≤7.40 | 3.3 (1.6–6.8) | 46.4% (28.0–65.8) | 80.5% (76.5–83.7) | 10.8% (6.1–18.1) | 96.7% (94.5–98.0) |
| Mean PI | ≥1.9 | 1.6 (0.7–3.5) | 28.6% (14.0–48.9) | 80.0% (76.4–83.2) | 6.8% (3.2–13.3) | 95.7% (93.3–97.3) |
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