Objective
The purpose of this study was to determine whether a customized standard of large-for-gestational age (LGA) identifies pregnancies with increased perinatal risk.
Study Design
We evaluated 7510 estimates of fetal weight to generate a fetal growth curve. Next, we analyzed the gestational age at delivery, physiologic and pathological variables from 5072 pregnancies to predict birthweight, and calculated a customized ideal birthweight and cutoff for LGA. In a separate analysis of 32,271 pregnancies, rates of macrosomia-related adverse outcomes were compared in pregnancies that had been identified as LGA by a customized standard (LGA cust ) and those pregnancies that had been identified as LGA or macrosomic by conventional standards.
Results
LGA cust pregnancies carried increased risk of shoulder dystocia, third- or fourth-degree laceration, and cephalopelvic disproportion. LGA cust pregnancies that did not meet conventional criteria for LGA/macrosomia were at increased risk of all measured outcomes.
Conclusion
A customized standard of LGA identifies a previously unrecognized population that is at increased risk of perinatal morbidity.
With increasing birthweight, the risk of dysfunctional labor, shoulder dystocia, birth trauma, and genital tract injury increases. Published evidence consistently demonstrates an increased risk of perinatal and neonatal morbidity with delivery of a macrosomic or large-for-gestational-age (LGA) infant. Nonetheless, a universally accepted definition of macrosomia and a clinical standard of care for treatment of the macrosomic fetus remain elusive.
For Editors’ Commentary, see Table of Contents
See related editorial, page 457
Increased rates of obstetric complications have been reported with delivery of an LGA infant and with birthweights that exceed 4000, 4500, or 5000 g. Conway and Langer have proposed divergent management schemes for diabetic pregnancies that are based on estimated fetal weight (EFW) at 37 weeks’ gestation, with expectant management of pregnancies with appropriate fetal growth, prophylactic cesarean delivery of infants with an EFW of >4250 g, and induction of labor if the EFW is >90th percentile but <4250 g. National practice guidelines state that 4500 g is the appropriate threshold for the designation of fetal macrosomia but suggest that prophylactic cesarean delivery should be reserved for pregnancies with an EFW that exceeds 5000 g. These disparate criteria reflect the lack of a universally accepted standard of excessive fetal growth.
Customized growth curves that are adjusted to reflect individual factors such as maternal height, weight, race, parity, and fetal sex, have been purported to improve the ability to identify abnormal fetal growth and pregnancies that are at increased risk of adverse outcomes related to growth restriction. The British Royal College of Obstetricians and Gynaecologists has recognized customized growth curves as the preferred clinical standard. The usefulness of customized growth standards in the identification of pregnancies that are at increased risk of morbid outcomes associated with delivery of a large infant has not been investigated thoroughly. The purpose of this retrospective study was to determine whether the use of a customized birthweight standard could improve the ability of caregivers to identify pregnancies at increased risk of macrosomia-related morbidity.
Materials and Methods
All data were collected from the Magee Obstetric Medical and Infant database. The Magee Obstetric Medical and Infant database, established in 1995, routinely collects comprehensive maternal, fetal, and neonatal outcomes from electronic and medical record data on all women who deliver at Magee-Womens Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA. The database is surveyed periodically at random to maintain its accuracy by direct comparison with patient charts. Personal identifying information in the database was eliminated to ensure confidentiality.
Determination of intrauterine growth standard
A subset of subjects included in the Magee Obstetric Medical and Infant database had ultrasound data available for analysis. We performed a retrospective analysis of 7510 EFWs that were obtained during clinically indicated sonograms in pregnancies that resulted in delivery of a live singleton at term at Magee-Womens Hospital between March 2004 and May 2007. All pregnancies that were analyzed to generate an intrauterine growth standard were dated by either first-trimester or mid-trimester ultrasound scanning that corroborated dating based on the last menstrual period. EFWs obtained during the sonogram that established or confirmed pregnancy dating were not used for purposes of analysis. Thus, all subjects whose data were analyzed had a minimum of 2 sonograms that were performed during their pregnancy, and all EFWs analyzed were obtained from ultrasound scans performed on subjects with previously dated pregnancies. We used the formula of Hadlock et al based on abdominal circumference and head circumference to calculate 97.8% of the estimates of fetal weight. If a head circumference could not be obtained, EFW was calculated with Hadlock’s formula based on abdominal circumference and femur length. We excluded subjects with diabetes mellitus (gestational or pregestational), chronic hypertension, gestational hypertension, preeclampsia, or congenital anomalies. The intent of excluding these pregnancies and those pregnancies that resulted in preterm delivery was to develop a standard of fetal growth in the nonpathologic pregnancy. The demographic characteristics of the population evaluated are listed in Table 1 . With the use of polynomial regression, the gestational age (GA) at the time of ultrasound scan was used to predict the log-transformation of EFW.
| Characteristic | Measurement |
|---|---|
| Maternal age at ultrasound scanning, y a | 29.8 ± 6.2 |
| Gestational age at ultrasound scanning, wk a | 32.2 ± 4.5 |
| Body mass index, kg/m 2 a | 25.4 ± 6.6 |
| Parity, n (%) | |
| 0 | 3312 (44.1) |
| 1 | 2565 (34.2) |
| 2 | 1044 (13.9) |
| 3 | 377 (5.0) |
| ≥4 | 212 (2.8) |
| Male fetus, n (%) | 3719 (49.6) |
| Race, n (%) | |
| White | 5654 (75.3) |
| African American | 1487 (19.8) |
| Other | 369 (4.9) |
| Self-reported tobacco use, n (%) | 1140 (15.2) |
| Self-reported cocaine use, n (%) | 48 (0.6) |
| Self-reported marijuana use, n (%) | 71 (1.0) |
Determination of customized birthweight standards
Following methods established by Gardosi et al, multivariable linear regression was used to generate a predictive model of birthweight, using demographic and clinical variables as covariates. Five thousand seventy-two women who delivered live, full-term singleton infants without congenital anomalies at Magee-Womens Hospital between March 2004 and May 2007 were evaluated. The demographic characteristics of this population are listed in Table 2 . Information was extracted from the same database that was used to generate a fetal growth curve; as a consequence, there is overlap between the populations that were used for these analyses. While subjects with diabetes mellitus, hypertension, or preeclampsia were not excluded from birthweight analysis, subjects with missing data for any of the variables analyzed were excluded. Physiologic variables (maternal height, weight, parity, race, fetal sex, and GA at delivery) and pathologic factors (smoking history, illicit drug use, diabetes mellitus, and hypertension/preeclampsia) were included. Covariates were selected by backward elimination with a significance level of .05.
| Characteristic | Measurement |
|---|---|
| Maternal age, y a | 29.9 ± 6.1 |
| Gestational age at delivery, wk a | 39.1 ± 1.2 |
| Body mass index, kg/m 2 a | 25.6 ± 6.4 |
| Parity, n (%) | |
| 0 | 2438 (48.1) |
| 1 | 1655 (32.6) |
| 2 | 658 (13.0) |
| 3 | 215 (4.2) |
| ≥4 | 106 (2.1) |
| Male fetus, n (%) | 2559 (50.5) |
| Race, n (%) | |
| White | 3946 (77.8) |
| African American | 864 (17.0) |
| Native American | 12 (0.2) |
| Hispanic | 14 (0.3) |
| Asian | 121 (2.4) |
| Other | 103 (2.0) |
| Self-reported tobacco use, n (%) | 698 (13.8) |
| Self-reported cocaine use, n (%) | 26 (0.5) |
| Self-reported marijuana use, n (%) | 43 (0.9) |
| Pregnancy-related hypertension, n (%) | |
| Gestational hypertension | 300 (5.9) |
| Mild preeclampsia | 225 (4.4) |
| Severe preeclampsia | 22 (0.4) |
| Eclampsia | 2 (0.04) |
| Superimposed preeclampsia | 23 (0.5) |
| Diabetes mellitus, n (%) | |
| Class A1/A2 | 375 (7.4) |
| Class B | 41 (0.8) |
| Class C | 14 (0.3) |
| Class D | 6 (0.1) |
| Class R | 4 (0.1) |
| Class F | 1 (0.02) |
After a predictive model of birthweight had been generated, an ideal birthweight for 40 weeks’ GA was calculated, based exclusively on nonpathologic variables (maternal height, weight, parity, race, and fetal sex) and a standardized GA of 40 weeks. With the calculation of an ideal birthweight at term, a cutoff value for 10th and 90th percentile was generated. These values were based on the standard deviation/mean birthweight (coefficient of variance) multiplied by the Z score for 10th and 90th percentile, –1.28 and 1.28, respectively. The mean birthweight for the study population was 3417 g, with an SD of 485 g, which yielded a coefficient of variance of 0.14. The resulting formula was used for the calculation of 10th and 90th percentile: ideal birthweight at term ± (ideal birthweight at term × 1.28 × 0.14).
Once ideal birthweight at term and the corresponding 10th and 90th percentile were calculated, the ideal birthweight and corresponding cutoff for 10th and 90th percentile could be extrapolated for any GA, using the intrauterine growth curve previously generated. With this method, a customized ideal birthweight and a customized standard for suboptimal or excessive growth could be generated for any pregnancy at any GA.
Morbidity in customized vs population-based classification of LGA
After generating a model of customized fetal growth, we compared morbid outcomes relating to macrosomia in pregnancies that were classified as LGA using customized vs conventional population-based birthweight standards. A separate population of 32,271 pregnancies that delivered at Magee-Womens Hospital from 2003-2008, distinct from that used to generate a fetal growth curve or customized birthweight standard, was used for this analysis. The demographic composition of this population is listed in Table 3 . This population was meant to represent a general obstetric population and included all women who delivered live-born infants with available records that were not included in previous analyses. We did not exclude subjects with multiple gestations, congenital anomalies, or preterm delivery. We classified pregnancies as LGA cust if the birthweight was greater than the customized cutoff for the 90th percentile. Pregnancies were classified as LGA pop if the recorded birthweight was higher than the 90th percentile for GA according to the national standards published by Alexander et al. Multivariate logistic regression was used to compare the risk of shoulder dystocia, cesarean section delivery for cephalopelvic disproportion (CPD), and third- or fourth-degree laceration in pregnancies that were classified as LGA pop and LGA cust and in pregnancies with birthweights in excess of 4000 or 4500 g. Covariates were selected by backward elimination with a significance level of .05.
| Characteristic | Measurement |
|---|---|
| Maternal age, y a | 29.4 ± 6.1 |
| Gestational age at delivery, wk a | 38.5 ± 2.29 |
| Pregravid body mass index, kg/m 2 a | 25.1 ± 5.8 |
| Cesarean delivery, n (%) | 8554 (26.5) |
| Parity, n (%) | |
| 0 | 14,632 (45.3) |
| 1 | 10,557 (32.7) |
| 2 | 4590 (14.2) |
| 3 | 1564 (4.9) |
| ≥4 | 928 (2.9) |
| Male fetus, n (%) | 16,636 (51.6) |
| Race, n (%) | |
| White | 24,781 (76.8) |
| African American | 5763 (17.9) |
| Native American | 194 (0.6) |
| Hispanic | 108 (0.3) |
| Asian | 509 (1.6) |
| Other | 635 (2.0) |
| Self-reported tobacco use, n (%) | 4682 (14.5) |
| Self-reported cocaine use, n (%) | 295 (0.9) |
| Self-reported marijuana use, n (%) | 429 (1.3) |
| Pregnancy-related hypertension, n (%) | |
| Gestational hypertension | 1718 (5.3) |
| Mild preeclampsia | 1325 (4.1) |
| Severe preeclampsia | 446 (1.4) |
| Eclampsia | 20 (0.06) |
| Superimposed preeclampsia | 179 (0.6) |
| Diabetes mellitus, n (%) | |
| Class A1/A2 | 1368 (4.2) |
| Class B | 140 (0.4) |
| Class C | 54 (0.2) |
| Class D | 27 (0.08) |
| Class R | 18 (0.06) |
| Class F | 4 (0.01) |
| Multiple gestation, n (%) | 761 (2.36) |
| Congenital anomaly, n (%) | 1130 (3.50) |
Stay updated, free articles. Join our Telegram channel
Full access? Get Clinical Tree
