Abstract
Purpose
The objective of this retrospective cohort study was to compare fetal growth during the second and third trimesters for ovulation induction with intrauterine insemination (IUI), fresh embryo transfer (ET), frozen embryo transfer (FET), and spontaneous conception following infertility.
Methods
Three hundred ninety-five women with viable pregnancies confirmed at a single academic fertility center participated. All women achieved pregnancy either by treatment or spontaneously after a diagnosis of infertility. Inclusion criteria included autologous singleton pregnancies. Exclusion criteria included pregnancies from donor oocytes, twins, unavailable ultrasound data, and treatment methods with small number of participants. Primary outcomes of interest were head circumference (HC), abdominal circumference (AC), HC/AC ratio, and estimated fetal weight (EFW). Conditional growth curve models were created, and growth curves were selected for each outcome of interest.
Results
For ovulation induction with IUI, fresh ET, FET, and spontaneous conception, the slope analysis of growth curves for per-week growth rate of HC, AC, HC/AC ratio, and EFW demonstrated no difference. A subgroup analysis of fresh ET and FET groups, for same outcomes, also showed no difference.
Conclusion
These findings contribute to the very limited literature on fetal growth trajectories following infertility treatment and suggest no significant differences in fetal growth for ovulation induction with IUI, fresh ET, FET, and spontaneous conception following infertility. It is possible there were no differences in growth trajectories between these conception methods because the majority of children born following infertility are of normal birth weight. While results are reassuring, further research with larger populations is warranted.
Electronic supplementary material
The online version of this article (10.1007/s10815-020-01785-8) contains supplementary material, which is available to authorized users.
Keywords: Assisted reproductive technology, Fetal growth, Embryo transfer, Spontaneous conception
Introduction
Birth weight is a powerful predictor of childhood and chronic disease [1–4]. Although many children born after infertility treatment are of normal weight, singletons conceived after assisted reproductive technology (ART) are at increased risk for low birth weight compared with the general population [3–8]. Recent literature suggests some association between ART methods and birth weight. For example, singletons conceived after in vitro fertilization (IVF) by fresh embryo transfer (ET) are observed to have lower mean birth weights than their non-IVF counterparts, while those conceived by frozen embryo transfer (FET) are less likely to be small and more likely to be large-for-gestational age [9–16]. For many reasons, use of FET has increased over time. It is hypothesized that FET cycles more closely resemble spontaneous conception [9, 13] and that embryos surviving the freezing and thawing process of FET may also be of superior quality, compared with fresh ET [13].
Still, little is known about fetal growth during pregnancies conceived after infertility. Limited data available have focused on first trimester growth [17, 18]. One study, to date, has assessed growth during the second and third trimesters for different modes of conception [19].
To our knowledge, no study has yet compared fetal growth kinetics during the second and third trimesters for fresh ET and FET cycles. This investigation would seem prudent to better understand the observed birth weight differences between these two IVF methods. Specifically, this study aims to compare fetal growth kinetics for ovulation induction with intrauterine insemination (IUI), fresh ET, FET, and spontaneous conception following infertility, to evaluate differences between non-IVF and IVF methods as well as a control group with history of infertility.
Our main objective was to compare ultrasound biometric measures, including head circumference (HC), abdominal circumference (AC), HC/AC ratio, and estimated fetal weight (EFW) over time. A second objective was to identify any significant differences in delivery outcomes for the different modes of conception studied.
Materials and methods
Study population
A prospective cohort of 893 women from a single academic center for fertility and reproductive health, with viable pregnancies confirmed by first trimester ultrasound, was recruited at 8 weeks of gestation and followed until delivery to study pregnancy outcomes following infertility. All subjects had achieved pregnancy between the years of 2011 and 2018, either by treatment or spontaneously after a previous diagnosis of infertility. Demographic and perinatal information was obtained for each participant throughout her pregnancy course (see Data collection for details). All participants provided informed written consent, and the study was approved by the Institutional Review Board.
For our study question, we implemented a retrospective secondary analysis of ultrasound data obtained from the above study population and included subjects with ultrasound reports available from the second and third trimesters. All ultrasounds were performed at the academic center or at an outside affiliated imaging facility. Inclusion criteria included autologous singleton pregnancies resulting in live births for the following conception methods of interest: ovulation induction with IUI, fresh ET, FET (natural and programmed cycles), and spontaneous conception after infertility. The methods for selecting FET in natural or programmed cycles have been previously described by von Versen-Höynck [17]. Autologous pregnancy was defined as pregnancy conceived through use of an individual’s own oocyte rather than a donor oocyte. Spontaneous conception after infertility was defined as conception occurring without the use of fertility medications, IUI, or IVF, among a subfertile cohort. Exclusion criteria included pregnancies from donor oocytes, twins identified by the presence of multiple gestational sacs on any ultrasound, unavailable ultrasound data (missing hardcopy reports from outside imaging facilities during transition from paper to electronic medical records), and treatment methods with small number of participants, such as IUI in a natural cycle and ovulation induction with no IUI. Given the scant number of FET stimulated cycles (ovulation induction in the context of FET), these were also excluded from final analysis.
Groups for analysis were constructed based on mode of conception.
Data collection
Electronic or paper medical records were used to obtain each participant’s baseline demographic information, including maternal weight, height, gravidity and parity, comorbidities, infertility diagnoses, conception, and ultrasound data. Study participants also completed a questionnaire at the time of consent (at the time of the first obstetric ultrasound demonstrating a viable intrauterine pregnancy). This questionnaire included maternal age, maternal weight, race, ethnicity, social habits, and past pregnancy history. Following delivery at the academic institution or another hospital, delivery outcomes were further extracted from medical records, such as gestational age at birth, fetal sex, birth weight, Apgar scores, neonatal complications, placental abnormalities, and the presence of gestational hypertension or diabetes in pregnancy.
Ultrasound data from the second and third trimesters were accessed through review of either the patient’s medical chart or the institution’s ClickView application. Gestational age at the time of each ultrasound was calculated based on conception data and initial ultrasound date. For IUI and IVF cycles, the dates of IUI and ET were used to calculate a precise gestational age. For spontaneous conceptions, dating was determined by home ovulation predictor kits or last menstrual period confirmed by an initial dating ultrasound. Formulas used to calculate gestational age can be found in Supplemental Materials.
The primary aim of our study was to examine fetal growth characteristics in the second and third trimesters of gestation. Primary outcome variables of interest were HC, AC, HC/AC ratio, and EFW. Secondary outcome variables of interest were delivery outcomes.
Statistical analysis
All data was entered into a REDCap database system, which was hosted by the academic institution [20]. All analyses were conducted using R 3.5.0 [21].
A conditional growth curve modeling approach was used to estimate the effect of different conception methods on growth rates for different ultrasound biometrics, including HC, AC, HC/AC ratio, and EFW. Of these, EFW was considered a key outcome of interest.
Gestational age was centered at 20 weeks based on visual inspection of the data. Linear, quadratic, and cubic growth curves were constructed for each outcome of interest. A growth curve was then chosen for each outcome based on which curve appeared to best fit the plotted data points.
First- and second-order slopes for curves were then determined. The first-order slope estimated the average constant per-week growth rate for each outcome by conception method, following adjustment for confounders—age, parity, height, weight, weight gain, hypertensive disorder, gestational diabetes, infant sex, and race—which were included as independent variables during regression analysis. The second-order slope, applicable to quadratic and cubic growth curves only, showed how the first-order growth rates changed with time. Calculated p values corresponded to whether there was any difference in slopes between the conception methods.
As a sensitivity analysis, an intergroup comparison was also performed between second trimester (18–23 weeks) EFW, third trimester (30–36 weeks) EFW, and birth weight to detect possible differences in growth rate between trimesters. Using multiple linear regression models, weight was modeled as a linear function of trimester and conception method.
For demographics and delivery data, continuous and dichotomous variables were identified. Continuous variables were expressed as a mean ± standard deviation (SD), and p values were calculated for correlation. For dichotomous variables, chi-square p values were calculated. For neonate complications and placental abnormalities, multinomial regression models were used to calculate p value correlates, using logistic or linear regression depending on the distribution of the outcome variable.
Results
Clinical characteristics and demographics
Of 893 eligible participants during the study period, 395 women met the inclusion criteria. The groups for analysis were as follows: ovulation induction with IUI (n = 69), fresh ET after controlled ovarian stimulation (n = 82), FET (n = 146) with natural (n = 76) and programmed cycles (n = 70), and spontaneous conception after infertility (n = 98).
The flowchart in Fig. 1 depicts the inclusion and exclusion process for study participants.
Fig. 1.
Flowchart depicting inclusion and exclusion process for study participants
Baseline demographics, including maternal characteristics and infertility diagnoses, were comparable between the groups with few exceptions (Table 1). Women who achieved spontaneous conception had a gravidity mean of 2, compared with 1 for other modes of conception. The lowest percentage of BMI > 30 (4%) was found in the spontaneous conception group. Participants were primarily of Asian or White race for all groups. For infertility diagnoses, individuals with diminished ovarian reserve were of highest percentage in the fresh ET group (30%) compared with other conception modes, while PCOS was of highest percentage in the IUI group (20%). Male factor was lowest in spontaneous conception (9%).
Table 1.
Demographics and infertility diagnosis
| Ovulation induction with IUI | Fresh ET | FET programmed | FET natural | Spontaneous | |
|---|---|---|---|---|---|
| n = | 69 | 82 | 70 | 76 | 98 |
| A. Demographics | |||||
| Maternal characteristics | |||||
| Age, years (mean ± SD) | 35.5 + 3.8 | 36.7 + 4.4 | 35.5 + 4.5 | 36.8 + 4.4 | 34.6 + 4.0 |
| Age > 35 (%) | 58.0 | 63.6 | 50.7 | 66.2 | 43.3 |
| Gravidity (mean ± SD) | 1.3 + 1.4 | 1.3 + 1.3 | 1.5 + 1.8 | 1.7 + 1.6 | 2.1 + 1.8 |
| Nulliparity (%) | 31.9 | 36.6 | 34.3 | 25.3 | 23.7 |
| BMI (mean ± SD) | 24.4 + 4.7 | 24.3 + 5.0 | 23.4 + 4.0 | 24.4 + 5.1 | 23.5 + 4.3 |
| BMI > 30 (%) | 13.0 | 12.4 | 10.0 | 10.5 | 4.1 |
| Height, cm (mean ± SD) | 163.5 + 6.1 | 163.6 + 8.7 | 164.7 + 6.9 | 164.6 + 6.5 | 164.2 + 7.0 |
| Weight gain in pregnancy, kg (mean ± SD) | 14.1 + 7.2 | 11.6 + 6.2 | 10.7 + 5.0 | 11.6 + 6.5 | 12.6 + 9.2 |
| Hypertension (%) | 0 | 6.1 | 2.9 | 2.6 | 4.1 |
| Diabetes (%) | 1.5 | 0.0 | 0 | 2.6 | 2.0 |
| Smoking (%) | 0.0 | 1.2 | 1.4 | 0 | 1.0 |
| Illicit drug use (%) | 2.9 | 6.1 | 1.4 | 1.3 | 6.1 |
| Race (%) | |||||
| AIAN | 1.5 | 0 | 0 | 0 | 0 |
| Asian | 49.3 | 39.0 | 52.9 | 52.6 | 35.7 |
| African American | 0 | 0 | 0 | 1.3 | 0 |
| NHPI | 1.5 | 1.2 | 0 | 0 | 0 |
| White | 37.6 | 56.1 | 39.9 | 39.5 | 55.1 |
| Other | 10.1 | 3.7 | 2.9 | 5.3 | 8.2 |
| Unknown | 0 | 0 | 4.3 | 1.3 | 1.0 |
| B. Infertility diagnosis (%) | |||||
| Advanced maternal age | 0 | 7.3 | 2.9 | 5.3 | 1.0 |
| Diminished ovarian reserve | 15.9 | 30.5 | 22.9 | 17.1 | 10.2 |
| Male factor | 13.0 | 34.2 | 34.3 | 30.3 | 9.2 |
| PCOS | 20.3 | 6.1 | 15.7 | 5.3 | 12.2 |
| Other ovulation disorder | 4.4 | 6.1 | 7.1 | 1.3 | 6.1 |
| Tubal | 1.5 | 9.8 | 5.7 | 7.9 | 8.2 |
| Uterine | 2.9 | 0 | 5.7 | 2.6 | 5.1 |
| Endometriosis | 0 | 3.7 | 4.3 | 11.8 | 6.1 |
| Recurrent pregnancy loss | 8.7 | 11.0 | 10.0 | 14.5 | 41.8 |
| Single gene disorder | 0 | 2.4 | 5.7 | 10.5 | 2.0 |
| Sex selection | 0 | 3.7 | 1.4 | 0 | 0 |
| Single female | 1.5 | 1.2 | 2.9 | 0 | 0 |
| Same sex partner | 1.5 | 0 | 1.4 | 0 | 0 |
| Unexplained | 31.9 | 17.1 | 14.3 | 21.1 | 9.2 |
| Other | 2.9 | 2.4 | 2.9 | 1.3 | 6.1 |
Data are presented as the percentage of subjects meeting criteria for each infertility diagnosis per conception group
It should be noted that some subjects met more than one diagnosis per group
Choosing growth curve models
A total of 1024 ultrasound examinations were available for review. Based on visual inspection of first-, second-, and third-degree growth curves for the parameters, HC, AC, and HC/AC ratio, growth was best modeled as a linear function of time (Supplemental Figs. 2a-c). For EFW, growth was best modeled as a quadratic function of time (Supplemental Fig. 2d). EFW percentiles were initially calculated with Hadlock’s formula [22]; however, plotted percentile data points did not display any relationship with time, so a growth curve was not presented for this measure.
Similarly, growth curves were applied to each outcome for the IVF subgroups, fresh ET and FET, to determine if there was any difference between these two groups (Supplemental Figs. 3a-d).
Fetal growth kinetics
First- and second-order slopes for EFW models only were calculated for each curve to determine growth rates, as depicted in Tables 2 and 3. Of note, growth rates were interpreted as an average rate at 20 weeks and a weekly rate acceleration for each week following 20 weeks. This was due to the gestational age variable being centered at 20 weeks, or 140 days, as this was the most common time during gestation in which a second trimester ultrasound had been performed.
Table 2.
Growth kinetics for ultrasound biometrics by mode of conception
| Variable of interest | Conception method | First-order slope 95% CI | First-order slope p value | Second-order slope 95% CI | Second-order slope p value |
|---|---|---|---|---|---|
| Head circumference (cm) | Ovulation induction with IUI | 0.949 (0.882, 1.016) | .60 | ||
| IVF | 0.984 (0.946, 1.021) | ||||
| Spontaneous conception | 0.959 (0.899, 1.019) | ||||
| Abdominal circumference (cm) | Ovulation induction with IUI | 1.054 (0.998, 1.110) | .78 | ||
| IVF | 1.071 (1.039, 1.103) | ||||
| Spontaneous conception | 1.054 (1.003, 1.104) | ||||
| Head to abdomen ratio | Ovulation induction with IUI | − 0.009 (− 0.011, − 0.008) | .95 | ||
| IVF | − 0.009 (− 0.010, − 0.008) | ||||
| Spontaneous conception | − 0.009 (− 0.011, − 0.007) | ||||
| Estimated fetal weight (g) | Ovulation induction with IUI | 66.759 (54.529, 78.990) | .60 | 10.39 (8.93, 11.84) | .16 |
| IVF | 61.040 (54.897, 67.183) | 11.80 (11.06, 12.54) | |||
| Spontaneous conception | 58.534 (48.393, 68.675) | 12.16 (10.90, 13.42) |
Table 3.
Growth kinetics for ultrasound biometrics by subgroup analysis of fresh ET and FET
| Outcome | Conception method | First-order slope 95% CI | First-order slope p value | Second-order slope 95% CI | Second-order slope p value |
|---|---|---|---|---|---|
| Head circumference (cm) | IVF fresh ET | 1.015 (0.955, 1.076) | .29 | ||
| IVF FET | 0.976 (0.934, 1.018) | ||||
| Abdominal circumference (cm) | IVF fresh ET | 1.100 (1.061, 1.138) | .16 | ||
| IVF FET | 1.066 (1.040, 1.092) | ||||
| Head to abdomen ratio | IVF fresh ET | − 0.010 (− 0.011, − 0.008) | .47 | ||
| IVF FET | − 0.009 (− 0.010, − 0.007) | ||||
| Estimated fetal weight (g) | IVF fresh ET | 57.237 (47.230, 67.244) | .39 | 12.06 (10.74, 13.38) | .68 |
| IVF FET | 62.690 (55.134, 70.246) | 11.71 (10.70, 12.72) |
First-order slopes showed average weekly growth rate for fetal weight at 20 weeks, for HC, AC, HC/AC, and EFW. In Table 2, the HC first-order slope value of 0.959 refers to an average weekly growth rate of 0.959 cm for HC at 20 weeks. Second-order slopes showed the acceleration of the growth rate each week after 20 weeks. From the same table, the EFW second-order slope value of 11.80 refers to an increase of approximately 11.80 g per week each week following 20 weeks (i.e., 23.60 g at 21 weeks).
For ovulation induction with IUI, IVF including fresh ET and FET, and spontaneous conception, the slope analysis of linear or quadratic growth curves for per-week growth rate of HC, AC, HC/AC, and EFW demonstrated no difference (p values were .60, .78, .95, and .60, respectively). Change in EFW over time was also not different between groups (p = .16).
Similarly, Table 3 demonstrates a subgroup analysis of growth for fresh ET and FET groups and same outcomes of interest, which showed no difference (p values were .39, .16, .47, and .39, respectively). There was no change in EFW over time between the subgroups (p = .68). A subgroup analysis of FET natural and programmed groups yielded comparable results, and the sensitivity analysis comparing growth between second and third trimesters showed no statistically significant difference between conception methods (p = .60).
Delivery outcomes
Table 4 displays delivery outcomes by conception method. No significant differences were found among the groups for Apgar scores, preterm birth, birth defects, NICU admissions, or neonatal complications. The most frequent known neonatal complication among all groups was jaundice. Mean birth weight for fresh ET neonates was 3262 g ± 614 g; for natural FET, 3415 g ± 551 g; and, for programmed FET, 3272 g ± 587 g (p = .30). There was no difference in placental abnormalities, with majority of placentas in all groups deemed normal. Similarly, no differences were notable for maternal conditions developed during pregnancy.
Table 4.
Neonatal, placental, and maternal outcomes at delivery
| Ovulation induction IUI | Fresh ET | FET programmed | FET natural | Spontaneous | p value | |
|---|---|---|---|---|---|---|
| n = | 69 | 82 | 70 | 76 | 98 | |
| Neonatal outcomes | ||||||
| Gestational age, days (mean ± SD) | 272.5 + 13.3 | 271.9 + 16.1 | 268.8 + 20.7 | 273.7 + 12.9 | 271.0 + 15.0 | .83 |
| Infant sex (%) | ||||||
| Female | 37.7 | 45.0 | 38.5 | 48.7 | 42.7 | |
| Male | 62.3 | 55.0 | 61.5 | 51.4 | 57.3 | .54 |
| Infant weight, g (mean ± SD) | 3216.2 + 518.6 | 3261.9 + 614.4 | 3272.4 + 587.1 | 3415.2 + 551.2 | 3226.9 + 90.3 | .33 |
| Small-for-gestational-age (%) | 8.8 | 12.8 | 7.7 | 6.9 | 6.5 | .74 |
| Large-for-gestational-age (%) | 2.9 | 3.9 | 7.7 | 9.7 | 6.5 | .47 |
| Apgar score (mean ± SD) | 8.8 + 1.0 | 8.8 + 0.7 | 8.8 + 0.9 | 8.9 + 0.4 | 8.8 + 0.7 | .89 |
| Preterm birth (%) | 7.3 | 10.0 | 18.2 | 8.3 | 7.8 | .34 |
| Birth defect (%) | 3.1 | 3.8 | 6.4 | 5.9 | 6.7 | .68 |
| Live birth (%) | 100 | 100 | 100 | 100 | 100 | |
| NICU admission (%) | 4.4 | 8.8 | 16.9 | 8.1 | 5.6 | .10 |
| Neonate complications (%) | ||||||
| Hypoglycemia | 0 | 1.2 | 0 | 2.7 | 1.1 | |
| Jaundice | 18.8 | 14.8 | 18.8 | 21.3 | 26.9 | |
| Hypothermia | 0 | 2.5 | 5.8 | 1.3 | 3.2 | |
| Seizure | 0 | 0 | 1.5 | 0 | 0 | |
| Infection | 0 | 2.5 | 1.5 | 0 | 1.1 | |
| Sepsis | 0 | 1.2 | 1.5 | 1.3 | 0 | |
| RDS | 0 | 2.5 | 4.4 | 1.3 | 2.2 | |
| Neonatal death | 0 | 1.2 | 0 | 0 | 0 | |
| Unknown | 37.7 | 34.6 | 42.0 | 44.0 | 29.0 | |
| Other | 0 | 1.2 | 0 | 2.7 | 3.2 | |
| None | 43.5 | 45.7 | 33.3 | 30.7 | 40.9 | .42 |
| Placental outcomes (%) | ||||||
| Abruption | 0 | 1.3 | 1.5 | 0 | 2.2 | |
| Previa | 1.5 | 3.8 | 5.8 | 1.3 | 1.1 | |
| Accreta | 0 | 0 | 4.4 | 1.3 | 0 | |
| Velamentous cord insertion | 0 | 0 | 1.5 | 0 | 1.1 | |
| Marginal cord insertion | 2.9 | 2.5 | 0 | 1.3 | 2.2 | |
| None | 95.7 | 92.5 | 89.9 | 96.0 | 94.4 | .44 |
| Maternal outcomes (%) | ||||||
| Hypertensive disorder of pregnancy | 9.0 | 13.8 | 18.8 | 8.0 | 11.4 | .57 |
| Gestational diabetes | 24.6 | 21.3 | 26.1 | 17.3 | 11.2 | .08 |
Discussion
This study found no significant difference in fetal growth during the second and third trimesters between ovulation induction with IUI, fresh ET, FET, and spontaneous conception following infertility. We believe our study is the first to compare later trimester growth patterns for the IVF methods of fresh ET and FET and the first to compare these methods to spontaneous conception in an infertile population.
Limited studies assessing the effect of mode of conception on fetal growth have primarily focused on the first trimester [17, 18]. Von Versen-Höynck et al. found that crown rump length (CRL) measurements differed as early as 6 to 8 weeks of gestation, with smaller CRL found in fresh ET compared with FET groups [17]. At the same time, Sundheimer and colleagues found no difference in fetal growth-to-placental weight parameters, ratios, or neonatal birth measurements for non-IVF, IVF, and spontaneous conception [18]. For the latter, fresh ET and FET groups were not compared.
If differences in embryonic growth are indeed present early in gestation, we would expect these differences to persist during the second and third trimesters. This would make sense based on traditional understanding of fetal growth kinetics, which suggests that greatest physiological variation in fetal weight emerges during the latter half of pregnancy, with maximal fetal growth velocity often observed during the last 12 weeks of gestation [23]. Our results did not demonstrate significant differences in late trimester growth or birth weight for fresh ET, FET natural, and FET programmed groups, which contrasts with literature describing larger birth weight for FET as compared with fresh ET cycles [10–16]. An association between FET and increased birth weight may be confounded by other factors, such as gestational age at birth, neonatal sex, and maternal factors [24], and our adjustment for these factors may explain why we found no association. It may also be that differences do, in fact, exist for those infants with abnormal growth curves, but that we were unable to capture these differences in our analysis as most neonates had normal birth weights.
Ginod et al. are the only other study, known to us, that has assessed the influence of conception mode on fetal growth during the second and third trimesters. In their study, EFW and birth weight z-scores were calculated and compared with those in reference curves for each trimester. Their results did detect a significantly greater EFW for IVF/ICSI (intracytoplasmic sperm injection), FET, and IUI groups from the second trimester onward, compared with standard reference curves by Papageorghiou and Ego. Birth weights in IVF/ICSI and IUI groups were significantly smaller, but FET showed a non-significant trend toward greater birth weight [19]. Fresh ET was not compared. As no other studies have assessed fetal growth in this manner, it is difficult to draw clear conclusions at this time regarding our different study results. However, we can expect differences to exist in the setting of different study designs and patient populations.
Our study is further unique in its analysis of later trimester growth parameters as a function of mode of conception among an exclusively infertile population. Research, to date, has been mixed regarding the potential influence of infertility on fetal outcomes [2, 9, 25, 26]. Some suggest an increase in growth abnormalities and pregnancy complications for infertile groups regardless of infertility treatment [2, 9], while others have not observed these trends [25, 26]. By focusing on an infertile population, we hoped to better elucidate the effect of mode of conception on fetal growth rather than a confounding effect of infertility. Given inconsistencies in existing data, however, we would encourage future studies to include both infertile and fertile controls. A clearer understanding of fetal growth patterns in an infertile population could better delineate physiological mechanisms that could be targeted to improve neonatal outcomes. It was overall reassuring that there were no significant differences in pregnancy and neonatal complications among our conception groups studied.
This study is not without limitations. While the total number of participants was comparable with other studies on the same topic, it may be that our subgroups had too small of numbers to detect a statistically significant difference in second and third trimester growth based on the selected ultrasound biometric measures. The retrospective nature of this study also did not allow for precise timing of ultrasounds. Consistently timed ultrasound during the second and third trimesters could facilitate creation of cleaner growth curves with simpler interpretation.
In conclusion, the data described here contribute to the very limited literature on fetal growth trajectories following infertility treatment. While results are overall reassuring, additional research with larger populations is warranted.
Electronic supplementary material
(DOCX 651 kb).
Acknowledgments
We would like to thank Raquel R. Fleischmann for assistance with data collection and Wendy Y. Zhang for primary proofreading of data tables.
Study data were collected and managed using REDCap electronic data capture tools hosted at the academic institution. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for data integration and interoperability with external sources.
Funding information
This work was financially supported by the National Institute of Child Health and Human Development (Grant No. P01 HD065647-01A1) and the German Research Foundation (Grant No. VE490/8-1).
Footnotes
Publisher’s note
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Contributor Information
Melody Besharati, Email: besharati.melody@gmail.com.
Frauke von Versen-Höynck, Email: frauke.von.versen@gmx.de.
Kris Kapphahn, Email: kikapp@stanford.edu.
Valerie Lynn Baker, Email: valbaker@jhmi.edu.
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