External validation and clinical usefulness of first-trimester prediction models for small- and large-for-gestational-age infants: a prospective cohort study

Design, rationale and protocol for Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs): an observational cohort study

INTRODUCTION

Given the increasing prevalence of both obesity and pre-diabetes in pregnant adults, there is growing interest in identifying hyperglycaemia in early pregnancy to optimise maternal and perinatal outcomes. Multiple organisations recommend first-trimester diabetes screening for individuals with risk factors; however, the benefits and drawbacks of detecting glucose abnormalities more mild than overt diabetes in early gestation and the best screening method to detect such abnormalities remain unclear.

METHODS AND ANALYSIS

The goal of the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) is to evaluate how early pregnancy glycaemia, measured using continuous glucose monitoring and oral glucose tolerance testing, relates to the diagnosis of gestational diabetes (GDM) at 24-28 weeks' gestation (maternal primary outcome) and large-for-gestational-age birth weight (newborn primary outcome). Secondary objectives include relating early pregnancy glycaemia to other adverse pregnancy outcomes and comprehensively detailing longitudinal changes in glucose over the course of pregnancy. GO MOMs enrolment began in April 2021 and will continue for 3.5 years with a target sample size of 2150 participants.

ETHICS AND DISSEMINATION

GO MOMs is centrally overseen by Vanderbilt University's Institutional Review Board and an Observational Study Monitoring Board appointed by National Institute of Diabetes and Digestive and Kidney Diseases. GO MOMs has potential to yield data that will improve understanding of hyperglycaemia in pregnancy, elucidate better approaches for early pregnancy GDM screening, and inform future clinical trials of early GDM treatment.

TRIAL REGISTRATION NUMBER

NCT04860336.

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review

BACKGROUND

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies.

OBJECTIVES

To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice.

SEARCH STRATEGY

MEDLINE, EMBASE and Cochrane Library were searched until June 2022.

SELECTION CRITERIA

We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance.

DATA COLLECTION AND ANALYSIS

Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool).

MAIN RESULTS

A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias.

CONCLUSIONS

There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

Evaluation of screening performance of first-trimester competing-risks prediction model for small-for-gestational age in Asian population

OBJECTIVE

To examine the external validity of the Fetal Medicine Foundation (FMF) competing-risks model for the prediction of small-for-gestational age (SGA) at 11-14 weeks' gestation in an Asian population.

METHODS

This was a secondary analysis of a multicenter prospective cohort study in 10 120 women with a singleton pregnancy undergoing routine assessment at 11-14 weeks' gestation. We applied the FMF competing-risks model for the first-trimester prediction of SGA, combining maternal characteristics and medical history with measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF) concentration. We calculated risks for different cut-offs of birth-weight percentile (< 10th , < 5th or < 3rd percentile) and gestational age at delivery (< 37 weeks (preterm SGA) or SGA at any gestational age). Predictive performance was examined in terms of discrimination and calibration.

RESULTS

The predictive performance of the competing-risks model for SGA was similar to that reported in the original FMF study. Specifically, the combination of maternal factors with MAP, UtA-PI and PlGF yielded the best performance for the prediction of preterm SGA with birth weight < 10th percentile (SGA < 10th ) and preterm SGA with birth weight < 5th percentile (SGA < 5th ), with areas under the receiver-operating-characteristics curve (AUCs) of 0.765 (95% CI, 0.720-0.809) and 0.789 (95% CI, 0.736-0.841), respectively. Combining maternal factors with MAP and PlGF yielded the best model for predicting preterm SGA with birth weight < 3rd percentile (SGA < 3rd ) (AUC, 0.797 (95% CI, 0.744-0.850)). After excluding cases with pre-eclampsia, the combination of maternal factors with MAP, UtA-PI and PlGF yielded the best performance for the prediction of preterm SGA < 10th and preterm SGA < 5th , with AUCs of 0.743 (95% CI, 0.691-0.795) and 0.762 (95% CI, 0.700-0.824), respectively. However, the best model for predicting preterm SGA < 3rd without pre-eclampsia was the combination of maternal factors and PlGF (AUC, 0.786 (95% CI, 0.723-0.849)). The FMF competing-risks model including maternal factors, MAP, UtA-PI and PlGF achieved detection rates of 42.2%, 47.3% and 48.1%, at a fixed false-positive rate of 10%, for the prediction of preterm SGA < 10th , preterm SGA < 5th and preterm SGA < 3rd , respectively. The calibration of the model was satisfactory.

CONCLUSION

The screening performance of the FMF first-trimester competing-risks model for SGA in a large, independent cohort of Asian women is comparable with that reported in the original FMF study in a mixed European population. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

External validation of Fetal Medicine Foundation competing-risks model for midgestation prediction of small-for-gestational-age neonates in Spanish population

OBJECTIVE

To examine the external validity of the new Fetal Medicine Foundation (FMF) competing-risks model for prediction in midgestation of small-for-gestational-age (SGA) neonates.

METHODS

This was a single-center prospective cohort study of 25 484 women with a singleton pregnancy undergoing routine ultrasound examination at 19 + 0 to 23 + 6 weeks' gestation. The FMF competing-risks model for the prediction of SGA combining maternal factors and midgestation estimated fetal weight by ultrasound scan (EFW) and uterine artery pulsatility index (UtA-PI) was used to calculate risks for different cut-offs of birth-weight percentile and gestational age at delivery. The predictive performance was evaluated in terms of discrimination and calibration.

RESULTS

The validation cohort was significantly different in composition compared with the FMF cohort in which the model was developed. In the validation cohort, at a 10% false-positive rate (FPR), maternal factors, EFW and UtA-PI yielded detection rates of 69.6%, 38.7% and 31.7% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks' gestation, respectively. The respective values for SGA < 3rd percentile were 75.7%, 48.2% and 38.1%. Detection rates in the validation cohort were similar to those reported in the FMF study for SGA with delivery at < 32 weeks but lower for SGA with delivery at < 37 and ≥ 37 weeks. Predictive performance in the validation cohort was similar to that reported in a subgroup of the FMF cohort consisting of nulliparous and Caucasian women. Detection rates in the validation cohort at a 15% FPR were 77.4%, 50.0% and 41.5% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks, respectively, which were similar to the respective values reported in the FMF study at a 10% FPR. The model had satisfactory calibration.

CONCLUSION

The new competing-risks model for midgestation prediction of SGA developed by the FMF performs well in a large independent Spanish population. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Impact of Maternal Monocyte to High-density Lipoprotein Cholesterol Ratio on the Incidence of Large-for-gestational-age Newborns: An Observational Cohort Study

BACKGROUND AND OBJECTIVES

Monocyte to high-density lipoprotein cholesterol ratio (MHR) has recently been identified as a new marker of inflammation and oxidative stress. However, it is unknown whether maternal MHR is associated with fetal weight at birth. Therefore, our objective was to analyze the association between maternal MHR and the frequency of small/large for gestational age (SGA/LGA) newborns in this retrospective cohort study.

METHODS

We retrospectively analyzed hospitalization records and laboratory data and obtained results from consecutive pregnant women in whom the blood lipid level had been investigated along with the blood cell count. Linear regression and logistic regression analyses were performed to estimate the associations of maternal MHR with birth weight and SGA/LGA.

RESULTS

Monocyte counts and MHR were positively associated with birth weight/LGA risk (monocyte [1-109/L increase] for birth weight: β: 170.24, 95% confidence interval [CI]: 41.72-298.76, LGA: odds ratio [OR]: 7.67; 95% CI: 2.56-22.98; MHR [1-109/mmol increase] for birth weight: β: 294.84, 95% CI: 170.23-419.44, LGA: OR: 7.97; 95% CI: 3.06-20.70), whereas high-density lipoprotein cholesterol (HDL-C) levels were negatively associated with birth weight/LGA risk [1 mmol/L increase for birth weight (β: -99.83, 95% CI: -130.47 to -69.19), for LGA: (OR: 0.57, 95% CI: 0.45-0.73). Obese pregnant women (body mass index [BMI] ≥30 kg/m2) with higher MHR (tertile 3: >0.33 109/mmol) significantly increased LGA risk by 6.39 fold (95% CI: 4.81, 8.49) compared to those with low MHR (tertile 1-2: ≤0.33 109/mmol) and normal weight (BMI <25 kg/m2).

CONCLUSION

Maternal MHR is associated with LGA risk, and this association might be further modified by BMI.

Assessment of first-trimester utero-placental vascular morphology by 3D power Doppler ultrasound image analysis using a skeletonization algorithm: the Rotterdam Periconception Cohort

STUDY QUESTION

Can three-dimensional (3D) Power Doppler (PD) ultrasound and a skeletonization algorithm be used to assess first-trimester development of the utero-placental vascular morphology?

SUMMARY ANSWER

The application of 3D PD ultrasonography and a skeletonization algorithm facilitates morphologic assessment of utero-placental vascular development in the first trimester and reveals less advanced vascular morphologic development in pregnancies with placenta-related complications than in pregnancies without placenta-related complications.

WHAT IS KNOWN ALREADY

Suboptimal development of the utero-placental vasculature is one of the main contributors to the periconceptional origin of placenta-related complications. The nature and attribution of aberrant vascular structure and branching patterns remain unclear, as validated markers monitoring first-trimester utero-placental vascular morphologic development are lacking.

STUDY DESIGN, SIZE, DURATION

In this prospective observational cohort, 214 ongoing pregnancies were included before 10 weeks gestational age (GA) at a tertiary hospital between January 2017 and July 2018, as a subcohort of the ongoing Rotterdam Periconception Cohort study.

PARTICIPANTS/MATERIALS, SETTING, METHODS

By combining 3D PD ultrasonography and virtual reality, utero-placental vascular volume (uPVV) measurements were obtained at 7, 9 and 11 weeks GA. A skeletonization algorithm was applied to the uPVV measurements to generate the utero-placental vascular skeleton (uPVS), a network-like structure containing morphologic characteristics of the vasculature. Quantification of vascular morphology was performed by assigning a morphologic characteristic to each voxel in the uPVS (end-, vessel-, bifurcation- or crossing-point) and calculating total vascular network length. A Mann–Whitney U test was performed to investigate differences in morphologic development of the first-trimester utero-placental vasculature between pregnancies with and without placenta-related complications. Linear mixed models were used to estimate trajectories of the morphologic characteristics in the first trimester.

MAIN RESULTS AND THE ROLE OF CHANCE

All morphologic characteristics of the utero-placental vasculature increased significantly in the first trimester (P < 0.005). In pregnancies with placenta-related complications (n = 54), utero-placental vascular branching was significantly less advanced at 9 weeks GA (vessel points P = 0.040, bifurcation points P = 0.050, crossing points P = 0.020, total network length P = 0.023). Morphologic growth trajectories remained similar after adjustment for parity, conception mode, foetal sex and occurrence of placenta-related complications.

LIMITATIONS, REASONS FOR CAUTION

The tertiary setting of this prospective observational study provides high internal, but possibly limited external, validity. Extrapolation of the study’s findings should therefore be addressed with caution.

WIDER IMPLICATIONS OF THE FINDINGS

The uPVS enables assessment of morphologic development of the first-trimester utero-placental vasculature. Further investigation of this innovative methodology needs to determine its added value for the assessment of (patho-) physiological utero-placental vascular development.

STUDY FUNDING/COMPETING INTEREST(S)

This research was funded by the Department of Obstetrics and Gynecology of the Erasmus MC, University Medical Centre, Rotterdam, The Netherlands. There are no conflicts of interest.

TRIAL REGISTRATION NUMBER

Registered at the Dutch Trial Register (NTR6854).

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.

Risk scores for predicting small for gestational age infants in Japan: The TMM birthree cohort study

This study aimed to construct a prediction model for small-for-gestational-age (SGA) infants in Japan by creating a risk score during pregnancy. A total of 17,073 subjects were included in the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study, a prospective cohort study. A multiple logistic regression model was used to construct risk scores during early and mid-gestational periods (11–17 and 18–21 weeks of gestation, respectively). The risk score during early gestation comprised the maternal age, height, body mass index (BMI) during early gestation, parity, assisted reproductive technology (ART) with frozen-thawed embryo transfer (FET), smoking status, blood pressure (BP) during early gestation, and maternal birth weight. The risk score during mid-gestation also consisted of the maternal age, height, BMI during mid-gestation, weight gain, parity, ART with FET, smoking status, BP level during mid-gestation, maternal birth weight, and estimated fetal weight during mid-gestation. The C-statistics of the risk scores during early- and mid-gestation were 0.658 (95% confidence interval [CI]: 0.642–0.675) and 0.725 (95% CI: 0.710–0.740), respectively. In conclusion, the predictive ability of the risk scores during mid-gestation for SGA infants was acceptable and better than that of the risk score during early gestation.

Fetal nondiabetic-macrosomia: risk factors for pregnancy adverse outcome and comparison of two growth curves in the prediction of cesarean section

BACKGROUND

Randomized trials reported no difference whether induction or expectant management is performed in non-diabetic women with large for gestational age babies but no tool has been validated for the prediction of high risk cases.

AIM

Assessing the performance of different growth curves in the prediction of complications.

METHODS

Data from 1066 consecutive non-diabetic women who delivered babies ≥4000 g were collected. Logistic regression analysis was used to analyze the impact of the maternal variables on: instrumental delivery, shoulder dystocia (SD), perineal tears, cesarean section (CS), and postpartum hemorrhage. Intergrowth21 curves and customized Gardosi's curves were compared in terms of prediction of adverse outcomes.

FINDINGS

Induction of labor was performed in 23.1% cases. The rate of CS was 17%. Hemorrhage, fetal distress, and SD occurred in 2%, 1.3%, and 2.7% of cases, respectively. Induction was significantly associated with instrumental delivery (p < .001), CS (p = .001), third and fourth degree perineal tears (p = .031), and post-partum hemorrhage (p = .02). The cutoff of 90th percentile according to Intergrowth21 did not show significant performance in predicting CS, while the same cutoff according to the Gardosi curves showed an OR 1.92 (CI 1.30-2.84) (p = .0009).

DISCUSSION

Gardosi curves showed a better performance in predicting the risk of CS versus Intergrowth curves. Induction is significantly associated with adverse outcome in non-diabetic women with LGA babies.

Family socioeconomic position and abnormal birth weight: evidence from a Chinese birth cohort

BACKGROUND

Birth weight is a strong determinant of infant short- and long-term health outcomes. Family socioeconomic position (SEP) is usually positively associated with birth weight. Whether this association extends to abnormal birth weight or there exists potential mediator is unclear.

METHODS

We analyzed data from 14,984 mother-infant dyads from the Born in Guangzhou Cohort Study. We used multivariable logistic regression to assess the associations of a composite family SEP score quartile with macrosomia and low birth weight (LBW), and examined the potential mediation effect of maternal pre-pregnancy body mass index (BMI) using causal mediation analysis.

RESULTS

The prevalence of macrosomia and LBW was 2.62% (n = 392) and 4.26% (n = 638). Higher family SEP was associated with a higher risk of macrosomia (OR 1.30, 95% CI 0.93-1.82; OR 1.53, 95% CI 1.11-2.11; and OR 1.59, 95% CI 1.15-2.20 for the 2nd, 3rd, and 4th SEP quartile respectively) and a lower risk of LBW (OR 0.69, 95% CI 0.55-0.86; OR 0.76, 95% CI 0.61-0.94; and OR 0.61, 95% CI 0.48-0.77 for the 2nd, 3rd, and 4th SEP quartile respectively), compared to the 1st SEP quartile. We found that pre-pregnancy BMI did not mediate the associations of SEP with macrosomia and LBW.

CONCLUSIONS

Socioeconomic disparities in fetal macrosomia and LBW exist in Southern China. Whether the results can be applied to other populations should be further investigated.

External validation and clinical usefulness of first-trimester prediction models for small- and large-for-gestational-age infants: a prospective cohort study

Objective

To assess the external validity of all published first‐trimester prediction models based on routinely collected maternal predictors for the risk of small‐ and large‐for‐gestational‐age (SGA and LGA) infants. Furthermore, the clinical potential of the best‐performing models was evaluated.

Design

Multicentre prospective cohort.

Setting

Thirty‐six midwifery practices and six hospitals (in the Netherlands).

Population

Pregnant women were recruited at <16 weeks of gestation between 1 July 2013 and 31 December 2015.

Methods

Prediction models were systematically selected from the literature. Information on predictors was obtained by a web‐based questionnaire. Birthweight centiles were corrected for gestational age, parity, fetal sex, and ethnicity.

Main outcome measures

Predictive performance was assessed by means of discrimination (C‐statistic) and calibration.

Results

The validation cohort consisted of 2582 pregnant women. The outcomes of SGA <10th percentile and LGA >90th percentile occurred in 203 and 224 women, respectively. The C‐statistics of the included models ranged from 0.52 to 0.64 for SGA (n = 6), and from 0.60 to 0.69 for LGA (n = 6). All models yielded higher C‐statistics for more severe cases of SGA (<5th percentile) and LGA (>95th percentile). Initial calibration showed poor‐to‐moderate agreement between the predicted probabilities and the observed outcomes, but this improved substantially after recalibration.

Conclusion

The clinical relevance of the models is limited because of their moderate predictive performance, and because the definitions of SGA and LGA do not exclude constitutionally small or large infants. As most clinically relevant fetal growth deviations are related to ‘vascular’ or ‘metabolic’ factors, models predicting hypertensive disorders and gestational diabetes are likely to be more specific.

Tweetable abstract

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.