Predicting the Risk of Macrosomia at Mid-Pregnancy Among Non-Diabetics: A Retrospective Cohort Study

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.

Binge alcohol drinking before pregnancy is closely associated with the development of macrosomia: Korean pregnancy registry cohort

Background

Alcohol drinking during pregnancy has been well-known to cause the detrimental effects on fetal development; however, the adverse effects of pre-pregnancy drinking are largely unknown. We investigate whether alcohol drinking status before pregnancy is associated with the risk for macrosomia, an offspring’s adverse outcome, in a Korean pregnancy registry cohort (n = 4,542) enrolled between 2013 and 2017.

Methods

Binge drinking was defined as consuming ≥5 drinks on one occasion and ≥2 times a week, and a total 2,886 pregnant, included in the final statistical analysis, were divided into 3 groups: never, non-binge, and binge drinking.

Results

The prevalence of macrosomia was higher in binge drinking before pregnancy than those with never or non-binge drinking (7.5% vs. 3.2% or 2.9%, p = 0.002). Multivariable logistic regression analysis demonstrated an independent association between macrosomia and prepregnancy binge drinking after adjusting for other confounders (adjusted odds ratio = 2.29; 95% CI, 1.08–4.86; p = 0.031). The model added binge drinking before pregnancy led to improvement of 10.6% (95% CI, 2.03–19.07; p = 0.0006) in discrimination from traditional risk prediction models.

Conclusion

Together, binge drinking before pregnancy might be an independent risk factor for developing macrosomia. Intensified intervention for drinking alcohol in women who are planning a pregnancy is important and may help prevent macrosomia.

Predicting the risk of fetal macrosomia at pregnancy in Shandong province: a case-control study

BACKGROUND AND AIM

Macrosomia is used to describe an infant born with excessively high weight, and it brings lots of unexpected risks in clinical work. Macrosomia causes considerable challenges for both physicians and pregnant women. Our objectives were to identify factors in gravida to be associated with the risk of macrosomia, to guide clinical prevention and treatment.

METHODS

The study assessed risk factors of macrosomia by comparison with normal birth weight neonates, and a case-control study was conducted at Shandong Provincial Maternity and Child Healthcare Hospital. We followed and selected the relevant indicators of gravida who gave birth to macrosomia or normal infants, and applied statistical analysis to identify clinical indicators related to macrosomia.

RESULTS

Maternal blood glucose (OR 3.88 (1.07, 14.15)), history of abnormal conception (OR 18.44 (1.05, 322.89)), situation of menarche (OR 13.53 (1.28, 142.66)), and menstrual cycle of gravida (OR 13.24 (1.17, 150.24)) were significant influencing factors of macrosomia, but did not appear in the univariate analysis. Adding gestational age at delivery (OR 4.00 (1.45, 11.09)), triglyceride (OR 0.01 (<0.01, 0.40)), and MBI (OR 46.35 (2.08, >99.99)) of pregnant women, the area under the curve (AUC) curve was drawn for forecasting the risk of macrosomia, and the value of AUC was 0.9174. The triglyceride blood index of pregnant women was the only one that was inversely proportional to the probability of giving birth to macrosomic infants. The low-density lipoprotein (LDL) (OR 0.29 (0.12, 0.72)) and total cholesterol (OR 0.39 (0.20, 0.75)) were important factors in univariate analysis, and both of them were negative correlation factors of macrosomia. All influencing factors in multivariate analysis were selected for drawing a receiver operating characteristic (ROC) curve, and the value of the AUC was 0.9174.

CONCLUSIONS

This analysis could therefore accurately predict the risk of pregnant women who would deliver macrosomic infants.