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Updated: Jul 1, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review
Lauren Ewington1,2, Naomi Black1,2, Charlotte Leeson1,2
1Division of Biomedical Sciences, University of Warwick, Coventry, UK.
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.
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