Am J Perinatol
DOI: 10.1055/a-2689-2368
Original Article

Forward Weight Prediction among Small for Gestational Age, Average for Gestational Age, and Large for Gestational Age Neonates

1   Carilion Clinic, Roanoke, Virginia, United States
,
Nneoma Edokobi
1   Carilion Clinic, Roanoke, Virginia, United States
,
1   Carilion Clinic, Roanoke, Virginia, United States
,
Benny Antony Amaraselvam
2   Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States
,
Ryan Bradley
1   Carilion Clinic, Roanoke, Virginia, United States
,
Monica Ahrens
1   Carilion Clinic, Roanoke, Virginia, United States
2   Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States
,
Megan Whitham
1   Carilion Clinic, Roanoke, Virginia, United States
2   Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States
› Author Affiliations
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Abstract

Objective

This study was aimed to evaluate the accuracy of the gestation-adjusted projection (GAP) forward projection model among neonates classified as small (SGA), appropriate (AGA), or large for gestational age (LGA), and to assess the impact of elevated maternal body mass index (BMI) on prediction accuracy. The GAP model uses percentile-based extrapolation to predict birth weight from remote ultrasounds, maintaining fetal weight percentile from scan to delivery, unlike traditional methods relying on static weight estimates near delivery.

Study Design

We conducted a retrospective review (2016–2023) of singleton, liveborn, nonanomalous pregnancies delivered after 28 weeks. Exclusions included multiples, major anomalies, stillbirth, and missing third-trimester growth ultrasounds or mid-gestational anatomical surveys. Among 1,559 records reviewed, 554 (35.5%) met inclusion criteria, with exclusions primarily due to missing third-trimester growth ultrasounds and mid-gestational anatomical surveys. This represents approximately 5.6% of total deliveries during the study period, reflecting our specific inclusion requirement for third-trimester growth assessments beyond routine prenatal care. GAP prediction accuracy was defined as birth weight prediction within 10% of actual, consistent with prior literature. Percent error and absolute percent error were also evaluated.

Results

Median absolute percent error for the cohort was 8.56% (interquartile range: 3.9, 15.2). Accuracy within 10% of actual birth weight was achieved in 51.4% of normal weight, 64.1% of overweight, and 58.0% of obese patients (p = 0.031). SGA infants were more often underestimated (median error: −15.79%) than AGA (−5.05%) or LGA (1.18%) infants (p < 0.001). Accuracy within 10% was achieved in 64.8% of AGA, 29.9% of SGA, and 66.9% of LGA infants (p < 0.001).

Conclusion

The GAP model demonstrates better accuracy in pregnancies with elevated maternal BMI and similar accuracy for LGA and AGA infants. Findings support its potential value in high-risk groups, such as those with obesity or suspected LGA.

Key Points

  • GAP model shows 8.6% median error in weight estimate.

  • SGA often underestimated; LGA more accurate.

  • GAP performs well in overweight and obese patients.



Publication History

Received: 28 June 2025

Accepted: 24 August 2025

Accepted Manuscript online:
25 August 2025

Article published online:
05 September 2025

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