A Service Evaluation of Antenatal Detection of Small-for-Gestational-Age Infants in a UK National Health Service Trust

Introduction Infants with a birthweight below the 10th centile are classified as small-for-gestational age (SGA). SGA is closely associated with fetal growth restriction (FGR), and both represent significant risk factors for stillbirth. The UK continues to report comparatively high stillbirth rates, and antenatal detection of SGA is a recognised strategy for reducing this risk. This service evaluation assessed the antenatal detection rate of SGA infants at Mid Yorkshire Hospitals NHS Trust (MYHT). Methods This was a retrospective observational cohort service evaluation conducted between January and March 2020 using total-population sampling of all eligible cases. Using the BadgerNet maternity system, 124 SGA infants were identified, of which 110 were included after applying eligibility criteria. Data were collected on 12 potential risk factors, including maternal age, medical comorbidities, and history of SGA births. The overall detection rate for the Trust was calculated, and a multivariable (binary) logistic regression was performed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, New York, United States) following descriptive analysis in Microsoft Excel. Missing data were addressed using multiple imputation, and supplementary analyses were conducted for contextual understanding. Results A total of 42.7% of SGA infants were detected in the antenatal period; 97.9% of these were identified following referral for an abnormal symphyseal fundal height (SFH) measurement. The regression model was statistically significant, χ²(12) = 47.7-50.9, p < 0.001. Maternal smoking was associated with a 3.9-fold increase in detection (95% CI, 1.01-14.9), while referral after an abnormal SFH measurement increased the likelihood of detection by 154-fold (95% CI, 14.9-1603.4). Conclusions The antenatal detection rate of SGA infants at MYHT mirrors the national average, yet most cases remain undetected across the UK. Detection was primarily driven by referral after abnormal SFH measurements, underscoring the value of routine screening. Maternal smoking also independently increased detection, reflecting its strong association with fetal growth restriction. National adoption of customised centiles in systems such as BadgerNet may improve alignment with clinical practice, support earlier recognition of at-risk pregnancies, and contribute to reducing stillbirth rates.

Authors

Institutions

Publication Details

Journal
Cureus
Published
2025-10-19
DOI
https://doi.org/10.7759/cureus.94901
Primary Topic
Neonatal Respiratory Health Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Service Evaluation of Antenatal Detection of Small-for-Gestational-Age Infants in a UK National Health Service Trust

Hebah Ali, Tulika Singh, Tharanya Thiruchelvam
Cureus
Neonatal Respiratory Health Research
article

A Service Evaluation of Antenatal Detection of Small-for-Gestational-Age Infants in a UK National Health Service Trust

Hebah Ali, Tulika Singh, Tharanya Thiruchelvam
article en

Abstract

Introduction Infants with a birthweight below the 10th centile are classified as small-for-gestational age (SGA). SGA is closely associated with fetal growth restriction (FGR), and both represent significant risk factors for stillbirth. The UK continues to report comparatively high stillbirth rates, and antenatal detection of SGA is a recognised strategy for reducing this risk. This service evaluation assessed the antenatal detection rate of SGA infants at Mid Yorkshire Hospitals NHS Trust (MYHT). Methods This was a retrospective observational cohort service evaluation conducted between January and March 2020 using total-population sampling of all eligible cases. Using the BadgerNet maternity system, 124 SGA infants were identified, of which 110 were included after applying eligibility criteria. Data were collected on 12 potential risk factors, including maternal age, medical comorbidities, and history of SGA births. The overall detection rate for the Trust was calculated, and a multivariable (binary) logistic regression was performed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, New York, United States) following descriptive analysis in Microsoft Excel. Missing data were addressed using multiple imputation, and supplementary analyses were conducted for contextual understanding. Results A total of 42.7% of SGA infants were detected in the antenatal period; 97.9% of these were identified following referral for an abnormal symphyseal fundal height (SFH) measurement. The regression model was statistically significant, χ²(12) = 47.7-50.9, p < 0.001. Maternal smoking was associated with a 3.9-fold increase in detection (95% CI, 1.01-14.9), while referral after an abnormal SFH measurement increased the likelihood of detection by 154-fold (95% CI, 14.9-1603.4). Conclusions The antenatal detection rate of SGA infants at MYHT mirrors the national average, yet most cases remain undetected across the UK. Detection was primarily driven by referral after abnormal SFH measurements, underscoring the value of routine screening. Maternal smoking also independently increased detection, reflecting its strong association with fetal growth restriction. National adoption of customised centiles in systems such as BadgerNet may improve alignment with clinical practice, support earlier recognition of at-risk pregnancies, and contribute to reducing stillbirth rates.

CureusVol. 17(10)
University of Leeds (GB)
Openalex Percentile: Top 57%
Neonatal Respiratory Health Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.