Scott W White1,2,3, Julie A Marsh4, Stephen J Lye5, Laurent Briollais5, John P Newnham1,2,3, Craig E Pennell1,2,3. 1. a School of Women's and Infants' Health, The University of Western Australia , Perth , Western Australia , Australia . 2. b Women and Infants Research Foundation, King Edward Memorial Hospital , Perth , Western Australia , Australia . 3. c Maternal Fetal Medicine Service, King Edward Memorial Hospital , Perth , Western Australia , Australia . 4. d School of Mathematics and Statistics, The University of Western Australia , Perth , Western Australia , Australia , and. 5. e Samuel Lunenfeld Research Institute, Mt Sinai Medical Centre, The University of Toronto , Toronto , Ontario , Canada.
Abstract
OBJECTIVE: To develop customized biometric charts to better define abnormal fetal growth. METHODS: A total of 1056 singleton fetuses from the Raine Study underwent serial ultrasound biometry (abdominal circumference [AC], head circumference, and femur length) at 18, 24, 28, 34, and 38 weeks' gestation. Customized biometry trajectories were developed adjusting for epidemiological influences upon fetal biometry using covariates available at 18 weeks gestation. Prediction accuracy (areas under the receiver operating characteristic curve [AUC] and 95% confidence interval [95%CI]) was evaluated by repeated random sub-sampling cross-validation methodology. RESULTS: The model for derived estimated fetal weight (EFW) performed well for EFW less than 10th predicted percentile (AUC = 0.695, 95%CI, 0.692-0.699) and EFW greater than 90th predicted percentile (AUC = 0.705, 95%CI, 0.702-0.708). Fetal AC was also well predicted for growth restriction (AUC = 0.789, 95%CI, 0.784-0.794) and macrosomia (AUC = 0.796, 95%CI, 0.793-0.799). Population-derived, sex-specific charts misclassified 7.9% of small fetuses and 10.7% of large fetuses as normal. Conversely, 9.2% of those classified as abnormally grown by population-derived charts were considered normal by customized charts, potentially leading to complications of unnecessary intervention. CONCLUSIONS: Customized fetal biometric charts may offer improved ability for clinicians to detect deviations from optimal fetal growth and influence pregnancy management.
OBJECTIVE: To develop customized biometric charts to better define abnormal fetal growth. METHODS: A total of 1056 singleton fetuses from the Raine Study underwent serial ultrasound biometry (abdominal circumference [AC], head circumference, and femur length) at 18, 24, 28, 34, and 38 weeks' gestation. Customized biometry trajectories were developed adjusting for epidemiological influences upon fetal biometry using covariates available at 18 weeks gestation. Prediction accuracy (areas under the receiver operating characteristic curve [AUC] and 95% confidence interval [95%CI]) was evaluated by repeated random sub-sampling cross-validation methodology. RESULTS: The model for derived estimated fetal weight (EFW) performed well for EFW less than 10th predicted percentile (AUC = 0.695, 95%CI, 0.692-0.699) and EFW greater than 90th predicted percentile (AUC = 0.705, 95%CI, 0.702-0.708). Fetal AC was also well predicted for growth restriction (AUC = 0.789, 95%CI, 0.784-0.794) and macrosomia (AUC = 0.796, 95%CI, 0.793-0.799). Population-derived, sex-specific charts misclassified 7.9% of small fetuses and 10.7% of large fetuses as normal. Conversely, 9.2% of those classified as abnormally grown by population-derived charts were considered normal by customized charts, potentially leading to complications of unnecessary intervention. CONCLUSIONS: Customized fetal biometric charts may offer improved ability for clinicians to detect deviations from optimal fetal growth and influence pregnancy management.
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