How to enhance the applicability of a risk prediction model for term small-for-gestational-age neonates in clinical settings?

ObjectiveTo construct a simple term small-for-gestational-age (SGA) neonate prediction model that is clinically practical.MethodsThis analysis was based on the Born in Guangzhou Cohort Study (BIGCS). Mothers who had a singleton pregnancy, delivered a term neonate, and had an ultrasonography within 30 + 0 to 32 + 6 weeks of gestation were included. Term SGA was defined with customized population percentiles. Prediction models were constructed with backward selection logistic regression in a four-step approach, where model 1 contained fetal biometrics only, models 2 and 3 included maternal features and a time factor (weeks between ultrasonography and delivery), respectively; and model 4 contained all features mentioned. The prediction performance of individual models was evaluated based on area under the curve (AUC) and a calibration test was performed.ResultsThe prevalence of SGA in the study population of 21 346 women was 11.5%. With a complete-case analysis approach, data of 19 954 women were used for model construction and validation. The AUC of the four models were 0.781, 0.793, 0.823, and 0.834, respectively, and all were well-calibrated. Model 3 consisted of fetal biometrics and corrected for time to delivery was chosen as the final model to build risk prediction graphs for clinical use.ConclusionA prediction model derived from fetal biometrics in early third trimester is satisfactory to predict SGA. A simple SGA prediction model based on fetal biometrics that requires no additional tests and no prior knowledge of the patient is clinically useful.