Supplementary Material for: Fetal Biometry: A Method for Comparing Local Curve Populations with Those from Major Reference Standards

<b><i>Objectives:</i></b> This study aimed to present a statistical method for assessing potential differences between fetal growth standard curves and local curve population. <b><i>Methods:</i></b> This was an observational repeated measures longitudinal study. We used a simulation model to generate random distribution of the international population from the IG-21st for fetal AC using the original equations of means and standard deviations (SD) obtained by the fractional polynomial method. A general linear model (GLM) allowed us to calculate new equations originating from simulated intergrowth-21st data (SIM_IG21st) and to compare them, by visual inspection of the estimated coefficients and their 95% CI, with the original published. We used further GLMs for evaluating the goodness of fitting of our local curve and comparing the relative equations of means and SD with those of SIM_IG21st. Finally, the impact of percentile differences between the 2 curves was quantified. <b><i>Results:</i></b> SIM_IG21st data yielded very similar coefficients than those of IG-21st reference to such an extent that means and SD and percentiles of interest were identical to the original. The comparison between SIM_IG21st curve and local curves showed a nonsignificant intercept and a slight difference of the 2 slopes (GA and GA³) for the equations of the mean. As a result, the local curve resulted in greater AC values. A difference in the intercept but not in the slopes (GA², GA³, and GA³ * lnGA) was instead reported for the equations of the SD. In the percentile comparison, the local curve resulted in an overestimation of the 3rd and the 10th percentile that corresponded to the 4th and 12th percentiles of SIM_IG21st, respectively. <b><i>Conclusion:</i></b> This statistical method allows sonographers to assess potential differences between standard curves and local curve population, enabling a more proper identification of abnormal growth trajectories.