Fetal Weight Estimation Using Automated Fractional Limb Volume With 2-Dimensional Size Parameters: A Multicenter Study.

OBJECTIVES

To develop new fetal weight prediction models using automated fractional limb volume (FLV).

METHODS

A prospective multicenter study measured fetal biometry within 4 to 7 days of delivery. Three-dimensional data acquisition included the automated FLV that was based on 50% of the humerus diaphysis (fractional arm volume [AVol]) or 50% of the femur diaphysis (fractional thigh volume [TVol]) length. A regression analysis provided population sample-specific coefficients to develop 4 weight estimation models. Estimated and actual birth weights (BWs) were compared for the mean percent difference ± standard deviation of the percent differences. Systematic errors were analyzed by the Student t test, and random errors were compared by the Pitman test.

RESULTS

A total of 328 pregnancies were scanned before delivery (BW range, 825-5470 g). Only 71.3% to 72.6% of weight estimations were within 10% of actual BW using original published models by Hadlock et al (Am J Obstet Gynecol 1985; 151:333-337) and INTERGROWTH-21st (Ultrasound Obstet Gynecol 2017; 49:478-486). All predictions were accurate by using sample-specific model coefficients to minimize bias in making these comparisons (Hadlock, 0.4% ± 8.7%; INTERGROWTH-21st, 0.5% ± 10.0%; AVol, 0.3% ± 7.4%; and TVol, 0.3% ± 8.0%). Both AVol- and TVol-based models improved the percentage of correctly classified BW ±10% in 83.2% and 83.9% of cases, respectively, compared to the INTERGROWTH-21st model (73.8%; P < .01). For BW of less than 2500 g, all models slightly overestimated BW (+2.0% to +3.1%). For BW of greater than 4000 g, AVol (-2.4% ± 6.5%) and TVol (-2.3% ± 6.9%) models) had weight predictions with small systematic errors that were not different from zero (P > .05). For these larger fetuses, both AVol and TVol models correctly classified BW (±10%) in 83.3% and 87.5% of cases compared to the others (Hadlock, 79.2%; INTERGROWTH-21st, 70.8%) although these differences did not reach statistical significance.

CONCLUSIONS

In this cohort, the inclusion of automated FLV measurements with conventional 2-dimensional biometry was generally associated with improved weight predictions.