Fetal Weight Estimation Using Automated Fractional Limb Volume With 2‐Dimensional Size Parameters: A Multicenter Study. (5th February 2020)

• Record Type: Journal Article
• Authors: Lee, Wesley; Mack, Lauren M.; Sangi‐Haghpeykar, Haleh; Gandhi, Rajshi; Wu, Qingqing; Kang, Li; Canavan, Timothy P.; Gatina, Renata; Schild, Ralf L.
• Is Part Of: Journal of ultrasound in medicine. Volume 39:Number 7(2020)
• Page Start: 1317
• Page End: 1324
• Publication Date: 2020-02-05
• Subjects: birth weight -- fractional arm volume -- fractional thigh volume -- 3‐dimensional ultrasound; Ultrasonics in medicine -- Periodicals; Ultrasonics; Ultrasonography; Ultrasonics in medicine; Electronic journals; Periodicals; Periodicals; 616.07543
• DOI: 10.1002/jum.15224
• Languages: English
• ISSNs: 0278-4297

Abstract

Abstract : 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.