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Fetal Weight Estimation Using Automated Fractional Limb Volume With 2‐Dimensional Size Parameters
Author(s) -
Lee Wesley,
Mack Lauren M.,
SangiHaghpeykar Haleh,
Gandhi Rajshi,
Wu Qingqing,
Kang Li,
Canavan Timothy P.,
Gatina Renata,
Schild Ralf L.
Publication year - 2020
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.1002/jum.15224
Subject(s) - medicine , diaphysis , nuclear medicine , population , fetal weight , mean difference , ultrasound , sample size determination , reproducibility , birth weight , femur , confidence interval , statistics , surgery , pregnancy , mathematics , radiology , environmental health , biology , genetics
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.