Larciprete Giovanni, Valensise Herbert, Barbati Giulia, Di Pierro Giuseppe, Jarvis Sheba, Deaibess Therese, Gioia Stefano, Giacomello Francesco, Cirese Elio, Arduini Domenico
Department of Perinatal Medicine, Tor Vergata University, and AFaR (Associazione Fatebenefratelli per la Ricerca), Fatebenefratelli Isola Tiberina Hospital, Rome, Italy.
J Obstet Gynaecol Res. 2007 Oct;33(5):635-40. doi: 10.1111/j.1447-0756.2007.00624.x.
The aim of this study was to explore a birthweight prediction model using ultrasound determined tissue thickness (SCTT) parameters.
We measured routine ultrasonographic biometric parameters and in addition, fetal SCTT in 201 healthy singleton pregnancies. Mid-arm fat and lean mass, mid-thigh fat and lean mass, subscapular fat mass and abdominal fat mass (AFM) were measured in order to calculate a birthweight prediction model. Ultrasound measurements were analyzed using an 'anovarepeated measures model'. The growth rate (beta-slope) of the selected parameters was computed and the correlation coefficient with the birthweight and the Kendall rank correlation tau, were calculated.
From the ultrasound determined SCTT parameters, only abdominal circumference (AC), AFM, and MTLM showed a statistically significant trend. The beta-slope of mid-thigh lean mass was excluded since it exhibited significant correlation with the beta-slope of AFM. The final regression model could be calculated as: birthweight (gr.) = intercept +alpha(1)(AFM beta-slope) + alpha(2)(AC beta-slope), where alpha(1), alpha(2) represent regression coefficients.
We provide a graphical birthweight prediction model for clinical practice using conventional and specific ultrasound measurements of fetal subcutaneous tissue thickness. This model is based upon an overall analysis of the ultrasound estimated body components.
本研究旨在探索一种利用超声测定的组织厚度（SCTT）参数的出生体重预测模型。
我们测量了201例健康单胎妊娠的常规超声生物测量参数，此外还测量了胎儿的SCTT。测量上臂中部脂肪和瘦体重、大腿中部脂肪和瘦体重、肩胛下脂肪量和腹部脂肪量（AFM），以计算出生体重预测模型。使用“方差分析重复测量模型”分析超声测量结果。计算所选参数的生长率（β斜率），并计算其与出生体重的相关系数以及肯德尔等级相关系数tau。
从超声测定的SCTT参数来看，只有腹围（AC）、AFM和MTLM显示出统计学上的显著趋势。大腿中部瘦体重的β斜率被排除，因为它与AFM的β斜率表现出显著相关性。最终回归模型可计算为：出生体重（克）=截距+α(1)（AFMβ斜率）+α(2)（ACβ斜率），其中α(1)、α(2)代表回归系数。
我们提供了一种用于临床实践的出生体重预测模型，该模型使用胎儿皮下组织厚度的常规和特定超声测量。该模型基于对超声估计身体成分的全面分析。
