Prediction of intra-twin birth weight discordance by binary logistic regression analysis

Identification of women at high risk of intra-twin birth weight discordance is helpful in obstetric care of these pregnancies. The aim of this study is to establish an intra-twin birth weight discordance prediction model.

We created an intra-twin birth weight discordance prediction model by logistic regression, based on the 1995-1997 register twin birth data of the USA. The twin sets were randomly divided into two groups: group 1 to establish the prediction model and group 2 to validate the prediction model. Intra-twin birth weight discordance was defined as birth weight discordance > 25%. The prediction model was validated by receiver operating characteristic curve.

A birth weight discordance prediction model including maternal age (beta = 0.069), parity (beta = 0.250), fetal gender concordance (beta = 0.041), maternal hypertension (beta = 0.368), eclampsia (beta = 0.316), other medical complication (beta = 0.165), and smoking (beta = 0.164) was established, yielded a 0.558 area under the receiver operating characteristic curve. The sensitivity, specificity, and positive predictive values were 38.1, 69.7, and 10.8%, respectively, at the cut-off value of 0.09 in group 2.

A birth weight discordance prediction model that includes seven variables available during pregnancy has been established with acceptable diagnostic performance.