title: Prediction of intra-twin birth weight discordance by binary logistic regression analysis.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  A birth weight discordance prediction model that includes seven variables available during pregnancy has been established with acceptable diagnostic performance.
authors.affiliation: School of Public Health, Central South University, Changsha, Hunan, PR China. swwen@ohri.ca
authors.forename: Shi Wu
authors.initials: SW
authors.lastname: Wen
authors.forename: Hongzhuan
authors.initials: H
authors.lastname: Tan
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Yang
authors.forename: Mark
authors.initials: M
authors.lastname: Walker
mesh_terms: D019540:Area Under Curve; D001724:Birth Weight; D004522:Educational Status; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D016015:Logistic Models; D008297:Male; D008423:Maternal Age; D010331:Paternal Age; D011237:Predictive Value of Tests; D011247:Pregnancy; D011272:Pregnancy, Multiple; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012737:Sex Factors; D014427:Twins