Prediction of small for gestational age by logistic regression in twins

BACKGROUND: Small for gestational age (SGA) is one of the major determinants of perinatal mortality and morbidity, and may relate in adult diseases. Early prediction of SGA could be helpful for health care providers and public health workers in guiding antenatal management and prevention. The reported methods of SGA prediction are not satisfactory because the diagnostic performance is poor and the interval between prediction and delivery is too short. AIMS: To establish a SGA prediction model for twin pregnancies based on variables obtainable in early gestation. METHODS: We used a large twin registry United States data (1995-1997). The study subjects were randomly divided into two groups: group 1 to establish the prediction model by logistic regression and group 2 to validate the prediction model. SGA was defined as birth weight for gestational age z scores less than 10th percentiles. Pair of twin was the unit of analysis. Two sets of multiple logistic regression analyses with different outcome measures - one or both twins SGAs and both twins SGAs - were used to establish the prediction model. RESULTS: The sensitivity, specificity, and positive predictive value were 52.3, 62.5, and 21.5%, respectively, at the cutoff value 0.16 in a SGA prediction model based on maternal race, education, marital status, parity, prenatal care visit initiation, cigarette smoking, and paternal race. CONCLUSIONS: A prediction model based on determinants that can be obtained at early gestation might be useful in the management of pregnancies with high risk of SGA in twins.