Competing-risks model in screening for pre-eclampsia in twin pregnancy by maternal characteristics and medical history.

Abstract

A survival-time regression model for gestational age at delivery with pre-eclampsia (PE) in singleton pregnancy, using maternal demographic characteristics and medical history, was reported previously. The objective of this study was to extend this model to dichorionic (DC) and monochorionic (MC) twin pregnancy. The study population included 1789 DC and 430 MC twin pregnancies and 93 297 singleton pregnancies. A survival-time model for gestational age at delivery with PE was developed from variables of maternal characteristics and medical history. The risk of PE with delivery < 37 weeks and < 42 weeks in twin pregnancies was determined and compared with that in singleton pregnancies. In singleton pregnancies comprising women of Caucasian racial origin, mean weight of 69 kg at 12 weeks' gestation, mean height of 164 cm, nulliparous, with spontaneous conception, no family history of PE and no history of diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, the mean of the Gaussian distribution of gestational age at delivery with PE was 55 weeks. In DC twins with PE, mean gestational age at delivery was shifted to the left by 8.2 (95% CI, 7.2-9.1) weeks and in MC twins it was shifted to the left by 10.0 (95% CI, 8.5-11.4) weeks. The risk of delivery with PE occurring at, or before, a specified gestational age is given by the area under the fitted distribution curve. For a reference population with the above characteristics, the estimated risk of PE < 37 weeks' gestation, assuming no other cause of delivery, was 0.6% for singletons, 9.0% for DC twins and 14.2% for MC twins; the respective values for PE < 42 weeks were 3.6%, 27.0% and 36.5%. A model based on maternal characteristics and medical history has been developed for estimation of patient-specific risks for PE in DC and MC twin pregnancy. Such estimation of the a-priori risk for PE is an essential first step in the use of Bayes' theorem to combine maternal factors with biomarkers for the continuing development of more effective methods of screening for the disease. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.