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  • Title: Mid-trimester beta-hCG levels incorporated in a multifactorial model for the prediction of severe pre-eclampsia.
    Author: Lee LC, Sheu BC, Shau WY, Liu DM, Lai TJ, Lee YH, Huang SC.
    Journal: Prenat Diagn; 2000 Sep; 20(9):738-43. PubMed ID: 11015703.
    Abstract:
    Pre-eclampsia remains a major cause of perinatal morbidity and mortality worldwide. Proposed predicting tests for early detection of pregnant women destined to develop pre-eclampsia remain unsatisfactory. The aim of this study was to investigate the clinical utility of combining mid-trimester maternal serum beta-human chorionic gonadotrophin (MShCG) levels with selected clinical determining factors as a multifactorial predictive test for pre-eclampsia. Thirty-nine cases with mild pre-eclampsia and 56 with severe pre-eclampsia were recruited as the study groups. Normotensive women (957) were enrolled as controls. Potential determining risk factors for severe pre-eclampsia were selected using a multiple logistic regression to build various combined prediction models. A receiver-operator characteristic curve was employed to assess the performance of each prediction test for pre-eclampsia. The prediction efficacy of each test was examined by the area under the curve (AUC). Our data show that mid-trimester MShCG levels significantly correlated with severity of pre-eclampsia (Spearman rank correlation coefficient=0.195, p<0.001). Women with mild pre-eclampsia had a 2.61-times greater chance, while women with severe pre-eclampsia had a 6.13-times greater chance of having MShCG exceeding 2.0 multiples of the median than did women with a normal pregnancy. A combined prediction model composed of MShCG levels, body mass index (BMI), parity, and age as a predictive test for severe pre-eclampsia was superior to MShCG levels alone (AUC 0.765 versus 0.648). The integrated multifactorial model could identify women at risk early on for developing severe pre-eclampsia, with a sensitivity of 70% and a specificity of 71%. Thus, we demonstrate a potentially effective and convenient method by which women at risk for developing severe pre-eclampsia can be identified early, based on a multifactorial predictive model composed of midtrimester MShCG levels, BMI, parity, and age.
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