A New Model for the Predicting the Risk of Preeclampsia in Twin Pregnancy - Han, Qing; Zheng, Shuisen; Chen, Rongxin; Zhang, Huale; Yan, Jianying (Frontiers in Physiology, 2022)
Objective: We aimed to develop an effective nomogram model for predicting the risk of preeclampsia in twin pregnancies. Method: The study was a retrospective cohort study of women pregnant with twins who attended antenatal care and labored between January 2015 and December 2020 at the Fujian Maternity and Child Health Hospital, China. We extracted maternal demographic data and clinical characteristics. Then we performed the least absolute shrinkage and selection operator regression combined with clinical significance to screen variables. Thereafter, multivariate logistic regression was used to construct a nomogram that predicted the risk of preeclampsia in twin pregnancies. Finally, the nomogram was validated using C-statistics (C-index) and calibration curves. Results: A total of 2,469 women with twin pregnancies were included, of whom 325 (13.16%) had preeclampsia. Multivariate logistic regression models revealed that serum creatinine, uric acid, mean platelet volume, high-density lipoprotein, lactate dehydrogenase, fibrinogen, primiparity, pre-pregnancy body mass index, and regular prenatal were independently associated with preeclampsia in twin pregnancies. The constructed predictive model exhibited a good discrimination and predictive ability for preeclampsia in twin pregnancies (concordance index 0.821). Conclusions: The model for the prediction of preeclampsia in twin pregnancies has high accuracy and specificity. It can be used to assess the risk of preeclampsia in twin pregnancies.