Objective: The objective was to create a nomogram for the individual prediction of preeclampsia (PE).
Study design: In a prospective population-based study that included 4777 patients, PE occurred in 2.4%. Age, body mass index, parity, previous preeclampsia (PPE), chronic hypertension, diastolic blood pressure (DBP), and proteinuria at first visit, and second trimester ultrasonography and umbilical artery Doppler resistance index (UARI) data were used to develop and calibrate a nomogram based on a multivariate logistic regression model.
Results: Based on multivariate analysis, nulliparity (P=0.002), PPE (P=0.004), DBP (P<0.0001), biparietal diameter (P=0.011), and UARI (P=0.08) were introduced into a nomogram. Based on these variables, the nomogram had good discrimination (area under the ROC curve=0.73, P<0.01) and calibration (unreliability index=-5.2 x 10(-4)). This nomogram was validated by bootstrapping.
Conclusion: Our nomogram predicts the probability of preeclampsia. After validation in an independent population, this tool could be used to plan a preventive trial.