A model for preconceptional prediction of recurrent early-onset preeclampsia: derivation and internal validation.
until further notice
SourceReproductive Sciences, 18, 11, (2011), pp. 1154-9
01 november 2011
Article / Letter to editor
Display more detailsDisplay less details
SubjectNCEBP 14: Cardiovascular diseases
OBJECTIVE: To develop a model to identify women at very low risk of recurrent early-onset preeclampsia. METHODS: We enrolled 407 women who had experienced early-onset preeclampsia in their first pregnancy, resulting in a delivery before 34 weeks' gestation. Preeclampsia was defined as hypertension (systolic blood pressure >/=140 mm Hg and/or diastolic blood pressure >/=90 mm Hg) after 20 weeks' gestation with de novo proteinuria (>/=300 mg urinary protein excretion/day). Based on the previous published evidence and expert opinion, 5 predictors (gestational age at previous birth, prior small-for-gestational-age newborn, fasting blood glucose, body mass index, and hypertension) were entered in a logistic regression model. Discrimination and calibration were evaluated after adjusting for overfitting by bootstrapping techniques. RESULTS: Early-onset disease recurred in 28 (6.9%) of 407 women. The area under the receiver operating characteristic (ROC) curve of the model was 0.65 (95% CI: 0.56-0.74). Calibration was good, indicated by a nonsignificant Hosmer-Lemeshow test (P = .11). Using a predicted absolute risk threshold of, for example, 4.6% (ie, women identified with an estimated risk either above or below 4.6%), the sensitivity was 100%, with a specificity of 26%. In such a strategy, no women who developed preeclampsia were missed, while 98 of the 407 women would be regarded as low risk of recurrent early-onset preeclampsia, not necessarily requiring intensified antenatal care. CONCLUSION: Our model may be helpful in the identification of women at very low risk of recurrent early-onset preeclampsia. Before widespread application, our model should be validated in other populations.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.