ArticleJournal of clinical hypertension (Greenwich, Conn.)2025
Development and Validation of a Nomogram for Predicting the Probability of Postpartum Chronic Hypertension in Women With Hypertensive Disorders of Pregnancy: A Multicenter, Cross-Sectional Study.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Women with hypertensive disorders of pregnancy (HDP) have a higher risk of developing chronic hypertension (CHT) postpartum, which can lead to increased cardiovascular events. Therefore, we aimed to develop and validate a nomogram to predict the probability of CHT in HDP women by analyzing traditional characteristics and pregnancy-related indices. A total of 688 HDP women who delivered at the three designated hospitals in China, during the period of January 2011 to June 2021, were randomly divided into 70% (n = 482) as the training set and the remaining 30% (n = 206) as the validation set. Predictors for CHT were extracted to establish a nomogram based on multivariate logistic analysis of the training set. The performance of the nomogram was evaluated by an internal validation. In total, 207 (30.1%) patients developed CHT after delivery. Maternal age, highest systolic blood pressure (SBP), highest diastolic blood pressure (DBP), peak alkaline phosphatase (ALP) levels, peak uric acid (UA) levels, and urine protein during pregnancy were independent predictors of the nomogram. Area under the curve (AUC) of the training set was 0.819 (95% CI: 0.778-0.860, p < 0.001) and 0.800 (95% CI: 0.739-0.862, p < 0.001) in the validation set. A good consistency between the nomogram model and standard diagnostic criteria was obtained (p > 0.05). Decision curve analysis (DCA) also showed a net benefit in the nomogram when the risk thresholds were 10%-90%. In conclusion, we developed a novel clinical nomogram to predict CHT risk in women with HDP, which was a useful and easy tool to identify high-risk individuals and performed well on internal validation.
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