A nomogram model for predicting maternal cardiovascular complications and neonatal adverse outcomes in pregnant patients with pulmonary arterial hypertension
Ruilin Ma, Jianjian Cui, Yanfang Zheng, Hui Tao, Wencong He, Zejun Yang, Yanan Li, Yin Zhao

TL;DR
This study creates a predictive model to assess risks for pregnant women with pulmonary arterial hypertension and their babies, using clinical indicators to guide early intervention.
Contribution
The paper introduces a novel nomogram model for predicting maternal cardiovascular complications and neonatal adverse outcomes in PAH pregnancies.
Findings
Nomogram models achieved high predictive accuracy with concordance indices of 0.96 and 0.91 for maternal complications.
Neonatal outcome predictors included gestational age, maternal platelet count, and B-type natriuretic peptide levels.
The model showed strong performance with area under the ROC curve values of 0.96 and 0.93 for maternal outcomes.
Abstract
Pulmonary arterial hypertension (PAH) during pregnancy significantly increases maternal and fetal mortality risk. We developed nomogram prediction models from retrospective data to assess maternal cardiovascular risks and neonatal adverse outcomes. Our study included 170 pregnant women, divided into training (70%) and validation (30%) sets. Predictors of outcomes were identified using logistic regression in the training set, and nomograms were constructed to predict maternal cardiovascular complications and neonatal adverse outcomes. Model performance was evaluated through internal validation. Predictors of cardiovascular complications included severe PAH (OR = 4.80), New York Heart Association (NYHA) classification ≥ III (OR = 25.94), ST-T changes (OR = 25.18), total bilirubin (OR = 1.49), albumin (OR = 0.87) and lactate dehydrogenase level (OR = 1.01). The nomogram showed high…
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Taxonomy
TopicsCardiovascular Issues in Pregnancy · Pulmonary Hypertension Research and Treatments · Pregnancy and preeclampsia studies
