Using Doppler ultrasound to assess fetal cardiac function and pregnancy outcomes in obstetric antiphospholipid syndrome pregnancies: a case-control study.

Abstract

This study aimed to evaluate fetal left ventricular function (LVF) in pregnant women with obstetric antiphospholipid syndrome (OAPS) by Doppler ultrasound and developed a clinical nomogram to predict adverse perinatal outcomes. In this prospective observational study, 105 pregnant women were enrolled and divided into the OAPS cohort (n = 60) and the control cohort (n = 45). Fetal cardiac function parameters were collected and compared between two cohorts. Univariate and multivariate analysis was conducted to select the risk factors associated with adverse perinatal outcomes, and a clinical nomogram was developed based on these selected risk factors. The predictive performance of corresponding indicators for adverse perinatal outcomes was evaluated using receiver operating characteristic (ROC) curve analysis. The OAPS cohort revealed an increase in the isovolumic relaxation time (IVRT) and myocardial performance index (MPI), a decrease in the ejection time (ET), middle cerebral artery pulsatility index (MCA-PI) and cerebroplacental ratio (CPR) compared to the control cohort. Through univariate and multivariate analysis, gravidity, CPR, and MPI were the risk factors associated with adverse perinatal outcomes. A model predicting adverse perinatal outcomes in OAPS pregnant women was constructed based on these three factors and visualized as a nomogram. The nomogram could accurately predict adverse perinatal outcomes with an area under the curve of 0.923 (95% CI: 0.858-0.982). This performance was better than evaluating individual factors such as MPI (0.825, 95% CI: 0.739-0.911) and CPR (0.816, 95% CI: 0.705-0.927) for efficacy. MPI can be used to assess fetal LVF and predict adverse perinatal outcomes. We developed a nomogram to predict adverse perinatal outcomes in OAPS women. This imaging-based evidence can provide timely clinical intervention, enabling personalized clinical decision-making.