Development and validation of nomograms to predict clinical outcomes of preeclampsia - Xia, Yan; Wang, Yao; Yuan, Shijin; Hu, Jiaming; Zhang, Lu; Xie, Jiamin; Zhao, Yang; Hao, Jiahui; Ren, Yanwei; Wu, Shengjun (Frontiers in Endocrinology, 2024)
Background: Pre-eclampsia (PE) is one of the most severe pregnancy-related diseases, however, reliable biomarkers are still lacking. In this study, we aim to develop models for predicting early-onset PE, severe PE, and gestation duration of PE patients. Methods: Eligible PE patients were enrolled and divided into training (n=253) and validation (n=108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and gestation duration of PE patients, respectively. Based on significant factors, nomograms were developed and evaluated by area under the curve (AUC) and calibration curve. Results: In training cohort, multiple gravidity experience (P=0.005), lower albumin (P<0.001), and higher lactate dehydrogenase (LDH, P<0.001) were significantly associated with early-onset PE; abortion history (P=0.017), prolonged thrombin time (TT, P<0.001), higher aspartate aminotransferase (P=0.002) and LDH (P=0.003) were significantly associated with severe PE; abortion history (P<0.001), gemellary pregnancy (P<0.001), prolonged TT (P<0.001), higher mean platelet volume (P=0.014), and LDH (P<0.001), lower albumin (P<0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each PE patients. AUC showed a good predictive performance. Calibration curves and decision curve analysis demonstrated a clinical practicability. Conclusion: Based on clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and gestation duration for pregnant women with PE, which help clinicians to early assess the clinical outcomes and design appropriate strategy for patients.