AUTHOR=Xia Yan , Wang Yao , Yuan Shijin , Hu Jiaming , Zhang Lu , Xie Jiamin , Zhao Yang , Hao Jiahui , Ren Yanwei , Wu Shengjun 

TITLE=Development and validation of nomograms to predict clinical outcomes of preeclampsia

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2024.1292458

DOI=10.3389/fendo.2024.1292458

ISSN=1664-2392

ABSTRACT=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.