Fig. 6

The establishment and validation of the prediction models for LOPE. A. The glmnet method utilized ten-fold cross-validation to analyze the relationship between the number of predictors and AUC values across various log(lambda) settings in the training dataset. B. A bar plot displays the coefficients of 15 clinical and laboratory indicators associated with LOPE, with MAP, MPV, and PCT being the three most positively correlated with LOPE, respectively. C. ROC curves are presented for both the clinical and laboratory marker models, as well as the clinical factor models, across diverse datasets. D. A pheatmap visualizes the correlations between 15 predictors and the PE risk prediction of the LOPE model in the training and EV set. E–F. The prediction scores exhibit significant negative correlations with gestational weeks at delivery (E) and birth weight (F). G. The prediction scores show significant positive correlations with SBP and DBP at admission among LOPE samples. H. The prediction scores demonstrate significant negative correlations with 24-hour urine protein levels in LOPE patients. I. A comparison of PE risk prediction of LOPE models is provided between FGR and control groups within LOPE samples