Fig. 5

The establishment and validation of the prediction models for EOPE. A. The ten-fold cross-validation for glmnet examined the relationship between the number of predictors and AUC values at different log (lambda) values in the training set. The left dashed line represents the Lambda.min value that maximizes the AUC value with the optimal combination of predictors, while the right dashed line indicates the lambda.1se value that yields a more regularized model with a cross-validated AUC within one standard error of the minimum. B. The coefficients of seven clinical and laboratory markers for predicting EOPE are presented, with MAP and AST/ALT being most positively and negatively correlated with EOPE, respectively. C. ROC curves are shown for the clinical and laboratory marker models as well as the clinical factor models in different datasets, including the training set (TS) and external validation set (EV). D. A pheatmap visualizes the correlations between seven predictors and the PE risk predicted by the ensemble EOPE model in the training and EV set. The vertical bar represents correlation coefficients, with red and blue showing high and low correlation respectively. E–F. The prediction scores presented significantly negative correlations with gestational weeks at delivery (E) and birth weight (F). G. The prediction scores showed significantly negative correlations with twenty-four hour urine protein levels in EOPE patients. H. Comparison of PE risk prediction among EOPE samples with different readings of + on dipstick analysis of urine specimens