ObjectiveTo explore the utilization of longitudinal data in constructing non-time-varying outcome prediction models and to compare the impact of different modeling approaches on prediction performance. MethodsClinical predictors were selected using univariate analysis and Lasso regression. Non-time-varying outcome prediction models were developed based on latent class trajectory analysis, the two-stage model, and logistic regression. Internal validation was performed using Bootstrapping resampling, and model performance was evaluated using ROC curves, PR curves, sensitivity, specificity and other relevant metrics. ResultsA total of 49 629 pregnant women were included in the study, with mean age of 31.42±4.13 years and pre-pregnancy BMI of 20.91±2.62kg/m². Fourteen predictors were incorporated into the final model. Prediction models utilizing longitudinal data demonstrated high accuracy, with AUROC values exceeding 0.90 and PR-AUC values greater than 0.47. The two-stage model based on late-pregnancy hemoglobin data showed the best performance, achieving AUROC of 0.93 (95%CI 0.92 to 0.94) and PR-AUC of 0.60 (95%CI 0.56 to 0.64). Internal validation confirmed robust model performance, and calibration curves indicated a good agreement between predicted and observed outcomes. ConclusionFor the longitudinal data, the two-stage model can well capture the dynamic change trajectory of the longitudinal data. For different clinical outcomes, the predictive value of repeated measurement data is different.