Rethinking Risk Prediction in Preeclampsia: From Biomarkers to Mechanistic Phenotypes and Longitudinal Models

Area of Science:

• Obstetrics and Gynecology
• Perinatal Medicine
• Biomarker Research

Background:

• Preeclampsia is a leading cause of maternal and perinatal mortality globally.
• Current predictive models have limited clinical utility due to treating preeclampsia as a single entity.
• Existing biomarkers and statistical approaches have not significantly improved risk stratification.

Purpose of the Study:

• To review current evidence on preeclampsia prediction.
• To advocate for integrating biological heterogeneity and temporal dynamics in predictive frameworks.
• To outline future research priorities for next-generation prediction.

Main Methods:

• Narrative review of existing literature on preeclampsia biomarkers and prediction.
• Synthesis of evidence supporting multimarker, phenotype-informed, and longitudinal approaches.
• Examination of statistical and machine learning models for clinical implementation.

Main Results:

• Preeclampsia is a heterogeneous syndrome with varying mechanistic phenotypes.
• Progress in prediction requires integrating heterogeneity and temporal dynamics, not just new biomarkers.
• Successful implementation necessitates calibration, validation, interpretability, and clinical usability.

Conclusions:

• Future preeclampsia prediction relies on dynamic, longitudinal risk assessment.
• Mechanistically grounded phenotyping and risk updating are crucial.
• Integrating risk stratification with interventions will enhance clinical decision-making.