Study Reveals: Preeclampsia Risk Models Lose Accuracy Over Time

A study reveals that preeclampsia risk models lose accuracy after 48 hours, calling for better prediction methods.

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Study Reveals: Preeclampsia Risk Models Lose Accuracy Over Time

Preeclampsia is a severe pregnancy-related condition that poses significant risks to both maternal and fetal health, often developing without warning. Affecting 5-20% of women diagnosed with it, preeclampsia can lead to complications such as organ failure, stroke, or even death if not appropriately managed. While early detection is key to managing and preventing severe outcomes, the current tools used to predict risks for these complications have shown limitations when used over extended periods of time. A recent study published in PLOS Medicine on February 4, 2025, explores the performance of existing preeclampsia risk models, revealing that their effectiveness diminishes over time, especially after the initial 48-hour window post-hospital admission. This finding suggests that improvements are needed in predictive models to help clinicians make better long-term decisions for pregnant women at risk of preeclampsia.

Understanding Preeclampsia and its Complications

Preeclampsia is typically characterized by high blood pressure and signs of damage to organs such as the kidneys or liver, which occur after the 20th week of pregnancy. It can lead to complications like eclampsia, premature birth, and placental abruption, all of which pose risks to both the mother and the baby. Managing preeclampsia often requires early intervention, and for many women, hospitalization is necessary to monitor and manage the condition.

Risk models like the PIERS (Pre-eclampsia Integrated Estimate of RiSk) Machine Learning (PIERS-ML) and fullPIERS have been used to predict the likelihood of adverse maternal outcomes in women diagnosed with preeclampsia. These models are based on clinical data and have been widely used for assessing the risks of women in the 48 hours following their hospital admission. However, the new research findings suggest that these prediction models do not perform well beyond the first two days after admission, highlighting the limitations of these tools when applied over longer periods.

Study Methodology: Evaluating the Performance of PIERS Models

To examine the performance of the PIERS-ML and fullPIERS models, the research team used data from 8,843 women diagnosed with preeclampsia between 2003 and 2016. These women were at a median gestational age of 36 weeks, and their data included clinical assessments made using both the PIERS-ML and fullPIERS models. This retrospective study allowed the researchers to evaluate how accurately these models predicted maternal health outcomes over time, particularly after the initial 48 hours of hospital admission.

The study assessed whether these models maintained their accuracy as the days passed after admission and whether they could still be relied upon to predict long-term outcomes for pregnant women with preeclampsia. It was found that while both models were effective in identifying very high-risk and very low-risk individuals initially, their predictive accuracy deteriorated significantly after 48 hours. In fact, the risk predictions for the larger high-risk and low-risk groups became notably less reliable with time, indicating that these models are not suitable for ongoing use beyond the first 48 hours of hospitalization.

Performance of PIERS Models: Short-Term vs. Long-Term Predictive Ability

The PIERS-ML model demonstrated the best performance for identifying extreme cases – that is, very high-risk and very low-risk groups – in the short term. However, its predictive ability for these groups declined over time, making it less effective for ongoing assessments. The study highlighted that the performance of the PIERS-ML model worsened when used repeatedly beyond 48 hours, which is critical for clinicians who need to continuously assess the health of patients at risk for preeclampsia.

The fullPIERS model, based on logistic regression, showed even worse performance over time than the PIERS-ML model. While the fullPIERS model had been previously used for predicting severe complications, the study revealed that it failed to perform adequately in identifying risk levels for preeclampsia complications after the initial hospital admission period. This finding underscores the need for better models that can predict outcomes over extended periods of time, not just during the first 48 hours.

Clinical Implications and Recommendations

The findings of the study provide essential insights for healthcare professionals managing women with preeclampsia. The authors of the study caution that while clinicians may still use the PIERS-ML and fullPIERS models beyond the 48-hour window, the predictions should be treated with increasing caution as the pregnancy progresses. The deterioration in performance over time means that clinicians cannot solely rely on these models for long-term risk prediction. This underlines the importance of combining risk prediction models with clinical judgment and monitoring over time to manage preeclampsia effectively.

The Need for New, Dynamic Models

As the study suggests, the current static models, which are designed and validated for initial assessments of risks, become increasingly inaccurate when used for repeated assessments over days. There is a clear need for new prediction models that can dynamically adjust to changing conditions and remain accurate over time. These models should consider not only the initial health data but also how the woman’s condition evolves during the course of the pregnancy.

New models should take into account a variety of factors, including the rate of change in clinical measurements, the impact of interventions, and any other variables that may influence the progression of preeclampsia. Additionally, the models should be validated over longer time periods to ensure that they remain reliable throughout the course of the condition. This is especially crucial as preeclampsia can develop and change rapidly, and early, accurate prediction is essential to managing the risk to both the mother and the baby.

The Role of Personalized Medicine in Managing Preeclampsia

Personalized medicine is an emerging field that tailors medical treatment to the individual characteristics of each patient. In the case of preeclampsia, personalized medicine could lead to better prediction models by incorporating genetic, environmental, and lifestyle factors into the assessment process. Understanding how these factors interact with preeclampsia could improve risk stratification and lead to more effective interventions.

Integrating personalized medicine into the prediction models could help clinicians better identify women who are at higher risk for severe complications, allowing them to tailor treatments and interventions more effectively. For example, genetic markers could be used to predict which women are more likely to develop severe forms of preeclampsia, while lifestyle factors like diet and exercise could be incorporated to understand how these variables impact the course of the disease.

 Addressing the Gaps in Preeclampsia Risk Models

The study published in PLOS Medicine underscores the limitations of existing preeclampsia risk models, particularly their declining performance over time. While the PIERS-ML and fullPIERS models have proven useful for initial assessments, their inability to maintain accuracy for ongoing risk stratification is a significant challenge. As preeclampsia continues to pose serious risks to maternal and fetal health, there is a pressing need for new prediction models that can remain reliable throughout the course of the disease.

Healthcare professionals should be aware of the limitations of current models and use them with caution when assessing long-term risks. The study calls for further research into dynamic, personalized models that can better predict the evolving risks of preeclampsia over time. With continued innovation in predictive medicine, it is hoped that more accurate, reliable tools will be developed to help manage preeclampsia and improve outcomes for both mothers and babies.