Predictive modeling of complications arising from early-onset preeclampsia in pregnant women.

BACKGROUND

Preeclampsia, a complex and unpredictable pregnancy complication, poses significant challenges in predicting maternal outcomes, often leading to heightened anxiety among patients, families, and clinicians. This study introduces an innovative approach to enhance the prediction of complications in early-onset preeclampsia, leveraging advanced machine learning techniques inspired by bio-algorithms.

OBJECTIVE

Our goal is to enhance the clinical management of preeclampsia by improving risk stratification and offering a more personalized approach to patient care.

DESIGN

A single-center, observational, retrospective cohort study with 246 singleton pregnancies diagnosed with early-onset preeclampsia between January 2007 and December 2020 was conducted at 12 de Octubre Hospital. Exclusions included pregnancies with congenital anomalies, lack of angiogenesis biomarker determination or loss of follow-up, resulting in a cohort of 234 patients.

METHODS

We employed innovative genetic algorithm strategies, integrating two distinct supervised machine learning models. These aim to accurately forecast key maternal risks associated with preeclampsia and determine the optimal timing for delivery. This approach culminates in a unique ensemble framework, comprising a primary model for assessing the risk of adverse outcomes and two specialized sub-models focusing on Hemolysis, Elevated Liver enzymes, and Low Platelets-abruption and temporal factors.

RESULTS

Our findings are promising. The mono-objective genetic algorithm strategy yielded predictive -scores of 68.3%, 83.1% ± 7.2%, and 71.5% ± 3.5% in the "Risk of Adverse Outcomes," "Hemolysis, Elevated Liver enzymes, and Low Platelets-Abruption," and "Time to Delivery" models, respectively. The multi-objective strategy, utilizing minimal yet powerful variable combinations, achieved predictive accuracies of 61.5%, 80.0% ± 6.2%, and 69.3% ± 7.2% with just five, four, and six features in the respective models. These results highlight the potential of our approach in enhancing clinical decision-making.

CONCLUSION

This study introduces a novel approach to risk stratification in early-onset preeclampsia, integrating baseline and delivery data within a machine learning framework. Our results demonstrate that refined risk prediction with a minimal number of variables can complement existing clinical tools. Further validation in larger cohorts is needed to confirm its potential impact on decision-making and maternal outcomes.