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 f -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.

Preeclampsia (PE), a major cause of perinatal morbidity and mortality, is frequently under-recognized as an early indicator of future cardiovascular (CV) disease. This study examines early-onset preeclampsia (eoPE) across three phases—pre-pregnancy, diagnosis, and follow-up—to dynamically reclassify risk of eoPE in the second-trimester and assess long-term CV implications. A case-control study involving 50 women with eoPE (diagnosed before 34 weeks) and 50 matched controls with uncomplicated pregnancies employed supervised machine learning to develop two predictive models: one for reevaluating first-trimester eoPE risk, with test sensitivity/specificity of 95.0% (92.2–97.8%)/99.0% (97.6–100.0%) and another for predicting post-pregnancy hypertension (HT), with test sensitivity/specificity of 74.1% (67.2–80.9%)/89.1% (85.5–92.8%). Metaheuristic methods identified key features for risk reevaluation and prediction, achieving high predictive performance using routine early pregnancy data and diagnostic information. These findings should be interpreted with caution due to the sample size limitations. Additionally, unsupervised machine learning on follow-up data (median 7.5 years postpartum) was used to explore how pregnancy conditions shape long-term health in eoPE patients.

To compare the risk of cardiovascular disease and the occurrence of cardiovascular events in the mid-long term after delivery, between women with and without a history of early-onset preeclampsia. A prospective case-control study has been conducted in Hospital Universitario 12 de Octubre, Madrid. 50 women with early-onset preeclampsia (diagnosed < 34 + 0 weeks) who delivered between 2008 and 2017 and a matched group (by age, parity, pregestational body mass index and date of delivery) of 50 women with uncomplicated pregnancies were recruited. In them, a 1-day visit for cardiovascular assessment was performed 3-12 years after delivery, consisting of the completion of blood and urine tests including oxidative stress analysis, vascular ultrasound to assess subclinical atherosclerosis and 24-hour blood pressure monitoring. Furthermore, Framingham10 and Framingham30 scales of cardiovascular disease risk were applied. Univariate analysis was used for comparisons, and the Kaplan-Meier method was performed to estimate their survival time until the development of a cardiovascular disease event (chronic hypertension, renal disease, myocardial infarction, thromboembolism and cerebrovascular disease). Patients were evaluated at a median of 7.5 years (interquartile range, 6.5-9) after delivery. Women with a history of early-onset preeclampsia vs controls showed significantly lower levels of hemoglobin (12.9 vs 13.7 g/dL), hematocrit (38.9 vs 40.8%), prothrombin activity (93.1 vs 99.8%), IgA (223.5 vs 279.9 mg/dL) and C3 factor (101.0 vs 110.5 mg/dL) and prolongated prothrombin time (12.4 vs 11.6 s). Early-onset preeclampsia cases showed worse blood pressure control, with higher percentages of over-limit systolic blood pressure (17.9 vs. 11.2%, p < 0.01) and diastolic blood pressure (28.1 vs. 18.7%, p < 0.01) readings in 24 h. There were no significant differences in the vascular ultrasound studies as well as in the estimated cardiovascular risk obtained with the Framingham scales. At the visit time, a cardiovascular event was present in 44% women with history of early-onset preeclampsia vs 10% in the control group (p < 0.01). The most common event was chronic hypertension, with a relative risk of 4.7 (95% confidence interval 1.7-13.0) for the early-onset preeclampsia group. Women with a history of early-onset preeclampsia, compared to their matched controls, showed a greater risk of cardiovascular disease mainly at the expense of a 4.7-fold risk of developing chronic hypertension, with a median follow-up of 7.5 years after delivery.

Background: Early-onset preeclampsia (eoPE), defined as preeclampsia occurring before 34 weeks of gestation, remains one of the leading causes of severe maternal morbidity, iatrogenic prematurity, and maternal mortality worldwide. Its heterogeneous and rapidly evolving clinical course makes the prediction of adverse outcomes particularly challenging. Although machine learning (ML)–based models have shown promise in stratifying eoPE risk and supporting clinical decision-making, robust prospective external validation is still lacking. The present study aims to address this gap through a multicenter validation of three ML models previously developed by our group to: (1) predict maternal complications, (2) estimate the likelihood of delivery within ≤ 7 days of diagnosis, and (3) identify the risk of hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome or placental abruption. Methods: This prospective multicenter cohort study (2024–2027) recruits pregnant women diagnosed with eoPE after informed consent. Clinical, analytical, and ultrasound data, including all variables used in the models, are recorded from early pregnancy to delivery. The first 100 cases will undergo quantitative external validation of three ML models and a semi-quantitative evaluation by clinicians, after which these cases will be used to retrain the models. The updated models will then be prospectively applied to the next 100 participants. Model predictions and physician´s assessment of the clinical utility of such predictions are generated through a secure web application accessible only after delivery. Discussion: This study addresses a critical gap in the prospective validation of ML-based tools for eoPE, emphasizing methodological rigor, data quality, and external validation in a multicenter setting. By integrating quantitative performance assessment with clinician-centered evaluation, the project seeks to ensure both statistical robustness and real-world clinical relevance. If successfully validated, these models could support more individualized and timely decision-making in eoPE management.