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Early-Onset Versus Late-Onset Preeclampsia in Bogotá, Colombia: Differential Risk Factor Identification and

Ayala-Ramírez Paola1,2, Mennickent Daniela3,4, Farkas Carlos3

  • 1Human Genetics Institute, Faculty of Medicine, Pontificia Universidad Javeriana, Bogotá 110231, Colombia.

Biomedicines
|August 28, 2025
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Summary

Early-onset preeclampsia (EOP) and late-onset preeclampsia (LOP) have distinct risk factors in Colombian women. Machine learning models identified education and hypertension history as key predictors for EOP.

Keywords:
Latin Americaartificial intelligenceearly-onset preeclampsiahypertension in pregnancylate-onset preeclampsiamachine learningrisk factorstraditional statistics

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Area of Science:

  • Obstetrics and Gynecology
  • Perinatal Medicine
  • Public Health

Background:

  • Preeclampsia (PE) is a leading cause of maternal and perinatal mortality, especially in low- and middle-income countries.
  • Early-onset PE (EOP) and late-onset PE (LOP) represent distinct clinical conditions with different underlying mechanisms.
  • Limited research exists on differential risk factors for EOP and LOP in Latin American populations.

Purpose of the Study:

  • To identify and evaluate clinical risk factors for predicting EOP and LOP.
  • To compare traditional statistical methods with machine learning (ML) for PE subtype prediction.
  • To develop context-specific prenatal care strategies for pregnant women in Bogotá, Colombia.

Main Methods:

  • A cross-sectional observational study of 190 pregnant women with PE (80 EOP, 110 LOP) in Bogotá.
  • Data collection via structured interviews and clinical records, including risk factors and perinatal outcomes.
  • Application of traditional statistics and eleven ML techniques for predictive modeling and feature importance analysis.

Main Results:

  • EOP was associated with higher maternal education and hypertension history; LOP with allergic history.
  • Linear discriminant analysis achieved the highest recall (0.71) for PE subtype prediction.
  • Key predictors identified by ML included education level, family history of perinatal death, and hypertension history.

Conclusions:

  • EOP and LOP present with unique clinical profiles in this Colombian cohort.
  • Integrating traditional statistics with ML enhances early risk stratification for PE subtypes.
  • Findings support the development of tailored prenatal care strategies in similar healthcare settings.