Systematic review of predictive models for pregnancy complications and their role in optimizing perinatal care
DOI:
https://doi.org/10.37800/RM.2.2026.737Keywords:
беременность, осложнения беременности, преэклампсия, преждевременные роды, гестационный сахарный диабет, прогнозирование, перинатальная помощьAbstract
Relevance: Pregnancy complications, including preeclampsia, preterm birth, and gestational diabetes mellitus, remain leading causes of maternal and perinatal morbidity and mortality. Modern approaches to pregnancy management require a transition from a universal model to a risk-based approach, grounded in predictive models for early identification of high-risk groups.
The study aimed to determine the role of contemporary predictive models in assessing the risk of pregnancy complications to optimize perinatal care.
Materials and Methods: A systematic review of the literature was conducted using PubMed, Scopus, Web of Science, and Cochrane Library databases for the period 2015–2025. Study selection was performed in accordance with PRISMA guidelines. The analysis included original studies and systematic reviews focused on the development and validation of predictive models for pregnancy complications. The quality of included studies was assessed using the PROBAST tool.
Results: A total of 25 studies were included in the review. Models for predicting preeclampsia were found to be the most developed, demonstrating high predictive accuracy (AUC up to 0.85). Models for preterm birth showed moderate accuracy (AUC 0.54–0.70), while models for gestational diabetes mellitus demonstrated consistent performance (AUC 0.68–0.85). The highest effectiveness was observed in combined models incorporating clinical parameters and biomarkers, as well as in machine learning-based models (AUC up to 0.90).
Conclusion: Predictive models are effective tools for the early detection of pregnancy complications and enable optimization of perinatal care through risk stratification. Further research is required to ensure external validation and adaptation of these models to national healthcare system settings.
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