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Related Experiment Video

Updated: Oct 19, 2025

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
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Recurrent preterm birth risk assessment for two delivery subtypes: A multivariable analysis.

Ilia Rattsev1,2, Natalie Flaks-Manov3, Angie C Jelin4,5

  • 1Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.

Journal of the American Medical Informatics Association : JAMIA
|September 24, 2021
PubMed
Summary

A new framework using a clinical phenotyping tool effectively assesses risk for recurrent preterm birth. This approach improves prediction accuracy for spontaneous and indicated preterm deliveries.

Keywords:
medical informaticspregnancy complicationspremature birthproportional hazards modelsrisk factors

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Area of Science:

  • Reproductive Medicine
  • Perinatal Epidemiology
  • Clinical Informatics

Background:

  • Recurrent preterm birth poses significant risks to maternal and infant health.
  • Accurate risk assessment is crucial for targeted interventions and improved outcomes.
  • Existing phenotyping tools require enhancement for comprehensive risk evaluation.

Purpose of the Study:

  • To develop and apply a novel 4-step framework for assessing recurrent preterm birth risk.
  • To extend an existing clinical phenotyping tool for enhanced predictive capabilities.
  • To analyze sociodemographic, clinical, and obstetric factors associated with recurrent preterm birth.

Main Methods:

  • Retrospective cohort study utilizing data from the Genomic and Proteomic Network for Preterm Birth Research Longitudinal Cohort Study (GPN-PBR LS).
  • Analysis of 52 risk factors, including sociodemographic, clinical, and obstetric history variables.
  • Application of Chi-square analysis, Kaplan-Meier estimates, and Cox proportional hazards models for univariate and multivariable analyses, stratified by delivery subtype.

Main Results:

  • Analysis included 428 women with a history of spontaneous preterm birth.
  • Key predictors identified: maternal age, race, income, marital status, previous C-section, delivery/abortion history, birth weight, cervical insufficiency, decidual hemorrhage, and placental dysfunction.
  • Stratified models showed improved performance (concordance 0.76 spontaneous, 0.87 indicated) compared to the naive model (0.72).

Conclusions:

  • The developed 4-step framework effectively analyzes risk factors for recurrent preterm birth in retrospective cohorts.
  • The framework demonstrates practical utility for future analyses with diverse data sources, including electronic health records.
  • This approach offers a robust method for identifying women at high risk of recurrent preterm birth.