Maternal preterm birth prediction in the United States: a case-control database study.

Area of Science:

• Reproductive Health
• Public Health
• Biostatistics

Background:

• Preterm birth is a significant global public health concern.
• Early prediction of preterm birth is crucial for timely interventions.
• This study aimed to create a practical preterm birth prediction model for clinical use.

Purpose of the Study:

• To develop and validate a predictive model for preterm birth.
• To identify key clinical and demographic factors associated with preterm birth.
• To establish a nomogram for accessible preterm birth risk assessment.

Main Methods:

• Utilized a case-control study design with data from the National Vital Statistics System (NVSS) (2018-2019).
• Employed univariate and multivariate logistic regression analyses to identify risk factors.
• Evaluated model performance using Area Under the Curve (AUC) and accuracy metrics.

Main Results:

• Analysis of over 6 million pregnancies revealed factors associated with reduced preterm birth risk (e.g., higher education, prenatal care) and increased risk (e.g., advanced maternal age, smoking, pre-existing conditions, previous preterm birth, plurality).
• The prediction model achieved an AUC of 0.688 and an accuracy of 0.762.
• A nomogram incorporating maternal and paternal information was developed for risk prediction.

Conclusions:

• The developed nomogram demonstrates good performance for predicting preterm birth risk.
• The identified predictors are clinically accessible, making the tool practical for healthcare settings.
• This nomogram offers a simple yet effective method for preterm birth prediction and management.