Development of a nomogram for individual preterm birth risk evaluation.

Area of Science:

• Perinatology and Obstetrics
• Reproductive Medicine
• Biostatistics and Predictive Modeling

Background:

• Preterm birth remains a leading cause of neonatal morbidity and mortality.
• Accurate risk stratification in high-risk pregnancies is crucial for timely intervention.
• Existing predictive models often lack personalization for individual patient profiles.

Purpose of the Study:

• To develop and validate a novel nomogram for personalized preterm birth risk assessment.
• To identify key clinical and paraclinical factors associated with preterm delivery in high-risk pregnancies.
• To create a user-friendly tool for clinical application in risk evaluation.

Main Methods:

• A prospective, multicentric study involving 813 high-risk asymptomatic pregnant women.
• Logistic regression modeling incorporating variables such as medical history, smoking, bacterial vaginosis, and cervical length.
• Model validation using bootstrap resampling and development of a downloadable risk calculator.

Main Results:

• Nine significant risk factors were identified, including history of preterm delivery, smoking, cervical length, and bacterial vaginosis.
• The developed nomogram demonstrated good predictive ability with an Area Under the Curve (AUC) of 0.77.
• The model showed excellent calibration (mean absolute error of 0.018) and identified an optimal risk threshold of 23.2%.

Conclusions:

• The validated nomogram provides a reliable tool for individual preterm birth risk assessment in high-risk pregnancies.
• This personalized approach can aid clinicians in better stratifying risk and potentially guiding management strategies.
• The availability of a downloadable calculator enhances the practical utility of the nomogram in clinical settings.