A Predictive Nomogram for Post-Cesarean Infections: Risk Factors and Clinical Implications

Area of Science:

• Obstetrics and Gynecology
• Infectious Disease Prevention
• Clinical Prediction Modeling

Background:

• Postoperative infections following cesarean sections significantly increase maternal morbidity and healthcare costs.
• Identifying specific risk factors is crucial for developing effective prevention strategies.

Purpose of the Study:

• To identify key risk factors for postoperative infections after cesarean sections.
• To develop and validate a predictive nomogram for assessing infection risk.

Main Methods:

• Retrospective study of 685 cesarean section patients.
• Multivariable logistic regression for risk factor identification.
• Nomogram development and validation using ROC analysis, calibration plots, and DCA.

Main Results:

• Diabetes mellitus, Group B Streptococcus (GBS) colonization, membrane rupture, complete cervical dilation, and >5 vaginal examinations were significant risk factors.
• The nomogram demonstrated strong predictive accuracy with an AUC of 0.786.
• Internal validation confirmed good calibration and clinical utility.

Conclusions:

• Excessive vaginal examinations, membrane rupture, cervical dilation, diabetes mellitus, and GBS colonization are key predictors of postoperative infection.
• The developed nomogram provides a valuable tool for clinicians to stratify infection risk and guide targeted prevention.