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

Abstract

Background: Material/Methods: Results: Conclusions: Postoperative infections following cesarean sections contribute to increased maternal morbidity, prolonged hospital stays, and elevated healthcare costs. Identifying risk factors and developing predictive models are essential for targeted prevention. A retrospective study of 685 cesarean section patients from January 2021 to December 2023 categorized them into infection (n=33) and non-infection (n=652) groups. Risk factors were identified using multivariable logistic regression. A nomogram was developed and validated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). Comparative analysis showed diabetes mellitus (39.4% vs 20.0%, P<0.001) and Group B Streptococcus (GBS) colonization (9.1% vs 2.4%, P=0.024) were more common in the infection group. Membrane rupture (57.6% vs 23.8%, P<0.001), complete cervical dilation (6.1% vs 0.9%, P=0.007), and >5 vaginal examinations (30.3% vs 10.0%, P<0.001) increased infection risk. The nomogram showed an AUC of 0.786 (95% CI: 0.681-0.856), sensitivity of 79.7%, and specificity of 76.8%. Internal validation confirmed a corrected C-index of 0.716 and excellent calibration (mean absolute error=0.008, Hosmer-Lemeshow c2=2.915, P=0.921). Decision curve analysis demonstrated superior net benefit over no or universal intervention. Key risk factors for postoperative infections include excessive vaginal examinations, membrane rupture, cervical dilation, diabetes mellitus, and GBS colonization. The nomogram offers strong predictive accuracy and clinical utility, aiding clinicians in stratifying infection risk and implementing targeted prevention.