Predictors of Infection after Cesarean Delivery

Document Type

Journal Article

Publication Date

3-31-2026

Journal

American journal of perinatology

DOI

10.1055/a-2835-1796

Abstract

Postoperative infections remain a significant complication of cesarean delivery (CD), with rates ranging from 3 to 30%. These infections increase healthcare costs, prolong recovery, and negatively impact maternal outcomes. Identifying risk factors for infection might help guide preventative strategies to mitigate this burden. The primary aim of this study was to develop and validate a predictive model for infection after CD using preoperative and perioperative characteristics.This study was a secondary analysis of a multicenter randomized trial comparing tranexamic acid versus placebo to prevent postpartum hemorrhage in individuals undergoing either scheduled or unscheduled CD. The primary outcome of this analysis was a composite of surgical site infection, endometritis, or pelvic abscess diagnosed within 6 weeks postpartum. Univariable and multivariable bootstrapped logistic regression models with stepwise selection were used to identify predictors of infection. Model performance was evaluated using the receiver operator curve area under the curve (AUC).Of the 10,995 participants, 287 (2.6%) developed an infection. Significant predictors included tobacco use (OR: 1.67, 95% CI: 1.21-2.31), BMI at delivery ≥ 30 kg/m (OR: 1.41, 95% CI: 1.04-1.91), labor before cesarean (OR: 1.71, 95% CI: 1.33-2.18), longer surgical duration (OR: 1.01 per minute, 95% CI: 1.01-1.02), uterine incision extension (OR: 1.54, 95% CI: 1.02-2.34), and the use of uterotonics other than oxytocin (OR: 1.48, 95% CI: 1.09-2.02). The predictive model demonstrated modest discrimination with an AUC of 0.64 (95% CI: 0.61-0.68).Multiple modifiable and nonmodifiable factors influence infection after CD. This predictive model offers a framework for assessing individualized risk, though its modest performance indicates that further refinement is necessary before it can be confidently applied in clinical decision-making. Future research should aim to enhance predictive accuracy and explore whether risk stratification meaningfully informs prevention or patient counseling strategies. · This study aimed to create a model to predict postpartum infection after cesarean delivery.. · Several factors were linked to higher infection risk after cesarean delivery. These included tobacco use, obesity, labor before cesarean, longer surgery, uterine incision extension, and uterotonic use.. · The model had moderate accuracy, with an AUC of 0.6.. · Common perioperative factors may help predict infection risk after cesarean delivery..

Department

Biostatistics and Bioinformatics

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