A model to predict risk of postpartum infection after Caesarean delivery*

Abstract

Purpose: The purpose of this study is to build and validate a statistical model to predict infection after caesarean delivery (CD).

Methods: Patient and surgical variables within 30 d of CD were collected on 2419 women. Postpartum infection included surgical site infection, urinary tract infection, endomyometritis and pneumonia. The data were split into model development and internal validation (1 January–31 August; N = 1641) and temporal validation subsets (1 September–31 December; N = 778). Logistic regression models were fit to the data with concordance index and calibration curves used to assess accuracy. Internal validation was performed with bootstrapping correcting for bias.

Results: Postoperative infection occurred in 8% (95% CI 7.3–9.9), with 5% meeting CDC criteria for surgical site infections (SSI) (95% CI 4.1–5.8). Eight variables were predictive for infection: increasing BMI, higher number of prior Caesarean deliveries, emergent Caesarean delivery, Caesarean for failure to progress, skin closure using stainless steel staples, chorioamnionitis, maternal asthma and lower gestational age. The model discriminated between women with and without infection on internal validation (concordance index = 0.71 95% CI 0.67–0.76) and temporal validation (concordance index = 0.70, 95% CI 0.62, 0.78).

Conclusions: Our model accurately predicts risk of infection after CD. Identification of patients at risk for postoperative infection allows for individualized patient care and counseling.

Keywords:

• Caesarean delivery
• postpartum infection
• statistical model

Disclosure statement

No financial benefit or interest has arisen from direct applications of this research for all authors.