A model to predict risk of postpartum infection after Caesarean delivery
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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.
Published Version (Please cite this version)10.1080/14767058.2017.1344632
Publication InfoChagin, Kevin M; Goje, O; Jelovsek, John E; Lachiewicz, Mark; & Moulton, LJ (2017). A model to predict risk of postpartum infection after Caesarean delivery. Journal of Maternal-Fetal and Neonatal Medicine. pp. 1-9. 10.1080/14767058.2017.1344632. Retrieved from https://hdl.handle.net/10161/15108.
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Associate Professor of Obstetrics and Gynecology
Dr. Jelovsek is the Vice Chair of Education and the Director of Data Science for Women’s Health in Department of Obstetrics & Gynecology (OBGYN) at Duke University. He is Board Certified in OBGYN by the American Board of OBGYN and Board Certified in Female Pelvic Medicine & Reconstructive Surgery by the American Board of OBGYN and American Board of Urology. He currently practices Female Pelvic Medicine and Reconstructive Surgery (FPMRS). He has expertise in the development and v