A model to predict risk of postpartum infection after Caesarean delivery*
| Autor: | J. Eric Jelovsek, Laura J. Moulton, Mark Lachiewicz, Kevin Chagin, Oluwatosin Goje |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adult
medicine.medical_specialty Caesarean delivery Decision Support Techniques Young Adult 03 medical and health sciences 0302 clinical medicine Pregnancy Risk Factors Postpartum infection medicine Humans Surgical Wound Infection 030212 general & internal medicine Gynecology Models Statistical 030219 obstetrics & reproductive medicine Cesarean Section Obstetrics business.industry Obstetrics and Gynecology Pneumonia Prognosis Chorioamnionitis Urinary Tract Infections Pediatrics Perinatology and Child Health Puerperal Infection Female Endometritis business |
| Zdroj: | The Journal of Maternal-Fetal & Neonatal Medicine. 31:2409-2417 |
| ISSN: | 1476-4954 1476-7058 |
| Popis: | The purpose of this study is to build and validate a statistical model to predict infection after caesarean delivery (CD).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.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).Our model accurately predicts risk of infection after CD. Identification of patients at risk for postoperative infection allows for individualized patient care and counseling. |
| Databáze: | OpenAIRE |
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