Post-cesarean Section Peritonitis at a Referral Hospital in Rwanda: Factors Associated with Maternal Morbidity and Mortality

Background: Post-cesarean section peritonitis is the leading cause of maternal morbidity and mortality at the main referral hospital in Rwanda. Published data on the management of post-cesarean section peritonitis is limited. This study examined predictors of maternal morbidity and mortality for post-cesarean peritonitis.

Methods: We performed a prospective observational cohort study at the University Teaching Hospital Kigali (CHUK) from January 1 until December 31 2015, followed by a retrospective chart review of all subjects with post-cesarean section peritonitis admitted to CHUK from January 1 until December 31, 2014. All patients admitted with the diagnosis of post-cesarean section peritonitis undergoing exploratory laparotomy at CHUK were enrolled. Patients were followed to either discharge or death. Study variables included baseline demographic/clinical characteristics, admission physical exam, intraoperative findings, and management. Data were analyzed using STATA version 14.

Results: Of the 167 patients enrolled, 81 survived without requiring hysterectomy (49%), 49 survived requiring hysterectomy (29%), and 36 died (22%). In the multivariate analysis, severe sepsis was the most significant predictor of mortality (RR=4.0 [2.2-7.7]) and uterine necrosis was the most significant predictor of hysterectomy (RR=6.3 [1.6-25.2]). There were high rates of antimicrobial resistance (AMR) among the bacterial isolates cultured from intra-abdominal pus, with 52% of bacteria resistant to third-generation cephalosporins.

Conclusions: Post-cesarean section peritonitis carries a high mortality rate in Rwanda. It is also associated with a high rate of hysterectomy. Understanding the disease process and identifying factors associated with outcomes can help guide management during admission.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.