Infections with multidrug resistant (MDR) bacteria in hospital settings have substantial implications in terms of clinical and economic outcomes. However, due to clinical and methodological heterogeneity, estimates about the attributable economic and clinical effects of healthcare-associated infections (HAI) due to MDR microorganisms (MDR HAI) remain unclear. The objective was to review and synthesize the evidence on the impact of MDR HAI in adults on hospital costs, length of stay, and mortality at discharge. Literature searches were conducted in PubMed/MEDLINE, and Google Scholar databases to select studies that evaluated the impact of MDR HAI on economic and clinical outcomes. Eligible studies were conducted in adults, in order to ensure homogeneity of populations, used propensity score matched cohorts or included explicit confounding control, and had confirmed antibiotic susceptibility testing. Risk of bias was evaluated, and effects were measured with ratios of means (ROM) for cost and length of stay, and risk ratios (RR) for mortality. A systematic search was performed on 14^th March 2019, re-run on the 10^th of June 2019 and extended the 3^rd of September 2019. Small effect sizes were assessed by examination of funnel plots. Sixteen articles (6,122 patients with MDR HAI and 8,326 patients with non-MDR HAI) were included in the systematic review of which 12 articles assessed cost, 19 articles length of stay, and 14 mortality. Compared to susceptible infections, MDR HAI were associated with increased cost (ROM 1.33, 95%CI [1.15; 1.54]), prolonged length of stay (ROM 1.27, 95%CI [1.18; 1.37]), and excess in-hospital mortality (RR 1.61, 95%CI [1.36; 1.90]) in the random effects models. Risk of publication bias was only found to be significant for mortality, and overall study quality good. MDR HAI appears to be strongly associated with increases in direct cost, prolonged length of stay and increased mortality. However, further comprehensive studies in this setting are warranted. PROSPERO (CRD42019126288).

To increase discriminatory accuracy (DA) for emergency cesarean sections (ECSs). We prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs) for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE), and analyzed a random forest model (RFM). We used the areas under the receiver-operating-characteristic (ROC) curves (AUCs) to assess their DA. The magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section) in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93–0.95). Putative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.