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Background: The adequate choice of perioperative antibiotic prophylaxis (PAP) could influence the risk of surgical site infections (SSIs) in general surgery. A new local PAP guideline was implemented in May 2017 and set the first-generation cefazolin (CFZ) instead the second-generation cefuroxime (CXM) as the new standard prophylactic antibiotic. The aim of this study was to compare the risk of SSIs after this implementation in intra-abdominal infections (IAIs) without sepsis. Methods: We performed a single center-quality improvement study at a 1500 bed sized university hospital in Germany analyzing patients after emergency surgery during 2016 to 2019 (n = 985), of which patients receiving CXM or CFZ were selected (n = 587). Propensity score matching was performed to ensure a comparable risk of SSIs in both groups. None-inferiority margin for SSIs was defined as 8% vs. 4%. Results: Two matched cohorts with respectively 196 patients were compared. The rate of SSIs was higher in the CFZ group (7.1% vs. 3.6%, p = 0.117) below the non-inferiority margin. The rate of other postoperative infections was significantly higher in the CFZ group (2.0% vs. 8.7%, p = 0.004). No other differences including postoperative morbidity, mortality or length-of-stay were observed. Conclusion: Perioperative antibiotic prophylaxis might be safely maintained by CFZ even in the treatment of intra-abdominal infections.
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.