%0 Journal Article
%T Influence of patient characteristics on microbial composition in surgical-site infections: insights from national surveillance study.
%A Peisl S
%A Guillen-Ramirez H
%A Sánchez-Taltavull D
%A Widmer A
%A Sommerstein R
%A Beldi G
%J Br J Surg
%V 111
%N 6
%D 2024 Jun 12
%M 38926136
%F 11.122
%R 10.1093/bjs/znae138
%X <B>BACKGROUND: </B>Although the impact of surgery- and patient-dependent factors on surgical-site infections (SSIs) have been studied extensively, their influence on the microbial composition of SSI remains unexplored. The aim of this study was to identify patient-dependent predictors of the microbial composition of SSIs across different types of surgery.<BR><B>METHODS: </B>This retrospective cohort study included 538 893 patients from the Swiss national infection surveillance programme. Multilabel classification methods, adaptive boosting and Gaussian Naive Bayes were employed to identify predictors of the microbial composition of SSIs using 20 features, including sex, age, BMI, duration of surgery, type of surgery, and surgical antimicrobial prophylaxis.<BR><B>RESULTS: </B>Overall, SSIs were recorded in 18 642 patients (3.8%) and, of these, 10 632 had microbiological wound swabs available. The most common pathogens identified in SSIs were Enterobacterales (57%), Staphylococcus spp. (31%), and Enterococcus spp. (28%). Age (mean feature importance 0.260, 95% c.i. 0.209 to 0.309), BMI (0.224, 0.177 to 0.271), and duration of surgery (0.221, 0.180 to 0.269) were strong and independent predictors of the microbial composition of SSIs. Increasing age and duration of surgical procedure as well as decreasing BMI were associated with a shift from Staphylococcus spp. to Enterobacterales and Enterococcus spp. An online application of the machine learning model is available for validation in other healthcare systems.<BR><B>CONCLUSIONS: </B>Age, BMI, and duration of surgery were key predictors of the microbial composition of SSI, irrespective of the type of surgery, demonstrating the relevance of patient-dependent factors to the pathogenesis of SSIs.<BR>Local infections are a frequent problem after surgery. The risk factors for surgical infections have been identified, but it is unclear which factors predict the type of microorganisms found in such infections. The aim of the present study was to assess patient factors affecting the composition of microorganisms in surgical infections. Data from 538 893 patients were analysed using standard statistics and machine learning methods. The results showed that age, BMI, and the duration of surgery were important in determining the bacteria found in the surgical-site infections. With increasing age, longer operations, and lower BMI, more bacteria stemming from the intestine were found in the surgical site, as opposed to bacteria from the skin. This knowledge may help in developing more personalized treatments for patients undergoing surgery in the future.