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Influence of patient characteristics on microbial composition in surgical-site infections: insights from national
Sarah Peisl1, Hugo Guillen-Ramirez1, Daniel Sánchez-Taltavull1
1Department of Visceral Surgery and Medicine, Bern University Hospital, University of Bern, Bern, Switzerland.
Background:
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.
Methods:
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.
Results:
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.
Conclusion:
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.
Insights
Patient age, body mass index (BMI), and surgery duration significantly influence the types of bacteria causing surgical-site infections (SSIs). These factors predict shifts in microbial composition, impacting infection development.
Area of Science:
- Medical Microbiology
- Infectious Diseases
- Machine Learning in Healthcare
Background:
- Surgical-site infections (SSIs) are a significant concern, with known impacts from surgical and patient factors.
- However, the specific influence of these factors on the microbial composition of SSIs has not been thoroughly investigated.
Purpose of the Study:
- To identify patient-dependent predictors associated with the microbial composition of SSIs.
- To explore how factors like age, BMI, and surgery duration shape SSI microbiology across various surgical procedures.
Main Methods:
- A retrospective cohort study analyzed data from 538,893 patients in the Swiss national infection surveillance program.
- Machine learning models, including adaptive boosting and Gaussian Naive Bayes, were used to identify predictors from 20 features.
- Analysis focused on patients with available microbiological wound swabs (10,632 out of 18,642 SSIs).
Main Results:
- Enterobacterales (57%), Staphylococcus spp. (31%), and Enterococcus spp. (28%) were the most common SSI pathogens.
- Age (mean importance 0.260), BMI (0.224), and surgery duration (0.221) were identified as strong, independent predictors of SSI microbial composition.
- Increased age and surgery duration, coupled with decreased BMI, correlated with a shift from Staphylococcus spp. to Enterobacterales and Enterococcus spp.
Conclusions:
- Patient age, BMI, and duration of surgery are critical predictors of SSI microbial composition, regardless of surgical type.
- These findings underscore the significant role of patient-specific factors in the pathogenesis of SSIs.
- An online application of the developed machine learning model is available for external validation.
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