Abstract：
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.
CONCLUSIONS: 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.
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.
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.
CONCLUSIONS: 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.
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.
摘要：
背景：尽管手术和患者依赖因素对手术部位感染（SSIs）的影响已得到广泛研究，它们对SSI微生物组成的影响仍未被探索。这项研究的目的是确定不同类型手术中SSI微生物组成的患者依赖性预测因子。
方法：这项回顾性队列研究包括来自瑞士国家感染监测计划的538893名患者。多标签分类方法，使用自适应增强和高斯朴素贝叶斯来识别使用20个特征的SSIs的微生物组成的预测因子，包括性,年龄,BMI,手术持续时间,手术类型,和外科抗菌药物预防。
结果：总体而言，SSIs记录了18642例患者（3.8%），其中，10632具有可用的微生物伤口拭子。SSIs中最常见的病原体是肠杆菌(57%)，葡萄球菌属。（31%），和肠球菌属。（28%）。年龄(平均特征重要性0.260，95%c.i.0.209至0.309)，BMI（0.224，0.177至0.271），和手术时间（0.221，0.180至0.269）是SSIs微生物组成的强大且独立的预测因子。年龄和手术时间的增加以及BMI的降低与葡萄球菌属的转变有关。肠杆菌和肠球菌属。机器学习模型的在线应用程序可用于其他医疗保健系统中的验证。
结论：年龄，BMI,手术时间和手术时间是SSI微生物组成的关键预测因素，不管手术类型如何,证明患者依赖性因素与SSIs发病机制的相关性。
局部感染是手术后常见的问题。已经确定了手术感染的危险因素，但是尚不清楚哪些因素可以预测此类感染中发现的微生物类型。本研究的目的是评估影响手术感染中微生物组成的患者因素。使用标准统计学和机器学习方法分析了538893名患者的数据。结果表明,年龄,BMI,手术时间对确定手术部位感染中的细菌很重要。随着年龄的增长,更长的操作，较低的BMI，在手术部位发现了更多来自肠道的细菌，与来自皮肤的细菌相反。这些知识可能有助于为将来接受手术的患者开发更个性化的治疗方法。
方法：这项回顾性队列研究包括来自瑞士国家感染监测计划的538893名患者。多标签分类方法，使用自适应增强和高斯朴素贝叶斯来识别使用20个特征的SSIs的微生物组成的预测因子，包括性,年龄,BMI,手术持续时间,手术类型,和外科抗菌药物预防。
结果：总体而言，SSIs记录了18642例患者（3.8%），其中，10632具有可用的微生物伤口拭子。SSIs中最常见的病原体是肠杆菌(57%)，葡萄球菌属。（31%），和肠球菌属。（28%）。年龄(平均特征重要性0.260，95%c.i.0.209至0.309)，BMI（0.224，0.177至0.271），和手术时间（0.221，0.180至0.269）是SSIs微生物组成的强大且独立的预测因子。年龄和手术时间的增加以及BMI的降低与葡萄球菌属的转变有关。肠杆菌和肠球菌属。机器学习模型的在线应用程序可用于其他医疗保健系统中的验证。
结论：年龄，BMI,手术时间和手术时间是SSI微生物组成的关键预测因素，不管手术类型如何,证明患者依赖性因素与SSIs发病机制的相关性。
局部感染是手术后常见的问题。已经确定了手术感染的危险因素，但是尚不清楚哪些因素可以预测此类感染中发现的微生物类型。本研究的目的是评估影响手术感染中微生物组成的患者因素。使用标准统计学和机器学习方法分析了538893名患者的数据。结果表明,年龄,BMI,手术时间对确定手术部位感染中的细菌很重要。随着年龄的增长,更长的操作，较低的BMI，在手术部位发现了更多来自肠道的细菌，与来自皮肤的细菌相反。这些知识可能有助于为将来接受手术的患者开发更个性化的治疗方法。
