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Abstract：
BACKGROUND: Surgical site infection (SSI) is an important cause of disease burden and healthcare costs. Fully manual surveillance is time-consuming and prone to subjectivity and inter-individual variability, which can be partly overcome by semi-automated surveillance. Algorithms used in orthopaedic SSI semi-automated surveillance have reported high sensitivity and important workload reduction. This study aimed to design and validate different algorithms to identify patients at high risk of SSI after hip or knee arthroplasty.
METHODS: Retrospective data from manual SSI surveillance between May 2015 and December 2017 were used as gold standard for validation. Knee and hip arthroplasty were included, patients were followed up for 90 days and European Centre for Disease Prevention and Control SSI classification was applied. Electronic health records data was used to generate different algorithms, considering combinations of the following variables: ≥1 positive culture, ≥ 3 microbiological requests, antimicrobial therapy ≥ 7 days, length of hospital stay ≥ 14 days, orthopaedics readmission, orthopaedics surgery and emergency department attendance. Sensitivity, specificity, negative and predictive value, and workload reduction were calculated.
RESULTS: In total 1631 surgical procedures were included, of which 67.5% (n = 1101) in women; patients\' median age was 69 years (IQR 62 to 77) and median Charlson index 2 (IQR 1 to 3). Most surgeries were elective (92.5%; n = 1508) and half were hip arthroplasty (52.8%; n = 861). SSI incidence was 3.8% (n = 62), of which 64.5% were deep or organ/space infections. Positive culture was the single variable with highest sensitivity (64.5%), followed by orthopaedic reintervention (59.7%). Twenty-four algorithms presented 90.3% sensitivity for all SSI types and 100% for deep and organ/space SSI. Workload reduction ranged from 59.7 to 67.7%. The algorithm including ≥ 3 microbiological requests, length of hospital stay ≥ 14 days and emergency department attendance, was one of the best options in terms of sensitivity, workload reduction and feasibility for implementation.
CONCLUSIONS: Different algorithms with high sensitivity to detect all types of SSI can be used in real life, tailored to clinical practice and data availability. Emergency department attendance can be an important variable to identify superficial SSI in semi-automated surveillance.
METHODS: Retrospective data from manual SSI surveillance between May 2015 and December 2017 were used as gold standard for validation. Knee and hip arthroplasty were included, patients were followed up for 90 days and European Centre for Disease Prevention and Control SSI classification was applied. Electronic health records data was used to generate different algorithms, considering combinations of the following variables: ≥1 positive culture, ≥ 3 microbiological requests, antimicrobial therapy ≥ 7 days, length of hospital stay ≥ 14 days, orthopaedics readmission, orthopaedics surgery and emergency department attendance. Sensitivity, specificity, negative and predictive value, and workload reduction were calculated.
RESULTS: In total 1631 surgical procedures were included, of which 67.5% (n = 1101) in women; patients\' median age was 69 years (IQR 62 to 77) and median Charlson index 2 (IQR 1 to 3). Most surgeries were elective (92.5%; n = 1508) and half were hip arthroplasty (52.8%; n = 861). SSI incidence was 3.8% (n = 62), of which 64.5% were deep or organ/space infections. Positive culture was the single variable with highest sensitivity (64.5%), followed by orthopaedic reintervention (59.7%). Twenty-four algorithms presented 90.3% sensitivity for all SSI types and 100% for deep and organ/space SSI. Workload reduction ranged from 59.7 to 67.7%. The algorithm including ≥ 3 microbiological requests, length of hospital stay ≥ 14 days and emergency department attendance, was one of the best options in terms of sensitivity, workload reduction and feasibility for implementation.
CONCLUSIONS: Different algorithms with high sensitivity to detect all types of SSI can be used in real life, tailored to clinical practice and data availability. Emergency department attendance can be an important variable to identify superficial SSI in semi-automated surveillance.
摘要：
背景：手术部位感染（SSI）是疾病负担和医疗费用的重要原因。完全人工监测耗时且容易产生主观性和个体间差异,可以通过半自动监控部分克服。骨科SSI半自动监测中使用的算法报告了高灵敏度和重要的工作量减少。本研究旨在设计和验证不同的算法，以识别髋关节或膝关节置换术后发生SSI的高风险患者。
方法：将2015年5月至2017年12月的手动SSI监测的回顾性数据用作验证的金标准。包括膝关节和髋关节置换术，患者随访90天,并应用欧洲疾病预防和控制中心SSI分类.电子健康记录数据被用来生成不同的算法，考虑以下变量的组合：≥1阳性培养，≥3个微生物要求，抗菌治疗≥7天，住院时间≥14天，骨科再入院,骨科手术和急诊科就诊。灵敏度,特异性，阴性和预测值，并计算了工作量的减少。
结果：共包括1631次外科手术，其中67.5%(n=1101)为女性;患者年龄中位数为69岁(IQR62~77),Charlson指数中位数为2(IQR1~3).大多数手术是选择性的（92.5％；n=1508），一半是髋关节置换术（52.8％；n=861）。SSI发生率为3.8%(n=62)，其中64.5%为深部或器官/空间感染。阳性培养是灵敏度最高的单变量(64.5%)，其次是骨科再干预(59.7%)。24种算法对所有SSI类型的灵敏度为90.3％，对深部和器官/空间SSI的灵敏度为100％。工作量减少从59.7％到67.7％不等。该算法包括≥3个微生物请求，住院时间≥14天，急诊科就诊，在灵敏度方面是最好的选择之一，工作量的减少和实施的可行性。
结论：在现实生活中可以使用具有高灵敏度的检测所有类型SSI的不同算法，根据临床实践和数据可用性量身定制。急诊科出勤可能是识别半自动监测中表面SSI的重要变量。
方法：将2015年5月至2017年12月的手动SSI监测的回顾性数据用作验证的金标准。包括膝关节和髋关节置换术，患者随访90天,并应用欧洲疾病预防和控制中心SSI分类.电子健康记录数据被用来生成不同的算法，考虑以下变量的组合：≥1阳性培养，≥3个微生物要求，抗菌治疗≥7天，住院时间≥14天，骨科再入院,骨科手术和急诊科就诊。灵敏度,特异性，阴性和预测值，并计算了工作量的减少。
结果：共包括1631次外科手术，其中67.5%(n=1101)为女性;患者年龄中位数为69岁(IQR62~77),Charlson指数中位数为2(IQR1~3).大多数手术是选择性的（92.5％；n=1508），一半是髋关节置换术（52.8％；n=861）。SSI发生率为3.8%(n=62)，其中64.5%为深部或器官/空间感染。阳性培养是灵敏度最高的单变量(64.5%)，其次是骨科再干预(59.7%)。24种算法对所有SSI类型的灵敏度为90.3％，对深部和器官/空间SSI的灵敏度为100％。工作量减少从59.7％到67.7％不等。该算法包括≥3个微生物请求，住院时间≥14天，急诊科就诊，在灵敏度方面是最好的选择之一，工作量的减少和实施的可行性。
结论：在现实生活中可以使用具有高灵敏度的检测所有类型SSI的不同算法，根据临床实践和数据可用性量身定制。急诊科出勤可能是识别半自动监测中表面SSI的重要变量。
