Julius Center for Health Sciences and Primary Care,University Medical Center Utrecht, University of Utrecht,Utrecht,The Netherlands.
Department of Medical Microbiology,University Medical Center Utrecht, University of Utrecht,Utrecht,The Netherlands.
Infect Control Hosp Epidemiol. 2019 May;40(5):574-578. doi: 10.1017/ice.2019.36. Epub 2019 Mar 14.
Surveillance of surgical site infections (SSIs) is important for infection control and is usually performed through retrospective manual chart review. The aim of this study was to develop an algorithm for the surveillance of deep SSIs based on clinical variables to enhance efficiency of surveillance.
Retrospective cohort study (2012-2015).
A Dutch teaching hospital.
We included all consecutive patients who underwent colorectal surgery excluding those with contaminated wounds at the time of surgery. All patients were evaluated for deep SSIs through manual chart review, using the Centers for Disease Control and Prevention (CDC) criteria as the reference standard.
We used logistic regression modeling to identify predictors that contributed to the estimation of diagnostic probability. Bootstrapping was applied to increase generalizability, followed by assessment of statistical performance and clinical implications.
In total, 1,606 patients were included, of whom 129 (8.0%) acquired a deep SSI. The final model included postoperative length of stay, wound class, readmission, reoperation, and 30-day mortality. The model achieved 68.7% specificity and 98.5% sensitivity and an area under the receiver operator characteristic (ROC) curve (AUC) of 0.950 (95% CI, 0.932-0.969). Positive and negative predictive values were 21.5% and 99.8%, respectively. Applying the algorithm resulted in a 63.4% reduction in the number of records requiring full manual review (from 1,606 to 590).
This 5-parameter model identified 98.5% of patients with a deep SSI. The model can be used to develop semiautomatic surveillance of deep SSIs after colorectal surgery, which may further improve efficiency and quality of SSI surveillance.
手术部位感染（SSI）监测对感染控制非常重要，通常通过回顾性手动图表审查来进行。本研究旨在开发一种基于临床变量的深部 SSI 监测算法，以提高监测效率。
回顾性队列研究（2012-2015 年）。
荷兰一所教学医院。
我们纳入了所有接受结直肠手术的连续患者，但不包括手术时污染伤口的患者。所有患者均通过手动图表审查评估深部 SSI，以疾病预防控制中心（CDC）标准为参考标准。
我们使用逻辑回归模型来确定有助于诊断概率估计的预测因素。采用自举法增加通用性，然后评估统计性能和临床意义。
共纳入 1606 例患者，其中 129 例（8.0%）发生深部 SSI。最终模型包括术后住院时间、伤口分类、再入院、再次手术和 30 天死亡率。该模型的特异性为 68.7%，敏感性为 98.5%，受试者工作特征（ROC）曲线下面积（AUC）为 0.950（95%CI，0.932-0.969）。阳性预测值和阴性预测值分别为 21.5%和 99.8%。应用该算法可将需要全面手动审查的记录数量减少 63.4%（从 1606 份减少到 590 份）。
该 5 个参数模型可识别出 98.5%的深部 SSI 患者。该模型可用于开发结直肠手术后深部 SSI 的半自动监测，从而进一步提高 SSI 监测的效率和质量。
