A diagnostic algorithm for the surveillance of deep surgical site infections after colorectal surgery. (14th March 2019)

• Record Type: Journal Article
• Authors: Mulder, Tessa; Kluytmans-van den Bergh, Marjolein F.Q.; van Mourik, Maaike S.M.; Romme, Jannie; Crolla, Rogier M.P.H.; Bonten, Marc J.M.; Kluytmans, Jan A.J.W.
• Is Part Of: Infection control and hospital epidemiology. Volume 40:Number 5(2019)
• Page Start: 574
• Page End: 578
• Publication Date: 2019-03-14
• Subjects: Nosocomial infections -- Epidemiology -- Periodicals; Health facilities -- Sanitation -- Periodicals; Hospital buildings -- Sanitation -- Periodicals; Cross Infection -- Periodicals; Epidemiology -- Periodicals; Hospitals -- Periodicals; Infection Control -- Periodicals; 614.44
• DOI: 10.1017/ice.2019.36
• Languages: English
• ISSNs: 0899-823X

Abstract

Abstract: Objective: 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. Design: Retrospective cohort study (2012–2015). Setting: A Dutch teaching hospital. Participants: 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. Analysis: 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. Results: 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). Conclusions: 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.