OBJECTIVE: Surgical site infection (SSI) surveillance represents a key method of nosocomial infection control programs worldwide. However, most SSI surveillance systems are considered to be poorly cost effective regarding human and economic resources required for data collection and patient follow up. This study aims to assess the efficacy of using hospital discharge databases (HDDs) as a routine surveillance system for detecting hip or knee arthroplasty-related infections (HKAIs). METHODS: A case-control study was conducted among patients hospitalized in the Centre region of France between 2008 and 2010. HKAI cases were extracted from the HDD with various algorithms based on the International Classification of Diseases, Tenth Revision, and procedure codes. The control subjects were patients with hip or knee arthroplasty (HKA) without infection selected at random from the HDD during the study period. The gold standard was medical chart review. Sensitivity (Se), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) were calculated to evaluate the efficacy of the surveillance system. RESULTS: Among 18,265 hospital stays for HKA, corresponding to 17,388 patients, medical reports were checked for 1,010 hospital stays (989 patients). We identified 530 cases in total (incidence rate, 1% [95% confidence interval (CI), 0.4%-1.6%), and 333 cases were detected by routine surveillance. As compared with 480 controls, Se was 98%, Spe was 71%, PPV was 63%, and NPV was 99%. Using a more specific case definition, based on a sample of 681 hospital stays, Se was 97%, Spe was 95%, PPV was 87%, and NPV was 98%. CONCLUSIONS: This study demonstrates the potential of HDD as a tool for routine SSI surveillance after low-risk surgery, under conditions of having an appropriate algorithm for selecting infections.
OBJECTIVE: Surgical site infection (SSI) surveillance represents a key method of nosocomial infection control programs worldwide. However, most SSI surveillance systems are considered to be poorly cost effective regarding human and economic resources required for data collection and patient follow up. This study aims to assess the efficacy of using hospital discharge databases (HDDs) as a routine surveillance system for detecting hip or knee arthroplasty-related infections (HKAIs). METHODS: A case-control study was conducted among patients hospitalized in the Centre region of France between 2008 and 2010. HKAI cases were extracted from the HDD with various algorithms based on the International Classification of Diseases, Tenth Revision, and procedure codes. The control subjects were patients with hip or knee arthroplasty (HKA) without infection selected at random from the HDD during the study period. The gold standard was medical chart review. Sensitivity (Se), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) were calculated to evaluate the efficacy of the surveillance system. RESULTS: Among 18,265 hospital stays for HKA, corresponding to 17,388 patients, medical reports were checked for 1,010 hospital stays (989 patients). We identified 530 cases in total (incidence rate, 1% [95% confidence interval (CI), 0.4%-1.6%), and 333 cases were detected by routine surveillance. As compared with 480 controls, Se was 98%, Spe was 71%, PPV was 63%, and NPV was 99%. Using a more specific case definition, based on a sample of 681 hospital stays, Se was 97%, Spe was 95%, PPV was 87%, and NPV was 98%. CONCLUSIONS: This study demonstrates the potential of HDD as a tool for routine SSI surveillance after low-risk surgery, under conditions of having an appropriate algorithm for selecting infections.
Authors: Dean J Karavite; Matthew W Miller; Mark J Ramos; Susan L Rettig; Rachael K Ross; Rui Xiao; Naveen Muthu; A Russell Localio; Jeffrey S Gerber; Susan E Coffin; Robert W Grundmeier Journal: Appl Clin Inform Date: 2018-10-24 Impact factor: 2.342
Authors: Maaike S M van Mourik; Pleun Joppe van Duijn; Karel G M Moons; Marc J M Bonten; Grace M Lee Journal: BMJ Open Date: 2015-08-27 Impact factor: 2.692