Please use this identifier to cite or link to this item:
|Title:||A framework to develop semiautomated surveillance of surgical site infections: An international multicenter study|
|Author:||van Rooden, Stephanie M.|
Pujol Rojo, Miquel
Gomila Grange, Aina
Kluytmans, Jan A. J. W.
Rodríguez Baño, Jesús
van Mourik, Maaike S. M.
Surgical wound infection
|Publisher:||Cambridge University Press|
|Abstract:||Objective: Automated surveillance of healthcare-associated infections reduces workload and improves standardization, but it has not yet been adopted widely. In this study, we assessed the performance and feasibility of an easy implementable framework to develop algorithms for semiautomated surveillance of deep incisional and organ-space surgical site infections (SSIs) after orthopedic, cardiac, and colon surgeries. Design: Retrospective cohort study in multiple countries. Methods: European hospitals were recruited and selected based on the availability of manual SSI surveillance data from 2012 onward (reference standard) and on the ability to extract relevant data from electronic health records. A questionnaire on local manual surveillance and clinical practices was administered to participating hospitals, and the information collected was used to pre-emptively design semiautomated surveillance algorithms standardized for multiple hospitals and for center-specific application. Algorithm sensitivity, positive predictive value, and reduction of manual charts requiring review were calculated. Reasons for misclassification were explored using discrepancy analyses. Results: The study included 3 hospitals, in the Netherlands, France, and Spain. Classification algorithms were developed to indicate procedures with a high probability of SSI. Components concerned microbiology, prolonged length of stay or readmission, and reinterventions. Antibiotics and radiology ordering were optional. In total, 4,770 orthopedic procedures, 5,047 cardiac procedures, and 3,906 colon procedures were analyzed. Across hospitals, standardized algorithm sensitivity ranged between 82% and 100% for orthopedic surgery, between 67% and 100% for cardiac surgery, and between 84% and 100% for colon surgery, with 72%-98% workload reduction. Center-specific algorithms had lower sensitivity. Conclusions: Using this framework, algorithms for semiautomated surveillance of SSI can be successfully developed. The high performance of standardized algorithms holds promise for large-scale standardization.|
|Note:||Reproducció del document publicat a: https://doi.org/10.1017/ice.2019.321|
|It is part of:||Infection Control and Hospital Epidemiology, 2020, vol. 41, num. 2, p. 194-201|
|Appears in Collections:||Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))|
This item is licensed under a Creative Commons License