Automated surveillance system for surgical site infection in coronary artery bypass graft surgery in tertiary care hospitals
Issued Date
2025-01-01
Resource Type
ISSN
01966553
eISSN
15273296
Scopus ID
2-s2.0-105026816516
Pubmed ID
41371302
Journal Title
American Journal of Infection Control
Rights Holder(s)
SCOPUS
Bibliographic Citation
American Journal of Infection Control (2025)
Suggested Citation
Skuntaniyom S., Techasaensiri C., Muntajit T., Piebpien P. Automated surveillance system for surgical site infection in coronary artery bypass graft surgery in tertiary care hospitals. American Journal of Infection Control (2025). doi:10.1016/j.ajic.2025.12.001 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114020
Title
Automated surveillance system for surgical site infection in coronary artery bypass graft surgery in tertiary care hospitals
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background While screening-automated surveillance systems (SASS) for surgical site infections (SSIs) are widely used in high-resource settings, adoption remains limited in low- and middle-income countries, including Thailand. This study aimed to develop and validate an automated surveillance model tailored to the Thai health care context. Routine SSI surveillance following coronary artery bypass graft surgery in Thailand relies on direct method surveillance systems (DMSS), which are labor-intensive and require manual record review. Methods A retrospective validation study was conducted at a tertiary care hospital performing ∼120 coronary artery bypass graft procedures annually. Data from January 2020 to April 2022, when both DMSS and SASS were active, were analyzed to assess diagnostic performance and workload reduction. Results Among 4 algorithms tested, the “Possible SSI Surveillance Code” algorithm achieved 100% sensitivity (95% CI: 78.47-100), 91.69% specificity (95% CI: 88.18-94.23), a positive predictive value of 34.15% (95% CI: 21.56-49.45), and a negative predictive value of 100% (95% CI: 98.27-100). It also reduced manual workload by 87.91%. Conclusions SASS demonstrated high diagnostic accuracy and substantial workload reduction compared to DMSS. The selected algorithm provides a scalable model for enhancing SSI surveillance in low- and middle-income country settings and advancing digital health transformation.