Y Fan1, J Zou1, X Cao1, Y Wu1, F Gao1, L Xiong2. 1. Department of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 2. Department of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: lijuanxiong2016@126.com.
Abstract
BACKGROUND: Detection of healthcare-associated infection (HCAI) clusters is crucial in limiting disease transmission. AIM: To investigate whether data on antibiotic use can be an alternative indicator for the identification of HCAI clusters caused by multidrug-resistant organisms (MDROs). METHODS: We retrospectively analysed MDRO-related HCAIs and the 10 indicators of antibiotic use from four independent high-risk units at a tertiary hospital in China from January 2014 to January 2017. Spearman's correlation test was used to evaluate the correlations between the variables, and Shewhart chart algorithm was used to evaluate the performances of cluster identification. FINDINGS: We identified 856 MDRO-related HCAI cases. All indicators of antibiotic use were positively correlated with the incidence of MDRO-related HCAIs (r = 0.2-0.5; P < 0.05), except for the antibiotics utilization rate (AUR) for single-agent use (r = -0.191; P = 0.017) and the AUR of unrestricted drugs (r = -0.042, P = 0.601). Shewhart chart algorithm identified 22 clusters of MDRO-related HCAI. The AUR of special-grade antibiotics, the AUR for three agents used in combination, and the number of antibiotic varieties per patient displayed the optimal predictive values for detecting these 22 MDRO-related HCAI clusters. At an acceptable specific level of 75%, these three indicators were considered as the optimal surveillance indicators for detecting MDRO-related HCAI clusters, with sensitivities from 80.00% to 95.00%, and positive predictive values from 71.05% to 77.50%. CONCLUSION: The use of data on antibiotic use is a sensitive method for identifying clusters of MDRO-related HCAIs in high-risk units and may be a useful adjunctive method for HCAI surveillance.
BACKGROUND: Detection of healthcare-associated infection (HCAI) clusters is crucial in limiting disease transmission. AIM: To investigate whether data on antibiotic use can be an alternative indicator for the identification of HCAI clusters caused by multidrug-resistant organisms (MDROs). METHODS: We retrospectively analysed MDRO-related HCAIs and the 10 indicators of antibiotic use from four independent high-risk units at a tertiary hospital in China from January 2014 to January 2017. Spearman's correlation test was used to evaluate the correlations between the variables, and Shewhart chart algorithm was used to evaluate the performances of cluster identification. FINDINGS: We identified 856 MDRO-related HCAI cases. All indicators of antibiotic use were positively correlated with the incidence of MDRO-related HCAIs (r = 0.2-0.5; P < 0.05), except for the antibiotics utilization rate (AUR) for single-agent use (r = -0.191; P = 0.017) and the AUR of unrestricted drugs (r = -0.042, P = 0.601). Shewhart chart algorithm identified 22 clusters of MDRO-related HCAI. The AUR of special-grade antibiotics, the AUR for three agents used in combination, and the number of antibiotic varieties per patient displayed the optimal predictive values for detecting these 22 MDRO-related HCAI clusters. At an acceptable specific level of 75%, these three indicators were considered as the optimal surveillance indicators for detecting MDRO-related HCAI clusters, with sensitivities from 80.00% to 95.00%, and positive predictive values from 71.05% to 77.50%. CONCLUSION: The use of data on antibiotic use is a sensitive method for identifying clusters of MDRO-related HCAIs in high-risk units and may be a useful adjunctive method for HCAI surveillance.