Tal Mann1, Joseph Ellsworth2, Najia Huda3, Anupama Neelakanta4, Thomas Chevalier2, Kristin L Sims5, Sorabh Dhar6, Mary E Robinson2, Keith S Kaye6. 1. 1General ICU,Assaf-Harofeh Medical Center,Tsrifin,Israel. 2. 2Department of Corporate Quality and Safety/Epidemiology,Detroit Medical Center,Detroit,Michigan,United States. 3. 3Department of Medicine,Henry Ford Hospital,Detroit,Michigan,United States. 4. 4Carolina Health System,Charlotte,North Carolina,United States. 5. 5Detroit Medical Center,Detroit Michigan,United States. 6. 6Department of Medicine,Wayne State University/Detroit Medical Center,Detroit,Michigan,United States.
Abstract
OBJECTIVE: To develop an automated method for ventilator-associated condition (VAC) surveillance and to compare its accuracy and efficiency with manual VAC surveillance SETTING: The intensive care units (ICUs) of 4 hospitals METHODS: This study was conducted at Detroit Medical Center, a tertiary care center in metropolitan Detroit. A total of 128 ICU beds in 4 acute care hospitals were included during the study period from August to October 2013. The automated VAC algorithm was implemented and utilized for 1 month by all study hospitals. Simultaneous manual VAC surveillance was conducted by 2 infection preventionists and 1 infection control fellow who were blinded to each another's findings and to the automated VAC algorithm results. The VACs identified by the 2 surveillance processes were compared. RESULTS: During the study period, 110 patients from all the included hospitals were mechanically ventilated and were evaluated for VAC for a total of 992 mechanical ventilation days. The automated VAC algorithm identified 39 VACs with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 100%. In comparison, the combined efforts of the IPs and the infection control fellow detected 58.9% of VACs, with 59% sensitivity, 99% specificity, 91% PPV, and 92% NPV. Moreover, the automated VAC algorithm was extremely efficient, requiring only 1 minute to detect VACs over a 1-month period, compared to 60.7 minutes using manual surveillance. CONCLUSIONS: The automated VAC algorithm is efficient and accurate and is ready to be used routinely for VAC surveillance. Furthermore, its implementation can optimize the sensitivity and specificity of VAC identification.
OBJECTIVE: To develop an automated method for ventilator-associated condition (VAC) surveillance and to compare its accuracy and efficiency with manual VAC surveillance SETTING: The intensive care units (ICUs) of 4 hospitals METHODS: This study was conducted at Detroit Medical Center, a tertiary care center in metropolitan Detroit. A total of 128 ICU beds in 4 acute care hospitals were included during the study period from August to October 2013. The automated VAC algorithm was implemented and utilized for 1 month by all study hospitals. Simultaneous manual VAC surveillance was conducted by 2 infection preventionists and 1 infection control fellow who were blinded to each another's findings and to the automated VAC algorithm results. The VACs identified by the 2 surveillance processes were compared. RESULTS: During the study period, 110 patients from all the included hospitals were mechanically ventilated and were evaluated for VAC for a total of 992 mechanical ventilation days. The automated VAC algorithm identified 39 VACs with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 100%. In comparison, the combined efforts of the IPs and the infection control fellow detected 58.9% of VACs, with 59% sensitivity, 99% specificity, 91% PPV, and 92% NPV. Moreover, the automated VAC algorithm was extremely efficient, requiring only 1 minute to detect VACs over a 1-month period, compared to 60.7 minutes using manual surveillance. CONCLUSIONS: The automated VAC algorithm is efficient and accurate and is ready to be used routinely for VAC surveillance. Furthermore, its implementation can optimize the sensitivity and specificity of VAC identification.
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