• Tal Mann, Joseph Ellsworth, Najia Huda, Anupama Neelakanta, Thomas Chevalier, Kristin L Sims, Sorabh Dhar, Mary E Robinson, and Keith S Kaye.
• 1General ICU,Assaf-Harofeh Medical Center,Tsrifin,Israel.
• Infect Control Hosp Epidemiol. 2015 Sep 1; 36 (9): 999-1003.

ObjectiveTo develop an automated method for ventilator-associated condition (VAC) surveillance and to compare its accuracy and efficiency with manual VAC surveillanceSettingThe intensive care units (ICUs) of 4 hospitalsMethodsThis 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.ResultsDuring 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.ConclusionsThe 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.

46 keywords...

hide…