Building and Validating a Computerized Algorithm for Surveillance of Ventilator-Associated Events

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.