Researchers have found an automated ventilator-associated condition algorithm that can be used in surveillance to optimize the sensitivity and specificity of VAC identification, according to a study published in Infection Control & Hospital Epidemiology.
The automated VAC algorithm was implemented and utilized from August to October 2013 at the Detroit Medical Center, including a total of 128 intensive care unit beds in four acute care hospitals.
Simultaneous manual VAC surveillance was conducted by two infection preventionists and one infection control fellow who were blinded to each another's findings and to the automated VAC algorithm results. The VACs identified by the two surveillance processes were compared.
Here are five things to know from the study:
1. 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.
2. The automated VAC algorithm identified 39 VACs with sensitivity, specificity, positive predictive value and negative predictive value of 100 percent.
3. In comparison, the combined efforts of the IPs and the infection control fellow detected 58.9 percent of VACs, with 59 percent sensitivity, 99 percent specificity, 91 percent positive predictive value and 92 percent negative predictive value.
4. The automated VAC algorithm was extremely efficient, requiring only one minute to detect VACs over a one-month period.
5. Manual surveillance, on the other hand, took 60.7 minutes to detect VACs over a one-month period.
"The automated VAC algorithm is efficient and accurate and is ready to be used routinely for VAC surveillance," concluded the study authors.
More articles on ventilator-associated conditions and HAIs:
How HAIs lead to direct, indirect and unintended hospital costs
Nurse-physician collaboration may decrease HAIs, study finds
Non-ventilator hospital-acquired pneumonia: Are you addressing the hidden issue affecting more patients at a greater cost than VAP?