Automated Surveillance for Ventilator-Associated Events

Jennifer P. Stevens, George Silva, Jean Gillis, Victor Novack, Daniel Talmor, Michael Klompas, Michael D. Howell

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


BACKGROUND:The US Centers for Disease Control and Prevention has implemented a new, multitiered definition for ventilator-associated events (VAEs) to replace their former definition of ventilator-associated pneumonia (VAP). We hypothesized that the new definition could be implemented in an automated, efficient, and reliable manner using the electronic health record and that the new definition would identify different patients than those identified under the previous definition. METHODS:We conducted a retrospective cohort analysis using an automated algorithm to analyze all patients admitted to the ICU at a single urban, tertiary-care hospital from 2008 to 2013. RESULTS:We identified 26,466 consecutive admissions to the ICU, 10,998 (42%) of whom were mechanically ventilated and 675 (3%) of whom were identified as having any VAE. Any VAE was associated with an adjusted increased risk of death (OR, 1.91; 95% CI, 1.53-2.37;P<.0001). The automated algorithm was reliable (sensitivity of 93.5%, 95% CI, 77.2%-98.8%; specificity of 100%, 95% CI, 98.8%-100% vs a human abstractor). Comparison of patients with a VAE and with the former VAP definition yielded little agreement (κ = 0.06). CONCLUSIONS:A fully automated method of identifying VAEs is efficient and reliable within a single institution. Although VAEs are strongly associated with worse patient outcomes, additional research is required to evaluate whether and which interventions can successfully prevent VAEs.

Original languageEnglish
Pages (from-to)1612-1618
Number of pages7
Issue number6
StatePublished - 1 Dec 2014
Externally publishedYes

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine
  • Cardiology and Cardiovascular Medicine


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