title: Differentiating infectious and noninfectious ventilator-associated complications: A new challenge.
abstract: The purpose of this study was to develop an electronic search algorithm which reliably differentiates infectious and noninfectious ventilator-associated events (VAEs). This was a retrospective cohort study used to derive a predictive model. It took place at a tertiary care hospital campus.
  
  
  
  Participants included all ventilated patients who met the Centers for Disease Control and Prevention's National Health Safety Network definitions for VAEs between January 1, 2012, and December 31, 2013. There were 164 patients who experienced 185 VAEs in the study period.
  
  
  
  The most predictive variables were fever 2 days before VAE onset, oxygenation changes, and appearance of respiratory secretions. No other variable, including laboratory tests, radiologic findings, and vital sign values, reached statistical significance. A multivariate regression model was constructed, with 68% sensitivity and 75% specificity (receiver operator characteristic area under the curve [ROC-AUC], 0.83). This was modestly better than the clinical pulmonary infection score (CPIS), which had sensitivity of 50%, specificity of 59%, and ROC-AUC of 0.60.
  
  
  
  Although diagnosis of VAEs remains challenging, our data indicate that clinical signs and symptoms of a VAE may be present up to 2 days before they screen positive. Sputum, fever, and oxygenation requirements all were indicative, but aggregate models failed to create a sensitive and specific model for differentiation of VAEs. The existing clinical tool, the CPIS, is also insufficiently sensitive and specific. Further research is needed to create a clinically viable tool for differentiating VAE types at the bedside.
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN; Division of Infectious Diseases, Department of Medicine, Mayo Clinic, Rochester, MN. Electronic address: Ohoro.john@mayo.edu.
authors.forename: John C
authors.initials: JC
authors.lastname: O'Horo
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN; Division of Critical Care, Department of Anesthesiology, Mayo Clinic, Rochester, MN.
authors.forename: Rahul
authors.initials: R
authors.lastname: Kashyap
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Division of Critical Care, Department of Anesthesiology, Mayo Clinic, Rochester, MN.
authors.forename: Ronaldo
authors.initials: R
authors.lastname: Sevilla Berrios
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN; Division of Critical Care, Department of Anesthesiology, Mayo Clinic, Rochester, MN.
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
authors.affiliation: Division of Infectious Diseases, Department of Medicine, Mayo Clinic, Rochester, MN.
authors.forename: Priya
authors.initials: P
authors.lastname: Sampathkumar
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D053717:Pneumonia, Ventilator-Associated / Q000175:diagnosis* / Q000473:pathology; D012121:Respiration, Artificial / Q000009:adverse effects*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D062606:Tertiary Care Centers
keywords: Ventilator-associated event; clinical pulmonary infection score; electronic search algorithm; health care–acquired infection; nosocomial; ventilator-associated pneumonia