John C O'Horo1, Rahul Kashyap2, Ronaldo Sevilla Berrios3, Vitaly Herasevich2, Priya Sampathkumar4. 1. 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. 2. Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN; Division of Critical Care, Department of Anesthesiology, Mayo Clinic, Rochester, MN. 3. 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. 4. Division of Infectious Diseases, Department of Medicine, Mayo Clinic, Rochester, MN.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: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. RESULTS: 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. CONCLUSIONS: 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.
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