Previous investigations have suggested that elevated airway pressures increase the risk of ventilator-induced pneumothorax. However, risk factor analysis using multivariate techniques has not been done. We investigated the hypothesis that airway pressures would not independently correlate with pneumothorax when underlying disease was considered. All ventilated patients over a 1 yr period in the Hohenburg Critical Care Unit at the University of Alabama were followed until death or discharge from the ICU. Ventilator data were collected daily and the presence of pneumomediastinum and pneumothorax determined by review of chest radiographs. Maximal values of airway pressures, minute ventilation, tidal volume, and respiratory rate, as well as age, sex, and underlying disease, were entered into logistic regression analysis. A total of 168 patients was studied, and 20 experienced pneumothorax. Multivariate analysis of the entire ventilated population revealed that only the presence of ARDS independently correlated with pneumothorax. A similar analysis performed on the ARDS population revealed independent correlation only with male sex. Trends toward elevation in airway pressures were seen that did not reach statistical significance. We conclude that development of pneumothorax is most closely correlated with underlying disease, specifically ARDS, and that the associations previously noted between airway pressures and barotrauma largely relate to the occurrence of high airway pressures in ARDS.

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Background Real-time automated continuous sampling of electronic medical record data may expeditiously identify patients at risk for death and enable prompt life-saving interventions. We hypothesized that a real-time electronic medical record-based alert could identify hospitalized patients at risk for mortality. Methods An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least two of four systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The SIRS/OD alert was applied real-time to 312,214 patients in 24 hospitals and analyzed in two phases: training and validation datasets. Results In the training phase, 29,317 (18.8%) triggered the alert and 5.2% of such patients died whereas only 0.2% without the alert died (unadjusted odds ratio 30.1; 95% confidence interval [95%CI] 26.1, 34.5; P<0.0001). In the validation phase, the sensitivity, specificity, area under curve (AUC), positive and negative likelihood ratios for predicting mortality were 0.86, 0.82, 0.84, 4.9, and 0.16, respectively. Multivariate Cox-proportional hazard regression model revealed greater hospital mortality when the alert was triggered (adjusted Hazards Ratio 4.0; 95%CI 3.3, 4.9; P<0.0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P<0.0001). Conclusion An automated alert system that continuously samples electronic medical record-data can be implemented, has excellent test characteristics, and can assist in the real-time identification of hospitalized patients at risk for death.

Objective: We have employed our electronic medical record (EMR) in an effort to identify patients at the onset of severe sepsis through an automated analysis that identifies simultaneous occurrence of systemic inflammatory response syndrome (SIRS) and organ dysfunction. The purpose of this study was to determine the positive predictive value of this alert for severe sepsis and other important outcomes in hospitalized adults. Design: Prospective cohort. Setting: Banner Good Samaritan Medical Center, Phoenix AZ Patients: Forty adult inpatients who triggered alert logic within our EMR indicating simultaneous occurrence of SIRS and organ dysfunction. Interventions: Interview of bedside nurse and chart review within six hours of alert firing to determine the clinical event that triggered each alert. Results: Eleven of 40 patients (28%) had a major clinical event (immediately lifethreatening illness) associated with the alert firing. Severe sepsis or septic shock accounted for four of these-yielding a positive predictive value of 0.10 (95%CI: 0.04-0.23) of the alert for detection of severe sepsis. The positive predictive value of the alert for detection of major clinical events was 0.28 (95%CI: 0.16-0.43), and for detecting either a major or minor clinical event was 0.45 (95%CI: 0.31-0.60). Twenty-two of 40 patients (55%) experienced a false alert. Conclusions: Our first-generation SIRS/organ dysfunction alert has a low positive predictive value for severe sepsis, and generates many false alerts, but shows promise for the detection of acute clinical events that require immediate attention. We are currently investigating refinements of our automated alert system which we believe have potential to enhance patient safety.

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