Validation Study of an Automated Electronic Acute Lung Injury Screening Tool
- Helen C Azzam, MD, MPHaffa,
- Satjeet S Khalsaaffb,
- Richard Urbaniaffc,
- Chirag V Shah, MD, MSaffd,affe,
- Jason D Christie, MD, MSaffd,affe,
- Paul N Lanken, MDaffd,
- Barry D Fuchs, MDaffd
- aDivision of Infectious Diseases, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA
- bDepartment of Radiology, Medical Informatics Groups, University of Pennsylvania School of Medicine, Philadelphia, PA
- cDepartment of Information Services, Web Applications Group, University of Pennsylvania School of Medicine, Philadelphia, PA
- dPulmonary, Allergy and Critical Care Division, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA
- eCenter for Clinical Epidemiology & Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA
- Correspondence: Barry D. Fuchs, M.D., University of Pennsylvania Medical Center; Pulmonary; Allergy and Critical Care Division; 9.066 Founders Pavilion; 3400 Spruce Street; Philadelphia; PA 19104-4283; e-mail: <barry.fuchs{at}uphs.upenn.edu>
- Received 28 December 2008
- Accepted 14 April 2009
Abstract
Objective The authors designed an automated electronic system that incorporates data from multiple hospital information systems to screen for acute lung injury (ALI) in mechanically ventilated patients. The authors evaluated the accuracy of this system in diagnosing ALI in a cohort of patients with major trauma, but excluding patients with congestive heart failure (CHF).
Design Single-center validation study. Arterial blood gas (ABG) data and chest radiograph (CXR) reports for a cohort of intensive care unit (ICU) patients with major trauma but excluding patients with CHF were screened prospectively for ALI requiring intubation by an automated electronic system. The system was compared to a reference standard established through consensus of two blinded physician reviewers who independently screened the same population for ALI using all available ABG data and CXR images. The system's performance was evaluated (1) by measuring the sensitivity and overall accuracy, and (2) by measuring concordance with respect to the date of ALI identification (vs. reference standard).
Measurements One hundred ninety-nine trauma patients admitted to our level 1 trauma center with an initial injury severity score (ISS) ≥ 16 were evaluated for development of ALI in the first five days in an ICU after trauma.
Main Results The system demonstrated 87% sensitivity (95% confidence interval [CI] 82.3–91.7) and 89% specificity (95% CI 84.7–93.4). It identified ALI before or within the 24-hour period during which ALI was identified by the two reviewers in 87% of cases.
Conclusions An automated electronic system that screens intubated ICU trauma patients, excluding patients with CHF, for ALI based on CXR reports and results of ABGs is sufficiently accurate to identify many early cases of ALI.
