Validation of an electronic surveillance system for acute lung injury.
OBJECTIVE
Early detection of acute lung injury (ALI) is essential for timely implementation of evidence-based therapies and enrollment into clinical trials. We aimed to determine the accuracy of computerized syndrome surveillance for detection of ALI in hospitalized patients and compare it with routine clinical assessment.


METHODS
Using a near-real time copy of the electronic medical records, we developed and validated a custom ALI electronic alert (ALI "sniffer") based on the European-American Consensus Conference Definition and compared its performance against provider-derived documentation.


METHODS
A total of 3,795 consecutive critically ill patients admitted to nine multidisciplinary intensive care units (ICUs) of a tertiary care teaching institution were included.


RESULTS
ALI developed in 325 patients and was recognized by bedside clinicians in only 86 (26.5%). Under-recognition of ALI was associated with not implementing protective mechanical ventilation (median tidal volumes of 9.2 vs. 8.0 ml/kg predicted body weight, P < 0.001). ALI "sniffer" demonstrated excellent sensitivity of 96% (95% CI 94-98) and moderate specificity of 89% (95% CI 88-90) with a positive predictive value ranging from 24% (95% CI 13-40) in the heart-lung transplant ICU to 64% (95% CI 55-71) in the medical ICU.


CONCLUSIONS
The computerized surveillance system accurately identifies critically ill patients who develop ALI syndrome. Since the lack of ALI recognition is a barrier to the timely implementation of best practices and enrollment into research studies, computerized syndrome surveillance could be a useful tool to enhance patient safety and clinical research.
Herasevich
Vitaly
V
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA.
Yilmaz
Murat
M
Khan
Hasrat
H
Hubmayr
Rolf D
RD
Gajic
Ognjen
O
D055371:Acute Lung Injury / Q000175:diagnosis*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D019992:Databases as Topic; D042241:Early Diagnosis; D001330:Electronic Data Processing / Q000379:methods*; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D008910:Minnesota; D011159:Population Surveillance / Q000379:methods*; D011446:Prospective Studies