Correlation of pneumonia score with electronic nose signature: A prospective study.
OBJECTIVE
Ventilator-associated pneumonia (VAP) is a frequent complication in patients in surgical intensive care units. Pneumonia scores, chest radiography, and bronchoscopy are all employed, but there is no gold standard test for the diagnosis of VAP. The electronic nose, a sensor of volatile molecules, is well suited to testing the breath of mechanically ventilated patients. Our objective was to determine the potential use of an electronic nose as a diagnostic adjunct in the detection of VAP.


METHODS
We performed a prospective study of mechanically ventilated patients in a surgical intensive care unit. Clinical data, including temperature, white blood cell count, character and quantity of tracheal secretions, ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, and chest radiographs, were collected, and a pneumonia score between 0 and 10 was calculated. Exhaled gas was sampled from the expiratory limb of the ventilator circuit. The gases were assayed with a commercially available electronic nose. Multidimensional data reduction analysis was used to analyze the results.


RESULTS
Forty-four patients were studied. Fifteen patients had pneumonia scores of 7 or greater, and 29 patients had scores of 6 or less. With Fisher discriminant analysis and K-nearest neighbor analysis, the electronic nose was able to discriminate between the two groups.


CONCLUSIONS
The electronic nose is a new technology that is inexpensive, noninvasive, and portable. We demonstrate its ability to predict pneumonia, based on a well-recognized scoring system. This technology promises to serve as a diagnostic adjunct in the management of VAP.
Hockstein
Neil G
NG
Department of Otorhinolaryngology-Head and Neck Surgery, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA.
Thaler
Erica R
ER
Lin
Yuanqing
Y
Lee
D Daniel
DD
Hanson
C William
CW
D015374:Biosensing Techniques; D001944:Breath Tests / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D007362:Intensive Care Units; D016571:Neural Networks, Computer; D011014:Pneumonia / Q000175:diagnosis* / Q000209:etiology; D011446:Prospective Studies; D012121:Respiration, Artificial / Q000009:adverse effects; D012903:Smell