HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:
Guest Access | Sign In via User Name/Password

This Article Full Text Free Full Text (PDF) Free Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Article Archive Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (6) Citing Articles via Google Scholar Google Scholar Articles by Patel, M. M. Articles by Kline, J. A. Search for Related Content PubMed PubMed Citation Articles by Patel, M. M. Articles by Kline, J. A.

Neural Network Analysis of the Volumetric Capnogram to Detect Pulmonary Embolism^*

^* From the Department of Emergency Medicine (Drs. Patel and Kline), Carolinas Medical Center, Charlotte, NC, and Novametrix Corporation (Ms. Browning), Wallingford, CT.

Correspondence to: Jeffrey A. Kline, MD, Department of Emergency Medicine, Carolinas Medical Center, Charlotte, NC 28232; e-mail: Jkline{at}carolinas.org

Background: Pulmonary embolism (PE) produces ventilation/perfusion mismatch that may be manifested in various variables of the volume-based capnogram (VBC). We hypothesized that a neural network (NN) system could detect changes in VBC variables that reflect the presence of a PE.

Methods: A commercial VBC system was used to record multiple respiratory variables from consecutive expiratory breaths. Data from 12 subjects (n = 6 PE+ and n = 6 PE-) were used as input to a fully connected back-propagating NN for model development. The derived model was tested in a prospective, observational study at an urban teaching hospital. Volumetric capnograms were then collected on 53 test subjects: 30 subjects with PE confirmed by pulmonary angiography or diagnostic scintillation lung scan, and 23 subjects without PE based on pulmonary angiography. The derived NN model was applied to VBC data from the test population.

Results: Seventeen VBC variables were used by the derived NN model to generate a numeric probability of PE. When the derived NN model was applied to VBC data from the 53 test subjects, PE was detected with a sensitivity of 100% (95% CI = 89% to 100%) and a specificity of 48% (95% CI = 27% to 69%). The likelihood ratio positive [LR(+)] for the VBC-NN test was 1.82 and the LR (-) was 0.1.

Conclusion: This study demonstrates the feasibility of developing a rapid, noninvasive breath test for diagnosing PE using volumetric capnography and NN analysis.

Key Words: artificial intelligence • capnography • diagnosis • pulmonary embolism • respiratory monitoring • thromboembolism

This article has been cited by other articles:

				F. Verschuren, G. Liistro, R. Coffeng, F. Thys, J. Roeseler, F. Zech, and M. Reynaert Volumetric Capnography as a Screening Test for Pulmonary Embolism in the Emergency Department Chest, March 1, 2004; 125(3): 841 - 850. [Abstract] [Full Text] [PDF]

				J. A. Kline, E. G. Israel, E. A. Michelson, B. J. O'Neil, M. C. Plewa, and D. C. Portelli Diagnostic Accuracy of a Bedside D-dimer Assay and Alveolar Dead-Space Measurement for Rapid Exclusion of Pulmonary Embolism: A Multicenter Study JAMA, February 14, 2001; 285(6): 761 - 768. [Abstract] [Full Text] [PDF]