title: Neural network analysis of the volumetric capnogram to detect pulmonary embolism.
abstract: 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.
  
  
  CONCLUSIONS
  This study demonstrates the feasibility of developing a rapid, noninvasive breath test for diagnosing PE using volumetric capnography and NN analysis.
authors.affiliation: Department of Emergency Medicine, Carolinas Medical Center, Charlotte, NC 28232, USA.
authors.forename: M M
authors.initials: MM
authors.lastname: Patel
authors.forename: D B
authors.initials: DB
authors.lastname: Rayburn
authors.forename: J A
authors.initials: JA
authors.lastname: Browning
authors.forename: J A
authors.initials: JA
authors.lastname: Kline
mesh_terms: D000792:Angiography; D019296:Capnography / Q000379:methods*; D005260:Female; D006784:Hospitals, Teaching; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D015588:Observer Variation; D011446:Prospective Studies; D011651:Pulmonary Artery / Q000000981:diagnostic imaging; D011655:Pulmonary Embolism / Q000175:diagnosis* / Q000378:metabolism / Q000503:physiopathology; D011877:Radionuclide Imaging; D012680:Sensitivity and Specificity; D013990:Tidal Volume; D014505:Urban Population; D014692:Ventilation-Perfusion Ratio