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.
- Artificial intelligence
- Pulmonary embolism
- Respiratory monitoring
ASJC Scopus subject areas
- Pulmonary and Respiratory Medicine
- Critical Care and Intensive Care Medicine
- Cardiology and Cardiovascular Medicine