Background Although currently most widely used in mechanical ventilation and cardiopulmonary resuscitation, features of the carbon dioxide (CO2) waveform produced through capnometry have been shown to correlate with V/Q mismatch, dead space volume, type of breathing pattern, and small airway obstruction. This study applied feature engineering and machine learning techniques to capnography data collected by the N-Tidal™ device across four clinical studies to build a classifier that could distinguish CO2 recordings (capnograms) of patients with COPD from those without COPD. Methods Capnography data from four longitudinal observational studies (CBRS, GBRS, CBRS2 and ABRS) was analysed from 295 patients, generating a total of 88,186 capnograms. CO2 sensor data was processed using TidalSense’s regulated cloud platform, performing real-time geometric analysis on CO2 waveforms to generate 82 physiologic features per capnogram. These features were used to train machine learning classifiers to discriminate COPD from ‘non-COPD’ (a group that included healthy participants and those with other cardiorespiratory conditions); model performance was validated on independent test sets. Results The best machine learning model (XGBoost) performance provided a class-balanced AUROC of 0.985 ± 0.013, positive predictive value (PPV) of 0.914 ± 0.039 and sensitivity of 0.915 ± 0.066 for a diagnosis of COPD. The waveform features that are most important for driving classification are related to the alpha angle and expiratory plateau regions. These features correlated with spirometry readings, supporting their proposed properties as markers of COPD. Conclusion The N-Tidal™ device can be used to accurately diagnose COPD in near-real-time, lending support to future use in a clinical setting. Trial registration: Please see NCT03615365, NCT02814253, NCT04504838 and NCT03356288.

Background: Although currently most widely used in mechanical ventilation and cardiopulmonary resuscitation, features of the carbon dioxide waveform produced through capnometry have been shown to correlate with V/Q mismatch, dead space volume, type of breathing pattern, and small airway obstruction. This study applied feature engineering and machine learning techniques to capnography data collected by the N-TidalTMdevice across four clinical studies to build a classifier that could distinguish CO2recordings (capnograms) of patients with COPD from those without COPD. Methods: Capnography data from four longitudinal observational studies (CBRS, GBRS, CBRS2 and ABRS) was analysed from 295 patients, generating a total of 88,186 capnograms. CO2sensor data was processed using Cambridge Respiratory Innovations′ regulated cloud platform, performing realtime geometric analysis on CO2waveforms to generate 82 physiologic features per capnogram. These features were used to train machine learning classifiers to discriminate COPD from 'non-COPD' (a group that included healthy participants and those with other cardiorespiratory conditions); model performance was validated on independent test sets. Results: The best machine learning model (XGBoost) performance provided a class−balanced AUROC of 0.968 ± 0.017 and a positive predictive value (PPV) of 0.911 ± 0.028 for a diagnosis of COPD. The waveform features that are most important for driving classification are related to the alpha angle and expiratory plateau regions. These features correlated with spirometry readings, supporting their proposed properties as markers of COPD. Conclusion: The N-Tidal device can be used to accurately diagnose COPD in near-real-time, lending support to future use in a clinical setting.