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On the temporal characterisation of novel capnometric indices for personalised monitoring and treatment optimisation of COPD

Background: The absence of appropriately sensitive or specific real-time respiratory metrics which are physiologically representative of lung function in normal tidal breathing has, until now, prevented the development of intelligent personalised devices which would facilitate early diagnosis of respiratory deterioration in chronic respiratory disorders such as COPD. Aim: To characterise the temporal progression of COPD with metrics derived from point-of-care capnometry in a population of COPD patients and healthy controls. Methods: 30 patients and 7 healthy controls used new generation LED-based personal capnometers three times daily for up to six weeks. Temporal parameters characterising the changing exhaled CO2 waveforms (angles, tangents, timepoints, ETCO2 values) were extracted by automated mathematical shape analysis. Results: A range of mathematical indices derived from analysis of changing capnometry signatures have allowed categorisation of our COPD cohort into those with (1) minimally changing, (2) increasing and (3) decreasing ETCO2 values (figure) which requires further clinical correlation. Alpha angles clearly distinguish COPD patients (126°-162°) from healthy controls (109-123°). Conclusions: Novel respiratory indices derived from changing capnometry waveform shapes can potentially facilitate temporal disease monitoring and ultimately optimised management of COPD.