Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients

Respiratory Research

Michal Ronen¹  Yael Shostak²  Dror Rosengarten²  Barak Pertzov²  Moshe Heching²  Dorit Shitenberg²  Mordechai R. Kramer² 

[1]Medtronic, Patient Monitoring, Jerusalem, Israel

[2]The Pulmonary Division, Pulmonary Institute, Rabin Medical Center, Beilinson Campus, 49100, Petach Tikva, Israel

[3]Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

关键词: Asthma;    COPD;    Airway obstruction;    FEV1;    Capnography;    Model;    Neural network;   

DOI  :  10.1186/s12931-021-01747-3

来源: Springer

【 摘 要 】

BackgroundCapnography waveform contains essential information regarding physiological characteristics of the airway and thus indicative of the level of airway obstruction. Our aim was to develop a capnography-based, point-of-care tool that can estimate the level of obstruction in patients with asthma and COPD.MethodsTwo prospective observational studies conducted between September 2016 and May 2018 at Rabin Medical Center, Israel, included healthy, asthma and COPD patient groups. Each patient underwent spirometry test and continuous capnography, as part of, either methacholine challenge test for asthma diagnosis or bronchodilator reversibility test for asthma and COPD routine evaluation. Continuous capnography signal, divided into single breaths waveforms, were analyzed to identify waveform features, to create a predictive model for FEV1 using an artificial neural network. The gold standard for comparison was FEV1 measured with spirometry.Measurements and main resultsOverall 160 patients analyzed. Model prediction included 32/88 waveform features and three demographic features (age, gender and height). The model showed excellent correlation with FEV1 (R = 0.84), R2 achieved was 0.7 with mean square error of 0.13.ConclusionIn this study we have developed a model to evaluate FEV1 in asthma and COPD patients. Using this model, as a point-of-care tool, we can evaluate the airway obstruction level without reliance on patient cooperation. Moreover, continuous FEV1 monitoring can identify disease fluctuations, response to treatment and guide therapy.Trial registrationclinical trials.gov, NCT02805114. Registered 17 June 2016, https://clinicaltrials.gov/ct2/show/NCT02805114

【 授权许可】

CC BY