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

Abstract

Background Capnography 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. Methods Two 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 results Overall 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. Conclusion In 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 registration clinical trials.gov, NCT02805114. Registered 17 June 2016, https://clinicaltrials.gov/ct2/show/NCT02805114

Development of a model for predicting the severity of chronic obstructive pulmonary disease

“…After adjusting for potential confounders, we found that the parameters that could distinguish between mild/moderate and severe cases were vascular disorders, HDL, plasma fibrinogen, fructosamine, standard bicarbonate concentration, pCO 2 , age, eosinophil count, lymphocyte ratio, and apolipoprotein A1. The factors that could distinguish between mild/moderate and very severe cases were vascular disorders, HDL, plasma fibrinogen, fructosamine, pO 2 , plasma carbon dioxide concentration, standard bicarbonate concentration, pCO 2 , age, sex, allergic diseases, eosinophil count, lymphocyte (14,17). For instance, Chen et al developed a model for predicting disease severity in patients hospitalized for COPD exacerbation and found that neutrophil count percentage and demographic parameters were associated with a higher risk of COPD exacerbation; the area under the receiver operating characteristic curve was 0.84 (14).…”

Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer

“…These features included the following: α, β, γ, and δ angles (Fig. 2 ) [ 11 – 13 ]; gradients and residuals derived from fitting curves to phases, such as the expiratory plateau [ 14 ]; absolute and short-term variability of pCO 2 [ 15 ]; curvature and other higher-order time-based features such as the ratio of the expiratory to inspiratory phase [ 12 , 13 , 16 ]; and area under the curve (AUC), which is commonly calculated in volumetric capnography [ 16 , 17 ]. These features have a basis in the respiratory physiology literature, and in many cases have been hypothesized to relate to clinical airway obstruction.…”

Machine Diagnosis of Chronic Obstructive Pulmonary Disease using a Novel Fast-Response Capnometer

“…As the number of breaths per capnogram varied, the median and standard deviation were calculated for the per-breath features in each capnogram. These features included the following: α, β, γ , and δ angles (Figure 2) [11, 12, 13]; gradients and residuals derived from fitting curves to phases, such as the expiratory plateau [14]; absolute and short-term variability of pCO 2 [15]; curvature and other higher-order time-based features such as the ratio of the expiratory to inspiratory phase [12, 13, 16]; and area under the curve (AUC), which is commonly calculated in volumetric capnography [16, 17]. These features have a basis in the respiratory physiology literature, and in many cases have been hypothesized to relate to clinical airway obstruction.…”