Physiological Impairments on Respiratory Oscillometry and Future Exacerbations in Chronic Obstructive Pulmonary Disease Patients without a History of Frequent Exacerbations.
Yi ZhangNaoya TanabeHiroshi ShimaYusuke ShiraisiTsuyoshi OgumaAtsuyasu SatoShigeo MuroSusumu SatoToyohiro HiraiPublished in: COPD (2022)
Respiratory oscillometry allows measuring respiratory resistance and reactance during tidal breathing and may predict exacerbations in patients with chronic obstructive pulmonary disease (COPD). While the Global Initiative for Chronic Obstructive Lung Disease (GOLD) advocates the ABCD classification tool to determine therapeutic approach based on symptom and exacerbation history, we hypothesized that in addition to spirometry, respiratory oscillometry complemented the ABCD tool to identify patients with a high risk of exacerbations. This study enrolled male outpatients with stable COPD who were prospectively followed-up over 5 years after completing mMRC scale and COPD assessment test (CAT) questionnaires, post-bronchodilator spirometry and respiratory oscillometry to measure resistance, reactance, and resonant frequency (Fres), and emphysema quantitation on computed tomography. Total 134 patients were classified into the GOLD A, B, C, and D groups ( n = 48, 71, 5, and 9) based on symptoms on mMRC and CAT and a history of exacerbations in the previous year. In univariable analysis, higher Fres was associated with an increased risk of exacerbation more strongly than other respiratory oscillometry indices. Fres was closely associated with forced expiratory volume in 1 sec (FEV 1 ). In multivariable Cox-proportional hazard models of the GOLD A and B groups, either lower FEV 1 group or higher Fres group was associated with a shorter time to the first exacerbation independent of the GOLD group (A vs B) and emphysema severity. Adding respiratory oscillometry to the ABCD tool may be useful for risk estimation of future exacerbations in COPD patients without frequent exacerbation history.
Keyphrases
- chronic obstructive pulmonary disease
- lung function
- end stage renal disease
- cystic fibrosis
- chronic kidney disease
- newly diagnosed
- ejection fraction
- computed tomography
- peritoneal dialysis
- prognostic factors
- machine learning
- depressive symptoms
- ms ms
- physical activity
- intensive care unit
- silver nanoparticles
- quality improvement
- deep learning
- acute respiratory distress syndrome
- mechanical ventilation
- pet ct