Abstract
BACKGROUND: Individuals with chronic obstructive pulmonary disease (COPD) commonly experience exacerbations, which may require hospital admission. Early detection of exacerbations, and therefore early treatment, could be crucial in preventing admission and improving outcomes. Our previous research has demonstrated that the pattern analysis of peripheral oxygen saturation (S(p) O(2) ) fluctuations provides novel insights into the engagement of the respiratory control system in response to physiological stress (hypoxia). Therefore, this pilot study tested the hypothesis that the pattern of S(p) O(2) variations in overnight recordings of individuals with COPD would distinguish between stable and exacerbation phases of the disease. METHODS: Overnight pulse oximetry data from 11 individuals with COPD, who exhibited exacerbation after a period of stable disease, were examined. Stable phase recordings were conducted overnight and one night prior to exacerbation recordings were also analyzed. Pattern analysis of S(p) O(2) variations was carried examined using sample entropy (for assessment of irregularity), the multiscale entropy (complexity), and detrended fluctuation analysis (self-similarity). RESULTS: S(p) O(2) variations displayed a complex pattern in both stable and exacerbation phases of COPD. During an exacerbation, S(p) O(2) entropy increased (p = 0.029) and long-term fractal-like exponent (α2) decreased (p = 0.002) while the mean and standard deviation of S(p) O(2) time series remained unchanged. Through ROC analyses, S(p) O(2) entropy and α2 were both able to classify the COPD phases into either stable or exacerbation phase. With the best positive predictor value (PPV) for sample entropy (PPV = 70%) and a cut-off value of 0.454. While the best negative predictor value (NPV) was α2 (NPV = 78%) with a cut-off value of 1.00. CONCLUSION: Alterations in S(p) O(2) entropy and the fractal-like exponent have the potential to detect exacerbations in COPD. Further research is warranted to examine if S(p) O(2) variability analysis could be used as a novel objective method of detecting exacerbations.