A network physiology approach to oxygen saturation variability during normobaric hypoxia.

NEW FINDINGS: What is the central question of this study? What is the physiological interpretation of SpO2 fluctuations observed during normobaric hypoxia in healthy individuals? What is the main finding and its importance? There is a significant flow of information between SpO2 and other cardio-respiratory time series during graded hypoxia. Analysis of the pattern of SpO2 variations has potential for non-invasive assessment of the engagement of respiratory control system in health and disease. ABSTRACT: Peripheral capillary oxygen saturation ( SpO2 ) exhibits a complex pattern of fluctuations during hypoxia. The physiological interpretation of SpO2 variability is not well understood. In this study, we tested the hypothesis that SpO2 fluctuation carries information about integrated cardio-respiratory control in healthy individuals using a network physiology approach. We explored the use of transfer entropy in order to compute the flow of information between cardio-respiratory signals during hypoxia. Twelve healthy males (mean (SD) age 22 (4) years) were exposed to four simulated environments (fraction of inspired oxygen ( FIO2 ): 0.12, 0.145, 0.17, and 0.2093) for 45 min, in a single blind randomized controlled design. The flow of information between different physiological parameters ( SpO2 , respiratory frequency, tidal volume, minute ventilation, heart rate, end-tidal pressure of O2 and CO2 ) were analysed using transfer entropy. Normobaric hypoxia was associated with a significant increase in entropy of the SpO2 time series. The transfer entropy analysis showed that, particularly at FIO2 0.145 and 0.12, the flow of information between SpO2 and other physiological variables exhibits a bidirectional relationship. While reciprocal interactions were observed between different cardio-respiratory parameters during hypoxia, SpO2 remained the main hub of this network. SpO2 fluctuations during graded hypoxia exposure carry information about cardio-respiratory control. Therefore, SpO2 entropy analysis has the potential for non-invasive assessment of the functional connectivity of respiratory control system in various healthcare settings.

Application of oxygen saturation variability analysis for the detection of exacerbation in individuals with COPD: A proof-of-concept study.

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 (Sp O2 ) 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 Sp O2 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 Sp O2 variations was carried examined using sample entropy (for assessment of irregularity), the multiscale entropy (complexity), and detrended fluctuation analysis (self-similarity). RESULTS: Sp O2 variations displayed a complex pattern in both stable and exacerbation phases of COPD. During an exacerbation, Sp O2 entropy increased (p = 0.029) and long-term fractal-like exponent (α2) decreased (p = 0.002) while the mean and standard deviation of Sp O2 time series remained unchanged. Through ROC analyses, Sp O2 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 Sp O2 entropy and the fractal-like exponent have the potential to detect exacerbations in COPD. Further research is warranted to examine if Sp O2 variability analysis could be used as a novel objective method of detecting exacerbations.

Effects of Normobaric Hypoxia on Oxygen Saturation Variability.

Background: The study is the first to evaluate the effects of graded normobaric hypoxia on SpO2 variability in healthy individuals. Materials and Methods: Twelve healthy males (mean [standard deviation] age 22 [4] years) were exposed to four simulated environments (fraction of inspired oxygen [FIO2]: 0.12, 0.145, 0.17, and 0.21) for 45 minutes, in a balanced crossover design. Results: Sample entropy, a tool that quantifies the irregularity of pulse oximetry fluctuations, was used as a measure of SpO2 variability. SpO2 entropy increased as the FIO2 decreased, and there was a strong significant negative correlation between mean SpO2 and its entropy during hypoxic exposure (r = -0.841 to -0.896, p < 0.001). In addition, SpO2 sample entropy, but not mean SpO2, was correlated (r = 0.630-0.760, p < 0.05) with dyspnea in FIO2 0.17, 0.145, and 0.12 and importantly, SpO2 sample entropy at FIO2 0.17 was correlated with dyspnea at FIO2 0.145 (r = 0.811, p < 0.01). Conclusions: These findings suggest that SpO2 variability analysis may have the potential to be used in a clinical setting as a noninvasive measure to identify the negative sequelae of hypoxemia.

Which heart rate variability index is an independent predictor of mortality in cirrhosis?

BACKGROUND: Liver cirrhosis is associated with reduced heart rate variability (HRV), which indicates impaired integrity of cardiovascular control in this patient population. There are several different indices for HRV quantification. The present study was designed to: 1) determine which of the HRV indices is best at predicting mortality in patients with cirrhosis; 2) verify if such ability to predict mortality is independent of the severity of hepatic failure. METHODS: Ten minutes electrocardiogram was recorded in 74 patients with cirrhosis. Heart rate fluctuations were quantified using statistical, geometrical and non-linear analysis. The patients were followed-up for 18months and information was collected on the occurrence of death/liver transplantation. RESULTS: During the follow-up period, 24 patients (32%) died or were transplanted for hepatic decompensation. Cox's regression analysis showed that SDNN (total HRV), cSDNN (corrected SDNN), SD1 (short-term HRV), SD2 (long-terms HRV) and spectral indices could predict survival in these patients. However, only SD2 and cSDNN were shown to be independent of MELD in predicting survival. The prognostic value of HRV indices was independent of age, gender, use of beta blockers, and the aetiology of liver disease. CONCLUSION: Two HRV indices were identified that could predict mortality in patients with cirrhosis, independently of MELD. These indices are potentially useful tools for survival prediction.

Pattern Analysis of Oxygen Saturation Variability in Healthy Individuals: Entropy of Pulse Oximetry Signals Carries Information about Mean Oxygen Saturation.

Pulse oximetry is routinely used for monitoring patients' oxygen saturation levels with little regard to the variability of this physiological variable. There are few published studies on oxygen saturation variability (OSV), with none describing the variability and its pattern in a healthy adult population. The aim of this study was to characterize the pattern of OSV using several parameters; the regularity (sample entropy analysis), the self-similarity [detrended fluctuation analysis (DFA)] and the complexity [multiscale entropy (MSE) analysis]. Secondly, to determine if there were any changes that occur with age. The study population consisted of 36 individuals. The "young" population consisted of 20 individuals [Mean (±1 SD) age = 21.0 (±1.36 years)] and the "old" population consisted of 16 individuals [Mean (±1 SD) age = 50.0 (±10.4 years)]. Through DFA analysis, OSV was shown to exhibit fractal-like patterns. The sample entropy revealed the variability to be more regular than heart rate variability and respiratory rate variability. There was also a significant inverse correlation between mean oxygen saturation and sample entropy in healthy individuals. Additionally, the MSE analysis described a complex fluctuation pattern, which was reduced with age (p < 0.05). These findings suggest partial "uncoupling" of the cardio-respiratory control system that occurs with aging. Overall, this study has characterized OSV using pre-existing tools. We have showed that entropy analysis of pulse oximetry signals carries information about body oxygenation. This may have the potential to be used in clinical practice to detect differences in diseased patient subsets.