Physiological network approach to prognosis in cirrhosis: A shifting paradigm

Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.

Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery.

METHODS

This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals.

RESULTS

The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences.

CONCLUSIONS

The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress.

Therapeutic effects of long-term HBOT on Alzheimer's disease neuropathologies and cognitive impairment in APPswe/PS1dE9 mice

Alzheimer's disease (AD) is the most common neurodegenerative disorder with the pathological hallmarks of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs) in the brain. Although there is a hope that anti-amyloid monoclonal antibodies may emerge as a new therapy for AD, the high cost and side effect is a big concern. Non-drug therapy is attracting more attention and may provide a better resolution for the treatment of AD. Given the fact that hypoxia contributes to the pathogenesis of AD, hyperbaric oxygen therapy (HBOT) may be an effective intervention that can alleviate hypoxia and improve AD. However, it remains unclear whether long-term HBOT intervention in the early stage of AD can slow AD progression and ultimately prevent cognitive impairment in this disease. In this study we applied consecutive 3-month HBOT interventions on 3-month-old APPswe/PS1dE9 AD mice which represent the early stage of AD. When the APPswe/PS1dE9 mice at 9-month-old which represent the disease stage we measured cognitive function, 24-h blood oxygen saturation, Aβ and tau pathologies, vascular structure and function, and neuroinflammation in APPswe/PS1dE9 mice. Our results showed that long-term HBOT can attenuate the impairments in cognitive function observed in 9-month-old APPswe/PS1dE9 mice. Most importantly, HBOT effectively reduced the progression of Aβ plaques deposition, hyperphosphorylated tau protein aggregation, and neuronal and synaptic degeneration in the AD mice. Further, long-term HBOT was able to enhance blood oxygen saturation level. Besides, long-term HBOT can improve vascular structure and function, and reduce neuroinflammation in AD mice. This study is the first to demonstrate that long-term HBOT intervention in the early stage of AD can attenuate cognitive impairment and AD-like pathologies. Overall, these findings highlight the potential of long-term HBOT as a disease-modifying approach for AD treatment.

Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery

COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.

Exploring the Hidden Complexity: Entropy Analysis in Pulse Oximetry of Female Athletes

This study examines the relationship between physiological complexity, as measured by Approximate Entropy (ApEn) and Sample Entropy (SampEn), and fitness levels in female athletes. Our focus is on their association with maximal oxygen consumption (VO2,max). Our findings reveal a complex relationship between entropy metrics and fitness levels, indicating that higher fitness typically, though not invariably, correlates with greater entropy in physiological time series data; however, this is not consistent for all individuals. For Heart Rate (HR), entropy measures suggest stable patterns across fitness categories, while pulse oximetry (SpO2) data shows greater variability. For instance, the medium fitness group displayed an ApEn(HR) = 0.57±0.13 with a coefficient of variation (CV) of 22.17 and ApEn(SpO2) = 0.96±0.49 with a CV of 46.08%, compared to the excellent fitness group with ApEn(HR) = 0.60±0.09 with a CV of 15.19% and ApEn(SpO2) =0.85±0.42 with a CV of 49.46%, suggesting broader physiological responses among more fit individuals. The larger standard deviations and CVs for SpO2 entropy may indicate the body's proficient oxygen utilization at higher levels of physical demand. Our findings advocate for combining entropy metrics with wearable sensor technology for improved biomedical analysis and personalized healthcare.

Antibiotic intervention exacerbated oxidative stress and inflammatory responses in SD rats under hypobaric hypoxia exposure

The gut microbiota plays a crucial role in maintaining host nutrition, metabolism, and immune homeostasis, particularly in extreme environmental conditions. However, the regulatory mechanisms of the gut microbiota in animal organisms hypobaric hypoxia exposure require further study. We conducted a research by comparing SD rats treated with an antibiotic (ABX) cocktail and untreated SD rats that were housed in a low-pressure oxygen chamber (simulating low pressure and hypoxic environment at 6000 m altitude) for 30 days. After the experiment, blood, feces, and lung tissues from SD rats were collected for analysis of blood, 16S rRNA amplicon sequencing, and non-targeted metabolomics. The results demonstrated that the antibiotic cocktail-treated SD rats exhibited elevated counts of neutrophil (Neu) and monocyte (Mon) cells, an enrichment of sulfate-reducing bacteria (SBC), reduced levels of glutathione, and accumulated phospholipid compounds. Notably, the accumulation of phospholipid compounds, particularly lysophosphatidic acid (LPA), lipopolysaccharide (LPS), and lysophosphatidylcholine (LPC), along with the aforementioned changes, contributed to heightened oxidative stress and inflammation in the organism. In addition, we explored the resistance mechanisms of SD rats in low-oxygen and low-pressure environments and found that increasing the quantity of the Prevotellaceae and related beneficial bacteria (especially Lactobacillus) could reduce oxidative stress and inflammation. These findings offer valuable insights into enhancing the adaptability of low-altitude animals under hypobaric hypoxia exposure.

Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet

An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.

Characterisation of the acute hypoxic response using breathing variability parameters: a pilot study in humans

PURPOSE

We aimed to investigate respiratory rate variability (RRV) and tidal volume (V_t) variability during exposure to normobaric hypoxia (i.e., reduction in the fraction of inspired oxygen - FiO₂), and the association of the changes in RRV and V_t variability with the changes in pulse oxygen saturation (SpO₂).

METHODS

Thirty healthy human participants (15 females) were exposed to: (1) 15-min normoxia, (2) 10-min hypoxia simulating 2200m, (3) 10-min hypoxia simulating 4000m, (4) 10-min hypoxia simulating 5000m, (5) 15-min recovery in normoxia. Linear regression modelling was applied with SpO₂ (dependent variable) and the changes in RRV and V_t variability (independent variables), controlling for FiO₂, age, sex, changes in heart rate (HR), changes in HR variability (HRV), and changes in minute ventilation (V_E).

RESULTS

When modelling breathing parameter variability as root-mean-square standard deviation (RMSSD), a significant independent association of the changes in RRV with the changes in SpO₂ was found (B=-4.3e-04, 95% CI=-8.3e-04/-2.1e-05, p=0.04). The changes in V_t variability showed no significant association with the changes in SpO₂ (B=-1.6, 95% CI=-5.5/2.4, p=0.42). When modelling parameters variability as SD, a significant independent association of the changes in RRV with the changes in SpO₂ was found (B=-8.2e-04, 95% CI=-1.5e-03/-9.4e-05, p=0.03). The changes in V_t variability showed no significant association with the changes in SpO₂ (B=1.4, 95% CI=-5.8/8.6, p=0.69).

CONCLUSION

Higher RRV is independently associated with lower SpO₂ during acute hypoxic exposure, while V_t variability parameters are not. Therefore, RRV may be a potentially interesting parameter to characterize individual responses to acute hypoxia.

Carotid-body modulation through meditation in stage-I hypertensive subjects: Study protocol of a randomized and controlled study

Adjunctive therapy for hypertension is in high demand for clinical research. Therefore, several meta-analyses have provided sufficient evidence for meditation as an adjunct therapy, without being anchored on reliable physiological grounds. Meditation modulates the autonomic nervous system. Herein, we propose a hierarchical-dependent effect for the carotid body (CB) in attenuating blood pressure (BP) and ventilatory variability (VV) fine-tuning due to known nerve connections between the CB, prefrontal brain, hypothalamus, and solitary tract nucleus. The aim of this exploratory study was to investigate the role of CB in the possible decrease in BP and changes in VV that could occur in response to meditation. This was a prospective, single-center, parallel-group, randomized, controlled clinical trial with concealed allocation. Eligible adult subjects of both sexes with stage 1 hypertension will be randomized into 1 of 2 groups: transcendental meditation or a control group. Subjects will be invited to 3 visits after randomization and 2 additional visits after completing 8 weeks of meditation or waiting-list control. Thus, subjects will undergo BP measurements in normoxia and hyperoxia, VV measurements using the Poincaré method at rest and during exercise, and CB activity measurement in the laboratory. The primary outcome of this study was the detection of changes in BP and CB activity after 8 weeks. Our secondary outcome was the detection of changes in the VV at rest and during exercise. We predict that interactions between hyperoxic deactivation of CB and meditation; Will reduce BP beyond stand-alone intervention or alternatively; Meditation will significantly attenuate the effects of hyperoxia as a stand-alone intervention. In addition, VV can be changed, partially mediated by a reduction in CB activity. Trial registration number: ReBEC registry (RBR-55n74zm). Stage: pre-results.

Comparison of oxygen supplementation in very preterm infants: Variations of oxygen saturation features and their application to hypoxemic episode based risk stratification

Background

Oxygen supplementation is commonly used to maintain oxygen saturation (SpO₂) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO₂ signal features and assess their performance in hypoxemia risk screening during NICU monitoring.

Subjects and methods

SpO₂ data from 25 infants with gestational age <32 weeks and birthweight <2,000 g who underwent a cross over trial of low-flow nasal cannula (NC) and digitally-set servo-controlled oxygen environment (OE) supplementations was considered in this secondary analysis. Features pertaining to signal distribution, variability and complexity were estimated and analyzed for differences between the supplementations. Univariate and regularized multivariate logistic regression was applied to identify relevant features and develop screening models for infants likely to experience a critically high number of IH per day of observation. Their performance was assessed using area under receiver operating curves (AUROC), accuracy, sensitivity, specificity and F1 scores.

Results

While most SpO₂ measures remained comparable during both supplementations, signal irregularity and complexity were elevated while on OE, pointing to more volatility in oxygen saturation during this supplementation mode. In addition, SpO₂ variability measures exhibited early prognostic value in discriminating infants at higher risk of critically many IH events. Poincare plot variability at lag 1 had AUROC of 0.82, 0.86, 0.89 compared to 0.63, 0.75, 0.81 for the IH number, a clinical parameter at observation times of 30 min, 1 and 2 h, respectively. Multivariate models with two features exhibited validation AUROC > 0.80, F1 score > 0.60 and specificity >0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring.

Conclusion

Analysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care.

Reduced oxygen saturation entropy is associated with poor prognosis in critically ill patients with sepsis

Recent studies have found that oxygen saturation (SpO2 ) variability analysis has potential for noninvasive assessment of the functional connectivity of cardiorespiratory control systems during hypoxia. Patients with sepsis have suboptimal tissue oxygenation and impaired organ system connectivity. Our objective with this report was to highlight the potential use for SpO2 variability analysis in predicting intensive care survival in patients with sepsis. MIMIC-III clinical data of 164 adults meeting Sepsis-3 criteria and with 30 min of SpO2 and respiratory rate data were analyzed. The complexity of SpO2 signals was measured through various entropy calculations such as sample entropy and multiscale entropy analysis. The sequential organ failure assessment (SOFA) score was calculated to assess the severity of sepsis and multiorgan failure. While the standard deviation of SpO2 signals was comparable in the non-survivor and survivor groups, non-survivors had significantly lower SpO2 entropy than those who survived their ICU stay (0.107 ± 0.084 vs. 0.070 ± 0.083, p < 0.05). According to Cox regression analysis, higher SpO2 entropy was a predictor of survival in patients with sepsis. Multivariate analysis also showed that the prognostic value of SpO2 entropy was independent of other covariates such as age, SOFA score, mean SpO2 , and ventilation status. When SpO2 entropy was combined with mean SpO2 , the composite had a significantly higher performance in prediction of survival. Analysis of SpO2 entropy can predict patient outcome, and when combined with SpO2 mean, can provide improved prognostic information. The prognostic power is on par with the SOFA score. This analysis can easily be incorporated into current ICU practice and has potential for noninvasive assessment of critically ill patients.

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.