Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern

[Exploratory study on quantitative analysis of nocturnal breathing patterns in patients with acute heart failure based on wearable devices]

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.

Interictal respiratory variability predicts severity of hypoxemia after generalized convulsive seizures

OBJECTIVE

Severe respiratory dysfunction induced by generalized convulsive seizures (GCS) is now thought to be a common mechanism for sudden unexpected death in epilepsy (SUDEP). In a mouse model of seizure-induced death, increased interictal respiratory variability was reported in mice that later died of respiratory arrest after GCS. We studied respiratory variability in epilepsy patients as a predictive tool for severity of postictal hypoxemia, a potential biomarker for SUDEP risk. We then explored the relationship between respiratory variability and central CO2 drive, measured by the hypercapnic ventilatory response (HCVR).

METHODS

We reviewed clinical, video-electroencephalography, and respiratory (belts, airflow, pulse oximeter, and HCVR) data of epilepsy patients. Mean, SD, and coefficient of variation (CV) of interbreath interval (IBI) were calculated. Primary outcomes were: (1) nadir of capillary oxygen saturation (SpO2 ) and (2) duration of oxygen desaturation. Poincaré plots of IBI were created. Covariates were evaluated in univariate models, then, based on Akaike information criteria (AIC), multivariate regression models were created.

RESULTS

Of 66 GCS recorded in 131 subjects, 30 had interpretable respiratory data. In the multivariate model with the lowest AIC value, duration of epilepsy was a significant predictor of duration of oxygen desaturation. Duration of tonic phase and CV of IBI during the third postictal minute correlated with SpO2 nadir, whereas CV of IBI during non-rapid eye movement sleep had a negative correlation. Poincaré plots showed that long-term variability was significantly greater in subjects with ≥200 s of postictal oxygen desaturation after GCS compared to those with <200 s desaturation. Finally, HCVR slope showed a negative correlation with measures of respiratory variability.

SIGNIFICANCE

These results indicate that interictal respiratory variability predicts severity of postictal oxygen desaturation, suggesting its utility as a potential biomarker. They also suggest that interictal respiratory control may be abnormal in some patients with epilepsy.

The protective effect of inhaled corticosteroid on lung inflammation and breathing pattern complexity in a rat model of asthma

Asthma is a heterogeneous disease in which the complexity of the breathing pattern reduces as the severity of the disease increases. Since the pathophysiological basis of reduced breathing pattern complexity in asthma is unclear, in this study, we investigated the effect of reducing inflammation using an inhaled corticosteroid (fluticasone propionate) on the breathing pattern of a rat model of asthma. Detrended fluctuation analysis, sample entropy, and cross-sample entropy analysis of both inter-breath interval and respiratory volume time series showed that early treatment with inhaled corticosteroids not only diminishes lung inflammation and airway hyper-responsiveness, but also has a protective effect against the reduction of breathing pattern complexity due to asthma. However, late treatment had a partial effect on asthma-induced respiratory pattern changes. Since inflammation is a key factor in shifting breathing dynamics away from normal fluctuations, these findings further emphasize the importance of early treatment of asthma with corticosteroids.

The use of time-of-flight camera to assess respiratory rates and thoracoabdominal depths in patients with chronic respiratory disease

INTRODUCTION

Over the last 5 years, the analysis of respiratory patterns presents a growing usage in clinical and research purposes, but there is still currently a lack of easy-to-use and affordable devices to perform such kind of evaluation.

OBJECTIVES

The aim of this study is to validate a new specifically developed method, based on Kinect sensor, to assess respiratory patterns against spirometry under various conditions.

METHODS

One hundred and one participants took parts in one of the three validations studies. Twenty-five chronic respiratory disease patients (14 with chronic obstructive pulmonary disease (COPD) [65 ± 10 years old, FEV₁  = 37 (15% predicted value), VC = 62 (20% predicted value)], and 11 with lung fibrosis (LF) [64 ± 14 years old, FEV₁  = 55 (19% predicted value), VC = 62 (20% predicted value)]) and 76 healthy controls (HC) were recruited. The correlations between the signal of the Kinect (depth and respiratory rate) and the spirometer (tidal volume and respiratory rate) were computed in part 1. We then included 66 HC to test the ability of the system to detect modifications of respiratory patterns induced by various conditions known to modify respiratory pattern (cognitive load, inspiratory load and combination) in parts 2 and 3.

RESULTS

There is a strong correlation between the depth recorded by the Kinect and the tidal volume recorded by the spirometer: r = 0.973 for COPD patients, r = 0.989 for LF patients and r = 0.984 for HC. The Kinect is able to detect changes in breathing patterns induced by different respiratory disturbance conditions, gender and oral task.

CONCLUSIONS

Measurements performed with the Kinect sensors are highly correlated with the spirometer in HC and patients with COPD and LF. Kinect is also able to assess respiratory patterns under various loads and disturbances. This method is affordable, easy to use, fully automated and could be used in the current clinical context. Respiratory patterns are important to assess in daily clinics. However, there is currently no affordable and easy-to-use tool to evaluate these parameters in clinics. We validated a new system to assess respiratory patterns using the Kinect sensor in patients with chronic respiratory diseases.

A systematic review and meta-analysis of heart rate variability in COPD

Background

Chronic obstructive pulmonary disease (COPD) is associated with disruption in autonomic nervous control of the heart rhythm. We present here quantitative evidence of the reduction in HRV measures as well as the challenges to clinical application of HRV in COPD clinics.

Method

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we search in June 2022 Medline and Embase databases for studies reporting HRV in COPD patients using relevant medical subject headings (MeSH) terms. The quality of included studies was assessed using the modified version of the Newcastle-Ottawa Scale (NOS). Descriptive data were extracted, while standardized mean difference was computed for changes in HRV due to COPD. Leave-one-out sensitivity test was performed to assess exaggerated effect size and funnel plots to assess publication bias.

Results

The databases search yielded 512 studies, of which we included 27 that met the inclusion criteria. The majority of the studies (73%) had a low risk of bias and included a total of 839 COPD patients. Although there were high between-studies heterogeneity, HRV time and frequency domains were significantly reduced in COPD patients compared with controls. Sensitivity test showed no exaggerated effect sizes and the funnel plot showed general low publication bias.

Conclusion

COPD is associated with autonomic nervous dysfunction as measured by HRV. Both sympathetic and parasympathetic cardiac modulation were decreased, but there is still a predominance of sympathetic activity. There is high variability in the HRV measurement methodology, which affects clinical applicability.

Evaluation of the effects of dexamethasone in modulating breathing pattern decomplexification in rats with 2-chloroethyl ethyl sulfide-induced lung injury

"Background and Objectives: Sulfur mustard is a functional alkylating chemical warfare agent that gives rise to appalling lung injury. In people with pulmonary diseases, including asthma, the complication of respirational dynamics is reduced. However, the complexity of breathing patterns in lung injury caused by chemical agents is not clear. In the current study, the outcome of 2-chloroethyl ethyl sulfide (CEES), and mustard analogue, upon breathing pattern of rats without or with treatment were reviewed. Methods: The interbeat interval (IBI) and respiratory volume (RV) data have been acquired from spontaneous respiration rats with lung injury by CEES using a whole-body plethysmograph. We calculated mean and coefficient of variation, alpha exponent derived from detrended fluctuation analysis (DFA), and sample entropy of IBI and RV. Finding: Entropy examination of respiratory variation displayed reduced inconsistency (less complication) in the breathing pattern of this rat model of lung injury. The mustard analogue also led to increased lung inflammation in damaged rats. However, treatment by NAC and dexamethasone had a compelling impact on the complication of the breathing rhythm and lung inflammation of rats with lung injury. Conclusion: Our findings show that inflammation could be the possible origin of respiratory dynamics shifting apart from the normal variation in CEES-induced lung injury"

Temporal variations in the pattern of breathing: techniques, sources, and applications to translational sciences

The breathing process possesses a complex variability caused in part by the respiratory central pattern generator in the brainstem; however, it also arises from chemical and mechanical feedback control loops, network reorganization and network sharing with nonrespiratory motor acts, as well as inputs from cortical and subcortical systems. The notion that respiratory fluctuations contain hidden information has prompted scientists to decipher respiratory signals to better understand the fundamental mechanisms of respiratory pattern generation, interactions with emotion, influences on the cortical neuronal networks associated with cognition, and changes in variability in healthy and disease-carrying individuals. Respiration can be used to express and control emotion. Furthermore, respiration appears to organize brain-wide network oscillations via cross-frequency coupling, optimizing cognitive performance. With the aid of information theory-based techniques and machine learning, the hidden information can be translated into a form usable in clinical practice for diagnosis, emotion recognition, and mental conditioning.

Respiratory pattern complexity in newly-diagnosed asthmatic patients

BACKGROUND

The intensity of respiratory symptoms and expiratory airflow limitations in asthma fluctuate over time. Some studies have reported variable complexity of the respiratory patterns in asthmatic patients. Thus, we conducted a novel study to assess the correlation between asthma severity and breathing pattern dynamics in newly-diagnosed asthmatic patients.

METHODS

A total of 20 newly-diagnosed asthmatic patients (7 male, 13 female) and 20 healthy cases (11 male, 9 female) were included. The respiratory patterns of all participants and the asthma severity for asthmatic patients were measured using a spirometer (before and after a bronchodilator exposure) and airflow recorder, respectively. The peak-to-peak intervals and the amplitude of peaks were considered as the inter-breath interval (IBI) and lung volume (LV) series. The Detrended Fluctuation Analysis (DFA), Sample Entropy (SampEn), Multi-scale Entropy (MSE), short-term (SD1) and long-term (SD2) variability, and IBI and LV Cross-Sample Entropy of the respiratory pattern dynamics were calculated using MATLAB (Mathwork, USA).

RESULTS

Asthma patients showed notable increase in the average of sample entropy in both IBI and LV parameters (p = 0.025 and p = 0.018, respectively) and also decreased synchronization between IBI and LV (p = 0.042). The multi-scale sample entropy of both IBI and LV was significantly higher in asthmatic patients (p < 0.05). Furthermore, SD1 and SD2 were higher in the patients with asthma (p < 0.05). Significant correlations were detected between spirometric (forced expiratory flow (FEF) change, pre FEF, pre forced expiratory volume in one second (FEV1) / forced vital capacity (FVC), FVC change) and respiratory pattern (mean-IBI, mean-LV, mean-respiratory rate (RR), coefficient of variation (CV)-IBI, CV-LV, cross-sample entropy) parameters (p < 0.05). Furthermore, we identified a negative correlation between CV of IBI and asthma severity (r = -0.52, p = 0.021).

CONCLUSION

Here, we took a novel approach and observed increased irregularity (more complexity) in the breathing pattern of patients newly-diagnosed with asthma. Remarkable correlations were detected between breathing complexity markers and spirometric indices along with disease severity in asthmatic patients. Thus, our data suggests respiratory pattern indices could be utilized as an indicator of asthma and its severity. However, more clinical data are required to support this conclusion.

Paternal preconception exposure to chronic morphine alters respiratory pattern in response to morphine in male offspring

The clinical use of opioids is restricted by its deleterious impacts on respiratory system. Gaining a better understanding of an individual's susceptibility to adverse opioid effects is important to recognize patients at risk. Ancestral drug addiction has been shown to be associated with alterations in drug responsiveness in the progenies. In the current study, we sought to evaluate the effects of preconception paternal morphine consumption on respiratory parameters in response to acute morphine in male offspring during adulthood, using plethysmography technique. Male Wistar rats administered 10 days of increasing doses of morphine in the period of adolescence. Thereafter, following a 30-day abstinence time, adult males copulated with naïve females. The adult male offspring were examined for breathing response to morphine. Our results indicated that sires who introduce chronic morphine during adolescence leads to increase irregularity of respiratory pattern and asynchronization between inter-breath interval (IBI) and respiratory volume (RV) time series in male offspring. These findings provide evidence that chronic morphine use by parents even before pregnancy can affect respiratory pattern and response to morphine in the offspring.

Allergen disrupts amygdala-respiration coupling

Allergic asthma affects both the respiratory function and central nervous system. Communication between the amygdala and respiratory control system is critical for regulating breathing function. To date, no study provides the effect of allergic inflammation on amygdala-respiration coupling. Here, we simultaneously recorded respiration and local field potentials of the amygdala during awake immobility in a rat model of allergic asthma. A decreased synchrony was found between amygdala and respiration in asthmatic rats. Allergen also reduced the modulatory effect of the respiration phase on amygdala power at delta, theta and gamma2 (80-120 Hz) frequencies. Moreover, in the animal model of allergic asthma, delta and theta oscillations strongly coordinate local gamma2 activity in the amygdala. These findings suggest that allergen can induce brain alterations and therefore shed light on future works to address how disruption of amygdala-respiration coupling contributes to respiratory dysfunction in allergic asthma.

Effects of Cardiac Resynchronization Therapy on Cardio-Respiratory Coupling

In this study, the effect of cardiac resynchronization therapy (CRT) on the relationship between the cardiovascular and respiratory systems in heart failure subjects was examined for the first time. We hypothesized that alterations in cardio-respiratory interactions, after CRT implantation, quantified by signal complexity, could be a marker of a favorable CRT response. Sample entropy and scaling exponents were calculated from synchronously recorded cardiac and respiratory signals 20 min in duration, collected in 47 heart failure patients at rest, before and 9 months after CRT implantation. Further, cross-sample entropy between these signals was calculated. After CRT, all patients had lower heart rate and CRT responders had reduced breathing frequency. Results revealed that higher cardiac rhythm complexity in CRT non-responders was associated with weak correlations of cardiac rhythm at baseline measurement over long scales and over short scales at follow-up recording. Unlike CRT responders, in non-responders, a significant difference in respiratory rhythm complexity between measurements could be consequence of divergent changes in correlation properties of the respiratory signal over short and long scales. Asynchrony between cardiac and respiratory rhythm increased significantly in CRT non-responders during follow-up. Quantification of complexity and synchrony between cardiac and respiratory signals shows significant associations between CRT success and stability of cardio-respiratory coupling.

Changes in quantifiable breathing pattern components predict asthma control: an observational cross-sectional study

Background

Breathing pattern disorders are frequently reported in uncontrolled asthma. At present, this is primarily assessed by questionnaires, which are subjective. Objective measures of breathing pattern components may provide additional useful information about asthma control. This study examined whether respiratory timing parameters and thoracoabdominal (TA) motion measures could predict and classify levels of asthma control.

Methods

One hundred twenty-two asthma patients at STEP 2- STEP 5 GINA asthma medication were enrolled. Asthma control was determined by the Asthma Control Questionnaire (ACQ7-item) and patients divided into ‘well controlled’ or ‘uncontrolled’ groups. Breathing pattern components (respiratory rate (RR), ratio of inspiration duration to expiration duration (Ti/Te), ratio of ribcage amplitude over abdominal amplitude during expiration phase (RCampe/ABampe), were measured using Structured Light Plethysmography (SLP) in a sitting position for 5-min. Breath-by-breath analysis was performed to extract mean values and within-subject variability (measured by the Coefficient of Variance (CoV%). Binary multiple logistic regression was used to test whether breathing pattern components are predictive of asthma control. A post-hoc analysis determined the discriminant accuracy of any statistically significant predictive model.

Results

Fifty-nine out of 122 asthma patients had an ACQ7-item < 0.75 (well-controlled asthma) with the rest being uncontrolled (n = 63). The absolute mean values of breathing pattern components did not predict asthma control (R² = 0.09) with only mean RR being a significant predictor (p < 0.01). The CoV% of the examined breathing components did predict asthma control (R² = 0.45) with all predictors having significant odds ratios (p < 0.01). The ROC curve showed that cut-off points > 7.40% for the COV% of the RR, > 21.66% for the CoV% of Ti/Te and > 18.78% for the CoV% of RCampe/ABampe indicated uncontrolled asthma.

Conclusion

The within-subject variability of timing parameters and TA motion can be used to predict asthma control. Higher breathing pattern variability was associated with uncontrolled asthma suggesting that irregular resting breathing can be an indicator of poor asthma control.

Rhythmic air-puff into nasal cavity modulates activity across multiple brain areas: A non-invasive brain stimulation method to reduce ventilator-induced memory impairment

Mechanical ventilation (MV) can result in long-term brain impairments that are resistant to treatment. The mechanisms underlying MV-induced brain function impairment remain unclear. Since nasal airflow modulates brain activity, here we evaluated whether reinstating airflow during MV could influence the memory performance of rats after recovery. Rats were allocated into two study groups: one group received rhythmic air-puff into the nasal cavity during MV and a control group that underwent ventilation without air-puff. During MV, air-puffs induced time-locked event potentials in OB, mPFC and vHPC and significantly increased the oscillatory activity at the air-puff frequency. Furthermore, in mPFC and vHPC, (but not in OB), delta and theta oscillations were more prominent during air-puff application. After recovery, working memory performance was significantly higher in the air-puff group compared to control. Our study thus suggests a promising non-invasive brain stimulation approach to alleviate the neurological complications of prolonged mechanical ventilation.

Derivation of Respiratory Metrics in Health and Asthma

The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates.

Estimation of the Lung Function Using Acoustic Features of the Voluntary Cough

Spirometry test, a measure of the patient's lung function, is the gold standard for diagnosis and monitoring of chronic pulmonary diseases. Spirometry is currently being done in hospital settings by having the patients blow the air out of their lungs forcefully and into the spirometer's tubes under the supervision and constant guidance of clinicians. This test is expensive, cumbersome and not easily applicable to every-day monitoring of these patients. The lung mechanism when performing a cough is very similar to when spirometry test is done. That includes a big inhalation, air compression and forceful exhalation. Therefore, it is reasonable to assume that obstruction of lung airways should have a similar effect on both cough features and spirometry measures. This paper explores the estimation of lung obstruction using cough acoustic features. A total number of 3695 coughs were collected from patients from 4 different conditions and 4 different severity categories along with their lung function measures in a clinical setting using a smartphone's microphone and a hospital-grade spirometry lab. After feature-set optimization and model hyperparameter tuning, the lung obstruction was estimated with MAE (Mean Absolute Error) of 8% for COPD and 9% for asthma populations. In addition to lung obstruction estimation, we were able to classify patients' disease state with 91% accuracy and patients' severity within each disease state with 95% accuracy.Clinical Relevance- This enables effort-independent estimation of lung function spirometry parameters which could potentially lead to passive monitoring of pulmonary patients.

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Background

There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility.

Objective

In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics.

Methods

A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison.

Results

Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84).

Conclusions

A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation.

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 [F_IO₂]: 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 F_IO₂ 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 F_IO₂ 0.17, 0.145, and 0.12 and importantly, SpO₂ sample entropy at F_IO₂ 0.17 was correlated with dyspnea at F_IO₂ 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.

Allergen-induced anxiety-like behavior is associated with disruption of medial prefrontal cortex - amygdala circuit

Anxiety is prevalent in asthma, and is associated with disease severity and poor quality of life. However, no study to date provides direct experimental evidence for the effect of allergic inflammation on the structure and function of medial prefrontal cortex (mPFC) and amygdala, which are essential regions for modulating anxiety and its behavioral expression. We assessed the impact of ovalbumin (OVA)-induced allergic inflammation on the appearance of anxiety-like behavior, mPFC and amygdala volumes using MRI, and the mPFC-amygdala circuit activity in sensitized rats. Our findings exhibited that the OVA challenge in sensitized rats induced anxiety-like behavior, and led to more activated microglia and astrocytes in the mPFC and amygdala. We also found a negative correlation between anxiety-like behavior and amygdala volume. Moreover, OVA challenge in sensitized rats was associated with increases in mPFC and amygdala activity, elevation of amygdala delta-gamma coupling, and the enhancement of functional connectivity within mPFC-amygdala circuit – accompanied by an inverted direction of information transferred from the amygdala to the mPFC. We indicated that disrupting the dynamic interactions of the mPFC-amygdala circuit may contribute to the induction of anxiety-related behaviors with asthma. These findings could provide new insight to clarify the underlying mechanisms of allergic inflammation-induced psychiatric disorders related to asthma.

Distraction of olfactory bulb-medial prefrontal cortex circuit may induce anxiety-like behavior in allergic rhinitis

Allergic rhinitis is a chronic inflammatory disease of the upper respiratory tract, which is associated with high incidence of anxiety symptom. There is evidence that medial prefrontal cortex modulates anxiety-related behaviors and receives projections from olfactory bulb. Since olfactory dysfunction has been reported in allergic rhinitis, we aimed to evaluate anxiety-like behavior and oscillations of olfactory bulb-medial prefrontal cortex circuit in an animal model of allergic rhinitis. The number of open arm entries in elevated zero maze was significantly reduced in sensitized rats exposed to intranasal ovalbumin compared to the control group, which was indicating the enhancement of anxiety-like behavior in allergic rhinitis animals. Analysis of local field potentials in olfactory bulb and medial prefrontal cortex during immobility and exploration state showed that anxiety-like behavior induced by allergic rhinitis was in association with increased activity of medial prefrontal cortex and enhancement of olfactory bulb-medial prefrontal cortex coupling in delta and theta bands. Moreover, in allergic rhinitis animals, theta strongly coordinates local gamma activity in olfactory bulb and medial prefrontal cortex, which means to have a strong local theta/gamma coupling. We suggested that disruption of olfactory bulb-medial prefrontal cortex circuit due to allergic reactions might have a governing role for inducing anxiety-like behavior in the allergic rhinitis experimental model.

Airway obstruction is associated with reduced variability in specific parts of the tidal breathing flow-volume curve in young children

Lung function testing in subjects with limited cooperation, such as young children, the elderly or the disabled, is often extremely challenging even when using methods that need a low level of cooperation like impulse oscillometry (IOS). Hence, measurement and analysis of tidal breathing have been studied as an alternative option for conventional lung function methods in detecting airway obstruction. However, attempts to determine clinically useful obstruction-related tidal breathing indices, such as peak points or line fits from the tidal breathing flow–volume (TBFV) curves, have produced very limited outcomes. A more recent approach has focused on looking at the tidal breathing curve variability over time. The tidal breathing indices and the mathematical methods for their quantification analysed in early studies have been diverse; for instance, approximate entropy [1, 2] and noise limit of the flow signal [3], interquartile range of various tidal breathing indices [4, 5], and coefficient of variability of tidal volume [6]. A common finding has been that lower airway obstruction is associated with reduced variability in the tidal breathing pattern.

Impedance pneumography recordings during sleep at night show reduced variability in specific parts of the expiratory flow–volume curve in children with recurrent wheezehttp://bit.ly/2Wkfap0

Sources of breathing pattern variability in the respiratory feedback control loop

The variability of the breath-to-breath breathing pattern, and its alterations in disease, may hold information of physiologic and/or diagnostic value. We hypothesized that this variability arises from the way that noise is processed within the respiratory feedback control loop, and that pathologic alterations to specific components within the system give rise to characteristic alterations in breathing pattern variability. We explored this hypothesis using a computational model of the respiratory control system that integrates mechanical factors, gas exchange processes, and chemoreceptor signals to simulate breathing patterns subject to the influences of random variability in each of the system components. We found that the greatest changes in the coefficient of variation (CV) of both breathing amplitude and timing were caused by increases in lung resistance and impairments in gas exchange, both common features of pulmonary disease. This suggests that breathing pattern variability may reflect discernible deterministic processes involved in the control of breathing.

Estimation of respiratory variables from thoracoabdominal breathing distance: a review of different techniques and calibration methods

The precise measurement of respiratory variables, such as tidal volume, minute ventilation, and respiratory rate, is necessary to monitor respiratory status, overcome several diseases, improve patient health conditions and reduce health care costs. This measurement has conventionally been performed by breathing into a mouthpiece connected to a flow rate measuring device. However, a mouthpiece can be uncomfortable for the subject and is difficult to use for long-term monitoring. Other noninvasive systems and devices have been developed that do not require a mouthpiece to quantitatively measure respiratory variables. These techniques are based on measuring size changes of the rib cage (RC) and abdomen (ABD), as lung volume is known to be a function of these variables. Among these systems, we distinguish respiratory inductive plethysmography (RIP), respiratory magnetometer plethysmography (RMP), and optoelectronic plethysmography devices. However, these devices should be previously calibrated for the correct evaluation of respiratory variables. The most popular calibration methods are isovolume manoeuvre calibration (ISOCAL), qualitative diagnostic calibration (QDC), multiple linear regression (MLR) and artificial neural networks (ANNs). The aim of this review is first to present how thoracoabdominal breathing distances can be used to estimate respiratory variables and second to present the different techniques and calibration methods used for this purpose.

Entropy Analysis for the Evaluation of Respiratory Changes Due to Asbestos Exposure and Associated Smoking

Breathing is a complex rhythmic motor act, which is created by integrating different inputs to the respiratory centres. Analysing nonlinear fluctuations in breathing may provide clinically relevant information in patients with complex illnesses, such as asbestosis. We evaluated the effect of exposition to asbestos on the complexity of the respiratory system by investigating the respiratory impedance sample entropy (SampEnZrs) and recurrence period density entropy (RPDEnZrs). Similar analyses were performed by evaluating the airflow pattern sample entropy (SampEnV') and recurrence period density entropy (RPDEnV'). Groups of 34 controls and 34 asbestos-exposed patients were evaluated in the respiratory impedance entropy analysis, while groups of 34 controls and 30 asbestos-exposed patients were investigated in the analysis of airflow entropy. Asbestos exposition introduced a significant reduction of RPDEnV' in non-smoker patients (p < 0.0004), which suggests that the airflow pattern becomes less complex in these patients. Smoker patients also presented a reduction in RPDEnV' (p < 0.05). These finding are consistent with the reduction in respiratory system adaptability to daily life activities observed in these patients. It was observed a significant reduction in SampEnV' in smoker patients in comparison with non-smokers (p < 0.02). Diagnostic accuracy evaluations in the whole group of patients (including non-smokers and smokers) indicated that RPDEnV' might be useful in the diagnosis of respiratory abnormalities in asbestos-exposed patients, showing an accuracy of 72.0%. In specific groups of non-smokers, RPDEnV' also presented adequate accuracy (79.0%), while in smoker patients, SampEnV' and RPDEnV' presented adequate accuracy (70.7% and 70.2%, respectively). Taken together, these results suggest that entropy analysis may provide an early and sensitive functional indicator of interstitial asbestosis.

The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities

The Poincaré plot is a geometrical technique used to visualize and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calculating the correlation between sequential data points in a time-series, rather than between two consecutive points. By incorporating this so-called lag, we hope to integrate a temporal aspect into quantifying the correlation, to depict whether a physiological system holds prolonged association between events separated by time. In doing so, it attempts to instantaneously characterize the intrinsic behavior of a complex system. We tested this hypothesis on three different physiological time-series: heart rate variability in patients with liver cirrhosis, respiratory rhythm in asthma and body temperature fluctuation in patients with cirrhosis, to evaluate the potential application of the extended Poincaré method in clinical practice. When studying the cardiac inter-beat intervals, the extended Poincaré plot revealed a stronger autocorrelation for patients with decompensated liver cirrhosis compared to less severe cases using Pearson's correlation coefficient. In addition, long-term variability (known as SD2 in the extended Poincaré plot) appeared as an independent prognostic variable. This holds significance by acting as a non-invasive tool to evaluate patients with chronic liver disease and potentially facilitate transplant selection as an adjuvant to traditional criteria. For asthmatics, employing the extended Poincaré plot allowed for a non-invasive tool to differentially diagnose various classifications of respiratory disease. In the respiratory inter-breath interval analysis, the receiver operating characteristic (ROC) curve provided evidence that the extension of the Poincaré plot holds a greater advantage in the classification of asthmatic patients, over the traditional Poincaré plot. Lastly, the analysis of body temperature from patients using the extended Poincaré plot helped identify inpatients from outpatients with cirrhosis. Through these analyses, the extended Poincaré plot provided unique and additional information which could potentially make a difference in clinical practice. Conclusively, the potential use of our work lies in its possible application of predicting mortality for the organ allocation procedure in patients with cirrhosis and non-invasively distinguish between atopic and non-atopic asthma.

Protective effect of heart rate variability biofeedback on stress-induced lung function impairment in asthma

Psychological stress can provoke airway constriction in asthmatic patients, which may be because of autonomic nervous system dysfunction in asthma. We investigated the effect of enhancing respiratory sinus arrhythmia using heart rate variability biofeedback (HRV-BF) on spirometry performance and HRV indices during stress induced by Stroop Color-Word interference test in asthmatic patients and healthy volunteers. Stress caused decrease in FEV1%, FVC%, and PEF% compared to baseline in asthmatic patients, but not in healthy subjects. A single short duration episode of HRV-BF not only had a protective effect on stress-induced airway constriction, but also significantly augmented the level of FEV1% and FVC% as compared with their own baseline. Also, there was a significant correlation between HRV changes and the augmentation of spirometry performance in asthmatic patients receiving HRV-BF. Our findings indicated that even a single short duration episode of HRV-BF can decrease susceptibility to stress-induced lung function impairment in patients with asthma, which may be through the modulation of respiratory sinus arrhythmia.

The effect of fractal-like mechanical ventilation on vital signs in a rat model of acute-on-chronic liver failure

OBJECTIVE

The network of interactions between different organs is impaired in liver cirrhosis. Liver cirrhosis is associated with multi-system involvement, which eventually leads to multiple organ failure. This process is accelerated by a precipitating factor such as bacterial infection, which leads to respiratory distress, circulatory shock, neural dysfunction and very high mortality. Cirrhotic patients often have blunted respiratory sinus arrhythmia and impaired cardio-respiratory variability. Fractal-like mechanical ventilation is reported to enhance respiratory sinus arrhythmia and attenuate respiratory distress in experimental models. In the present study we hypothesise that fractal-like mechanical ventilation may improve the outcome of cirrhotic rats with multiple organ failure.

APPROACH

Cirrhosis was induced by chronic biliary obstruction in rats. Acute multiple organ failure was induced by intraperitoneal injection of bacterial endotoxin in cirrhotic rats. The effect of conventional mechanical ventilation (with constant tidal volume and respiratory rate) or fractal-like ventilation (with the same average but variable tidal volume and respiratory rate) were assessed on vital signs, oxygen saturation and plasma alanine aminotransferase in anaesthetised cirrhotic rats.

MAIN RESULTS

We demonstrated that fractal-like mechanical ventilation was accompanied by improved oxygen saturation, reduced heart rate and decreased liver injury following injection of bacterial endotoxin. Moreover, variable mechanical ventilation in cirrhotic rats reduced mortality and prevented a fall in short-term heart rate variability following endotoxin challenge in comparison with rats with constant mechanical ventilation.

SIGNIFICANCE

We suggest further investigations into the beneficial effects of fractal-like ventilation strategy in critically ill patients with liver failure requiring organ support and mechanical ventilation.

Clinical and experimental aspects of breathing modulation by inflammation

Neuroinflammation is produced by local or systemic alterations and mediated mainly by glia, affecting the activity of various neural circuits including those involved in breathing rhythm generation and control. Several pathological conditions, such as sudden infant death syndrome, obstructive sleep apnea and asthma exert an inflammatory influence on breathing-related circuits. Consequently breathing (both resting and ventilatory responses to physiological challenges), is affected; e.g., responses to hypoxia and hypercapnia are compromised. Moreover, inflammation can induce long-lasting changes in breathing and affect adaptive plasticity; e.g., hypoxic acclimatization or long-term facilitation. Mediators of the influences of inflammation on breathing are most likely proinflammatory molecules such as cytokines and prostaglandins. The focus of this review is to summarize the available information concerning the modulation of the breathing function by inflammation and the cellular and molecular aspects of this process. I will consider: 1) some clinical and experimental conditions in which inflammation influences breathing; 2) the variety of experimental approaches used to understand this inflammatory modulation; 3) the likely cellular and molecular mechanisms.

Omalizumab improves forced expiratory volume in 1 second in patients with severe asthma

Introduction

Asthma is a multiphenotypic disease, and therapeutic managenment in patients with severe asthma is particulary difficult, with conventional treatment of severe asthma showing poor efficacy.

Aim

To analyse forced expiratory volume in 1 s (FEV₁) following the adminstration of omalizumab.

Material and methods

Six patinents (mean age: 50 ±12.6) with severe, uncontrolled asthma according to the GINA guidelines were enrolled in the study.

Results

Treatment with omalizumab increased in all subjects FEV₁ by 17.28 ±13.4% after months and 18.57 ±13.4% after 12 months of treatment.

Conclusions

These results provides further evidence that therapy with omalizumab improves spiromtric parameters in severe asthma.

Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review

Asthma is a chronic respiratory disease featured with unpredictable flare-ups, for which continuous lung function monitoring is the key for symptoms control. To find new indices to individually classify severity and predict disease prognosis, continuous physiological data collected from monitoring devices is being studied from different perspectives. Entropy, as an analysis method for quantifying the inner irregularity of data, has been widely applied in physiological signals. However, based on our knowledge, there is no such study to summarize the complexity differences of various physiological signals in asthmatic patients. Therefore, we organized a systematic review to summarize the complexity differences of important signals in patients with asthma. We searched several medical databases and systematically reviewed existing asthma clinical trials in which entropy changes in physiological signals were studied. As a conclusion, we find that, for airflow, heart rate variability, center of pressure and respiratory impedance, their entropy values decrease significantly in asthma patients compared to those of healthy people, while, for respiratory sound and airway resistance, their entropy values increase along with the progression of asthma. Entropy of some signals, such as respiratory inter-breath interval, shows strong potential as novel indices of asthma severity. These results will give valuable guidance for the utilization of entropy in physiological signals. Furthermore, these results should promote the development of management and diagnosis of asthma using continuous monitoring data in the future.

Variable ventilation decreases airway responsiveness and improves ventilation efficiency in a rat model of asthma

The optimal ventilation strategy in patients receiving mechanical ventilation for severe asthma remains unclear. The effect of conventional ventilation (with constant tidal volume and respiratory rate) and variable ventilation (with the same average but variable tidal volume and respiratory rate) on peak airway pressure and airway exacerbation induced by increasing doses of methacholine was compared in a rat model of asthma. The respiratory rate and tidal volume data were obtained from a spontaneously breathing intact rat during immobility using a whole-body plethysmograph. Peak airway pressure and airway responsiveness to cumulative doses of methacholine were significantly affected by ventilation mode and they were lower in variable ventilation group than in the conventional ventilation group. Also, variable ventilation improved oxygen saturation compared to conventional ventilation. Our results indicate that variable ventilation decreases airway responsiveness and enhances ventilation efficiency in a rat model of asthma. We suggest further investigations on beneficial effects of variable ventilation strategy in mechanically ventilated patients with severe asthma.

Exercise ventilatory irregularity can be quantified by approximate entropy to detect breathing pattern disorder

BACKGROUND

Breathing pattern disorder (BPD) is a prevalent cause of exertional dyspnea and yet there is currently no reliable objective measure for its diagnosis. We propose that statistical analysis of ventilatory irregularity, quantified by approximate entropy (ApEn), could be used to detect BPD when applied to cardiopulmonary exercise test (CPET) data. We hypothesised that ApEn of ventilatory variables (tidal volume (V_T), breathing frequency (B_f), minute ventilation (V_E)) would be greater, i.e. more irregular, in patients with BPD than healthy controls.

METHODS

We evaluated ventilatory ApEn in 20 adults (14 female) with exertional dyspnoea, undergoing CPET and independently diagnosed with BPD by a specialist respiratory physiotherapist. Data were compared with 15 age- gender- and BMI-matched controls. ApEn for V_T, B_f and V_E were calculated for an incremental cycle exercise test.

RESULTS

Patients with BPD more frequently rated breathlessness as the reason for exercise limitation and had a lower mean (SD) peak oxygen uptake compared with controls: 80 (18) vs. 124 (27) % predicted (P < 0.001). ApEn was significantly greater for V_T (p = .006) and V_E (p = .002) in BPD than controls. ApEn V_E was inversely related (r² = 0.24, p = .03) to peak oxygen uptake in BPD but not controls. ROC analysis revealed that ApEn V_E > 0.88, conferred a sensitivity and specificity of 70% and 87% respectively, for detection of BPD.

CONCLUSIONS

Non-linear statistical interrogation of CPET-acquired ventilatory data has utility in the detection of BPD. A simple calculation of approximate entropy of ventilation, during an incremental cardiopulmonary exercise test, provides a quantitative method to detect BPD.

Effect of Rho-kinase inhibition on complexity of breathing pattern in a guinea pig model of asthma

Asthma represents an episodic and fluctuating behavior characterized with decreased complexity of respiratory dynamics. Several evidence indicate that asthma severity or control is associated with alteration in variability of lung function. The pathophysiological basis of alteration in complexity of breathing pattern in asthma has remained poorly understood. Regarding the point that Rho-kinase is involved in pathophysiology of asthma, in present study we investigated the effect of Rho-kinase inhibition on complexity of respiratory dynamics in a guinea pig model of asthma. Male Dunkin Hartley guinea pigs were exposed to 12 series of inhalations with ovalbumin or saline. Animals were treated by the Rho-kinase inhibitor Y-27632 (1mM aerosols) prior to each allergen challenge. We recorded respiration of conscious animals using whole-body plethysmography. Exposure to ovalbumin induced lung inflammation, airway hyperresponsiveness and remodeling including goblet cell hyperplasia, increase in the thickness of airways smooth muscles and subepithelial collagen deposition. Complexity analysis of respiratory dynamics revealed a dramatic decrease in irregularity of respiratory rhythm representing less complexity in asthmatic guinea pigs. Inhibition of Rho-kinase reduced the airway remodeling and hyperreponsiveness, but had no significant effect on lung inflammation and complexity of respiratory dynamics in asthmatic animals. It seems that airway hyperresponsiveness and remodeling do not significantly affect the complexity of respiratory dynamics. Our results suggest that inflammation might be the probable cause of shift in the respiratory dynamics away from the normal fluctuation in asthma.

Effect of airway remodeling and hyperresponsiveness on complexity of breathing pattern in rat

The complexity of respiratory dynamics is decreased, in association with disease severity, in patients with asthma. However, the pathophysiological basis of decreased complexity of breathing pattern in asthma is not clear. In the present study, we investigated the effect of airway remodeling and hyperresponsiveness induced by repeated bronchoconstriction (using methacholine) on breathing pattern in rats with or without allergen-induced sensitization. Entropy analysis of respiratory variability showed decreased irregularity (less complexity) of respiratory rhythm in this rat model of asthma. Airway remodeling and hyperresponsiveness induced by repeated bronchoconstriction also led to increased regularity of respiratory dynamics in sensitized rats. However, these airway alterations had no significant effect on the complexity of breathing pattern in non-sensitized rats. Our results indicate that mechanical respiratory alterations cannot per se attenuate the complexity of respiratory dynamics, unless there is an underlying inflammation. We suggest further studies on underlying mechanisms of breathing variability with focus on respiratory control alterations due to airway inflammation.

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.

Heart rhythm characterization through induced physiological variables

Atrial fibrillation remains a major cause of morbi-mortality, making mass screening desirable and leading industry to actively develop devices devoted to automatic AF detection. Because there is a tendency toward mobile devices, there is a need for an accurate, rapid method for studying short inter-beat interval time series for real-time automatic medical monitoring. We report a new methodology to efficiently select highly discriminative variables between physiological states, here a normal sinus rhythm or atrial fibrillation. We generate induced variables using the first ten time derivatives of an RR interval time series and formally express a new multivariate metric quantifying their discriminative power to drive state variable selection. When combined with a simple classifier, this new methodology results in 99.9% classification accuracy for 1-min RR interval time series (n = 7,400), with heart rate accelerations and jerks being the most discriminant variables. We show that the RR interval time series can be drastically reduced from 60 s to 3 s, with a classification accuracy of 95.0%. We show that heart rhythm characterization is facilitated by induced variables using time derivatives, which is a generic methodology that is particularly suitable to real-time medical monitoring.

Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning

PURPOSE

The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique.

METHOD

We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. [Formula: see text] was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer ([Formula: see text]) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km[Formula: see text], treadmill running at 9 and 12  km [Formula: see text] and ergometer cycling at 90 and 110 W).

RESULTS

The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between [Formula: see text] and [Formula: see text] (bias) was higher for the MLR model ([Formula: see text] L) than for the ANN model ([Formula: see text] L).

CONCLUSION

Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.

Discriminating Tuberculous Pleural Effusion from Malignant Pleural Effusion Based on Routine Pleural Fluid Biomarkers, Using Mathematical Methods

BACKGROUND

The differential diagnosis of tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) is difficult because the biochemical profiles are similar. The present study aimed to differentiate TPE from MPE, using a decision tree and a weighted sparse representation-based classification (WSRC) method, based on the best combination of routine pleural effusion fluid biomarkers.

MATERIALS AND METHODS

The routine biomarkers of pleural fluid, including differential cell count, lactate dehydrogenase (LDH), protein, glucose and adenosine deaminase (ADA), were measured in 236 patients (100 with TPE and 136 with MPE). A Sequential Forward Selection (SFS) algorithm was employed to obtain the best combination of parameters for the classification of pleural effusions. Moreover, WSRC was compared to the standard sparse representation-based classification (SRC) and the Support Vector Machine (SVM) methods for classification accuracy.

RESULTS

ADA provided the highest diagnostic performance in differentiating TPE from MPE, with 91.91% sensitivity and 74.0% specificity. The best combination of parameters for discriminating TPE from MPE included age, ADA, polynuclear leukocytes and lymphocytes. WSRC outperformed the SRC and SVM methods, with an area under the curve of 0.877, sensitivity of 93.38%, and specificity of 82.0%. The generated flowchart of the decision tree demonstrated 87.2% accuracy for discriminating TPE from MPE.

CONCLUSION

This study indicates that a decision tree and a WSRC are novel, noninvasive, and inexpensive methods, which can be useful in discriminating between TPE and MPE, based on the combination of routine pleural fluid biomarkers.