Reduced breathing variability as a predictor of unsuccessful patient separation from mechanical ventilation

Ventilator Weaning and Extubation

Increasing evidence supports specific approaches to liberate patients from invasive ventilation including the use of liberation protocols, inspiratory assistance during spontaneous breathing trials (SBTs), early extubation of patients with chronic obstructive pulmonary disease to noninvasive ventilation, and prophylactic use of noninvasive support strategies after extubation. Additional research is needed to elucidate the best criteria to identify patients who are ready to undergo an SBT and to inform optimal screening frequency, the best SBT technique and duration, extubation assessments, and extubation decision-making. Additional clarity is also needed regarding the optimal timing to measure and report extubation success.

Dyspnoea in acutely ill mechanically ventilated adult patients: an ERS/ESICM statement

This statement outlines a review of the literature and current practice concerning the prevalence, clinical significance, diagnosis and management of dyspnoea in critically ill, mechanically ventilated adult patients. It covers the definition, pathophysiology, epidemiology, short- and middle-term impact, detection and quantification, and prevention and treatment of dyspnoea. It represents a collaboration of the European Respiratory Society and the European Society of Intensive Care Medicine. Dyspnoea ranks among the most distressing experiences that human beings can endure. Approximately 40% of patients undergoing invasive mechanical ventilation in the intensive care unit (ICU) report dyspnoea, with an average intensity of 45 mm on a visual analogue scale from 0 to 100 mm. Although it shares many similarities with pain, dyspnoea can be far worse than pain in that it summons a primal fear response. As such, it merits universal and specific consideration. Dyspnoea must be identified, prevented and relieved in every patient. In the ICU, mechanically ventilated patients are at high risk of experiencing breathing difficulties because of their physiological status and, in some instances, because of mechanical ventilation itself. At the same time, mechanically ventilated patients have barriers to signalling their distress. Addressing this major clinical challenge mandates teaching and training, and involves ICU caregivers and patients. This is even more important because, as opposed to pain which has become a universal healthcare concern, very little attention has been paid to the identification and management of respiratory suffering in mechanically ventilated ICU patients.

Dyspnoea in acutely ill mechanically ventilated adult patients: an ERS/ESICM statement

This statement outlines a review of the literature and current practice concerning the prevalence, clinical significance, diagnosis and management of dyspnoea in critically ill, mechanically ventilated adult patients. It covers the definition, pathophysiology, epidemiology, short- and middle-term impact, detection and quantification, and prevention and treatment of dyspnoea. It represents a collaboration of the European Respiratory Society (ERS) and the European Society of Intensive Care Medicine (ESICM). Dyspnoea ranks among the most distressing experiences that human beings can endure. Approximately 40% of patients undergoing invasive mechanical ventilation in the intensive care unit (ICU) report dyspnoea, with an average intensity of 45 mm on a visual analogue scale from 0 to 100 mm. Although it shares many similarities with pain, dyspnoea can be far worse than pain in that it summons a primal fear response. As such, it merits universal and specific consideration. Dyspnoea must be identified, prevented and relieved in every patient. In the ICU, mechanically ventilated patients are at high risk of experiencing breathing difficulties because of their physiological status and, in some instances, because of mechanical ventilation itself. At the same time, mechanically ventilated patients have barriers to signalling their distress. Addressing this major clinical challenge mandates teaching and training, and involves ICU caregivers and patients. This is even more important because, as opposed to pain which has become a universal healthcare concern, very little attention has been paid to the identification and management of respiratory suffering in mechanically ventilated ICU patients.

Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials

Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019-2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795-1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434-0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model's prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.

The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation

Variability characterizes breathing, cellular respiration, and the underlying quantum effects. Variability serves as a mechanism for coping with changing environments; however, this hypothesis does not explain why many of the variable phenomena of respiration manifest randomness. According to the constrained disorder principle (CDP), living organisms are defined by their inherent disorder bounded by variable boundaries. The present paper describes the mechanisms of breathing and cellular respiration, focusing on their inherent variability. It defines how the CDP accounts for the variability and randomness in breathing and respiration. It also provides a scheme for the potential role of respiration variability in the energy balance in biological systems. The paper describes the option of using CDP-based artificial intelligence platforms to augment the respiratory process's efficiency, correct malfunctions, and treat disorders associated with the respiratory system.

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.

Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study

BACKGROUND

Extremely preterm infants are frequently subjected to mechanical ventilation. Current prediction tools of extubation success lacks accuracy.

METHODS

Multicenter study including infants with birth weight ≤1250 g undergoing their first extubation attempt. Clinical data and cardiorespiratory signals were acquired before extubation. Primary outcome was prediction of extubation success. Automated analysis of cardiorespiratory signals, development of clinical and cardiorespiratory features, and a 2-stage Clinical Decision-Balanced Random Forest classifier were used. A leave-one-out cross-validation was done. Performance was analyzed by ROC curves and determined by balanced accuracy. An exploratory analysis was performed for extubations before 7 days of age.

RESULTS

A total of 241 infants were included and 44 failed (18%) extubation. The classifier had a balanced accuracy of 73% (sensitivity 70% [95% CI: 63%, 76%], specificity 75% [95% CI: 62%, 88%]). As an additional clinical-decision tool, the classifier would have led to an increase in extubation success from 82% to 93% but misclassified 60 infants who would have been successfully extubated. In infants extubated before 7 days of age, the classifier identified 16/18 failures (specificity 89%) and 73/105 infants with success (sensitivity 70%).

CONCLUSIONS

Machine learning algorithms may improve a balanced prediction of extubation outcomes, but further refinement and validation is required.

IMPACT

A machine learning-derived predictive model combining clinical data with automated analyses of individual cardiorespiratory signals may improve the prediction of successful extubation and identify infants at higher risk of failure with a good balanced accuracy. Such multidisciplinary approach including medicine, biomedical engineering and computer science is a step forward as current tools investigated to predict extubation outcomes lack sufficient balanced accuracy to justify their use in future trials or clinical practice. Thus, this individualized assessment can optimize patient selection for future trials of extubation readiness by decreasing exposure of low-risk infants to interventions and maximize the benefits of those at high risk.

Comprehensive breathing variability indices enhance the prediction of extubation failure in patients on mechanical ventilation

BACKGROUND AND OBJECTIVE

Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10-15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored.

METHODS

Here, we aimed to develop novel breathing variability indices to predict extubation failure among invasively ventilated patients. First, breath-to-breath basic and comprehensive respiratory parameters were computed from continuous ventilation waveforms 1 h before extubation. Subsequently, the basic and advanced variability methods were applied to the respiratory parameter sequences to derive comprehensive breathing variability indices, and their role in predicting extubation failure was assessed. Finally, after reducing the feature dimensionality using the forward search method, the combined effect of the indices was evaluated by inputting them into the machine learning models, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost).

RESULTS

The coefficient of variation of the dynamic mechanical power per breath (CV-MPd[J/breath]) exhibited the highest area under the receiver operating characteristic curve (AUC) of 0.777 among the individual indices. Furthermore, the XGBoost model obtained the best AUC (0.902) by combining multiple selected variability indices.

CONCLUSIONS

These results suggest that the proposed novel breathing variability indices can improve extubation failure prediction in invasively ventilated patients.

The psychophysiology of the sigh: II: The sigh from the psychological perspective

A sigh is a distinct respiratory behavior with specific psychophysiological roles. In two accompanying reviews we will discuss the physiological and psychological functions of the sigh. The present review will focus on the psychological functions of the sigh. We discuss the regulatory effects of a sigh, and argue how these effects may become maladaptive when sighs occur excessively. The adaptive role of a sigh is discussed in the context of regulation of psychophysiological states. We propose that sighs facilitate transitions from one psychophysiological state to the next, and this way contribute to psychophysiological flexibility, via a hypothesized resetting mechanism. We discuss how a sigh resets respiration, by controlling mechanical and metabolic properties of respiration associated with respiratory symptoms. Next, we elaborate on a sigh resetting emotional states by facilitating emotional transitions. We attempt to explain the adaptive and maladaptive functions of a sigh in the framework of stochastic resonance, in which we propose occasional, spontaneous sighs to be noise contributing to psychophysiological regulation, while excessive sighs result in psychophysiological dysregulation. In this context, we discuss how sighs can contribute to therapeutic interventions, either by increasing sighs to improve regulation in case of a lack of sighing, or by decreasing sighs to restore regulation in case of excessive sighing. Finally, a research agenda on the psychology of sighs is presented.

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.

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

Background

Measuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it.

Methods

In this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation.

Results

By analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved.

Conclusion

The results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

Automated evaluation of respiratory signals to provide insight into respiratory drive

The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals and is highly active throughout life displaying rhythmic activity. The repetitive activation of the DIAm (and of other muscles driven by central pattern generator activity) presents an opportunity to analyze these physiological data on a per-event basis rather than pooled on a per-subject basis. The present study highlights the development and implementation of a graphical user interface-based algorithm using an analysis of critical points to detect the onsets and offsets of individual respiratory events across a range of motor behaviors, thus facilitating analyses of within-subject variability. The algorithm is designed to be robust regardless of the signal type (e.g., EMG or transdiaphragmatic pressure). Our findings suggest that this approach may be particularly beneficial in reducing animal numbers in certain types of studies, for assessments of perturbation studies where the effects are relatively small but potentially physiologically meaningful, and for analyses of respiratory variability.

A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients

BACKGROUND

Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice.

OBJECTIVES

This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible.

METHODS

This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital's Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., "patients weaned within 24 h" (n = 1042) and "patients not weaned within 24 h" (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model.

RESULTS

The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables.

CONCLUSIONS

The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment.

Proportional assist ventilation relieves clinically significant dyspnea in critically ill ventilated patients

INTRODUCTION

Dyspnea is common and often severe symptom in mechanically ventilated patients. Proportional assist ventilation (PAV) is an assist ventilatory mode that adjusts the level of assistance to the activity of respiratory muscles. We hypothesized that PAV reduce dyspnea compared to pressure support ventilation (PSV).

PATIENTS AND METHODS

Mechanically ventilated patients with clinically significant dyspnea were included. Dyspnea intensity was assessed by the Dyspnea-Visual Analog Scale (D-VAS) and the Intensive Care-Respiratory Distress Observation Scale (IC-RDOS) at inclusion (PSV-Baseline), after personalization of ventilator settings in order to minimize dyspnea (PSV-Personalization), and after switch to PAV. Respiratory drive was assessed by record of electromyographic activity of inspiratory muscles, the proportion of asynchrony was analyzed.

RESULTS

Thirty-four patients were included (73% males, median age of 66 [57-77] years). The D-VAS score was lower with PSV-Personalization (37 mm [20‒55]) and PAV (31 mm [14‒45]) than with PSV-Baseline (62 mm [28‒76]) (p < 0.05). The IC-RDOS score was lower with PAV (4.2 [2.4‒4.7]) and PSV-Personalization (4.4 [2.4‒4.9]) than with PSV-Baseline (4.8 [4.1‒6.5]) (p < 0.05). The electromyographic activity of parasternal intercostal muscles was lower with PAV and PSV-Personalization than with PSV-Baseline. The asynchrony index was lower with PAV (0% [0‒0.55]) than with PSV-Baseline and PSV-Personalization (0.68% [0‒2.28] and 0.60% [0.31‒1.41], respectively) (p < 0.05).

CONCLUSION

In mechanically ventilated patients exhibiting clinically significant dyspnea with PSV, personalization of PSV settings and PAV results in not different decreased dyspnea and activity of muscles to a similar degree, even though PAV was able to reduce asynchrony more effectively.

Comparative Efficacy and Safety of Four Different Spontaneous Breathing Trials for Weaning From Mechanical Ventilation: A Systematic Review and Network Meta-Analysis

Background: Spontaneous breathing trial (SBT) has been used to predict the optimal time of weaning from ventilator. However, it remains controversial which trial should be preferentially selected. We aimed to compare and rank four common SBT modes including automatic tube compensation (ATC), pressure support ventilation (PSV), continuous positive airway pressure (CPAP), and T-piece among critically ill patients receiving mechanical ventilation (MV).

Methods: We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) to identify studies that investigated the comparative efficacy and safety of at least two SBT strategies among critically ill patients up to May 17, 2020. We estimated the surface under the cumulative ranking curve (SUCRA) to rank SBT techniques, and determined the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation method. Primary outcome was weaning success. Secondary outcomes were reintubation, SBT success, duration of acute care, and intensive care unit (ICU) mortality. Statistical analysis was conducted by using RevMan 5.4, Stata, and R software.

Results: We enrolled 24 trials finally. Extubation success rate was significantly higher in ATC than that in T-piece (OR, 0.28; 95% CI, 0.13–0.64) or PSV (OR, 0.53; 95% CI, 0.32–0.88). For SBT success, ATC was better than other SBT techniques, with a pooled OR ranging from 0.17 to 0.42. For reintubation rate, CPAP was worse than T-piece (OR, 2.76; 95% CI, 1.08 to 7.06). No significant difference was detected between SBT modes for the length of stay in ICU or long-term weaning unit (LWU). Similar result was also found for ICU mortality between PSV and T-piece. Majority direct results were confirmed by network meta-analysis. Besides, ATC ranks at the first, first, and fourth place with a SUCRA of 91.7, 99.7, and 39.9%, respectively in increasing weaning success and SBT success and in prolonging ICU or LWU length of stay among four SBT strategies. The confidences in evidences were rated as low for most comparisons.

Conclusion: ATC seems to be the optimal choice of predicting successful weaning from ventilator among critically ill patients. However, randomized controlled trials (RCTs) with high quality are needed to further establish these findings.

Breathing variability during propofol/remifentanil procedural sedation with a single additional dose of midazolam or s-ketamine: a prospective observational study

Purpose

Regulation of spontaneous breathing is highly complex and may be influenced by drugs administered during the perioperative period. Because of their different pharmacological properties we hypothesized that midazolam and s-ketamine exert different effects on the variability of minute ventilation (MV), tidal volume (TV) and respiratory rate (RR).

Methods

Patients undergoing procedural sedation (PSA) with propofol and remifentanil received a single dose of midazolam (1–3 mg, n = 10) or s-ketamine (10–25 mg, n = 10). We used non-invasive impedance-based respiratory volume monitoring to record RR as well as changes in TV and MV. Variability of these three parameters was calculated as coefficients of variation.

Results

TV and MV decreased during PSA to a comparable extent in both groups, whereas there was no significant change in RR. In line with our hypothesis we observed marked differences in breathing variability. The variability of MV (– 47.5% ± 24.8%, p = 0.011), TV (– 42.1% ± 30.2%, p = 0.003), and RR (– 28.5% ± 29.3%, p = 0.011) was significantly reduced in patients receiving midazolam. In contrast, variability remained unchanged in patients receiving s-ketamine (MV + 16% ± 45.2%, p = 0.182; TV +12% ± 47.7%, p = 0.390; RR +39% ± 65.2%, p = 0.129). After termination of PSA breathing variables returned to baseline values.

Conclusions

While midazolam reduces respiratory variability in spontaneously breathing patients undergoing procedural sedation, s-ketamine preserves variability suggesting different effects on the regulation of spontaneous breathing.

Automated Classification of Whole Body Plethysmography Waveforms to Quantify Breathing Patterns

Whole body plethysmography (WBP) monitors respiratory rate and depth but conventional analysis fails to capture the diversity of waveforms. Our first purpose was to develop a waveform cluster analysis method for quantifying dynamic changes in respiratory waveforms. WBP data, from adult Sprague-Dawley rats, were sorted into time domains and principle component analysis was used for hierarchical clustering. The clustering method effectively sorted waveforms into categories including sniffing, tidal breaths of varying duration, and augmented breaths (sighs). We next used this clustering method to quantify breathing after opioid (fentanyl) overdose and treatment with ampakine CX1942, an allosteric modulator of AMPA receptors. Fentanyl caused the expected decrease in breathing, but our cluster analysis revealed changes in the temporal appearance of inspiratory efforts. Ampakine CX1942 treatment shifted respiratory waveforms toward baseline values. We conclude that this method allows for rapid assessment of breathing patterns across extended data recordings. Expanding analyses to include larger portions of recorded WBP data may provide insight on how breathing is affected by disease or therapy.

Feasibility of implementing Extubation Advisor, a clinical decision support tool to improve extubation decision-making in the ICU: a mixed-methods observational study

OBJECTIVES

Although spontaneous breathing trials (SBTs) are standard of care to extubation readiness, no tool exists that optimises prediction and standardises assessment. In this study, we evaluated the feasibility and clinical impressions of Extubation Advisor (EA), a comprehensive clinical extubation decision support (CDS) tool.

DESIGN

Phase I mixed-methods observational study.

SETTING

Two Canadian intensive care units (ICUs).

PARTICIPANTS

We included patients on mechanical ventilation for ≥24 hours and clinicians (respiratory therapists and intensivists) responsible for extubation decisions.

INTERVENTIONS

Components included a predictive model assessment, feasibility evaluation, questionnaires and interviews with clinicians.

RESULTS

We enrolled 117 patients, totalling 151 SBTs and 80 extubations. The incidence of extubation failure was 11% in low-risk patients and 21% in high-risk patients stratified by the predictive model; 38% failed extubation when both the model and clinical impression were at high risk. The tool was well rated: 94% and 75% rated the data entry and EA report as average or better, respectively. Interviews (n=15) revealed favourable impressions regarding its user interface and functionality, but unexpectedly, also concerns regarding EA's potential impact on respiratory therapists' job security.

CONCLUSIONS

EA implementation was feasible, and users perceived it to have potential to support extubation decision-making. This study helps to understand bedside implementation of CDS tools in a multidisciplinary ICU.

TRIAL REGISTRATION NUMBER

NCT02988167.

Breathing variability-implications for anaesthesiology and intensive care

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.

A5 noradrenergic neurons and breathing control in neonate rats

The pontine A5 noradrenergic group contributes to the maturation of the respiratory system before birth in rats. These neurons are connected to the neural network responsible for respiratory rhythmogenesis. In the present study, we investigated the participation of A5 noradrenergic neurons in neonates (P7-8 and P14-15) in the control of ventilation during hypoxia and hypercapnia in in vivo experiments using conjugated saporin anti-dopamine beta-hydroxylase (DβH-SAP) to specifically ablate noradrenergic neurons. Thus, DβH-SAP (420 ng/μL) or saporin (SAP, control) was injected into the A5 region of neonatal male Wistar rats. Hypoxia reduced respiratory variability in control animals; however, A5 lesion prevented this effect in P7-8 rats. Our data suggest that noradrenergic neurons of the A5 region in neonate rats do not participate in the control of ventilation under baseline and hypercapnic conditions, but exert an inhibitory modulation on breathing variability under hypoxic challenge in early life (P7-8).

Prediction of extubation outcome in mechanically ventilated patients: Development and validation of the Extubation Predictive Score (ExPreS)

Despite the best efforts of intensive care units (ICUs) professionals, the extubation failure rates in mechanically ventilated patients remain in the range of 5%–30%. Extubation failure is associated with increased risk of death and longer ICU stay. This study aimed to identify respiratory and non-respiratory parameters predictive of extubation outcome, and to use these predictors to develop and validate an “Extubation Predictive Score (ExPreS)” that could be used to predict likelihood of extubation success in patients receiving invasive mechanical ventilation (IMV). Derivation cohort was composed by patients aged ≥18 years admitted to the ICU and receiving IMV through an endotracheal tube for >24 hours. The weaning process followed the established ICU protocol. Clinical signs and ventilator parameters of patients were recorded during IMV, in the end phase of weaning in pressure support ventilation (PSV) mode, with inspiratory pressure of 7 cm H₂O over the PEEP (positive end expiratory pressure). Patients who tolerated this ventilation were submitted to spontaneous breathing trial (SBT) with T-tube for 30 minutes. Those who passed the SBT and a subsequent cuff-leak test were extubated. The primary outcome of this study was extubation success at 48 hours. Parameters that showed statistically significant association with extubation outcome were further investigated using the receiver operating characteristics (ROC) analysis to assess their predictive value. The area under the curve (AUC) values were used to select parameters for inclusion in the ExPreS. Univariable logistic regression analysis and ROC analysis were performed to evaluate the performance of ExPreS. Patients’ inclusion and statistical analyses for the prospective validation cohort followed the same criteria used for the derivation cohort and the decision to extubate was based on the ExPreS result. In the derivation cohort, a total of 110 patients were extubated: extubation succeeded in 101 (91.8%) patients and failed in 9 (8.2%) patients. Rapid shallow-breathing index (RSBI) in SBT, dynamic lung compliance, duration of IMV, muscle strength, estimated GCS, hematocrit, and serum creatinine were significantly associated with extubation outcome. These parameters, along with another parameter—presence of neurologic comorbidity—were used to create the ExPreS. The AUC value for the ExPreS was 0.875, which was higher than the AUCs of the individual parameters. The total ExPreS can range from 0 to 100. ExPreS ≥59 points indicated high probability of success (OR = 23.07), while ExPreS ≤44 points indicated low probability of success (OR = 0.82). In the prospective validation cohort, 83 patients were extubated: extubation succeeded in 81 (97.6%) patients and failed in 2 (2.4%) patients. The AUC value for the ExPreS in this cohort was 0.971. The multiparameter score that we propose, ExPreS, shows good accuracy to predict extubation outcome in patients receiving IMV in the ICU. In the prospective validation, the use of ExPreS decreased the extubation failure rate from 8.2% to 2.4%, even in a cohort of more severe patients.

Early abolition of cough reflex predicts mortality in deeply sedated brain-injured patients

Background

Deep sedation may hamper the detection of neurological deterioration in brain-injured patients. Impaired brainstem reflexes within the first 24 h of deep sedation are associated with increased mortality in non-brain-injured patients. Our objective was to confirm this association in brain-injured patients.

Methods

This was an observational prospective multicenter cohort study involving four neuro-intensive care units. We included acute brain-injured patients requiring deep sedation, defined by a Richmond Assessment Sedation Scale (RASS) < −3. Neurological assessment was performed at day 1 and included pupillary diameter, pupillary light, corneal and cough reflexes, and grimace and motor response to noxious stimuli. Pre-sedation Glasgow Coma Scale (GCS) and Simplified Acute Physiology Score (SAPS-II) were collected, as well as the cause of death in the Intensive Care Unit (ICU).

Results

A total of 137 brain-injured patients were recruited, including 70 (51%) traumatic brain-injured patients, 40 (29%) vascular (subarachnoid hemorrhage or intracerebral hemorrhage). Thirty patients (22%) died in the ICU. At day 1, the corneal (OR 2.69, p = 0.034) and cough reflexes (OR 5.12, p = 0.0003) were more frequently abolished in patients that died in the ICU. In a multivariate analysis, abolished cough reflex was associated with ICU mortality after adjustment to pre-sedation GCS, SAPS-II, RASS (OR: 5.19, 95% CI [1.92–14.1], p = 0.001) or dose of sedatives (OR: 8.89, 95% CI [2.64–30.0], p = 0.0004).

Conclusion

Early (day 1) cough reflex abolition is an independent predictor of mortality in deeply sedated brain-injured patients. Abolished cough reflex likely reflects a brainstem dysfunction that might result from the combination of primary and secondary neuro-inflammatory cerebral insults revealed and/or worsened by sedation.

Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm's performance was compared versus the gold standard (the ventilator's waveform recordings for CP-VI were scored visually by three experts; Fleiss' kappa = 0.90 (0.87-0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient's own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78-0.86) and 0.78 (0.78-0.85), and accuracies of 0.93 (0.89-0.93) and 0.89 (0.89-0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.

High Breath-by-Breath Variability Is Associated With Extubation Failure in Children

OBJECTIVES

Extubation failure is multifactorial, and most tools to assess extubation readiness only evaluate snapshots of patient physiology. Understanding variability in respiratory variables may provide additional information to inform extubation readiness assessments.

DESIGN

Secondary analysis of prospectively collected physiologic data of children just prior to extubation during a spontaneous breathing trial. Physiologic data were cleaned to provide 40 consecutive breaths and calculate variability terms, coefficient of variation and autocorrelation, in commonly used respiratory variables (i.e., tidal volume, minute ventilation, and respiratory rate). Other clinical variables included diagnostic and demographic data, median values of respiratory variables during spontaneous breathing trials, and the change in airway pressure during an occlusion maneuver to measure respiratory muscle strength (maximal change in airway pressure generated during airway occlusion [PiMax]). Multivariable models evaluated independent associations with reintubation and prolonged use of noninvasive respiratory support after extubation.

SETTING

Acute care, children's hospital.

PATIENTS

Children were included from the pediatric and cardiothoracic ICUs who were greater than 37 weeks gestational age up to and including 18 years who were intubated greater than or equal to 12 hours with planned extubation. We excluded children who had a contraindication to an esophageal catheter or respiratory inductance plethysmography bands.

INTERVENTIONS

Noninterventional study.

MEASUREMENTS AND MAIN RESULTS

A total of 371 children were included, 32 of them were reintubated. Many variability terms were associated with reintubation, including coefficient of variation and autocorrelation of the respiratory rate. After controlling for confounding variables such as age and neurologic diagnosis, both coefficient of variation of respiratory rate(p < 0.001) and low PiMax (p = 0.002) retained an independent association with reintubation. Children with either low PiMax or high coefficient of variation of respiratory rate had a nearly three-fold higher risk of extubation failure, and when these children developed postextubation upper airway obstruction, reintubation rates were greater than 30%.

CONCLUSIONS

High respiratory variability during spontaneous breathing trials is independently associated with extubation failure in children, with very high rates of extubation failure when these children develop postextubation upper airway obstruction.

Novel mechanical ventilator weaning predictive model

Mechanical ventilation (MV) is a common life support system in intensive care units. Accurate identification of patients who are capable of being extubated can shorten the MV duration and potentially reduce MV-related complications. Therefore, prediction of patients who can successfully be weaned from the mechanical ventilator is an important issue. The electronic medical record system (EMRs) has been applied and developed in respiratory therapy in recent years. It can increase the quality of critical care. However, there is no perfect index available that can be used to determine successful MV weaning. Our purpose was to establish a novel model that can predict successful weaning from MV. Patients' information was collected from the Kaohsiung Medical University Hospital respiratory therapy EMRs. In this retrospective study, we collected basic information, classic weaning index, and respiratory parameters during spontaneous breathing trials of patients eligible for extubation. According to the results of extubation, patients were divided into successful extubation and extubation failure groups. This retrospective cohort study included 169 patients. Statistical analysis revealed successful extubation predictors, including sex; height; oxygen saturation; Glasgow Coma Scale; Acute Physiology and Chronic Health Evaluation II score; pulmonary disease history; and the first, 30th, 60th, and 90th minute respiratory parameters. We built a predictive model based on these predictors. The area under the curve of this model was 0.889. We established a model for predicting the successful extubation. This model was novel to combine with serial weaning parameters and thus can help intensivists to make extubation decisions easily.

Variations in respiratory rate do not reflect changes in tidal volume or minute ventilation after major abdominal surgery

Monitoring of postoperative pulmonary function usually includes respiratory rate and oxygen saturation measurements. We hypothesized that changes in postoperative respiratory rate do not correlate with changes in tidal volume or minute ventilation. In addition, we hypothesized that variability of minute ventilation and tidal volume is larger than variability of respiratory rate. Respiratory rate and changes in tidal volume and in minute ventilation were continuously measured in 27 patients during 24 h following elective abdominal surgery, using an impedance-based non-invasive respiratory volume monitor (ExSpiron, Respiratory Motion, Waltham, MA, US). Coefficients of variation were used as a measure for variability of respiratory rate, tidal volume and minute ventilation. Data of 38,149 measurements were analyzed. We found no correlation between respiratory rate and tidal volume or minute ventilation (r² = 0.02 and 0.01). Mean respiratory rate increased within the first 24 h after abdominal surgery from 13.9 ± 2.5 to 16.2 ± 2.4 breaths/min (p = 0.008), while tidal volume and minute ventilation remained unchanged (p = 0.90 and p = 0.18). Of interest, variability of respiratory rate (0.21 ± 0.06) was significantly smaller than variability of tidal volume (0.37 ± 0.12, p < 0.001) and minute ventilation (0.41 ± 0.12, p < 0.001). Changes in postoperative respiratory rate do not allow conclusions about changes in tidal volume or minute ventilation. We suggest that postoperative alveolar hypoventilation may not be recognized by monitoring respiratory rate alone. Variability of respiratory rate is smaller than variability in tidal volume and minute ventilation, suggesting that adaptations of alveolar ventilation to metabolic needs may be predominately achieved by variations in tidal volume.

Mechanisms of the breathing contribution to bodily self-consciousness in healthy humans: Lessons from machine-assisted breathing?

Previous studies investigated bodily self-consciousness (BSC) by experimentally exposing subjects to multisensory conflicts (i.e., visuo-tactile, audio-tactile, visuo-cardiac) in virtual reality (VR) that involve the participant's torso in a paradigm known as the full-body illusion (FBI). Using a modified FBI paradigm, we found that synchrony of visuo-respiratory stimulation (i.e., a flashing outline surrounding an avatar in VR; the flash intensity depending on breathing), is also able to modulate BSC by increasing self-location and breathing agency toward the virtual body. Our aim was to investigate such visuo-respiratory effects and determine whether respiratory motor commands contributes to BSC, using non-invasive mechanical ventilation (i.e., machine-delivered breathing). Seventeen healthy participants took part in a visuo-respiratory FBI paradigm and performed the FBI during two breathing conditions: (a) "active breathing" (i.e., participants actively initiate machine-delivered breaths) and (b) "passive breathing" (i.e., breaths' timing was determined by the machine). Respiration rate, tidal volume, and their variability were recorded. In line with previous results, participants experienced subjective changes in self-location, breathing agency, and self-identification toward the avatar's body, when presented with synchronous visuo-respiratory stimulation. Moreover, drift in self-location was reduced and tidal volume variability were increased by asynchronous visuo-respiratory stimulations. Such effects were not modulated by breathing control manipulations. Our results extend previous FBI findings showing that visuo-respiratory stimulation affects BSC, independently from breathing motor command initiation. Also, variability of respiratory parameters was influenced by visuo-respiratory feedback and might reduce breathing discomfort. Further exploration of such findings might inform the development of respiratory therapeutic tools using VR in patients.

Brainstem dysfunction in critically ill patients

The brainstem conveys sensory and motor inputs between the spinal cord and the brain, and contains nuclei of the cranial nerves. It controls the sleep-wake cycle and vital functions via the ascending reticular activating system and the autonomic nuclei, respectively. Brainstem dysfunction may lead to sensory and motor deficits, cranial nerve palsies, impairment of consciousness, dysautonomia, and respiratory failure. The brainstem is prone to various primary and secondary insults, resulting in acute or chronic dysfunction. Of particular importance for characterizing brainstem dysfunction and identifying the underlying etiology are a detailed clinical examination, MRI, neurophysiologic tests such as brainstem auditory evoked potentials, and an analysis of the cerebrospinal fluid. Detection of brainstem dysfunction is challenging but of utmost importance in comatose and deeply sedated patients both to guide therapy and to support outcome prediction. In the present review, we summarize the neuroanatomy, clinical syndromes, and diagnostic techniques of critical illness-associated brainstem dysfunction for the critical care setting.

A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.

An Evaluation of Risk Factors for Extubation Failure in Surgical Patients in Intensive Care

BACKGROUND

Reintubations following extubation from mechanical ventilation are costly, resulting in increased morbidity and mortality. The preparation for and timing of extubation from mechanical ventilation can reduce unplanned reintubations. Few studies have investigated reintubation in the surgical intensive care unit (SICU) population.

OBJECTIVE

To identify risk factors that predict extubation failure in nontrauma surgical postoperative intensive care patients.

METHODS

Retrospective analysis utilizing American College of Surgeons National Surgical Quality Improvement Program data and institutional clinical variables from July 1, 2013, to December 31, 2015, in a sample (N = 93) of surgical patients admitted postoperatively to a SICU with an endotracheal tube in place, requiring invasive mechanical ventilation. Logistic regression analysis was used to model extubation failure as a function of clinical variables in the 24 hours preceding extubation.

RESULTS

Of 93 patients, 70 were successfully extubated, and 23 experienced failure. Increasing respiratory rate in the 24 hours preceding extubation significantly predicted failure (odds ratio, 1.086; 95% confidence interval, 1.006-1.172; P = .034).

DISCUSSION

Elevated respiratory rates during the 24 hours preceding extubation are an underappreciated risk factor for extubation failure. This has direct implications for nurses who are assessing intensive care unit patients' readiness for extubation. Opportunity exists for nurses to better integrate respiratory rate data into extubation planning to improve unplanned reintubation rates in SICU patients.

Physiological Validation of an Airborne Ultrasound Based Surface Motion Camera for a Contactless Characterization of Breathing Pattern in Humans

Characterizing the breathing pattern in naturally breathing humans brings important information on respiratory mechanics, respiratory muscle, and breathing control. However, measuring breathing modifies breathing (observer effect) through the effects of instrumentation and awareness: measuring human breathing under true ecological conditions is currently impossible. This study tested the hypothesis that non-contact vibrometry using airborne ultrasound (SONAR) could measure breathing movements in a contactless and invisible manner. Thus, first, we evaluated the validity of SONAR measurements by testing their interchangeability with pneumotachograph (PNT) measurements obtained at the same time. We also aimed at evaluating the observer effect by comparing breathing variability obtained by SONAR versus SONAR-PNT measurements. Twenty-three healthy subjects (12 men and 11 women; mean age 33 years - range: 20-54) were studied during resting breathing while sitting on a chair. Breathing activity was described in terms of ventilatory flow measured using a PNT and, either simultaneously or sequentially, with a SONAR device measuring the velocity of the surface motion of the chest wall. SONAR was focused either anteriorly on the xiphoid process or posteriorly on the lower part of the costal margin. Discrete ventilatory temporal and volume variables and their coefficients of variability were calculated from the flow signal (PNT) and the velocity signal (SONAR) and tested for interchangeability (Passing-Bablok regression). Tidal volume (VT) and displacement were linearly related. Breathing frequency (BF), total cycle time (TT), inspiratory time (TI), and expiratory time (TE) met interchangeability criteria. Their coefficients of variation were not statistically significantly different with PNT and SONAR-only. This was true for both the anterior and the posterior SONAR measurements. Non-contact vibrometry using airborne ultrasound is a valid tool for measuring resting breathing pattern.

Expired Tidal Volume Variation in Extremely Low Birth Weight and Very Low Birth Weight Infants on Volume-Targeted Ventilation

In a prospective study we describe the delivery of small tidal volumes to extremely low birth weight (ELBW) and very low birth weight (VLBW) infants using a volume-targeted ventilation mode (VTV). Tidal volume delivery was consistent for both ELBW and VLBW infants independent of gestational age, birth weight, and the target volume.

Weaning from Mechanical Ventilation in ARDS: Aspects to Think about for Better Understanding, Evaluation, and Management

Acute respiratory distress syndrome (ARDS) is characterized by severe inflammatory response and hypoxemia. The use of mechanical ventilation (MV) for correction of gas exchange can cause worsening of this inflammatory response, called “ventilator-induced lung injury” (VILI). The process of withdrawing mechanical ventilation, referred to as weaning from MV, may cause worsening of lung injury by spontaneous ventilation. Currently, there are few specific studies in patients with ARDS. Herein, we reviewed the main aspects of spontaneous ventilation and also discussed potential methods to predict the failure of weaning in this patient category. We also reviewed new treatments (modes of mechanical ventilation, neuromuscular blocker use, and extracorporeal membrane oxygenation) that could be considered in weaning ARDS patients from MV.

Predictive factors of weaning from mechanical ventilation and extubation outcome: A systematic review

PURPOSE

To identify, describe and discuss the parameters used to predict weaning from mechanical ventilation and extubation outcomes.

METHODS

Systematic review of scientific articles using four electronic databases: PubMed, Embase, PEDro and Cochrane Library. Search terms included "weaning", "extubation", "withdrawal" and "discontinuation", combined with "mechanical ventilation" and "predictive factors", "predictive parameters" and "predictors for success". In this study, we included original articles that presented predictive factors for weaning or extubation outcomes in adult patients and not restricted to a single disease. Articles not written in English were excluded.

RESULTS

A total of 43 articles were included, with a total of 7929 patients and 56 different parameters related to weaning and extubation outcomes. Rapid Shallow Breathing Index (RSBI) was the most common predictor, discussed in 15 studies (2159 patients), followed by Age and Maximum Inspiratory Pressure in seven studies. The other 53 parameters were found in less than six studies.

CONCLUSION

There are several parameters used to predict weaning and extubation outcomes. RSBI was the most frequently studied and seems to be an important measurement tool in deciding whether to wean/extubate a patient. Furthermore, the results demonstrated that weaning and extubation should be guided by several parameters, and not only to respiratory ones.

Mid-cervical spinal cord contusion causes robust deficits in respiratory parameters and pattern variability

Mid-cervical spinal cord contusion disrupts both the pathways and motoneurons vital to the activity of inspiratory muscles. The present study was designed to determine if a rat contusion model could result in a measurable deficit to both ventilatory and respiratory motor function under “normal” breathing conditions at acute to chronic stages post trauma. Through whole body plethysmography and electromyography we assessed respiratory output from three days to twelve weeks after a cervical level 3 (C3) contusion. Contused animals showed significant deficits in both tidal and minute volumes which were sustained from acute to chronic time points. We also examined the degree to which the contusion injury impacted ventilatory pattern variability through assessment of Mutual Information and Sample Entropy. Mid-cervical contusion significantly and robustly decreased the variability of ventilatory patterns. The enduring deficit to the respiratory motor system caused by contusion was further confirmed through electromyography recordings in multiple respiratory muscles. When isolated via a lesion, these contused pathways were insufficient to maintain respiratory activity at all time points post injury. Collectively these data illustrate that, counter to the prevailing literature, a profound and lasting ventilatory and respiratory motor deficit may be modelled and measured through multiple physiological assessments at all time points after cervical contusion injury.

Variable mechanical ventilation

Objective

To review the literature on the use of variable mechanical ventilation and the main outcomes of this technique.

Methods

Search, selection, and analysis of all original articles on variable ventilation, without restriction on the period of publication and language, available in the electronic databases LILACS, MEDLINE^®, and PubMed, by searching the terms "variable ventilation" OR "noisy ventilation" OR "biologically variable ventilation".

Results

A total of 36 studies were selected. Of these, 24 were original studies, including 21 experimental studies and three clinical studies.

Conclusion

Several experimental studies reported the beneficial effects of distinct variable ventilation strategies on lung function using different models of lung injury and healthy lungs. Variable ventilation seems to be a viable strategy for improving gas exchange and respiratory mechanics and preventing lung injury associated with mechanical ventilation. However, further clinical studies are necessary to assess the potential of variable ventilation strategies for the clinical improvement of patients undergoing mechanical ventilation.

Neurally Adjusted Ventilatory Assist Is Associated with Greater Initial Extubation Success in Postoperative Congenital Heart Disease Patients when Compared to Conventional Mechanical Ventilation

Extubation failure is associated with considerable morbidity and mortality in postoperative patients with congenital heart disease (CHD). The study purpose was to investigate initial extubation success utilizing neurally adjusted ventilatory assist (NAVA) compared with pressure-regulated volume controlled, synchronized intermittent mandatory ventilation with pressure support (SIMV-PRVC + PS) for ventilatory weaning in patients who required prolonged mechanical ventilation (MV). Also, total days on MV, inotropes, sedation, analgesia, and pediatric intensive care unit (PICU) length of stay (LOS) between both groups were compared. This was a non-randomized pilot study utilizing historical controls (SIMV-PRVC + PS; n  = 40) compared with a prospective study population (NAVA; n  = 35) in a Level I PICU and was implemented to help future trial designs. All patients ( n  = 75) required prolonged MV ≥96 hours due to their complex postoperative course. Ventilator weaning initiation and management was standardized between both groups. Ninety-seven percent of the NAVA group was successfully extubated on the initial attempt, while 80% were in the SIMV-PRVC + PS group ( p  = 0.0317). Patients placed on NAVA were eight times more likely to have successful initial extubation (odds ratio [OR]: 8.50, 95% confidence interval [CI]: 1.01, 71.82). The NAVA group demonstrated a shorter median duration on MV (9.0 vs. 11.0 days, p  = 0.032), PICU LOS (9.0 vs. 13.5 days, p  < 0.0001), and shorter median duration of days on dopamine (8.0 vs. 11.0 days, p  = 0.0022), milrinone (9.0 vs. 12.0 days, p  = 0.0002), midazolam (8.0 vs. 12.0 days, p  < 0.0001), and fentanyl (9.0 vs. 12.5 days, p  < 0.0001) compared with the SIMV-PRVC + PS group. NAVA compared with SIMV-PRVC + PS was associated with a greater initial extubation success rate. NAVA should be considered as a mechanical ventilator weaning strategy in postoperative congenital heart disease (CHD) patients and warrants further investigation.

Comparison between a nurse-led weaning protocol and weaning based on physician's clinical judgment in tracheostomized critically ill patients: a pilot randomized controlled clinical trial

Background

Weaning protocols expedite extubation in mechanically ventilated patients, yet the literature investigating the application in tracheostomized patients remains scarce. The primary objective of this parallel randomized controlled pilot trial (RCT) was to assess the feasibility and safety of a nurse-led weaning protocol (protocol) compared to weaning based on physician’s clinical judgment (control) in tracheostomized critically ill patients.

Results

We enrolled 65 patients, 27 were in the protocol group and 38 in the control group. Of 27 patients in the protocol group, 1 (3.7%) died in the ICU, 24 (88.9%) were successfully weaned from tracheostomy, and 2 (7.4%) were transferred still on the ventilator. Of 38 patients in the control group, 2 (5.3%) died in the ICU, 22 (57.9%) were successfully weaned from tracheostomy, and 14 were transferred still on the ventilator (36.8%). Risk of being discharged from the ICU on the ventilator was higher in the control group (relative risk: 1.5, IC 95% 1.14–2.01). Concerning safety and feasibility, no patients were excluded after randomization. There was no crossover between the two study arms nor missing data, and no severe adverse event related to the study protocol application was recorded by the staff. Weaning time and rate of successful weaning were not different in the protocol group compared to the control group (long-rank test, p = 0.31 for MV duration, p = 0.45 for weaning time). Based on our results and assuming a 30% reduction of the weaning time for the protocol group, 280 patients would be needed for a RCT to establish efficacy.

Conclusions

In this pilot RCT we demonstrated that a nurse-led weaning protocol from tracheostomy was feasible and safe. A larger RCT is justified to assess efficacy.

Electronic supplementary material

The online version of this article (10.1186/s13613-018-0354-1) contains supplementary material, which is available to authorized users.

Time-of-day influences on respiratory sequelae following maximal electroshock-induced seizures in mice

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in refractory epilepsy patients. Although specific mechanisms underlying SUDEP are not well understood, evidence suggests most SUDEP occurs due to seizure-induced respiratory arrest. SUDEP also tends to happen at night. Although this may be due to circumstances in which humans find themselves at night, such as being alone without supervision or sleeping prone, or to independent influences of sleep state, there are a number of reasons why the night (i.e., circadian influences) could be an independent risk factor for SUDEP. We explored this possibility. Adult male WT mice were instrumented for EEG, EMG, and EKG recording and subjected to maximal electroshock (MES) seizures during wakefulness, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep during the nighttime/dark phase. These data were compared with data collected following seizures induced during the daytime/light phase. Seizures induced during the nighttime were similar in severity and duration to those induced during the daytime; however, seizures induced during the nighttime were associated with a lesser degree of respiratory dysregulation and postictal EEG suppression. Seizures induced during REM sleep during the nighttime were universally fatal, as is seen when seizures are induced during REM during the daytime. Taken together, these data implicate a role for time of day in influencing the physiological consequences of seizures that may contribute to seizure-induced death.NEW & NOTEWORTHY Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. SUDEP frequently occurs during the night, which has been attributed to an effect of sleep. We have shown that sleep state does indeed influence survival following a seizure. That SUDEP occurs during the night could also implicate a circadian influence. In this study we found that time of day independently affects the physiological consequences of seizures.

The effect of inspiratory muscle training on respiratory variables in a patient with ankylosing spondylitis: A case report

Ankylosing Spondylitis (AS) presents with both musculoskeletal and cardiorespiratory pathophysiological manifestations. Inspiratory muscle training (IMT) may be a useful intervention to address deficits in respiratory and functional status.

CASE DESCRIPTION

A 25-year-old male with AS initially sought treatment for low back and right hip pain, but 7 weeks of IMT was also provided due to abnormal respiratory performance.

OUTCOMES

At baseline, the patient presented with a resting respiratory rate (RR) of 14.5 breaths/minute, tidal volume (TV) of 0.76 L, minute ventilation (VE) of 10.87 L/min, and end tidal CO2 (PetCO2) of 30.56 mmHg. Baseline exercise test results revealed a VO2max of 44 ml/kg/min and VE to CO2 output (VE/VCO2) slope of 30. Baseline MIP, SMIP, and MEP were 54 cm H2O, 507 PTU, and 87 cm H2O, respectively, and increased to 176 cm H2O, 807 PTU, and 151 cm H2O, respectively, after IMT. The VO2max increased to 51 ml/kg/min with decreases in the VE/VCO2 slope (29), resting RR (12 breaths/minute), resting TV (0.52 L), and resting VE (6.83 L/min) after IMT. Improvements during postural challenges were also observed.

DISCUSSION

This case demonstrates the clinical utility of respiratory gas analysis and respiratory performance measures to identify functional deficits and manage a patient with AS. The improvements in respiratory performance at rest, during postural challenges, and during maximal exercise after a relatively short period of IMT highlights the role IMT may have to improve functional status in patients with AS. Further investigation of IMT in patients with AS is warranted.

Trends in mechanical ventilation: are we ventilating our patients in the best possible way?

This review addresses how the combination of physiology, medicine and engineering principles contributed to the development and advancement of mechanical ventilation, emphasising the most urgent needs for improvement and the most promising directions of future development. Several aspects of mechanical ventilation are introduced, highlighting on one side the importance of interdisciplinary research for further development and, on the other, the importance of training physicians sufficiently on the technological aspects of modern devices to exploit properly the great complexity and potentials of this treatment.

EDUCATIONAL AIMS

To learn how mechanical ventilation developed in recent decades and to provide a better understanding of the actual technology and practice.To learn how and why interdisciplinary research and competences are necessary for providing the best ventilation treatment to patients.To understand which are the most relevant technical limitations in modern mechanical ventilators that can affect their performance in delivery of the treatment.To better understand and classify ventilation modes.To learn the classification, benefits, drawbacks and future perspectives of automatic ventilation tailoring algorithms.

Measuring Ventilatory Activity with Structured Light Plethysmography (SLP) Reduces Instrumental Observer Effect and Preserves Tidal Breathing Variability in Healthy and COPD

The use of a mouthpiece to measure ventilatory flow with a pneumotachograph (PNT) introduces a major perturbation to breathing ("instrumental/observer effect") and suffices to modify the respiratory behavior. Structured light plethysmography (SLP) is a non-contact method of assessment of breathing pattern during tidal breathing. Firstly, we validated the SLP measurements by comparing timing components of the ventilatory pattern obtained by SLP vs. PNT under the same condition; secondly, we compared SLP to SLP+PNT measurements of breathing pattern to evaluate the disruption of breathing pattern and breathing variability in healthy and COPD subjects. Measurements were taken during tidal breathing with SLP alone and SLP+PNT recording in 30 COPD and healthy subjects. Measurements included: respiratory frequency (Rf), inspiratory, expiratory, and total breath time/duration (Ti, Te, and Tt). Passing-Bablok regression analysis was used to evaluate the interchangeability of timing components of the ventilatory pattern (Rf, Ti, Te, and Tt) between measurements performed under the following experimental conditions: SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT. The variability of different ventilatory variables was assessed through their coefficients of variation (CVs). In healthy: according to Passing-Bablok regression, Rf, TI, TE and TT were interchangeable between measurements obtained under the three experimental conditions (SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT). All the CVs describing "traditional" ventilatory variables (Rf, Ti, Te, Ti/Te, and Ti/Tt) were significantly smaller in SLP+PNT condition. This was not the case for more "specific" SLP-derived variables. In COPD: according to Passing-Bablok regression, Rf, TI, TE, and TT were interchangeable between measurements obtained under SLP vs. PNT and SLP+PNT vs. PNT, whereas only Rf, TE, and TT were interchangeable between measurements obtained under SLP+PNT vs. SLP. However, most discrete variables were significantly different between the SLP and SLP+PNT conditions and CVs were significantly lower when COPD patients were assessed in the SLP+PNT condition. Measuring ventilatory activity with SLP preserves resting tidal breathing variability, reduces instrumental observer effect and avoids any disruptions in breathing pattern induced by the use of PNT-mouthpiece-nose-clip combination.

Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies

OBJECTIVE

To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

DATA SOURCES

Literature review, research data, and expert opinion.

STUDY SELECTION

Not applicable.

DATA EXTRACTION

Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

DATA SYNTHESIS

Summary of presentations and discussion supported and supplemented by relevant literature.

CONCLUSIONS

There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Protective effects of ethyl pyruvate on lipopolysaccharide‑induced acute lung injury through inhibition of autophagy in neutrophils

Among a number of clinical factors, bacterial infection is one of the most common causes of acute lung injury (ALI), a serious complication that carries a high risk of mortality (~40%). During the process of ALI, intense local and systemic inflammation is elicited, which exacerbates the injury. Neutrophil infiltration into airspace is observed in early stage of ALI, and is required for the full development of ALI through an array of mechanisms, including the release of granule contents and the production of pro‑inflammatory cytokines, due to the overactivation of complement and cytokines. The present study noted that ethyl pyruvate alleviated ALI in lipopolysaccharide (LPS)‑induced ALI mice. Increased autophagy in neutrophils from ALI mice was observed, while ethyl pyruvate diminished autophagy in neutrophils and constrained granule release, and therefore myeloperoxidase (MPO) in bronchoalveolar lavage fluid and the production of proinflammatory cytokines. Using neutrophil cells, it was identified that autophagy was required for neutrophil activation and granule release, and that ethyl pyruvate caused neutrophil autophagy, leading to the restriction of granule release, and thus contributing to the mitigation of ALI. If autophagy was obviated through knockdown of key regulator of autophagy Atg5, the effects of ethyl pyruvate on granule release by neutrophils disappeared. Taken together, the results demonstrated that ethyl pyruvate alleviates ALI through inhibition of autophagy‑induced granule release by neutrophils, and this mechanism suggested a novel potential therapeutic target in autophagy regulation for ALI.

Slowing Down of Recovery as Generic Risk Marker for Acute Severity Transitions in Chronic Diseases

OBJECTIVE

We propose a novel paradigm to predict acute attacks and exacerbations in chronic episodic disorders such as asthma, cardiac arrhythmias, migraine, epilepsy, and depression. A better generic understanding of acute transitions in chronic dynamic diseases is increasingly important in critical care medicine because of the higher prevalence and incidence of these chronic diseases in our aging societies.

DATA SOURCES

PubMed, Medline, and Web of Science.

STUDY SELECTION

We selected studies from biology and medicine providing evidence of slowing down after a perturbation as a warning signal for critical transitions.

DATA EXTRACTION

Recent work in ecology, climate, and systems biology has shown that slowing down of recovery upon perturbations can indicate loss of resilience across complex, nonlinear biologic systems that are approaching a tipping point. This observation is supported by the empiric studies in pathophysiology and controlled laboratory experiments with other living systems, which can flip from one state of clinical balance to a contrasting one. We discuss examples of such evidence in bodily functions such as blood pressure, heart rate, mood, and respiratory regulation when a tipping point for a transition is near.

CONCLUSIONS

We hypothesize that in a range of chronic episodic diseases, indicators of critical slowing down, such as rising variance and temporal correlation, may be used to assess the risk of attacks, exacerbations, and even mortality. Identification of such early warning signals over a range of diseases will enhance the understanding of why, how, and when attacks and exacerbations will strike and may thus improve disease management in critical care medicine.

Variable ventilation from bench to bedside

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

When Interpolation-Induced Reflection Artifact Meets Time-Frequency Analysis

OBJECTIVE

While extracting the temporal dynamical features based on the time-frequency analyses, like the reassignment and synchrosqueezing transform, attracts more and more interest in biomedical data analysis, we should be careful about artifacts generated by interpolation schemes, in particular when the sampling rate is not significantly higher than the frequency of the oscillatory component we are interested in.

METHODS

We formulate the problem called the reflection effect and provide a theoretical justification of the statement. We also show examples in the anesthetic depth analysis with clear but undesirable artifacts.

RESULTS

The artifact associated with the reflection effect exists not only theoretically but practically as well. Its influence is pronounced when we apply the time-frequency analyses to extract the time-varying dynamics hidden inside the signal.

CONCLUSION

We have to carefully deal with the artifact associated with the reflection effect by choosing a proper interpolation scheme.

Automated analysis of respiratory behavior in extremely preterm infants and extubation readiness

BACKGROUND

Rates of extubation failure of extremely preterm infants remain high. Analysis of breathing patterns variability during spontaneous breathing under endotracheal tube continuous positive airway pressure (ETT-CPAP) is a potential tool to predict extubation readiness.

OBJECTIVE

To investigate if automated analysis of respiratory signals would reveal differences in respiratory behavior between infants that were successfully extubated or not.

METHODS

Respiratory Inductive Plethysmography (RIP) signals were recorded during ETT-CPAP just prior to extubation. Signals were digitized, and analyzed using an Automated Unsupervised Respiratory Event Analysis (AUREA). Extubation failure was defined as reintubation within 72 hr. Statistical differences between infants who were successfully extubated or failed were calculated.

RESULTS

A total of 56 infants were enrolled and one was excluded due to instability during the ETT-CPAP; 11 out of 55 infants studied failed extubation (20%). No differences in demographics were observed between the success and failure groups. Significant differences on the variability of some respiratory parameters or 'metrics' estimated by AUREA were observed between the 2 groups. Indeed, a simple classification using the variability of two metrics of respiratory behavior predicted extubation failure with high accuracy.

CONCLUSION

Automated analysis of respiratory behavior during a short ETT-CPAP period may help in the prediction of extubation readiness in extremely preterm infants.