Transcutaneous electromyographic respiratory muscle recordings to quantify patient-ventilator interaction in mechanically ventilated children

Respiratory entrainment related reverse triggering in mechanically ventilated children

BACKGROUND

The underlying pathophysiological pathways how reverse triggering is being caused are not fully understood. Respiratory entrainment may be one of these mechanisms, but both terms are used interchangeably. We sought to characterize reverse triggering and the relationship with respiratory entrainment among mechanically ventilated children with and without acute lung injury.

METHODS

We performed a secondary phyiology analysis of two previously published data sets of invasively mechanically ventilated children < 18 years with and without lung injury mechanically ventilated in a continuous or intermittent mandatory ventilation mode. Ventilator waveforms, electrical activity of the diaphragm measured with surface electromyography and oesophageal tracings were analyzed for entrained and non-entrained reverse triggered breaths.

RESULTS

In total 102 measurements (3110 min) from 67 patients (median age 4.9 [1.8 ; 19,1] months) were analyzed. Entrained RT was identified in 12 (12%) and non-entrained RT in 39 (38%) recordings. Breathing variability for entrained RT breaths was lower compared to non-entrained RT breaths. We did not observe breath stacking during entrained RT. Double triggering often occurred during non-entrained RT and led to an increased tidal volume. Patients with respiratory entrainment related RT had a shorter duration of MV and length of PICU stay.

CONCLUSIONS

Reverse triggering is not one entity but a clinical spectrum with different mechanisms and consequences.

TRIAL REGISTRATION

Not applicable.

Automated characterization of patient-ventilator interaction using surface electromyography

BACKGROUND

Characterizing patient-ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient.

METHODS

In this study, we recorded surface electromyography ([Formula: see text]) signals from the diaphragm and intercostal muscles and esophageal pressure ([Formula: see text]) in mechanically ventilated patients with ARDS. The sEMG recordings were preprocessed, and two different algorithms (triangle algorithm and adaptive thresholding algorithm) were used to automatically detect inspiratory patient effort. Based on the detected inspirations, major asynchronies (ineffective, auto-, and double triggers and double efforts), delayed and synchronous triggers were computationally classified. Reverse triggers were not considered in this study. Subsequently, asynchrony indices were calculated. For the validation of detected efforts, two experts manually annotated inspiratory patient activity in [Formula: see text], blinded toward each other, the [Formula: see text] signals, and the algorithmic results. We also classified patient-ventilator interaction and calculated asynchrony indices with manually detected inspirations in [Formula: see text] as a reference for automated asynchrony classification and asynchrony index calculation.

RESULTS

Spontaneous breathing activity was recognized in 22 out of the 36 patients included in the study. Evaluation of the accuracy of the algorithms using 3057 inspiratory efforts in [Formula: see text] demonstrated reliable detection performance for both methods. Across all datasets, we found a high sensitivity (triangle algorithm/adaptive thresholding algorithm: 0.93/0.97) and a high positive predictive value (0.94/0.89) against expert annotations in [Formula: see text]. The average delay of automatically detected inspiratory onset to the [Formula: see text] reference was [Formula: see text]79 ms/29 ms for the two algorithms. Our findings also indicate that automatic asynchrony index prediction is reliable. For both algorithms, we found the same deviation of [Formula: see text] to the [Formula: see text]-based reference.

CONCLUSIONS

Our study demonstrates the feasibility of automating the quantification of patient-ventilator asynchrony in critically ill patients using noninvasive sEMG. This may facilitate more frequent diagnosis of asynchrony and support improving patient-ventilator interaction.

Analysis and applications of respiratory surface EMG: report of a round table meeting

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

Surface electromyography to quantify neuro-respiratory drive and neuro-mechanical coupling in mechanically ventilated children

BACKGROUND

The patient's neuro-respiratory drive, measured as electrical activity of the diaphragm (EAdi), quantifies the mechanical load on the respiratory muscles. It correlates with respiratory effort but requires a dedicated esophageal catheter. Transcutaneous (surface) monitoring of respiratory muscle electromyographic (sEMG) signals may be considered a suitable alternative to EAdi because of its non-invasive character, with the additional benefit that it allows for simultaneously monitoring of other respiratory muscles. We therefore sought to study the neuro-respiratory drive and timing of inspiratory muscles using sEMG in a cohort of children enrolled in a pediatric ventilation liberation trial. The neuro-mechanical coupling, relating the pressure generated by the inspiratory muscles to the sEMG signals of these muscles, was also calculated.

METHODS

This is a secondary analysis of data from a randomized cross-over trial in ventilated patients aged < 5 years. sEMG recordings of the diaphragm and parasternal intercostal muscles (ICM), esophageal pressure tracings and ventilator scalars were simultaneously recorded during continuous spontaneous ventilation and pressure controlled-intermittent mandatory ventilation, and at three levels of pressure support. Neuro-respiratory drive, timing of diaphragm and ICM relative to the mechanical ventilator's inspiration and neuro-mechanical coupling were quantified.

RESULTS

Twenty-nine patients were included (median age: 5.9 months). In response to decreasing pressure support, both amplitude of sEMG (diaphragm: p = 0.001 and ICM: p = 0.002) and neuro-mechanical efficiency indices increased (diaphragm: p = 0.05 and ICM: p < 0.001). Poor correlations between neuro-respiratory drive and respiratory effort were found, with R²: 0.088 [0.021-0.152].

CONCLUSIONS

sEMG allows for the quantification of the electrical activity of the diaphragm and ICM in mechanically ventilated children. Both neuro-respiratory drive and neuro-mechanical efficiency increased in response to lower inspiratory assistance. There was poor correlation between neuro-respiratory drive and respiratory effort.

TRIAL REGISTRATION

ClinicalTrials.gov ID NCT05254691. Registered 24 February 2022, registered retrospectively.

Simultaneous measurement of diaphragm activity, chest impedance, and ECG using three standard cardiorespiratory monitoring electrodes

INTRODUCTION

Current cardiorespiratory monitoring in neonates with electrocardiogram (ECG) and chest impedance (CI) has limitations. Adding transcutaneous electromyography of the diaphragm (dEMG) may improve respiratory monitoring, but requires additional hardware. We aimed to determine the feasibility of measuring dEMG and ECG/CI simultaneously using the standard ECG/CI hardware, with its three electrodes repositioned to dEMG electrode locations.

METHODS

Thirty infants (median postmenstrual age 30.4 weeks) were included. First, we assessed the feasibility of extracting dEMG from the ECG-signal. If successful, the agreement between dEMG-based respiratory rate (RR), using three different ECG-leads, and a respiratory reference signal was assessed using the Bland-Altman analysis and the intraclass correlation coefficient (ICC). Furthermore, we studied the agreement between CI-based RR and the reference signal with the electrodes placed at the standard and dEMG position. Finally, we explored the quality of the ECG-signal at the different electrode positions.

RESULTS

In 15 infants, feasibility of measuring dEMG with the monitoring electrodes was confirmed. In the next 15 infants, comparing dEMG-based RR to the reference signal resulted in a mean difference and limits of agreement for ECG-lead I, II and III of 4.2 [-8.2 to 16.6], 4.3 [-10.7 to 19.3] and 5.0 [-14.2 to 24.2] breaths/min, respectively. ICC analysis showed a moderate agreement for all ECG-leads. CI-based RR agreement was similar at the standard and dEMG electrode position. An exploratory analysis suggested similar quality of the ECG-signal at both electrode positions.

CONCLUSION

Measuring dEMG using the ECG/CI hardware with its electrodes on the diaphragm is feasible, leaving ECG/CI monitoring unaffected.

Blind source separation of inspiration and expiration in respiratory sEMG signals

OBJECTIVE

Surface electromyography (sEMG) is a noninvasive option for monitoring respiratory effort in ventilated patients. However, respiratory sEMG signals are affected by crosstalk and cardiac activity. This work addresses the blind source separation (BSS) of inspiratory and expiratory electrical activity in single- or two-channel recordings. The main contribution of the presented methodology is its applicability to the addressed muscles and the number of available channels.

APPROACH

We propose a two-step procedure consisting of a single-channel cardiac artifact removal algorithm, followed by a single- or multi-channel BSS stage. First, cardiac components are removed in the wavelet domain. Subsequently, a nonnegative matrix factorization (NMF) algorithm is applied to the envelopes of the resulting wavelet bands. The NMF is initialized based on simultaneous standard pneumatic measurements of the ventilated patient.

MAIN RESULTS

The proposed estimation scheme is applied to twelve clinical datasets and simulated sEMG signals of the respiratory system. The results on the clinical datasets are validated based on expert annotations using invasive pneumatic measurements. In the simulation, three measures evaluate the separation success: The distortion and the correlation to the known ground truth and the inspiratory-to-expiratory signal power ratio. We find an improvement across all SNRs, recruitment patterns, and channel configurations. Moreover, our results indicate that the initialization strategy replaces the manual matching of sources after the BSS.

SIGNIFICANCE

The proposed separation algorithm facilitates the interpretation of respiratory sEMG signals. In crosstalk affected measurements, the developed method may help clinicians distinguish between inspiratory effort and other muscle activities using only noninvasive measurements.

Surface EMG-based quantification of inspiratory effort: a quantitative comparison with Pes

Background

Inspiratory patient effort under assisted mechanical ventilation is an important quantity for assessing patient–ventilator interaction and recognizing over and under assistance. An established clinical standard is respiratory muscle pressure \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{P}_{\mathrm{mus}}$$\end{document}Pmus, derived from esophageal pressure (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{P}_{\mathrm{es}}$$\end{document}Pes), which requires the correct placement and calibration of an esophageal balloon catheter. Surface electromyography (sEMG) of the respiratory muscles represents a promising and straightforward alternative technique, enabling non-invasive monitoring of patient activity.

Methods

A prospective observational study was conducted with patients under assisted mechanical ventilation, who were scheduled for elective bronchoscopy. Airway flow and pressure, esophageal/gastric pressures and sEMG of the diaphragm and intercostal muscles were recorded at four levels of pressure support ventilation. Patient efforts were quantified via the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{P}_{\mathrm{mus}}$$\end{document}Pmus-time product (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus), the transdiaphragmatic pressure-time product (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{di}}$$\end{document}PTPdi) and the EMG-time products (ETP) of the two sEMG channels. To improve the signal-to-noise ratio, a method for automatically selecting the more informative of the sEMG channels was investigated. Correlation between ETP and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus was assessed by determining a neuromechanical conversion factor \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}_{\mathrm{EMG}}$$\end{document}KEMG between the two quantities. Moreover, it was investigated whether this scalar can be reliably determined from airway pressure during occlusion maneuvers, thus allowing to quantify inspiratory effort based solely on sEMG measurements.

Results

In total, 62 patients with heterogeneous pulmonary diseases were enrolled in the study, 43 of which were included in the data analysis. The ETP of the two sEMG channels was well correlated with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{r}={0.79\pm 0.25}$$\end{document}r=0.79±0.25 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{r}={0.84\pm 0.16}$$\end{document}r=0.84±0.16 for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{r}={0.87\pm 0.09}$$\end{document}r=0.87±0.09). The neuromechanical conversion factor obtained by fitting ETP to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus varied widely between patients (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}_{\mathrm{EMG}}= {4.32\pm 3.73}\,{\hbox {cm}\hbox {H}_{2}\hbox {O}/\upmu \hbox {V}}$$\end{document}KEMG=4.32±3.73cm2O/μV) and was highly correlated with the scalar determined during occlusions (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{r}={0.95}$$\end{document}r=0.95, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{p}<{.001}$$\end{document}p<.001). The occlusion-based method for deriving \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus from ETP showed a breath-wise deviation to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${0.43\pm 1.73}\,{\hbox {cm}\hbox {H}_{2}\hbox {O}\,\hbox {s}}$$\end{document}0.43±1.73cm2Os across all datasets.

Conclusion

These results support the use of surface electromyography as a non-invasive alternative for monitoring breath-by-breath inspiratory effort of patients under assisted mechanical ventilation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03833-w.

Trends in Pediatric Patient-Ventilator Asynchrony During Invasive Mechanical Ventilation

OBJECTIVES

To explore the level and time course of patient-ventilator asynchrony in mechanically ventilated children and the effects on duration of mechanical ventilation, PICU stay, and Comfort Behavior Score as indicator for patient comfort.

DESIGN

Secondary analysis of physiology data from mechanically ventilated children.

SETTING

Mixed medical-surgical tertiary PICU in a university hospital.

PATIENTS

Mechanically ventilated children 0-18 years old were eligible for inclusion. Excluded were patients who were unable to initiate and maintain spontaneous breathing from any cause.

MEASUREMENTS AND MAIN RESULTS

Twenty-nine patients were studied with a total duration of 109 days. Twenty-two study days (20%) were excluded because patients were on neuromuscular blockade or high-frequency oscillatory ventilation, yielding 87 days (80%) for analysis. Patient-ventilator asynchrony was detected through analysis of daily recorded ventilator airway pressure, flow, and volume versus time scalars. Approximately one of every three breaths was asynchronous. The percentage of asynchronous breaths significantly increased over time, with the highest prevalence on the day of extubation. There was no correlation with the Comfort Behavior score. The percentage of asynchronous breaths during the first 24 hours was inversely correlated with the duration of mechanical ventilation. Patients with severe patient-ventilator asynchrony (asynchrony index > 10% or > 75th percentile of the calculated asynchrony index) did not have a prolonged duration of ventilation.

CONCLUSIONS

The level of patient-ventilator asynchrony increased over time was not related to patient discomfort and inversely related to the duration of mechanical ventilation.

The impact of critical illness on the expiratory muscles and the diaphragm assessed by ultrasound in mechanical ventilated children

Background

Critical illness has detrimental effects on the diaphragm, but the impact of critical illness on other major muscles of the respiratory pump has been largely neglected. This study aimed to determine the impact of critical illness on the most important muscles of the respiratory muscle pump, especially on the expiratory muscles in children during mechanical ventilation. In addition, the correlation between changes in thickness of the expiratory muscles and the diaphragm was assessed.

Methods

This longitudinal observational cohort study performed at a tertiary pediatric intensive care unit included 34 mechanical ventilated children (> 1 month– < 18 years). Thickness of the diaphragm and expiratory muscles (obliquus interna, obliquus externa, transversus abdominis and rectus abdominis) was assessed daily using ultrasound. Contractile activity was estimated from muscle thickening fraction during the respiratory cycle.

Results

Over the first 4 days, both diaphragm and expiratory muscles thickness decreased (> 10%) in 44% of the children. Diaphragm and expiratory muscle thickness increased (> 10%) in 26% and 20% of the children, respectively. No correlation was found between contractile activity of the muscles and the development of atrophy. Furthermore, no correlation was found between changes in thickness of the diaphragm and the expiratory muscles (P = 0.537). Decrease in expiratory muscle thickness was significantly higher in patients failing extubation compared to successful extubation (− 34% vs − 4%, P = 0.014).

Conclusions

Changes in diaphragm and expiratory muscles thickness develop rapidly after the initiation of mechanical ventilation. Changes in thickness of the diaphragm and expiratory muscles were not significantly correlated. These data provide a unique insight in the effects of critical illness on the respiratory muscle pump in children.

Acute respiratory failure in randomized trials of noninvasive respiratory support: A systematic review of definitions, patient characteristics, and criteria for intubation

PURPOSE

To examine the definitions of acute respiratory failure, the characteristics of recruited patients, and the criteria for intubation used in randomized trials.

METHODS

We searched MEDLINE for randomized trials of noninvasive respiratory support modalities in patients with de novo respiratory failure. We included trials from 1995 to 2017 that enrolled 40 or more patients and used intubation as an outcome.

RESULTS

We examined the reports of 53 trials that enrolled 7225 patients. There was wide variation in the use of variables for defining acute respiratory failure. Dyspnea was rarely measured and the increase in breathing effort was poorly defined. The characteristics of patients enrolled in trials changed over time and differed by the cause of respiratory failure. Intubation was poorly characterized. The criteria for intubation had more variables than the criteria for respiratory failure.

CONCLUSIONS

We identified deficiencies in the design and reporting of randomized trials, some of which can be remedied by investigators. We also found that patient characteristics differ by the type of respiratory failure. This knowledge can help clinician identify patients at the right moment to benefit from the tested interventions and investigators in developing criteria for enrollment in future trials.

Processing transcutaneous electromyography measurements of respiratory muscles, a review of analysis techniques

Transcutaneous electromyography (tc-EMG) has been used to measure the electrical activity of respiratory muscles during inspiration in various studies. Processing the raw tc-EMG signal of these inspiratory muscles has shown to be difficult as baseline noise, cardiac interference, cross-talk and motion artefacts can influence the signal quality. In this review we will discuss the most important sources of signal noise in tc-EMG of respiratory muscles and the various techniques described to suppress or reduce this signal noise. Furthermore, we will elaborate on the options available to develop or improve an algorithm that can be used to guide the approach for analysis of tc-EMG signals of inspiratory muscles in future research.