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

Potential Diaphragm Muscle Weakness-related Dyspnea Persists 2 Years after COVID-19 and Could Be Improved by Inspiratory Muscle Training: Results of an Observational and an Interventional Clinical Trial

Rationale: Diaphragm muscle weakness might underlie persistent exertional dyspnea, despite normal lung and cardiac function in individuals who were previously hospitalized for acute coronavirus disease (COVID-19) illness. Objectives: The authors sought, first, to determine the persistence and pathophysiological nature of diaphragm muscle weakness and its association with exertional dyspnea 2 years after hospitalization for COVID-19 and, second, to investigate the impact of inspiratory muscle training (IMT) on diaphragm and inspiratory muscle weakness and exertional dyspnea in individuals with long COVID. Methods: Approximately 2 years after hospitalization for COVID-19, 30 individuals (11 women, 19 men; median age, 58 years; interquartile range [IQR] = 51-63) underwent comprehensive (invasive) respiratory muscle assessment and evaluation of dyspnea. Eighteen with persistent diaphragm muscle weakness and exertional dyspnea were randomized to 6 weeks of IMT or sham training; assessments were repeated immediately after and 6 weeks after IMT completion. The primary endpoint was change in inspiratory muscle fatiguability immediately after IMT. Measurements and Main Results: At a median of 31 months (IQR = 23-32) after hospitalization, 21 of 30 individuals reported relevant persistent exertional dyspnea. Diaphragm muscle weakness on exertion and reduced diaphragm cortical activation were potentially related to exertional dyspnea. Compared with sham control, IMT improved diaphragm and inspiratory muscle function (sniff transdiaphragmatic pressure, 83 cm H2O [IQR = 75-91] vs. 100 cm H2O [IQR = 81-113], P = 0.02), inspiratory muscle fatiguability (time to task failure, 365 s [IQR = 284-701] vs. 983 s [IQR = 551-1,494], P = 0.05), diaphragm voluntary activation index (79% [IQR = 63-92] vs. 89% [IQR = 75-94], P = 0.03), and dyspnea (Borg score, 7 [IQR = 5.5-8] vs. 6 [IQR = 4-7], P = 0.03). Improvements persisted for 6 weeks after IMT completion. Conclusions: To the best of the authors' knowledge, this study is the first to identify a potential treatment for persisting exertional dyspnea in long COVID and provide a possible pathophysiological explanation for the treatment benefit. Clinical trial registered with www.clinicaltrials.gov (NCT04854863, NCT05582642).

Advanced waveform analysis of diaphragm surface EMG allows for continuous non-invasive assessment of respiratory effort in critically ill patients at different PEEP levels

BACKGROUND

Respiratory effort should be closely monitored in mechanically ventilated ICU patients to avoid both overassistance and underassistance. Surface electromyography of the diaphragm (sEMGdi) offers a continuous and non-invasive modality to assess respiratory effort based on neuromuscular coupling (NMCdi). The sEMGdi derived electrical activity of the diaphragm (sEAdi) is prone to distortion by crosstalk from other muscles including the heart, hindering its widespread use in clinical practice. We developed an advanced analysis as well as quality criteria for sEAdi waveforms and investigated the effects of clinically relevant levels of PEEP on non-invasive NMCdi.

METHODS

NMCdi was derived by dividing end-expiratory occlusion pressure (Pocc) by sEAdi, based on three consecutive Pocc manoeuvres at four incremental (+ 2 cmH2O/step) PEEP levels in stable ICU patients on pressure support ventilation. Pocc and sEAdi quality was assessed by applying a novel, automated advanced signal analysis, based on tolerant and strict cut-off criteria, and excluding inadequate waveforms. The coefficient of variations (CoV) of NMCdi after basic manual and automated advanced quality assessment were evaluated, as well as the effect of an incremental PEEP trial on NMCdi.

RESULTS

593 manoeuvres were obtained from 42 PEEP trials in 17 ICU patients. Waveform exclusion was primarily based on low sEAdi signal-to-noise ratio (Ntolerant = 155, 37%, Nstrict = 241, 51% waveforms excluded), irregular or abrupt cessation of Pocc (Ntolerant = 145, 35%, Nstrict = 145, 31%), and high sEAdi area under the baseline (Ntolerant = 94, 23%, Nstrict = 79, 17%). Strict automated assessment allowed to reduce CoV of NMCdi to 15% from 37% for basic quality assessment. As PEEP was increased, NMCdi decreased significantly by 4.9 percentage point per cmH2O.

CONCLUSION

Advanced signal analysis of both Pocc and sEAdi greatly facilitates automated and well-defined identification of high-quality waveforms. In the critically ill, this approach allowed to demonstrate a dynamic NMCdi (Pocc/sEAdi) decrease upon PEEP increments, emphasising that sEAdi-based assessment of respiratory effort should be related to PEEP dependent diaphragm function. This novel, non-invasive methodology forms an important methodological foundation for more robust, continuous, and comprehensive assessment of respiratory effort at the bedside.

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.

Individual response in patient's effort and driving pressure to variations in assistance during pressure support ventilation

BACKGROUND

During Pressure Support Ventilation (PSV) an inspiratory hold allows to measure plateau pressure (Pplat), driving pressure (∆P), respiratory system compliance (Crs) and pressure-muscle-index (PMI), an index of inspiratory effort. This study aims [1] to assess systematically how patient's effort (estimated with PMI), ∆P and tidal volume (Vt) change in response to variations in PSV and [2] to confirm the robustness of Crs measurement during PSV.

METHODS

18 patients recovering from acute respiratory failure and ventilated by PSV were cross-randomized to four steps of assistance above (+ 3 and + 6 cmH2O) and below (-3 and -6 cmH2O) clinically set PS. Inspiratory and expiratory holds were performed to measure Pplat, PMI, ∆P, Vt, Crs, P0.1 and occluded inspiratory airway pressure (Pocc). Electromyography of respiratory muscles was monitored noninvasively from body surface (sEMG).

RESULTS

As PSV was decreased, Pplat (from 20.5 ± 3.3 cmH2O to 16.7 ± 2.9, P < 0.001) and ∆P (from 12.5 ± 2.3 to 8.6 ± 2.3 cmH2O, P < 0.001) decreased much less than peak airway pressure did (from 21.7 ± 3.8 to 9.7 ± 3.8 cmH2O, P < 0.001), given the progressive increase of patient's effort (PMI from -1.2 ± 2.3 to 6.4 ± 3.2 cmH2O) in line with sEMG of the diaphragm (r = 0.614; P < 0.001). As ∆P increased linearly with Vt, Crs did not change through steps (P = 0.119).

CONCLUSION

Patients react to a decrease in PSV by increasing inspiratory effort-as estimated by PMI-keeping Vt and ∆P on a desired value, therefore, limiting the clinician's ability to modulate them. PMI appears a valuable index to assess the point of ventilatory overassistance when patients lose control over Vt like in a pressure-control mode. The measurement of Crs in PSV is constant-likely suggesting reliability-independently from the level of assistance and patient's effort.

Evaluation of three approaches used for respiratory measurement in healthy subjects

Objective. Respiration is one of the critical vital signs of human health status, and accurate respiratory monitoring has important clinical significance. There is substantial evidence that alterations in key respiratory parameters can be used to determine a patient's health status, aid in the selection of appropriate treatments, predict potentially serious clinical events and control respiratory activity. Although various approaches have been developed for respiration monitoring, no definitive conclusions have been drawn regarding the accuracy of these approaches because each has different advantages and limitations. In the present study, we evaluated the performance of three non-invasive respiratory measurement approaches, including transthoracic impedance (IMP), surface diaphragm electromyography-derived respiration (EMGDR) and electrocardiogram-derived respiration (ECGDR), and compared them with the direct measurement of airflow (FLW) in 33 male and 38 female healthy subjects in the resting state.Approach. The accuracy of six key respiratory parameters, including onset of inspiration (Ion), onset of expiration (Eon), inspiratory time (It), expiratory time (Et), respiratory rate (RR) and inspiratory-expiratory ratio (I:E), measured from the IMP, EMGDR and ECGDR, were compared with those annotated from the reference FLW.Main results. The correlation coefficients between the estimated inspiratory volume and reference value were 0.72 ± 0.20 for IMP, 0.62 ± 0.23 for EMGDR and 0.46 ± 0.21 for ECGDR (p< 0.01 among groups). The positive predictive value and sensitivity for respiration detection were 100% and 100%, respectively, for IMP, which were significantly higher than those of the EMGDR (97.2% and 95.5%,p< 0.001) and the ECGDR (96.9% and 90.0%,p< 0.001). Additionally, the mean error (ME) forIon,Eon,It,EtandRRdetection were markedly lower for IMP than for EMGDR and ECGDR (p< 0.001).Significance. Compared with EMGDR and ECGDR, the IMP signal had a higher positive predictive value, higher sensitivity and lower ME for respiratory parameter detection. This suggests that IMP is more suitable for dedicated respiratory monitoring and parameter evaluation.

Patient Self-Inflicted Lung Injury-A Narrative Review of Pathophysiology, Early Recognition, and Management Options

Patient self-inflicted lung injury (P-SILI) is a life-threatening condition arising from excessive respiratory effort and work of breathing in patients with lung injury. The pathophysiology of P-SILI involves factors related to the underlying lung pathology and vigorous respiratory effort. P-SILI might develop both during spontaneous breathing and mechanical ventilation with preserved spontaneous respiratory activity. In spontaneously breathing patients, clinical signs of increased work of breathing and scales developed for early detection of potentially harmful effort might help clinicians prevent unnecessary intubation, while, on the contrary, identifying patients who would benefit from early intubation. In mechanically ventilated patients, several simple non-invasive methods for assessing the inspiratory effort exerted by the respiratory muscles were correlated with respiratory muscle pressure. In patients with signs of injurious respiratory effort, therapy aimed to minimize this problem has been demonstrated to prevent aggravation of lung injury and, therefore, improve the outcome of such patients. In this narrative review, we accumulated the current information on pathophysiology and early detection of vigorous respiratory effort. In addition, we proposed a simple algorithm for prevention and treatment of P-SILI that is easily applicable in clinical practice.

Highlights from the Respiratory Failure and Mechanical Ventilation 2022 Conference

The Respiratory Intensive Care Assembly of the European Respiratory Society gathered in Berlin to organise the second Respiratory Failure and Mechanical Ventilation Conference in June 2022. The conference covered several key points of acute and chronic respiratory failure in adults. During the 3-day conference, ventilatory strategies, patient selection, diagnostic approaches, treatment and health-related quality of life topics were addressed by a panel of international experts. Lectures delivered during the event have been summarised by Early Career Members of the Assembly and take-home messages highlighted.

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.