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A non-invasive method to monitor respiratory muscle effort during mechanical ventilation

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Abstract

Purpose

This study introduces a method to non-invasively and automatically quantify respiratory muscle effort (Pmus) during mechanical ventilation (MV). The methodology hinges on numerically solving the respiratory system’s equation of motion, utilizing measurements of airway pressure (Paw) and airflow (Faw). To evaluate the technique’s effectiveness, Pmus was correlated with expected physiological responses. In volume-control (VC) mode, where tidal volume (VT) is pre-determined, Pmus is expected to be linked to Paw fluctuations. In contrast, during pressure-control (PC) mode, where Paw is held constant, Pmus should correlate with VT variations.

Methods

The study utilized data from 250 patients on invasive MV. The data included detailed recordings of Paw and Faw, sampled at 31.25 Hz and saved in 131.1-second epochs, each covering 34 to 41 breaths. The algorithm identified 51,268 epochs containing breaths on either VC or PC mode exclusively. In these epochs, Pmus and its pressure-time product (PmusPTP) were computed and correlated with Paw’s pressure-time product (PawPTP) and VT, respectively.

Results

There was a strong correlation of PmusPTP with PawPTP in VC mode (R² = 0.91 [0.76, 0.96]; n = 17,648 epochs) and with VT in PC mode (R² = 0.88 [0.74, 0.94]; n = 33,620 epochs), confirming the hypothesis. As expected, negligible correlations were observed between PmusPTP and VT in VC mode (R² = 0.03) and between PmusPTP and PawPTP in PC mode (R² = 0.06).

Conclusion

The study supports the feasibility of assessing respiratory effort during MV non-invasively through airway signal analysis. Further research is warranted to validate this method and investigate its clinical applications.

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Data availability

The datasets used and analyzed during the current study can be found in the Electronic Data Repository. The database storing the raw data is available from the author upon reasonable request.

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Acknowledgements

The author thanks the Commission for Educational Exchange between the United States, Belgium and Luxembourg and the Fulbright Scholarship Board. Their generous support as a Fulbright Research Scholar granted the author the time and resources necessary to develop the ideas that formed the basis of this research.

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Authors

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Single author manuscript. I take responsibility for all aspects of the research.

Corresponding author

Correspondence to Guillermo Gutierrez.

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Competing interests

The author has applied for a U.S. patent based on the information presented in the manuscript.

Ethical approval and consent to participate

The database used in the present study was collected during the conduct of several IRB approved studies (Nos. 101228, 110910, 111235) at The George Washington University Hospital, with the IRB allowing the use of deidentified data in further studies.

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Supplementary Material 1

Glossary

Crs

Respiratory system static compliance

ΔV(t) 

Lung volume change during insufflation

Faw

Airway flow

PEEPa

Applied positive end expiratory pressure

PEEPi

Intrinsic PEEP present at end expiration

PC

Pressure control ventilation mode

PS

Pressure support ventilation mode

Paw

airway pressure

PawPeak

Peak inspiratory pressure

PawPTP

Paw pressure time product

Pmus

Respiratory muscles pressure

Peak_Pmus

Peak respiratory muscles pressure

PmusPTP

Pmus pressure time product

Ppassive

Paw required for passively inflation of the respiratory system

rs

Respiratory system

Rrs

Respiratory system inspiratory airway resistance

RRVI

respiratory rate variability index

VC

Volume control ventilation mode

VT

Tidal volume

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Gutierrez, G. A non-invasive method to monitor respiratory muscle effort during mechanical ventilation. J Clin Monit Comput (2024). https://doi.org/10.1007/s10877-024-01164-z

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