• Guillermo Gutierrez.
• Professor Emeritus Medicine, Anesthesiology and Engineering, The George Washington University, 700 New Hampshire Ave, NW Suite 510, Washington, DC, 20037, USA. gutier@gwu.edu.
• J Clin Monit Comput. 2024 Oct 1; 38 (5): 112511341125-1134.

PurposeThis 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.MethodsThe 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.ResultsThere 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).ConclusionThe 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.© 2024. The Author(s), under exclusive licence to Springer Nature B.V.

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