Professor Emeritus Medicine, Anesthesiology and Engineering, The George Washington University, 700 New Hampshire Ave, NW Suite 510, Washington, DC, 20037, USA.
J Clin Monit Comput. 2024 Oct;38(5):1125-1134. doi: 10.1007/s10877-024-01164-z. Epub 2024 May 11.
This study introduces a method to non-invasively and automatically quantify respiratory muscle effort (P) during mechanical ventilation (MV). The methodology hinges on numerically solving the respiratory system's equation of motion, utilizing measurements of airway pressure (P) and airflow (F). To evaluate the technique's effectiveness, P was correlated with expected physiological responses. In volume-control (VC) mode, where tidal volume (V) is pre-determined, P is expected to be linked to P fluctuations. In contrast, during pressure-control (PC) mode, where P is held constant, P should correlate with V variations.
The study utilized data from 250 patients on invasive MV. The data included detailed recordings of P and F, 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, P and its pressure-time product (PPTP) were computed and correlated with P's pressure-time product (PPTP) and V, respectively.
There was a strong correlation of PPTP with PPTP in VC mode (R² = 0.91 [0.76, 0.96]; n = 17,648 epochs) and with V in PC mode (R² = 0.88 [0.74, 0.94]; n = 33,620 epochs), confirming the hypothesis. As expected, negligible correlations were observed between PPTP and V in VC mode (R² = 0.03) and between PPTP and PPTP in PC mode (R² = 0.06).
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.
本研究介绍了一种无创、自动量化机械通气（MV）期间呼吸肌做功（P）的方法。该方法的关键在于数值求解呼吸系统的运动方程，利用气道压力（P）和气流（F）的测量值。为了评估该技术的有效性，将 P 与预期的生理反应相关联。在容量控制（VC）模式下，潮气量（V）是预先确定的，预计 P 与 P 波动相关。相比之下，在压力控制（PC）模式下，P 保持恒定，P 应与 V 变化相关联。
本研究使用了 250 名接受有创 MV 的患者的数据。这些数据包括详细的 P 和 F 记录，以 31.25 Hz 的频率采样，并以 131.1 秒的时间段保存，每个时间段包含 34 至 41 次呼吸。该算法识别出 51268 个仅包含 VC 或 PC 模式下呼吸的时间段。在这些时间段内，计算了 P 和其压力-时间乘积（PPTP），并分别与 P 的压力-时间乘积（PPTP）和 V 相关联。
在 VC 模式下，PPTP 与 PPTP 之间存在很强的相关性（R²=0.91 [0.76, 0.96]；n=17648 个时间段），与 PC 模式下的 V 之间存在很强的相关性（R²=0.88 [0.74, 0.94]；n=33620 个时间段），验证了这一假设。正如预期的那样，在 VC 模式下，PPTP 与 V 之间的相关性可以忽略不计（R²=0.03），而在 PC 模式下，PPTP 与 PPTP 之间的相关性也可以忽略不计（R²=0.06）。
该研究支持通过气道信号分析无创地评估 MV 期间呼吸肌做功的可行性。需要进一步的研究来验证这种方法，并探讨其临床应用。
