• Hyun-Lim Yang, Seong-A Park, Hong Yeul Lee, Hyeonhoon Lee, and Ho-Geol Ryu.
• Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea; Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
• J Crit Care. 2024 Sep 23; 85: 154920154920.

PurposeEstimating tidal volume (VT) from electrocardiography (ECG) can be quite useful during deep sedation or spinal anesthesia since it eliminates the need for additional monitoring of ventilation. This study aims to validate and compare VT estimation methodologies based on ECG-derived respiration (EDR) using real-world clinical data.Materials And MethodsWe analyzed data from 90 critically ill patients for general analysis and two critically ill patients for constrained analysis. EDR signals were generated from ECG data, and VT was estimated using impedance-based respiration waveforms. Linear regression and deep learning models, both subject-independent and subject-specific, were evaluated using mean absolute error and Pearson correlation.ResultsThere was a strong short-term correlation between VT and the respiration waveform (r = 0.78 and 0.96), which weakened over longer periods (r = 0.23 and - 0.16). VT prediction models performed poorly in the general population (R2 = 0.17) but showed satisfactory performance in two constrained patient records using measured respiration waveforms (R2 = 0.84 to 0.94).ConclusionAlthough EDR-based VT estimation is promising, current methodologies are limited by noisy ICU ECG signals, but controlled environment data showed significant short-term correlations with measured respiration waveforms. Future studies should develop reliable EDR extraction procedures and improve predictive models to broaden clinical applications.Copyright © 2024. Published by Elsevier Inc.

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