• J Clin Monit Comput · Apr 2023

    Non-invasive over-distension measurements: data driven vs model-based.

    • Qianhui Sun, J Geoffrey Chase, Cong Zhou, Merryn H Tawhai, Jennifer L Knopp, Knut Möller, and Geoffrey M Shaw.
    • Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand. qianhui.sun@pg.canterbury.ac.nz.
    • J Clin Monit Comput. 2023 Apr 1; 37 (2): 389398389-398.

    AbstractClinical measurements offer bedside monitoring aiming to minimise unintended over-distension, but have limitations and cannot be predicted for changes in mechanical ventilation (MV) settings and are only available in certain MV modes. This study introduces a non-invasive, real-time over-distension measurement, which is robust, predictable, and more intuitive than current methods. The proposed over-distension measurement, denoted as OD, is compared with the clinically proven stress index (SI). Correlation is analysed via R2 and Spearman rs. The OD safe range corresponding to the unit-less SI safe range (0.95-1.05) is calibrated by sensitivity and specificity test. Validation is fulfilled with 19 acute respiratory distress syndrome (ARDS) patients data (196 cases), including assessment across ARDS severity. Overall correlation between OD and SI yielded R2 = 0.76 and Spearman rs = 0.89. Correlation is higher considering only moderate and severe ARDS patients. Calibration of OD to SI yields a safe range defined: 0 ≤ OD ≤ 0.8 cmH2O. The proposed OD offers an efficient, general, real-time measurement of patient-specific lung mechanics, which is more intuitive and robust than SI. OD eliminates the limitations of SI in MV mode and its less intuitive lung status value. Finally, OD can be accurately predicted for new ventilator settings via its foundation in a validated predictive personalized lung mechanics model. Therefore, OD offers potential clinical value over current clinical methods.© 2022. The Author(s), under exclusive licence to Springer Nature B.V.

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