critical care; Digital Twin; identification; Mechanical ventilation; prediction; Virtual patient; Base function; Critical care; Exponentials; Functional residual capacities; Identification; Lung mechanics; Patient specific; Positive end expiratory pressures; Virtual patients; Control and Systems Engineering
Abstract :
[en] Current methodology around mechanically ventilating a patient is generalized and determining a patient-specific positive-end-expiratory-pressure (PEEP) is not standardized, causing problems not only around the efficiency of ventilation but the risk mitigation of such. The inclusion of recruitment maneuvers with subsequent PEEP in mechanical ventilation (MV) have proven highly effective in recruiting lung volume and preventing alveolar collapse. These recruitment maneuvers reopen collapsed alveolar by providing a temporary increase in airway pressure. Utilizing patient-specific, personalized monitoring enables more appropriate delivery of ventilation, with a model that will evolve as the patients condition does by continuously modelling the patients lung mechanics and altering ventilation predictions based on their condition. This research analysed pressure controlled ventilation (PCV) using data from the Maastricht trial. This mode of ventilation sets driving pressure to minimise overdistension in the lungs. In this study, functional residual capacity has been analysed using hysteresis loop analysis (HLA) and three separate potential basis functions, Exponential (EXP), Parabolic (PARA) and Cumulative (CUMU). These basis function sets were compared based on their performance in predicting functional residual capacity (VFRC). Additional components of lung mechanics have been previously analysed and compared, however this particular research prioritized the accuracy of VFRC predictions. Data provided spanned across 15 patients and 4 different baseline PEEP levels ranging from 6 cmH2O through to 12 cmH2O. Up to 6 prediction steps were analysed from each baseline PEEP to determine the accuracy across a range of case numbers yielding 293 cases. The results showed that all three basis function sets displayed the highest R2 values for cumulative prediction steps 1-6. EXP and CUMU sets both yielded a final R2 of 0.88 and the PARA set had a final R2 value of 0.87. However, the EXP set showed higher R2 over each prediction step, yielding it the most efficient as the higher PEEP levels are more clinically relevant for invasive mechanical ventilation (IMV).
Disciplines :
Mechanical engineering Cardiovascular & respiratory systems
Author, co-author :
Klei, Trudy Caljé-Van Der; Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, New Zealand
Sun, Qianhui ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Zhou, Cong; Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, New Zealand
Chase, Geoff; Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, New Zealand
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Language :
English
Title :
Functional Residual Capacity Predictions through Three Personalized Basis Functions in a Virtual Patient Model for PCV
Publication date :
September 2024
Event name :
12th IFAC Symposium on Biological and Medical Systems BMS 2024
International Federation of Automatic Control (IFAC) - Biological and Medical Systems, TC 8.2.; International Federation of Automatic Control (IFAC) - TC 1.1. Modelling, Identification and Signal Processing; International Federation of Automatic Control (IFAC) - TC 1.2. Adaptive and Learning Systems; International Federation of Automatic Control (IFAC) - TC 2.1. Control Design; International Federation of Automatic Control (IFAC) - TC 4.3. Robotics
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