Critical care; Decision-making; Mechanical ventilation; Model-based protocol; Respiratory mechanics; Stochastic modelling; Humans; Retrospective Studies; Respiration, Artificial; Respiratory System; % reductions; Decisions makings; Model based approach; Model-based OPC; Patient specific; Stochastic-modeling; Stochastics; Radiological and Ultrasound Technology; Biomaterials; Biomedical Engineering; Radiology, Nuclear Medicine and Imaging; General Medicine
Abstract :
[en] BACKGROUND AND OBJECTIVE: Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration.
METHODS: A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each.
RESULTS: From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol.
CONCLUSIONS: Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.
Disciplines :
Anesthesia & intensive care
Author, co-author :
Lee, Jay Wing Wai; School of Engineering, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
Chiew, Yeong Shiong; School of Engineering, Monash University Malaysia, Subang Jaya, Selangor, Malaysia. chiew.yeong.shiong@monash.edu
Wang, Xin; School of Engineering, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
Mat Nor, Mohd Basri; Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia
Chase, J Geoffrey; Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
Desaive, Thomas ; Université de Liège - ULiège > GIGA > GIGA In silico medicine
Language :
English
Title :
Stochastic integrated model-based protocol for volume-controlled ventilation setting.
The authors would like to thank the Ministry of Energy, Science, Technology, Environment and Climate Change (MESTECC) research grant (IF0219I1060), MedTech Centre of Research Expertise, University of Canterbury, New Zealand, the New Zealand Ministry of Business Innovation and Employment (MBIE) Covid Innovation Action Fund (CIAF), and Monash University Malaysia Advance Engineering Platform (AEP) for supporting this research.
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