Co-ventilation; Decision support; Model-based method; Parallel ventilation; Humans; Respiration, Artificial; SARS-CoV-2; Tidal Volume; Ventilators, Mechanical; COVID-19; Decision supports; Lung model; Matchings; Mechanical; Mechanical ventilation; Model-based OPC; Radiological and Ultrasound Technology; Biomaterials; Biomedical Engineering; Radiology, Nuclear Medicine and Imaging; General Medicine
Abstract :
[en] BACKGROUND: Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV.
METHODS: The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation.
RESULTS: The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care.
CONCLUSION: This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.
Disciplines :
Anesthesia & intensive care
Author, co-author :
Wong, Jin Wai; School of Engineering, Monash University Malaysia, Selangor, Malaysia
Chiew, Yeong Shiong; School of Engineering, Monash University Malaysia, Selangor, Malaysia. chiew.yeong.shiong@monash.edu
Desaive, Thomas ; Université de Liège - ULiège > GIGA > GIGA In silico medicine
Chase, J Geoffrey; Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
Language :
English
Title :
Model-based patient matching for in-parallel pressure-controlled ventilation.
The authors would like to thank the 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.
Penarredonda JL. Covid-19: the race to build coronavirus ventilators. 2020. 2020.
Cohen IG, Crespo AM, White DB. Potential legal liability for withdrawing or withholding ventilators during COVID-19: assessing the risks and identifying needed reforms. JAMA. 2020;323(19):1901–2. DOI: 10.1001/jama.2020.5442
Ricci M, Gallina P. COVID-19—immunity from prosecution for physicians forced to allocate scarce resources: the Italian perspective. Crit Care. 2020;24(1):295. DOI: 10.1186/s13054-020-03028-9
Truog RD, Mitchell C, Daley GQ. The toughest triage—allocating ventilators in a pandemic. N Engl J Med. 2020;382:1973–5. DOI: 10.1056/NEJMp2005689
Neyman G, Irvin CB. A single ventilator for multiple simulated patients to meet disaster surge. Soc Acad Emerg Med. 2006;13:1246–9. DOI: 10.1197/j.aem.2006.05.009
Paladino L, et al. Increasing ventilator surge capacity in disasters: ventilation of four adult-human-sized sheep on a single ventilator with a modified circuit. Resuscitation. 2008;77(1):121–6. DOI: 10.1016/j.resuscitation.2007.10.016
Smith R, Brown J. Simultaneous ventilation of two healthy subjects with a single ventilator. Resuscitation. 2009;80(9):1087. DOI: 10.1016/j.resuscitation.2009.05.018
Beitler JR, et al. Ventilator sharing during an acute shortage caused by the COVID-19 Pandemic. Am J Respir Crit Care Med. 2020;202:600–4. DOI: 10.1164/rccm.202005-1586LE
de Jongh FH, et al. Ventilating two patients with one ventilator: technical setup and laboratory testing. ERJ Open Res. 2020. 10.1183/23120541.00256-2020. DOI: 10.1183/23120541.00256-2020
Srinivasan S, et al. Individualized system for augmenting ventilator efficacy (iSAVE): a rapidly deployable system to expand ventilator capacity. BioRxiv. 2020;13:1246.
SCCM, et al. Consensus statement on multiple patients per ventilator. SCCM Website. https://www.sccm.org/Disaster/Joint-Statement-on-Multiple-Patients-Per-Ventilato; 2020.
Chase JG, et al. In-parallel ventilator sharing during an acute shortage: too much risk for a wider uptake. Am J Respir Crit Care Med. 2020;202(9):1316–7. DOI: 10.1164/rccm.202006-2420LE
Branson RD, et al. Use of a single ventilator to support 4 patients: laboratory evaluation of a limited concept. Respir Care. 2012;57(3):399–403. DOI: 10.4187/respcare.01236
Lee JWW, et al., Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients. Ann Biomed Eng, 2021:1–16.
Chatburn RL, Branson RD, Hatipoğlu U. Multiplex ventilation: a simulation-based study of ventilating 2 patients with a single ventilator. Respir Care. 2020;65(7):920–31. DOI: 10.4187/respcare.07882
Major V, et al. Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction. Biomed Signal Process Control. 2016;23:1–9. DOI: 10.1016/j.bspc.2015.07.007
Chiew YS, et al. Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction. Comput Methods Programs Biomed. 2018;157:217–24. DOI: 10.1016/j.cmpb.2018.02.007
Gutierrez G, et al. Automatic detection of patient-ventilator asynchrony by spectral analysis of airway flow. Crit Care. 2011;15(4):R167. DOI: 10.1186/cc10309
Kannangara DO, et al. Estimating the true respiratory mechanics during asynchronous pressure controlled ventilation. Biomed Signal Process Control. 2016;30:70–8. DOI: 10.1016/j.bspc.2016.06.014
Major VJ, et al. Biomedical engineer’s guide to the clinical aspects of intensive care mechanical ventilation. Biomed Eng Online. 2018;17(1):169. DOI: 10.1186/s12938-018-0599-9
Chiew YS, et al. Feasibility of titrating PEEP to minimum elastance for mechanically ventilated patients. Pilot Feasibility Stud. 2015;1:9. DOI: 10.1186/s40814-015-0006-2
Ng QA, et al. Network data acquisition and monitoring system for intensive care mechanical ventilation treatment. IEEE Access. 2021;9:91859–73. DOI: 10.1109/ACCESS.2021.3092194
Chao DC, Scheinhorn DJ. Barotrauma vs volutrauma. Chest J. 1996;109(4):1127–8. DOI: 10.1378/chest.109.4.1127
Ricard JD, Dreyfuss D, Saumon G. Ventilator-induced lung injury. Eur Respir J. 2003;22(42_suppl):2s–9.
van Drunen E, et al. Expiratory model-based method to monitor ARDS disease state. Biomed Eng Online. 2013;12(1):57. DOI: 10.1186/1475-925X-12-57
Morton SE, et al. Optimising mechanical ventilation through model-based methods and automation. Ann Rev Control. 2019;48:369–82. DOI: 10.1016/j.arcontrol.2019.05.001
Szlavecz A, et al. The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management. Biomed Eng Online. 2014;13(1):140. DOI: 10.1186/1475-925X-13-140
Rees, S.E., The Intelligent Ventilator (INVENT) project: The role of mathematical models in translating physiological knowledge into clinical practice. Computer Methods Programs Biomed 2011;104, Supplement 1(0):S1–S29.
Ng QA, et al. Mechanical ventilation monitoring: development of a network data acquisition system. IFAC-PapersOnLine. 2020;53(2):15916–21. DOI: 10.1016/j.ifacol.2020.12.290
Chase JG, et al. Safe doubling of ventilator capacity: a last resort proposal for last resorts. Crit Care. 2020;24(1):222. DOI: 10.1186/s13054-020-02945-z
Gattinoni L, et al. Lung recruitment in patients with the acute respiratory distress syndrome. N Engl J Med. 2006;354(17):1775–86. DOI: 10.1056/NEJMoa052052
Gattinoni L, et al. Positive end-expiratory pressure: how to set it at the individual level. Ann Transl Med. 2017;5(14):288. DOI: 10.21037/atm.2017.06.64
O’Driscoll B, et al. British Thoracic Society Guideline for oxygen use in adults in healthcare and emergency settings. BMJ Open Respir Res. 2017;4(1):e000170. DOI: 10.1136/bmjresp-2016-000170
Poor H. Basics of mechanical ventilation. Berlin: Springer; 2018. DOI: 10.1007/978-3-319-89981-7
Network TARDS. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med. 2000;342(18):1301–8. DOI: 10.1056/NEJM200005043421801
Chiew YS, et al. Model-based PEEP optimisation in mechanical ventilation. Biomed Eng Online. 2011;10(1):111. DOI: 10.1186/1475-925X-10-111
Morton SE, et al. Predictive virtual patient modelling of mechanical ventilation: impact of recruitment function. Ann Biomed Eng. 2019;47(7):1626–41. DOI: 10.1007/s10439-019-02253-w
Kim KT, et al. Quantifying neonatal pulmonary mechanics in mechanical ventilation. Biomed Signal Process Control. 2019;52:206–17. DOI: 10.1016/j.bspc.2019.04.015
Bates JH. Lung mechanics: an inverse modeling approach. Cambridge: Cambridge University Press; 2009. DOI: 10.1017/CBO9780511627156
Chase JG, et al. Next-generation, personalised, model-based critical care medicine: a state-of-the-art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online. 2018;17:24. DOI: 10.1186/s12938-018-0455-y
Chase JG, et al. Digital twins in critical care: what, when, how, where, why? IFAC-PapersOnLine. 2021;54(15):310–5. DOI: 10.1016/j.ifacol.2021.10.274
Arunachalam GR, et al. Virtual mechanical ventilation protocol—a model-based method to determine MV settings. IFAC-PapersOnLine. 2020;53(2):16119–24. DOI: 10.1016/j.ifacol.2020.12.432
Lee JWW, et al. Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients. Comput Methods Progr Biomed. 2022;214:106577. DOI: 10.1016/j.cmpb.2021.106577
Arnal J-M, et al. Parameters for simulation of adult subjects during mechanical ventilation. Respir Care. 2018;63(2):158–68. DOI: 10.4187/respcare.05775