Zhou, C.; School of Civil Aviation, Northwestern Polytechnical University, China, Department of Mechanical Engineering, University of Canterbury, New Zealand
Chase, J. G.; Department of Mechanical Engineering, University of Canterbury, New Zealand
Knopp, J.; Department of Mechanical Engineering, University of Canterbury, New Zealand
Sun, Q.; Department of Mechanical Engineering, University of Canterbury, New Zealand
Tawhai, M.; Auckland Bio-Engineering Institute (ABI), University of Auckland, New Zealand
Möller, K.; Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
Heines, S. J.; Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
Bergmans, D. C.; Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
Shaw, G. M.; Department of Intensive Care, Christchurch, New Zealand
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Language :
English
Title :
Virtual patients for mechanical ventilation in the intensive care unit
Publication date :
2021
Journal title :
Computer Methods and Programs in Biomedicine
ISSN :
0169-2607
eISSN :
1872-7565
Publisher :
Elsevier Ireland Ltd
Volume :
199
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
TEC - Tertiary Education Commission
Funding text :
This work was supported by the NZ Tertiary Education Com- mission (TEC) fund MedTech CoRE (Centre of Research Excellence; #3705718) and the NZ National Science Challenge 7, Science for Technology and Innovation (2019-S3-CRS). The authors also ac- knowledge support from the EU H2020 R&I programme (MSCA- RISE-2019 call) under grant agreement #872488 —DCPM.
Carney, D., DiRocco, J., Nieman, G., Dynamic alveolar mechanics and ventilator-induced lung injury. Crit. Care Med. 33 (2005), S122–S128.
Halter, J.M., Steinberg, J.M., Schiller, H.J., DaSilva, M., Gatto, L.A., Landas, S., Nieman, G.F., Positive end-expiratory pressure after a recruitment maneuver prevents both alveolar collapse and recruitment/derecruitment. Am. J. Respir. Crit. Care Med. 167 (2003), 1620–1626.
Hess, D.R., Recruitment maneuvers and PEEP titration. Respir. Care 60 (2015), 1688–1704.
Sundaresan, A., Chase, J.G., Positive end expiratory pressure in patients with acute respiratory distress syndrome–The past, present and future. Biomed. Signal Process. Control 7 (2012), 93–103.
Bos, L.D., Martin-Loeches, I., Schultz, M.J., ARDS: challenges in patient care and frontiers in research. Eur. Respir. Rev., 2018, 27.
Rouby, J.J., Lu, Q., Goldstein, I., Selecting the right level of positive end-expiratory pressure in patients with acute respiratory distress syndrome. Am. J. Respir. Crit. Care Med. 165 (2002), 1182–1186.
Network, A.R.D.S, Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. New Engl. J. Med. 342 (2000), 1301–1308.
Amato, M.B.P., Barbas, C.S.V., Medeiros, D.M., Magaldi, R.B., Schettino, G.P., Lorenzi-Filho, G., Kairalla, R.A., Deheinzelin, D., Munoz, C., Oliveira, R., Takagaki, T.Y., Carvalho, C.R.R, Effect of a protective-ventilation strategy on mortality in the acute respiratory distress syndrome. N Engl. J. Med. 338 (1998), 347–354.
Major, V.J., Chiew, Y.S., Shaw, G.M., Chase, J.G., Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation. Biomed. Eng. Onl., 17, 2018, 169, 10.1186/s12938-018-0599-9.
Malhotra, A., Low-tidal-volume ventilation in the acute respiratory distress syndrome. N Engl J Med 357 (2007), 1113–1120, 10.1056/NEJMct074213.
Bellani, G., Guerra, L., Musch, G., Zanella, A., Patroniti, N., Mauri, T., Messa, C., Pesenti, A., Lung regional metabolic activity and gas volume changes induced by tidal ventilation in patients with acute lung injury. Am. J. Respir. Crit. Care Med. 183 (2011), 1193–1199.
Terragni, P., Rosboch, G., Lisi, A., Viale, A., Ranieri, V.M., How respiratory system mechanics may help in minimising ventilator-induced lung injury in ARDS patients. Eur. Respir. J. 22 (2003), 15s–21s.
Amato, M.B., Meade, M.O., Slutsky, A.S., Brochard, L., Costa, E.L., Schoenfeld, D.A., Stewart, T.E., Briel, M., Talmor, D., Mercat, A., Driving pressure and survival in the acute respiratory distress syndrome. New Engl. J. Med. 372 (2015), 747–755.
Protti, A., Andreis, D.T., Monti, M., Santini, A., Sparacino, C.C., Langer, T., Votta, E., Gatti, S., Lombardi, L., Leopardi, O., Lung stress and strain during mechanical ventilation: any difference between statics and dynamics?. Crit. Care Med. 41 (2013), 1046–1055.
Jain, S.V., Kollisch-Singule, M., Satalin, J., Searles, Q., Dombert, L., Abdel-Razek, O., Yepuri, N., Leonard, A., Gruessner, A., Andrews, P., The role of high airway pressure and dynamic strain on ventilator-induced lung injury in a heterogeneous acute lung injury model. Intens. Care Med. Exp., 5, 2017, 25.
Briel, M., Meade, M., Mercat, A., Brower, R.G., Talmor, D., Walter, S.D., Slutsky, A.S., Pullenayegum, E., Zhou, Q., Cook, D., Higher vs lower positive end-expiratory pressure in patients with acute lung injury and acute respiratory distress syndrome: systematic review and meta-analysis. Jama 303 (2010), 865–873.
Cavalcanti, A.B., Suzumura, É.A., Laranjeira, L.N., de Moraes Paisani, D., Damiani, L.P., Guimarães, H.P., Romano, E.R., de Moraes Regenga, M., Taniguchi, L.N.T., Teixeira, C, Effect of lung recruitment and titrated positive end-expiratory pressure (PEEP) vs low PEEP on mortality in patients with acute respiratory distress syndrome: a randomized clinical trial. Jama 318 (2017), 1335–1345.
Chase, J.G., Preiser, J.C., Dickson, J.L., Pironet, A., Chiew, Y.S., Pretty, C.G., Shaw, G.M., Benyo, B., Moeller, K., Safaei, S., Tawhai, M., Hunter, P., Desaive, T., 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. Onl., 17, 2018, 24, 10.1186/s12938-018-0455-y.
Tawhai, M., Clark, A., Chase, J., The lung physiome and virtual patient models: from morphometry to clinical translation. Morphologie 103 (2019), 131–138.
Dasta, J.F., McLaughlin, T.P., Mody, S.H., Piech, C.T., Daily cost of an intensive care unit day: the contribution of mechanical ventilation. Crit. Care Med. 33 (2005), 1266–1271.
Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., Gilbert, A., Fernandes, J.F., Bukhari, H.A., Wajdan, A., The ‘Digital Twin'to enable the vision of precision cardiology. Eur. Heart J., 2020.
Chase, J.G., Preiser, J.-C., Dickson, J.L., Pironet, A., Chiew, Y.S., Pretty, C.G., Shaw, G.M., Benyo, B., Moeller, K., Safaei, S., 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. Onl. 17 (2018), 1–29.
Langdon, R., Docherty, P.D., Schranz, C., Chase, J.G., Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics. Biomed. Eng. Onl., 16, 2017, 126, 10.1186/s12938-017-0415-y.
Langdon, R., Docherty, P.D., Chiew, Y.S., Chase, J.G., Extrapolation of a non-linear autoregressive model of pulmonary mechanics. Math. Biosci. 284 (2017), 32–39, 10.1016/j.mbs.2016.08.001.
Bates, J.H.T., Lung Mechanics: An Inverse Modeling Approach. 2009, Cambridge University Press.
Bates, J.H.T., Engineering in medicine and biology society. Ann. Int. Conf. IEEE, 2009, 170–172.
Morton, S.E., Knopp, J.L., Chase, J.G., Möller, K., Docherty, P., Shaw, G.M., Tawhai, M., Predictive virtual patient modelling of mechanical ventilation: impact of recruitment function. Ann. Biomed. Eng. 47 (2019), 1626–1641.
Morton, S.E., Dickson, J., Chase, J.G., Docherty, P., Desaive, T., Howe, S.L., Shaw, G.M., Tawhai, M., A virtual patient model for mechanical ventilation. Comput. Methods Progr. Biomed. 165 (2018), 77–87 https://doi.org/10.1016/j.cmpb.2018.08.004.
Morton, S.E., Knopp, J.L., Tawhai, M.H., Docherty, P., Heines, S.J., Bergmans, D.C., Möller, K., Chase, J.G., Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation. Comput. Methods Progr. Biomed., 2020, 105696.
Hamlington, K.L., Smith, B.J., Allen, G.B., Bates, J.H., Predicting ventilator-induced lung injury using a lung injury cost function. J. Appl. Physiol. 121 (2016), 106–114.
Ma, B., Bates, J., Modeling the complex dynamics of derecruitment in the lung. Ann. Biomed. Eng. 38 (2010), 3466–3477, 10.1007/s10439-010-0095-2.
Ma, B., Bates, J.H., Continuum vs. spring network models of airway-parenchymal interdependence. J. Appl. Physiol. 113 (2012), 124–129.
Mellenthin, M.M., Seong, S.A., Roy, G.S., Bartolák-Suki, E., Hamlington, K.L., Bates, J.H., Smith, B.J., Using injury cost functions from a predictive single-compartment model to assess the severity of mechanical ventilator-induced lung injuries. J. Appl. Physiol. 127 (2019), 58–70.
Bates, J.H., Smith, B.J., Ventilator-induced lung injury and lung mechanics. Ann. Transl. Med., 6, 2018.
Sun, Q., Zhou, C., Chase, J.G., Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation. Biomed. Signal Process. Control, 2020, 102003.
Bates, J.H., Allen, G.B., The estimation of lung mechanics parameters in the presence of pathology: a theoretical analysis. Ann. Biomed. Eng. 34 (2006), 384–392.
Steimle, K.L., Mogensen, M.L., Karbing, D.S., de la Serna, J.B., Andreassen, S., A model of ventilation of the healthy human lung. Comput. Methods Progr. Biomed. 101 (2011), 144–155.
Tawhai, M.H., Pullan, A., Hunter, P., Generation of an anatomically based three-dimensional model of the conducting airways. Ann. Biomed. Eng. 28 (2000), 793–802.
Tawhai, M.H., Hunter, P., Tschirren, J., Reinhardt, J., McLennan, G., Hoffman, E.A., CT-based geometry analysis and finite element models of the human and ovine bronchial tree. J. Appl. Physiol. 97 (2004), 2310–2321, 10.1152/japplphysiol.00520.2004.
Burrowes, K., De Backer, J., Smallwood, R., Sterk, P., Gut, I., Wirix-Speetjens, R., Siddiqui, S., Owers-Bradley, J., Wild, J., Maier, D., Multi-scale computational models of the airways to unravel the pathophysiological mechanisms in asthma and chronic obstructive pulmonary disease (AirPROM). Interface Focus, 3, 2013, 20120057.
Lauzon, A.-M., Bates, J.H., Donovan, G., Tawhai, M., Sneyd, J., Sanderson, M., A multi-scale approach to airway hyperresponsiveness: from molecule to organ. Front. Physiol., 3, 2012, 191.
Burrowes, K., Swan, A., Warren, N., Tawhai, M., Towards a virtual lung: multi-scale, multi-physics modelling of the pulmonary system. Philosoph. Trans. R. Soc. A: Math. Phys. Eng. Sci. 366 (2008), 3247–3263.
Morton, S.E., Knopp, J.L., Chase, J.G., Docherty, P., Howe, S.L., Möller, K., Shaw, G.M., Tawhai, M., Optimising mechanical ventilation through model-based methods and automation. Ann. Rev. Control, 2019 https://doi.org/10.1016/j.arcontrol.2019.05.001.
Zhou, C., Chase, J.G., A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring. Struct. Health Monitor., 2020, 1475921720920641.
Docherty, P.D., Chase, J.G., Lotz, T.F., Desaive, T., A graphical method for practical and informative identifiability analyses of physiological models: a case study of insulin kinetics and sensitivity. Biomed. Eng. Onl., 10, 2011, 39, 10.1186/1475-925X-10-39.
Schranz, C., Docherty, P.D., Chiew, Y.S., Chase, J.G., Moller, K., Structural identifiability and practical applicability of an alveolar recruitment model for ARDS patients. IEEE Trans. Biomed. Eng. 59 (2012), 3396–3404, 10.1109/TBME.2012.2216526.
Docherty, P.D., Schranz, C., Chiew, Y.-S., Möller, K., Chase, J.G., Reformulation of the pressure-dependent recruitment model (PRM) of respiratory mechanics. Biomed. Signal Process. Control 12 (2014), 47–53.
Schranz, C., Kretschmer, J., Möller, K., 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, 5220–5223 IEEE.
Docherty, P.D., Schranz, C., Chase, J.G., Chiew, Y.S., Moller, K., Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: a case-study of pulmonary mechanics. Comput. Methods Progr. Biomed. 114 (2014), e70–e78, 10.1016/j.cmpb.2013.06.017.
Zhou, C., Chase, J.G., Rodgers, G.W., Tomlinson, H., Xu, C., Physical parameter identification of structural systems with hysteretic pinching. Comput.-Aid. Civ. Infrastruct. Eng. 30 (2015), 247–262.
Zhou, C., Chase, J.G., Rodgers, G.W., Iihoshi, C., Damage assessment by stiffness identification for a full-scale three-story steel moment resisting frame building subjected to a sequence of earthquake excitations. Bull. Earthq. Eng. 15 (2017), 5393–5412.
Zhou, C., Chase, J.G., Rodgers, G.W., Degradation evaluation of lateral story stiffness using HLA-based deep learning networks. Adv. Eng. Inf. 39 (2019), 259–268.
Zhou, C., Chase, J.G., Rodgers, G.W., Support vector machines for automated modelling of nonlinear structures using health monitoring results. Mech. Syst. Signal Process., 149, 2021, 107201.
Peters, R.M., The energy cost (work) of breathing. Ann. Thorac. Surg. 7 (1969), 51–67.
Stahl, C.A., Moller, K., Schumann, S., Kuhlen, R., Sydow, M., Putensen, C., Guttmann, J., Dynamic versus static respiratory mechanics in acute lung injury and acute respiratory distress syndrome. Crit. Care Med. 34 (2006), 2090–2098.
Tsolaki, V., Siempos, I., Magira, E., Kokkoris, S., Zakynthinos, G.E., Zakynthinos, S., PEEP levels in COVID-19 pneumonia. Crit. Care, 24, 2020, 303, 10.1186/s13054-020-03049-4.
Kim, K.T., Morton, S., Howe, S., Chiew, Y.S., Knopp, J.L., Docherty, P., Pretty, C., Desaive, T., Benyo, B., Szlavecz, A., Model-based PEEP titration versus standard practice in mechanical ventilation: a randomised controlled trial. Trials, 21, 2020, 130.
Szlavecz, A., Chiew, Y.S., Redmond, D., Beatson, A., Glassenbury, D., Corbett, S., Major, V., Pretty, C., Shaw, G.M., Benyo, B., Desaive, T., Chase, J.G., 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, 13, 2014, 140, 10.1186/1475-925X-13-140.
Zhou, C., Chase, J.G., Rodgers, G.W., Xu, C., Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring. Mech. Syst. Signal Process. 84 (2017), 384–398.
Morton, S.E., Development of Virtual Patients for use in Mechanical Ventilation Doctor of Philosophy thesis. 2019, University of Canterbury.
Caironi, P., Carlesso, E., Cressoni, M., Chiumello, D., Moerer, O., Chiurazzi, C., Brioni, M., Bottino, N., Lazzerini, M., Bugedo, G., Lung recruitability is better estimated according to the Berlin definition of acute respiratory distress syndrome at standard 5 cm H2O rather than higher positive end-expiratory pressure: a retrospective cohort study. Crit. Care Med. 43 (2015), 781–790.
de Matos, G.F., Stanzani, F., Passos, R.H., Fontana, M.F., Albaladejo, R., Caserta, R.E., Santos, D.C., Borges, J.B., Amato, M.B., Barbas, C.S., How large is the lung recruitability in early acute respiratory distress syndrome: a prospective case series of patients monitored by computed tomography. Crit. Care, 16, 2012, R4.
Pan, C., Chen, L., Lu, C., Zhang, W., Xia, J.-A., Sklar, M.C., Du, B., Brochard, L., Qiu, H., Lung recruitability in COVID-19–associated acute respiratory distress syndrome: a single-center observational study. Am. J. Respir. Crit. Care Med. 201 (2020), 1294–1297.
Chiumello, D., Marino, A., Lazzerini, M., Caspani, M., Gattinoni, L., Lung recruitability in ARDS H1N1 patients. Intens. Care Med. 36 (2010), 1791–1792.
Costa, E.L., Borges, J.B., Melo, A., Suarez-Sipmann, F., Toufen, C., Bohm, S.H., Amato, M.B., Bedside estimation of recruitable alveolar collapse and hyperdistension by electrical impedance tomography. Intens. Care Med. 35 (2009), 1132–1137.
Baber, T.T., Noori, M.N., Random vibration of degrading, pinching systems. J. Eng. Mech. 111 (1985), 1010–1026.
Baber, T., Noori, M., Modelling general hysteresis behaviour and random vibration application. J. Vibration, Acoust. Stress,Reliab. Des. 108 (1986), 411–420.
Ismail, M., Ikhouane, F., Rodellar, J., The hysteresis Bouc-Wen model, a survey. Arch. Comput. Methods Eng. 16 (2009), 161–188.
Zhou, C., Chase, J.G., Rodgers, G.W., Xu, C., Tomlinson, H., Overall damage identification of flag-shaped hysteresis systems under seismic excitation. Smart Struct. Syst. 16 (2015), 163–181.
Zhou, C., Chase, J.G., Rodgers, G.W., Kuang, A., Gutschmidt, S., Xu, C., Performance evaluation of cwh base isolated building during two major earthquakes in christchurch. Bull. N. Z. Soc. Earthq. 48 (2015), 264–273.
Zhou, C., Chase, J.G., Rodgers, G.W., Efficient hysteresis loop analysis-based damage identification of a reinforced concrete frame structure over multiple events. J. Civ. Struct. Health Monitor., 2017, 1–16.
Chase, J.G., Desaive, T., Bohe, J., Cnop, M., De Block, C., Gunst, J., Hovorka, R., Kalfon, P., Krinsley, J., Renard, E., Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas. Crit. Care, 22, 2018, 182.