Virtual patients for mechanical ventilation in the intensive care unit

Digital twins; Hysteresis loop analysis; Hysteresis model; Lung mechanics; Mechanical ventilation; Virtual patient; Biological organs; Forecasting; Hysteresis; Hysteresis loops; Intensive care units; Mechanics; Physiological models; Pressure control; Ventilation; Accurate prediction; Accurate response; Nonlinear changes; Nonlinear hysteresis; Patient specific; Virtual patient models; Virtual patients; Patient treatment; Computer Simulation; Humans; Respiration, Artificial; Respiratory Mechanics; Ventilator-Induced Lung Injury

Abstract :

[en] Background: Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV. Methods: An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers. Results: Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH2O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, Vfrc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and Vfrc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and Vfrc errors are relatively small. Conclusions: Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy. © 2020

Disciplines :

Anesthesia & intensive care

Author, co-author :

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.

Available on ORBi :

since 09 February 2022

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Bibliography

Lobo, B., Hermosa, C., Abella, A., Gordo, F., Electrical impedance tomography. Ann. Transl. Med., 6, 2018.
Gajic, O., Dara, S.I., Mendez, J.L., Adesanya, A.O., Festic, E., Caples, S.M., Rana, R., Sauver, J.L.S., Lymp, J.F., Afessa, B, Ventilator-associated lung injury in patients without acute lung injury at the onset of mechanical ventilation. Crit. Care Med. 32 (2004), 1817–1824.
Villar, J., Ventilator or physician-induced lung injury. Minerva Anestesiol., 71, 2005, 255 - 258.
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.
Jawde, S.B., Walkey, A.J., Majumdar, A., O'Connor, G.T., Smith, B.J., Bates, J.H., Lutchen, K.R., Suki, B., tracking respiratory mechanics around natural breathing rates via variable ventilation. Sci. Rep. 10 (2020), 1–12.
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.
Tawhai, M.H., Bates, J.H.T, Multi-scale lung modeling. J. Appl. Physiol. 110 (2011), 1466–1472, 10.1152/japplphysiol.01289.2010.
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.
Chopra, A.K., Dynamics of structures. (Pearson education upper saddle river. NJ, 2012.
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.