Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation
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Knut Möller | J Geoffrey Chase | Merryn H Tawhai | Sophie E Morton | Jennifer L Knopp | Paul Docherty | Serge J Heines | Dennis C Bergmans | J. Chase | K. Möller | M. Tawhai | D. Bergmans | P. Docherty | J. Knopp | S. Heines | S. Morton | Serge J Heines | Dennis C. Bergmans | J. Chase
[1] C Schranz,et al. Model-based setting of inspiratory pressure and respiratory rate in pressure-controlled ventilation , 2014, Physiological measurement.
[2] Anake Pomprapa,et al. A mathematical model for carbon dioxide elimination: an insight for tuning mechanical ventilation , 2013, European Journal of Applied Physiology.
[3] G Saumon,et al. Role of tidal volume, FRC, and end-inspiratory volume in the development of pulmonary edema following mechanical ventilation. , 1993, The American review of respiratory disease.
[4] Christopher E. Hann,et al. Dynamic functional residual capacity can be estimated using a stress-strain approach , 2011, Comput. Methods Programs Biomed..
[5] D. Dreyfuss,et al. Barotrauma is volutrauma, but which volume is the one responsible? , 2005, Intensive Care Medicine.
[6] Fikret Berkes,et al. Communities and social enterprises in the age of globalization , 2007 .
[7] Umberto Lucangelo,et al. Lung mechanics at the bedside: make it simple , 2007, Current opinion in critical care.
[8] Arthur S Slutsky,et al. Ventilator-induced lung injury. , 2013, The New England journal of medicine.
[9] Stephen E. Rees,et al. Model-based advice for mechanical ventilation: From research (INVENT) to product (Beacon Caresystem) , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[10] Anake Pomprapa,et al. Automatic protective ventilation using the ARDSNet protocol with the additional monitoring of electrical impedance tomography , 2014, Critical Care.
[11] S. Lapinsky,et al. Bench-to-bedside review: Recruitment and recruiting maneuvers , 2004, Critical care.
[12] Edmund Koch,et al. Simulating physiological interactions in a hybrid system of mathematical models , 2014, Journal of Clinical Monitoring and Computing.
[13] Anake Pomprapa,et al. The dawn of physiological closed-loop ventilation—a review , 2020, Critical Care.
[14] Yeong Shiong Chiew,et al. Assessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient , 2016, Comput. Methods Programs Biomed..
[15] J. Geoffrey Chase,et al. Positive end expiratory pressure in patients with acute respiratory distress syndrome - The past, present and future , 2012, Biomed. Signal Process. Control..
[16] Steen Andreassen,et al. A model of ventilation of the healthy human lung , 2011, Comput. Methods Programs Biomed..
[17] J. Chase,et al. Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function , 2019, Annals of Biomedical Engineering.
[18] Matthias Briel,et al. Higher vs lower positive end-expiratory pressure in patients with acute lung injury and acute respiratory distress syndrome: systematic review and meta-analysis. , 2010, JAMA.
[19] C. Carvalho,et al. Effect of a protective-ventilation strategy on mortality in the acute respiratory distress syndrome. , 1998, The New England journal of medicine.
[20] Jason H. T. Bates,et al. The Estimation of Lung Mechanics Parameters in the Presence of Pathology: A Theoretical Analysis , 2006, Annals of Biomedical Engineering.
[21] J. Geoffrey Chase,et al. A virtual patient model for mechanical ventilation , 2018, Comput. Methods Programs Biomed..
[22] Yeong Shiong Chiew,et al. Model-based PEEP optimisation in mechanical ventilation , 2011, Biomedical engineering online.
[23] D. Karbing,et al. Model-based optimization of peep, a strategy and its implementation , 2015, Intensive Care Medicine Experimental.
[24] H. Abbona,et al. Modos controlados por presión versus volumen en la ventilación mecánica invasiva , 2013 .
[25] E. Moltchanova,et al. Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation , 2015 .
[26] P. Suter,et al. Effect of tidal volume and positive end-expiratory pressure on compliance during mechanical ventilation. , 1978, Chest.
[27] Zhanqi Zhao,et al. PEEP titration guided by ventilation homogeneity: a feasibility study using electrical impedance tomography , 2010, Critical care.
[28] A. Ben-Tal,et al. Computational models for the study of heart–lung interactions in mammals , 2012, Wiley interdisciplinary reviews. Systems biology and medicine.
[29] J. Geoffrey Chase,et al. Optimising mechanical ventilation through model-based methods and automation , 2019, Annual Reviews in Control.
[30] S. Leonhardt,et al. Closed-loop mechanical ventilation for lung injury: a novel physiological-feedback mode following the principles of the open lung concept , 2017, Journal of Clinical Monitoring and Computing.
[31] Jason H. T. Bates,et al. Lung Mechanics: An Inverse Modeling Approach , 2009 .
[32] Yeong Shiong Chiew,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 , 2018, BioMedical Engineering OnLine.
[33] Jason H T Bates,et al. Multi-scale lung modeling. , 2011, Journal of applied physiology.
[34] F. Gordo-Vidal,et al. Pressure versus volume controlled modes in invasive mechanical ventilation , 2013 .
[35] Yeong Shiong Chiew,et al. Visualisation of time-varying respiratory system elastance in experimental ARDS animal models , 2014, BMC Pulmonary Medicine.
[36] Steen Andreassen,et al. Prospective evaluation of a decision support system for setting inspired oxygen in intensive care patients. , 2010, Journal of critical care.
[37] Stephen E. Rees,et al. Determining the appropriate model complexity for patient-specific advice on mechanical ventilation , 2017, Biomedizinische Technik. Biomedical engineering.
[38] D. Dreyfuss,et al. Ventilator-induced lung injury: lessons from experimental studies. , 1998, American journal of respiratory and critical care medicine.
[39] J. Geoffrey Chase,et al. Biomedical engineer’s guide to the clinical aspects of intensive care mechanical ventilation , 2018, BioMedical Engineering OnLine.
[40] S. Spadaro,et al. Prospective evaluation of a decision support system providing advice on ventilator settings of: inspiratory oxygen, delivered pressure or volume, frequency and peep , 2015, Intensive Care Medicine Experimental.
[41] Stephan H. Böhm,et al. Use of dynamic compliance for open lung positive end‐expiratory pressure titration in an experimental study , 2007, Critical care medicine.
[42] J. Chase,et al. Validation of a Model-based Method for Estimating Functional Volume Gains during Recruitment Manoeuvres in Mechanical Ventilation , 2018 .
[43] Stephen E. Rees,et al. The Intelligent Ventilator (INVENT) project: The role of mathematical models in translating physiological knowledge into clinical practice , 2011, Comput. Methods Programs Biomed..
[44] J. Geoffrey Chase,et al. Structural Identifiability and Practical Applicability of an Alveolar Recruitment Model for ARDS Patients , 2012, IEEE Transactions on Biomedical Engineering.