Non-invasive over-distension measurements: data driven vs model-based

[1]  J. Chase,et al.  Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model , 2021, Comput. Biol. Medicine.

[2]  P. Pelosi,et al.  Individualized versus Fixed Positive End-expiratory Pressure for Intraoperative Mechanical Ventilation in Obese Patients: A Secondary Analysis , 2021, Anesthesiology.

[3]  Arthur S Slutsky,et al.  Effect of Lowering Tidal Volume on Mortality in ARDS Varies with Respiratory System Elastance. , 2021, American journal of respiratory and critical care medicine.

[4]  J. Chase,et al.  A Nonlinear Hysteretic Model for Automated Prediction of Lung Mechanics during Mechanical Ventilation , 2020, IFAC-PapersOnLine.

[5]  Geoffrey M. Shaw,et al.  Virtual patients for mechanical ventilation in the intensive care unit , 2020, Comput. Methods Programs Biomed..

[6]  J. Geoffrey Chase,et al.  Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation , 2020, Biomed. Signal Process. Control..

[7]  J Geoffrey Chase,et al.  A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring , 2020, Structural Health Monitoring.

[8]  J. Geoffrey Chase,et al.  Biomedical engineer’s guide to the clinical aspects of intensive care mechanical ventilation , 2018, BioMedical Engineering OnLine.

[9]  Bradford J. Smith,et al.  Ventilator-induced lung injury and lung mechanics. , 2018, Annals of translational medicine.

[10]  Haldun Akoglu,et al.  User's guide to correlation coefficients , 2018, Turkish journal of emergency medicine.

[11]  Xuan He,et al.  Stress Index Can Be Accurately and Reliably Assessed by Visually Inspecting Ventilator Waveforms , 2018, Respiratory Care.

[12]  Christa Boer,et al.  Correlation Coefficients: Appropriate Use and Interpretation , 2018, Anesthesia and analgesia.

[13]  F. Rapetti,et al.  Driving pressure and mechanical power: new targets for VILI prevention. , 2017, Annals of translational medicine.

[14]  G. Bugedo,et al.  Does the use of high PEEP levels prevent ventilator-induced lung injury? , 2017, Revista Brasileira de terapia intensiva.

[15]  Steffen Leonhardt,et al.  Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNt stuDy group , 2016, Thorax.

[16]  C. Newth,et al.  Monitoring of Children With Pediatric Acute Respiratory Distress Syndrome: Proceedings From the Pediatric Acute Lung Injury Consensus Conference , 2015, Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

[17]  Cong Zhou,et al.  Physical Parameter Identification of Structural Systems with Hysteretic Pinching , 2015, Comput. Aided Civ. Infrastructure Eng..

[18]  Arthur S Slutsky,et al.  Driving pressure and survival in the acute respiratory distress syndrome. , 2015, The New England journal of medicine.

[19]  Laura Piqueras,et al.  Adjusting tidal volume to stress index in an open lung condition optimizes ventilation and prevents overdistension in an experimental model of lung injury and reduced chest wall compliance , 2015, Critical Care.

[20]  Yeong Shiong Chiew,et al.  Visualisation of time-varying respiratory system elastance in experimental ARDS animal models , 2014, BMC Pulmonary Medicine.

[21]  Salvatore Grasso,et al.  Accuracy of Plateau Pressure and Stress Index to Identify Injurious Ventilation in Patients with Acute Respiratory Distress Syndrome , 2013, Anesthesiology.

[22]  P. Pelosi,et al.  Monitoring respiration: what the clinician needs to know. , 2013, Best practice & research. Clinical anaesthesiology.

[23]  A. Carvalho,et al.  Detection of Tidal Recruitment/Overdistension in Lung-Healthy Mechanically Ventilated Patients Under General Anesthesia , 2013, Anesthesia and analgesia.

[24]  A. Zangrillo,et al.  ECMO criteria for influenza A (H1N1)-associated ARDS: role of transpulmonary pressure , 2012, Intensive Care Medicine.

[25]  Yeong Shiong Chiew,et al.  Model-based PEEP optimisation in mechanical ventilation , 2011, Biomedical engineering online.

[26]  Zhanqi Zhao,et al.  Ventilation inhomogeneity is one criterion among many in multidimensional PEEP titration , 2010, Critical care.

[27]  E. Deitch,et al.  Low Tidal Volume and High Positive End-Expiratory Pressure Mechanical Ventilation Results in Increased Inflammation and Ventilator-Associated Lung Injury in Normal Lungs , 2010, Anesthesia and analgesia.

[28]  Zhanqi Zhao,et al.  PEEP titration guided by ventilation homogeneity: a feasibility study using electrical impedance tomography , 2010, Critical care.

[29]  Bruce Ratner.  The correlation coefficient: Its values range between +1/−1, or do they? , 2009 .

[30]  Thea Koch,et al.  Ability of dynamic airway pressure curve profile and elastance for positive end-expiratory pressure titration , 2008, Intensive Care Medicine.

[31]  R. Khemani,et al.  Respiratory monitoring during mechanical ventilation , 2007 .

[32]  Knut Möller,et al.  Dynamic versus static respiratory mechanics in acute lung injury and acute respiratory distress syndrome , 2006, Critical care medicine.

[33]  J. Guttmann,et al.  Alveolar recruitment in acute lung injury. , 2006, British journal of anaesthesia.

[34]  L. Goodman,et al.  What has computed tomography taught us about the acute respiratory distress syndrome? , 2001, American journal of respiratory and critical care medicine.

[35]  F. Leclerc,et al.  Overdistension in ventilated children , 2001, Critical care.

[36]  M J Tobin,et al.  How is mechanical ventilation employed in the intensive care unit? An international utilization review. , 2000, American journal of respiratory and critical care medicine.

[37]  A. Rossi,et al.  Impact of positive end-expiratory pressure on chest wall and lung pressure-volume curve in acute respiratory failure. , 1997, American journal of respiratory and critical care medicine.

[38]  P. Sly,et al.  Influence of nonlinearities on estimates of respiratory mechanics using multilinear regression analysis. , 1994, Journal of applied physiology.

[39]  Al-Jurf As.  Positive end-expiratory pressure. , 1981, Surgery, gynecology & obstetrics.

[40]  David W. Kaczka,et al.  Respiratory System Mechanics During Low Versus High Positive End-Expiratory Pressure in Open Abdominal Surgery: A Substudy of PROVHILO Randomized Controlled Trial , 2018, Anesthesia and analgesia.

[41]  Cong Zhou,et al.  Silicone phantom validation of breast cancer tumor detection using nominal stiffness identification in digital imaging elasto-tomography (DIET) , 2018, Biomed. Signal Process. Control..

[42]  Arthur S Slutsky,et al.  Acute Respiratory Distress Syndrome The Berlin Definition , 2012 .

[43]  J. Fisher,et al.  Identifying lung overdistention during mechanical ventilation by using volume‐pressure loops , 1988, Pediatric pulmonology.