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Lin N, Fan CJ, Li FY, Luo HR, Li YM, Duggal A, Benn BS, Yan T, Pan LL, Lai ZM. Research trends and hotspots in the field of electrical impedance tomography for mechanical ventilation: a bibliometric analysis. J Thorac Dis 2024; 16:2070-2081. [PMID: 38617762 PMCID: PMC11009609 DOI: 10.21037/jtd-24-98] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/01/2024] [Indexed: 04/16/2024]
Abstract
Background Electrical impedance tomography (EIT) is a relatively recent functional imaging technique that is both noninvasive and radiation free. EIT measures the associated voltage when a weak current is applied to the surface of the human body to determine the distribution of electrical resistance within tissues. We performed a bibliometrics-based review to explore the geographic hotspots of current research and future trends developing in the field of EIT for mechanical ventilation. Methods The Web of Science database was searched from its inception to June 25, 2023. CiteSpace software was used to visualize and analyze the relevant literature and identify the most impactful literature, trends, and hotspots. Results 363 articles describing EIT use in mechanical ventilation were identified. A fluctuating growth in the number of publications was observed from 1998 to 2023. Germany had the highest number of articles (n=154), followed by Italy (n=53) and China (n=52). A cluster analysis of keyword co-occurrence revealed that "titration", "ventilator-related lung injury", and "oxygenation" were the most actively researched terms associated with the use of EIT in mechanically ventilated patients. Conclusions Significant progress has been made in EIT research for mechanical ventilation. EIT research is limited to a small number of countries with a present research focus on the prevention and treatment of ventilator-related lung injury, oxygenation status, and prone ventilation. These topics are expected to remain research hotspots in the future.
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Affiliation(s)
- Nan Lin
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chong-Jiu Fan
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Fu-Yuan Li
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Hui-Rong Luo
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yu-Mei Li
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Abhijit Duggal
- Department of Critical Care Medicine, Respiratory Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
- Department of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Bryan S. Benn
- Pulmonary Department, Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ting Yan
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ling-Li Pan
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhong-Meng Lai
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
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Caljé-van der Klei T, Sun Q, Chase JG, Zhou C, Tawhai MH, Knopp JL, Möller K, Heines SJ, Bergmans DC, Shaw GM. Pulmonary response prediction through personalized basis functions in a virtual patient model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107988. [PMID: 38171168 DOI: 10.1016/j.cmpb.2023.107988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/16/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND AND OBJECTIVE Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings. METHODS This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps. RESULTS The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95. CONCLUSIONS The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.
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Affiliation(s)
- Trudy Caljé-van der Klei
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Qianhui Sun
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand; University of Liége, Liége, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Cong Zhou
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Merryn H Tawhai
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Jennifer L Knopp
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Knut Möller
- Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Serge J Heines
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
| | - Dennis C Bergmans
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
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3
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Bosso G, Sansone G, Papillo M, Giaquinto A, Orefice S, Allegorico E, Serra C, Minerva V, Mercurio V, Cannavacciuolo F, Dello Vicario F, Porta G, Pagano A, Numis FG. Lung ultrasound-guided PEEP titration in COVID-19 patients treated with CPAP. J Basic Clin Physiol Pharmacol 2023; 34:677-682. [PMID: 37463298 DOI: 10.1515/jbcpp-2023-0165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 07/20/2023]
Abstract
OBJECTIVES An increasing number of COVID-19 patients were treated with continuous positive airways pressure (CPAP). To evaluate the clinical effects of personalized positive end-expiratory pressure (PEEP) compared to standard fixed PEEP in COVID-19 patients requiring CPAP. METHODS This is a single center, prospective, randomized clinical study. Sixty-three COVID-19 patients with hypoxemic respiratory failure and bilateral pneumonia were randomized in two Groups: Group A received CPAP with fixed PEEP of 10 cm H2O, Group B performed the "PEEP trial", that consists in the evaluation of best PEEP defined as the PEEP value that precedes the echographic appearance of "lung pulse" determining a PaO2/FiO2 increase. Primary outcome was composite in-hospital mortality + intubation, secondary outcome was the percentage increase of PaO2/FiO2. As safety indicator, the incidence of pneumothorax was collected. RESULTS Thirty-two patients were enrolled in Group A and 31 in Group B. The two groups were comparable for clinical characteristics and laboratory parameters. The primary outcome occurred in 36 (57.1 %) patients: 23 (71.8 %) in Group A and 13 (41.9 %) in Group B (p<0.01). Mortality was higher in Group A (53.1 vs. 19.3 %, p<0.01), while intubation rate was comparable between groups. Group B showed a higher PaO2/FiO2 increase than Group A (34.9 vs. 13.1 %, p<0.01). Five cases of pneumothorax were reported in Group A, none in Group B. CONCLUSIONS Lung ultrasound-guided PEEP trial is associated with lower mortality in COVID-19 patients treated with CPAP. Identifying the best PEEP is useful to increase oxygenation and reduce the incidence of complications.
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Affiliation(s)
- Giorgio Bosso
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Gennaro Sansone
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Martina Papillo
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Alessandro Giaquinto
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Silvia Orefice
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Enrico Allegorico
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Claudia Serra
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Valentina Minerva
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Valentina Mercurio
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | | | - Ferdinando Dello Vicario
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Giovanni Porta
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Antonio Pagano
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
| | - Fabio Giuliano Numis
- Department of Emergency Medicine, COVID Care Unit, Santa Maria delle Grazie Hospital, Pozzuoli, Naples, Italy
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Warnaar RSP, Mulder MP, Fresiello L, Cornet AD, Heunks LMA, Donker DW, Oppersma E. Computational physiological models for individualised mechanical ventilation: a systematic literature review focussing on quality, availability, and clinical readiness. Crit Care 2023; 27:268. [PMID: 37415253 PMCID: PMC10327331 DOI: 10.1186/s13054-023-04549-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/24/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Individualised optimisation of mechanical ventilation (MV) remains cumbersome in modern intensive care medicine. Computerised, model-based support systems could help in tailoring MV settings to the complex interactions between MV and the individual patient's pathophysiology. Therefore, we critically appraised the current literature on computational physiological models (CPMs) for individualised MV in the ICU with a focus on quality, availability, and clinical readiness. METHODS A systematic literature search was conducted on 13 February 2023 in MEDLINE ALL, Embase, Scopus and Web of Science to identify original research articles describing CPMs for individualised MV in the ICU. The modelled physiological phenomena, clinical applications, and level of readiness were extracted. The quality of model design reporting and validation was assessed based on American Society of Mechanical Engineers (ASME) standards. RESULTS Out of 6,333 unique publications, 149 publications were included. CPMs emerged since the 1970s with increasing levels of readiness. A total of 131 articles (88%) modelled lung mechanics, mainly for lung-protective ventilation. Gas exchange (n = 38, 26%) and gas homeostasis (n = 36, 24%) models had mainly applications in controlling oxygenation and ventilation. Respiratory muscle function models for diaphragm-protective ventilation emerged recently (n = 3, 2%). Three randomised controlled trials were initiated, applying the Beacon and CURE Soft models for gas exchange and PEEP optimisation. Overall, model design and quality were reported unsatisfactory in 93% and 21% of the articles, respectively. CONCLUSION CPMs are advancing towards clinical application as an explainable tool to optimise individualised MV. To promote clinical application, dedicated standards for quality assessment and model reporting are essential. Trial registration number PROSPERO- CRD42022301715 . Registered 05 February, 2022.
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Affiliation(s)
- R S P Warnaar
- Cardiovascular and Respiratory Physiology, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands.
| | - M P Mulder
- Cardiovascular and Respiratory Physiology, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands
| | - L Fresiello
- Cardiovascular and Respiratory Physiology, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands
| | - A D Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands
| | - L M A Heunks
- Department of Intensive Care, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - D W Donker
- Cardiovascular and Respiratory Physiology, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands
- Intensive Care Centre, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - E Oppersma
- Cardiovascular and Respiratory Physiology, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands
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Mohamed M, Khalil M, Diab H, EL-Maraghy A. Utility of lung ultrasound in adjustment of the initial mechanical ventilation settings in patients with acute respiratory distress syndrome. EGYPTIAN JOURNAL OF CHEST DISEASES AND TUBERCULOSIS 2023. [DOI: 10.4103/ecdt.ecdt_35_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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6
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Ang CYS, Lee JWW, Chiew YS, Wang X, Tan CP, Cove ME, Nor MBM, Zhou C, Desaive T, Chase JG. Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107146. [PMID: 36191352 DOI: 10.1016/j.cmpb.2022.107146] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/17/2022] [Accepted: 09/17/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment. METHODS The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated. RESULTS This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol. CONCLUSION The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.
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Affiliation(s)
| | - Jay Wing Wai Lee
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | | | - Xin Wang
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Chee Pin Tan
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Matthew E Cove
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, 25200, Malaysia
| | - Cong Zhou
- Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA In-Silico Medicine, University of Liege, Liege, Belgium
| | - J Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
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7
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Tsolaki V, Zakynthinos GE. Simulation to minimise patient self-inflicted lung injury: are we almost there? Br J Anaesth 2022; 129:150-153. [PMID: 35729011 PMCID: PMC9551385 DOI: 10.1016/j.bja.2022.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/21/2022] [Accepted: 05/12/2022] [Indexed: 11/25/2022] Open
Abstract
Computational modelling has been used to enlighten pathophysiological issues in patients with acute respiratory distress syndrome (ARDS) using a sophisticated, integrated cardiopulmonary model. COVID-19 ARDS is a pathophysiologically distinct entity characterised by dissociation between impairment in gas exchange and respiratory system mechanics, especially in the early stages of ARDS. Weaver and colleagues used computational modelling to elucidate factors contributing to generation of patient self-inflicted lung injury, and evaluated the effects of various spontaneous respiratory efforts with different oxygenation and ventilatory support modes. Their findings indicate that mechanical forces generated in the lung parenchyma are only counterbalanced when the respiratory support mode reduces the intensity of respiratory efforts.
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Affiliation(s)
- Vasiliki Tsolaki
- Department of Intensive Care Medicine, General University of Larissa, University of Thessaly, Faculty of Medicine, Larissa, Thessaly, Greece.
| | - George E Zakynthinos
- Department of Intensive Care Medicine, General University of Larissa, University of Thessaly, Faculty of Medicine, Larissa, Thessaly, Greece
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8
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Hannon DM, Mistry S, Das A, Saffaran S, Laffey JG, Brook BS, Hardman JG, Bates DG. Modeling Mechanical Ventilation In Silico-Potential and Pitfalls. Semin Respir Crit Care Med 2022; 43:335-345. [PMID: 35451046 DOI: 10.1055/s-0042-1744446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.
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Affiliation(s)
- David M Hannon
- Anesthesia and Intensive Care Medicine, School of Medicine, NUI Galway, Ireland
| | - Sonal Mistry
- School of Engineering, University of Warwick, Coventry, United Kingdom
| | - Anup Das
- School of Engineering, University of Warwick, Coventry, United Kingdom
| | - Sina Saffaran
- Faculty of Engineering Science, University College London, London, United Kingdom
| | - John G Laffey
- Anesthesia and Intensive Care Medicine, School of Medicine, NUI Galway, Ireland
| | - Bindi S Brook
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Jonathan G Hardman
- Anesthesia and Critical Care, Injury Inflammation and Recovery Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Declan G Bates
- School of Engineering, University of Warwick, Coventry, United Kingdom
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9
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Zhou C, Chase JG, Sun Q, Knopp J, Tawhai MH, Desaive T, Möller K, Shaw GM, Chiew YS, Benyo B. Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model. Biomed Eng Online 2022; 21:16. [PMID: 35255922 PMCID: PMC8900099 DOI: 10.1186/s12938-022-00986-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 02/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. METHODS Changes in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. RESULTS Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony. CONCLUSIONS Experimental test-lung validation demonstrates the method's reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.
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Affiliation(s)
- Cong Zhou
- School of Civil Aviation & Yangtze River Delta Research Institute, Northwestern Polytechnical University, Xian, China
- Dept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - J. Geoffrey Chase
- Dept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Qianhui Sun
- Dept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer Knopp
- Dept of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Merryn H. Tawhai
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA-In Silico Medicine, Institute of Physics, University of Liege, Liege, Belgium
| | - Knut Möller
- Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Geoffrey M. Shaw
- Dept of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | | | - Balazs Benyo
- Dept of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
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10
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Lee JWW, Chiew YS, Wang X, Mat Nor MB, Chase JG, Desaive T. Stochastic integrated model-based protocol for volume-controlled ventilation setting. Biomed Eng Online 2022; 21:13. [PMID: 35148759 PMCID: PMC8832735 DOI: 10.1186/s12938-022-00981-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 01/21/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration. METHODS A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each. RESULTS From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol. CONCLUSIONS Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.
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Affiliation(s)
- Jay Wing Wai Lee
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor Malaysia
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor Malaysia
| | - Xin Wang
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor Malaysia
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia
| | - J. Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA In-Silico Medicine, University of Liege, Liege, Belgium
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11
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Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103367] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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12
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Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model. Comput Biol Med 2021; 141:105022. [PMID: 34801244 DOI: 10.1016/j.compbiomed.2021.105022] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND OBJECTIVE Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks. METHODS This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH2O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH2O ahead covering 6x2cmH2O PEEP steps. RESULTS Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH2O, compared with 4.76cmH2O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R2 = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH2O. Predicted dynamic functional residual capacity (Vfrc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R2 of 0.84. CONCLUSIONS Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.
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Knopp JL, Chase JG, Kim KT, Shaw GM. Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106300. [PMID: 34348200 DOI: 10.1016/j.cmpb.2021.106300] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/17/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort. METHODS Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH2O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results. RESULTS Average per-patient root-mean-squared model fit error was (median [interquartile range, IQR]) 0.9 [0.6-1.3] cmH2O, and average per-patient median Pp was -3.9 [-4.5- -3.0] cmH2O, with range -7.9 - -1.9 cmH2O. Per-patient E and R were 16.4 [13.6-21.8] cmH2O/L and 9.2 [6.4-13.1] cmH2O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R2=0.25-0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure. CONCLUSIONS The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.
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Affiliation(s)
- Jennifer L Knopp
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Kyeong Tae Kim
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Private Bag 4710, Christchurch, New Zealand
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14
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Zhou C, Chase JG, Knopp J, Sun Q, Tawhai M, Möller K, Heines SJ, Bergmans DC, Shaw GM, Desaive T. Virtual patients for mechanical ventilation in the intensive care unit. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105912. [PMID: 33360683 DOI: 10.1016/j.cmpb.2020.105912] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/12/2020] [Indexed: 06/12/2023]
Abstract
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.
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Affiliation(s)
- Cong Zhou
- School of Civil Aviation, Northwestern Polytechnical University, China; Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, New Zealand.
| | - Jennifer Knopp
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Qianhui Sun
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Merryn Tawhai
- Auckland Bio-Engineering Institute (ABI), University of Auckland, New Zealand
| | - Knut Möller
- Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Serge J Heines
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, the Netherlands
| | - Dennis C Bergmans
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, the Netherlands
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA-In Silico Medicine, Institute of Physics, University of Liege, Liege, Belgium
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Lee JWW, Chiew YS, Wang X, Tan CP, Mat Nor MB, Damanhuri NS, Chase JG. Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients. Ann Biomed Eng 2021; 49:3280-3295. [PMID: 34435276 PMCID: PMC8386681 DOI: 10.1007/s10439-021-02854-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/13/2021] [Indexed: 02/07/2023]
Abstract
While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.
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Affiliation(s)
- Jay Wing Wai Lee
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Yeong Shiong Chiew
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Xin Wang
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Chee Pin Tan
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Mohd Basri Mat Nor
- grid.440422.40000 0001 0807 5654Kulliyah of Medicine, International Islamic University Malaysia, 25200 Kuantan, Pahang Malaysia
| | - Nor Salwa Damanhuri
- grid.412259.90000 0001 2161 1343Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Bukit Bertajam, Pulau Pinang Malaysia
| | - J. Geoffrey Chase
- grid.21006.350000 0001 2179 4063Center of Bioengineering, University of Canterbury, Christchurch, 8041 New Zealand
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