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Perchiazzi G, Kawati R, Pellegrini M, Liangpansakul J, Colella R, Bollella P, Rangaiah P, Cannone A, Venkataramana DH, Perez M, Stramaglia S, Torsi L, Bellotti R, Augustine R. Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept. J Clin Monit Comput 2024:10.1007/s10877-024-01208-4. [PMID: 39162839 DOI: 10.1007/s10877-024-01208-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 08/07/2024] [Indexed: 08/21/2024]
Abstract
Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of metabolic acidosis. Data was collected from ten anesthetized, spontaneously breathing pigs divided randomly into two groups, one without dead space and the other with dead space at the beginning of the experiment. Each group underwent two equal sequences of pH lowering with pre-defined targets by continuous infusion of lactic acid. The inputs to ANNs were pH, ΔPaCO2 (variation of the arterial partial pressure of CO2), PaO2, and blood temperature which were sampled from the animal model. The output was the delta minute volume (ΔVM), (the change of minute volume as compared to the minute volume the animal had at the beginning of the experiment). The ANN performance was analyzed using mean squared error (MSE), linear regression, and the Bland-Altman (B-A) method. The animal experiment provided the necessary data to train the ANN. The best architecture of ANN had 17 intermediate neurons; the best performance of the finally trained ANN had a linear regression with R2 of 0.99, an MSE of 0.001 [L/min], a B-A analysis with bias ± standard deviation of 0.006 ± 0.039 [L/min]. ANNs can accurately estimate ΔVM using the same information that arrives at the respiratory centers. This performance makes them a promising component for the future development of closed-loop artificial ventilators.
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Affiliation(s)
- Gaetano Perchiazzi
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden.
- Hedenstierna Laboratoriet, Akademiska sjukhuset ing 40 3 tr, Uppsala, 75185, Sweden.
| | - Rafael Kawati
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden
| | - Mariangela Pellegrini
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden
| | - Jasmine Liangpansakul
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | | | - Paolo Bollella
- Department of Chemistry, University of Bari Aldo Moro, Bari, Italy
| | - Pramod Rangaiah
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
| | - Annamaria Cannone
- Department of Anaesthesia and Intensive Care, "Madonna delle Grazie" Hospital, Matera, Italy
| | | | - Mauricio Perez
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
| | - Luisa Torsi
- Department of Chemistry, University of Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
| | - Robin Augustine
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
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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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Patel B, Mumby S, Johnson N, Falaschetti E, Hansen J, Adcock I, McAuley D, Takata M, Karbing DS, Jabaudon M, Schellengowski P, Rees SE. Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol. Trials 2022; 23:47. [PMID: 35039050 PMCID: PMC8762446 DOI: 10.1186/s13063-021-05967-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/23/2021] [Indexed: 12/16/2022] Open
Abstract
Background The acute respiratory distress syndrome (ARDS) occurs in response to a variety of insults, and mechanical ventilation is life-saving in this setting, but ventilator-induced lung injury can also contribute to the morbidity and mortality in the condition. The Beacon Caresystem is a model-based bedside decision support system using mathematical models tuned to the individual patient’s physiology to advise on appropriate ventilator settings. Personalised approaches using individual patient description may be particularly advantageous in complex patients, including those who are difficult to mechanically ventilate and wean, in particular ARDS. Methods We will conduct a multi-centre international randomised, controlled, allocation concealed, open, pragmatic clinical trial to compare mechanical ventilation in ARDS patients following application of the Beacon Caresystem to that of standard routine care to investigate whether use of the system results in a reduction in driving pressure across all severities and phases of ARDS. Discussion Despite 20 years of clinical trial data showing significant improvements in ARDS mortality through mitigation of ventilator-induced lung injury, there remains a gap in its personalised application at the bedside. Importantly, the protective effects of higher positive end-expiratory pressure (PEEP) were noted only when there were associated decreases in driving pressure. Hence, the pressures set on the ventilator should be determined by the diseased lungs’ pressure-volume relationship which is often unknown or difficult to determine. Knowledge of extent of recruitable lung could improve the ventilator driving pressure. Hence, personalised management demands the application of mechanical ventilation according to the physiological state of the diseased lung at that time. Hence, there is significant rationale for the development of point-of-care clinical decision support systems which help personalise ventilatory strategy according to the current physiology. Furthermore, the potential for the application of the Beacon Caresystem to facilitate local and remote management of large numbers of ventilated patients (as seen during this COVID-19 pandemic) could change the outcome of mechanically ventilated patients during the course of this and future pandemics. Trial registration ClinicalTrials.gov identifier NCT04115709. Registered on 4 October 2019, version 4.0 Supplementary Information The online version contains supplementary material available at 10.1186/s13063-021-05967-2.
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Affiliation(s)
- Brijesh Patel
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Imperial College, London, UK.
| | - Sharon Mumby
- Airway Disease, National, Heart & Lung Institute, Imperial College, London, UK
| | - Nicholas Johnson
- Imperial Clinical Trials Unit, Stadium House, 68 Wood Lane, London, W12 7RH, UK
| | | | | | - Ian Adcock
- Airway Disease, National, Heart & Lung Institute, Imperial College, London, UK
| | - Danny McAuley
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University, Belfast, UK
| | - Masao Takata
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Imperial College, London, UK
| | - Dan S Karbing
- Respiratory and Critical Care Group, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Matthieu Jabaudon
- Department of Perioperative Medicine, University Hospital of Clermont-Ferrand, GReD, Université Clermont Auvergne, CNRS, INSERM, Clermont-Ferrand, France
| | - Peter Schellengowski
- Medical University of Vienna, Department of Medicine I, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Stephen E Rees
- Respiratory and Critical Care Group, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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Garfield B, Handslip R, Patel BV. Ventilator-Associated Lung Injury. ENCYCLOPEDIA OF RESPIRATORY MEDICINE 2022. [PMCID: PMC8128668 DOI: 10.1016/b978-0-08-102723-3.00237-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Ventilatory support, while life saving, can also cause or aggravate lung injury through several mechanisms which are encompassed within ventilator-associated lung injury (VALI). The important realizationin the acute respiratory distress syndrome that the “baby” lung resided in non-dependent areas led to the conceptualization of “lung rest” to reduce stress and strain to exposed alveolar units. We discuss concepts and mechanisms within VALI that ultimately induce maladaptive lung responses, as well as, current and future management strategies to detect and mitigate VALI at the bedside.
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Loo N, Chiew Y, Tan C, Mat-Nor M, Ralib A. A machine learning approach to assess magnitude of asynchrony breathing. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102505] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Clark AR, Burrowes KS, Tawhai MH. Integrative Computational Models of Lung Structure-Function Interactions. Compr Physiol 2021; 11:1501-1530. [PMID: 33577123 DOI: 10.1002/cphy.c200011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Anatomically based integrative models of the lung and their interaction with other key components of the respiratory system provide unique capabilities for investigating both normal and abnormal lung function. There is substantial regional variability in both structure and function within the normal lung, yet it remains capable of relatively efficient gas exchange by providing close matching of air delivery (ventilation) and blood delivery (perfusion) to regions of gas exchange tissue from the scale of the whole organ to the smallest continuous gas exchange units. This is despite remarkably different mechanisms of air and blood delivery, different fluid properties, and unique scale-dependent anatomical structures through which the blood and air are transported. This inherent heterogeneity can be exacerbated in the presence of disease or when the body is under stress. Current computational power and data availability allow for the construction of sophisticated data-driven integrative models that can mimic respiratory system structure, function, and response to intervention. Computational models do not have the same technical and ethical issues that can limit experimental studies and biomedical imaging, and if they are solidly grounded in physiology and physics they facilitate investigation of the underlying interaction between mechanisms that determine respiratory function and dysfunction, and to estimate otherwise difficult-to-access measures. © 2021 American Physiological Society. Compr Physiol 11:1501-1530, 2021.
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Affiliation(s)
- Alys R Clark
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kelly S Burrowes
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn H Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, 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 Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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Rees SE, Karbing DS. Determining the appropriate model complexity for patient-specific advice on mechanical ventilation. ACTA ACUST UNITED AC 2017; 62:183-198. [PMID: 27930361 DOI: 10.1515/bmt-2016-0061] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 09/29/2016] [Indexed: 11/15/2022]
Abstract
Mathematical physiological models can be applied in medical decision support systems. To do so requires consideration of the necessary model complexity. Models that simulate changes in the individual patient are required, meaning that models should have a complexity where parameters can be uniquely identified at the bedside from clinical data and where the models adequately represent the individual patient's (patho)physiology. This paper describes the models included in a system for providing decision support for mechanical ventilation. Models of pulmonary gas exchange, respiratory mechanics, acid-base, and respiratory control are described. The parameters of these models are presented along with the necessary clinical data required for their estimation and the parameter estimation process. In doing so, the paper highlights the need for simple, minimal models for application at the bedside, directed toward well-defined clinical problems.
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Affiliation(s)
- Stephen E Rees
- Respiratory and Critical Care group (rcare), Department of health science and Technology, Aalborg University
| | - Dan S Karbing
- Respiratory and Critical Care group (rcare), Department of health science and Technology, Aalborg University
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Rees SE, Karbing DS. Model-based advice for mechanical ventilation: From research (INVENT) to product (Beacon Caresystem). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5331-4. [PMID: 26737495 DOI: 10.1109/embc.2015.7319595] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper describes the structure and functionality of a physiological model-based system for providing advice on the settings of mechanical ventilation. Use of the system is presented with examples of patients on support and control modes of mechanical ventilation.
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A mathematical model approach quantifying patients' response to changes in mechanical ventilation: Evaluation in pressure support. J Crit Care 2015; 30:1008-15. [PMID: 26067844 DOI: 10.1016/j.jcrc.2015.05.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 04/30/2015] [Accepted: 05/12/2015] [Indexed: 12/19/2022]
Abstract
PURPOSE This article evaluates how mathematical models of gas exchange, blood acid-base status, chemical respiratory drive, and muscle function can describe the respiratory response of spontaneously breathing patients to different levels of pressure support. METHODS The models were evaluated with data from 12 patients ventilated in pressure support ventilation. Models were tuned with clinical data (arterial blood gas measurement, ventilation, and respiratory gas fractions of O2 and CO2) to describe each patient at the clinical level of pressure support. Patients were ventilated up to 5 different pressure support levels, for 15 minutes at each level to achieve steady-state conditions. Model-simulated values of respiratory frequency (fR), arterial pH (pHa), and end-tidal CO2 (FeCO2) were compared to measured values at each pressure support level. RESULTS Model simulations compared well to measured data with Bland-Altman bias and limits of agreement of fR of 0.7 ± 2.2 per minute, pHa of -0.0007 ± 0.019, and FeCO2 of -0.001 ± 0.003. CONCLUSION The models describe patients' fR, pHa, and FeCO2 response to changes in pressure support with low bias and narrow limits of agreement.
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