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Guy EF, Flett IL, Clifton JA, Calje-van der Klei T, Chen R, Knopp JL, Möller K, Chase JG. Pulmonary function testing dataset of pressure and flow, dynamic circumference, heart rate, and aeration monitoring. Data Brief 2024; 54:110386. [PMID: 38646196 PMCID: PMC11033070 DOI: 10.1016/j.dib.2024.110386] [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: 03/20/2024] [Revised: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 04/23/2024] Open
Abstract
Respiratory data was collected from 20 subjects, with an even sex distribution, in the low-risk clinical unit at the University of Canterbury. Ethical consent for this trial was granted by the University of Canterbury Human Research Ethics Committee (Ref: HREC 2023/30/LR-PS). Respiratory data were collected, for each subject, over three tests consisting of: 1) increasing set PEEP from a starting point of ZEEP using a CPAP machine; 2) test 1 repeated with two simulated apnoea's (breath holds) at each set PEEP; and 3) three forced expiratory manoeuvres at ZEEP. Data were collected using a custom pressure and flow sensor device, ECG, PPG, Garmin HRM Dual heartrate belt, and a Dräeger PulmoVista 500 Electrical Impedance Tomography (EIT) machine. Subject demographic data was also collected prior to the trial, in a questionnaire, with measurement equipment available. These data aim to inform the development of pulmonary mechanics models and titration algorithms.
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Affiliation(s)
- Ella F.S. Guy
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Isaac L. Flett
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jaimey A. Clifton
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | | | - Rongqing Chen
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Jennifer L. Knopp
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Knut Möller
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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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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Guy EF, Clifton JA, Knopp JL, Holder-Pearson LR, Chase JG. Respiratory pressure and split flow data collection device with rapid occlusion attachment. HARDWAREX 2023; 16:e00489. [PMID: 38058767 PMCID: PMC10696101 DOI: 10.1016/j.ohx.2023.e00489] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 11/10/2023] [Indexed: 12/08/2023]
Abstract
Respiratory model-based methods require datasets containing enough dynamics to ensure model identifiability for development and validation. Rapid expiratory occlusion has been used to identify elastance and resistance within a single breath. Currently accepted practice for rapid expiratory occlusion involves a 100 ms occlusion of the expiratory pathway. This article presents a low-cost modular rapid shutter attachment to enable identification of passive respiratory mechanics. Shuttering faster than 100 ms creates rapid expiratory occlusion without the added dynamics of muscular response to shutter closure, by eliminating perceived expiratory blockage via high shutter speed. The shutter attachment fits onto a non-invasive venturi-based flow meter with separated inspiratory and expiratory pathways, established using one-way valves. Overall, these elements allow comprehensive collection of respiratory pressure and flow datasets with relatively very rapid expiratory occlusion.
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Affiliation(s)
- Ella F.S. Guy
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jaimey A. Clifton
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L. Knopp
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Lui R. Holder-Pearson
- Department of Electrical Engineering, University of Canterbury, Christchurch, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Chen Y, Zhang K, Zhou C, Chase JG, Hu Z. Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses. Biomed Eng Online 2023; 22:102. [PMID: 37875890 PMCID: PMC10598979 DOI: 10.1186/s12938-023-01165-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/16/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop. METHODS Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors. RESULTS The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment. CONCLUSIONS The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.
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Affiliation(s)
- Yuhong Chen
- Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Kun Zhang
- Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Cong Zhou
- Department of Mechanical Engineering & Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
- Taicang Yangtze River Delta Research Institute, Suzhou, China.
| | - J Geoffrey Chase
- Department of Mechanical Engineering & Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Zhenjie Hu
- Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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5
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Ang CYS, Chiew YS, Wang X, Ooi EH, Nor MBM, Cove ME, Chase JG. Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107728. [PMID: 37531693 DOI: 10.1016/j.cmpb.2023.107728] [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: 04/12/2023] [Revised: 06/27/2023] [Accepted: 07/19/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model. METHODS A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles. RESULTS A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation. CONCLUSION VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.
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Affiliation(s)
| | | | - Xin Wang
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Ean Hin Ooi
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Pahang, Malaysia
| | - Matthew E Cove
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Hospital, Singapore
| | - J Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
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Agarwal A, Marion J, Nagy P, Robinson M, Walkey A, Sevransky J. How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials. Crit Care Clin 2023; 39:733-749. [PMID: 37704337 DOI: 10.1016/j.ccc.2023.03.006] [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] [Indexed: 09/15/2023]
Abstract
Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.
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Affiliation(s)
- Ankita Agarwal
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Emory Critical Care Center, Emory Healthcare, Atlanta, GA, USA
| | | | - Paul Nagy
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew Robinson
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Allan Walkey
- Department of Medicine - Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Jonathan Sevransky
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Emory Critical Care Center, Emory Healthcare, Atlanta, GA, USA.
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Guy EFS, Knopp JL, Lerios T, Chase JG. Airflow and dynamic circumference of abdomen and thorax for adults at varied continuous positive airway pressure ventilation settings and breath rates. Sci Data 2023; 10:481. [PMID: 37481681 PMCID: PMC10363111 DOI: 10.1038/s41597-023-02326-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/22/2023] [Indexed: 07/24/2023] Open
Abstract
Continuous positive airway pressure (CPAP) ventilation is a commonly prescribed respiratory therapy providing positive end-expiratory pressure (PEEP) to assist breathing and prevent airway collapse. Setting PEEP is highly debated and it is thus primarily titrated based on symptoms of excessive or insufficient support. However, titration periods are clinician intensive and can result in barotrauma or under-oxygenation during the process. Developing model-based methods to more efficiently personalise CPAP therapy based on patient-specific response requires clinical data of lung/CPAP interactions. To this end, a trial was conducted to establish a dataset of healthy subjects lung/CPAP interaction. Pressure, flow, and tidal volume were recorded alongside secondary measures of dynamic chest and abdominal circumference, to better validate model outcomes and assess breathing modes, muscular recruitment, and effort. N = 30 subjects (15 male; 15 female) were included. Self-reported asthmatics and smokers/vapers were included, offering a preliminary assessment of any potential differences in response to CPAP from lung stiffness changes in these scenarios. Additional demographics associated with lung function (sex, age, height, and weight) were also recorded.
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Affiliation(s)
- Ella F S Guy
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Jennifer L Knopp
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Theodore Lerios
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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8
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An identifiable model of lung mechanics to diagnose and monitor COPD. Comput Biol Med 2023; 152:106430. [PMID: 36543001 DOI: 10.1016/j.compbiomed.2022.106430] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/23/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Current methods to diagnose and monitor COPD employ spirometry as the gold standard to identify lung function reduction with reduced forced expiratory volume (FEV1)/vital capacity (VC) ratio. Current methods utilise linear assumptions regarding airway resistance, where nonlinear resistance modelling may provide rapid insight into patient specific condition and disease progression. This study examines model-based expiratory resistance in healthy lungs and those with progressively more severe COPD. METHODS Healthy and COPD pressure (P)[cmH2O] and flow (Q)[L/s] data is obtained from the literature, and 5 intermediate levels of COPD and responses are created to simulate COPD progression and assess model-based metric resolution. Linear and nonlinear single compartment models are used to identify changes in inspiratory (R1,insp) and linear (R1,exp)/nonlinear (R2Φ) expiratory resistance with disease severity and over the course of expiration. RESULTS R1,insp increases from 2.1 to 7.3 cmH2O/L/s, R1,exp increases from 2.4 to 10.0 cmH2O/L/s with COPD severity. Nonlinear R2Φ increases (mean R2Φ: 2.5 cmH2O/L/s (healthy) to 24.4 cmH2O/L/s (COPD)), with increasing end-expiratory nonlinearity as COPD severity increases. CONCLUSION Expiratory resistance is increasingly highly nonlinear with COPD severity. These results show a simple, nonlinear model can capture fundamental COPD dynamics and progression from regular breathing data, and such an approach may be useful for patient-specific diagnosis and monitoring.
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Victor MH, Maximo MROA, Matsumoto MMS, Pereira SM, Tucci MR. Mixed-integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3668. [PMID: 36509708 DOI: 10.1002/cnm.3668] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 11/01/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
Information about respiratory mechanics such as resistance, elastance, and muscular pressure is important to mitigate ventilator-induced lung injury. Particularly during pressure support ventilation, the available options to quantify breathing effort and calculate respiratory system mechanics are often invasive or complex. We herein propose a robust and flexible estimation of respiratory effort better than current methods. We developed a method for non-invasively estimating breathing effort using only flow and pressure signals. Mixed-integer quadratic programming (MIQP) was employed, and the binary variables were the switching moments of the respiratory effort waveform. Mathematical constraints, based on ventilation physiology, were set for some variables to restrict feasible solutions. Simulated and patient data were used to verify our method, and the results were compared to an established estimation methodology. Our algorithm successfully estimated the respiratory effort, resistance, and elastance of the respiratory system, resulting in more robust performance and faster solver times than a previously proposed algorithm that used quadratic programming (QP) techniques. In a numerical simulation benchmark, the worst-case errors for resistance and elastance were 25% and 23% for QP versus <0.1% and <0.1% for MIQP, whose solver times were 4.7 s and 0.5 s, respectively. This approach can estimate several breathing effort profiles and identify the respiratory system's mechanical properties in invasively ventilated critically ill patients.
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Affiliation(s)
- Marcus H Victor
- Medical Electrical Devices Laboratory (LabMed), Electronics Engineering, Aeronautics Institute of Technology, São Paulo, Brazil
- Divisão de Pneumologia, Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Marcos R O A Maximo
- Medical Electrical Devices Laboratory (LabMed), Electronics Engineering, Aeronautics Institute of Technology, São Paulo, Brazil
- Autonomous Computational Systems Lab (LAB-SCA), Computer Science Division, Aeronautics Institute of Technology, São Paulo, Brazil
| | - Monica M S Matsumoto
- Medical Electrical Devices Laboratory (LabMed), Electronics Engineering, Aeronautics Institute of Technology, São Paulo, Brazil
| | - Sérgio M Pereira
- Divisão de Pneumologia, Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Anesthesia and Pain Medicine, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Mauro R Tucci
- Divisão de Pneumologia, Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
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Ang CYS, Chiew YS, Wang X, Mat Nor MB, Cove ME, Chase JG. Predicting mechanically ventilated patients future respiratory system elastance - A stochastic modelling approach. Comput Biol Med 2022; 151:106275. [PMID: 36375413 DOI: 10.1016/j.compbiomed.2022.106275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/26/2022] [Accepted: 10/30/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND AND OBJECTIVE Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment. METHODS Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves. RESULTS Clinical validation shows all three models captured more than 98% (median) of future Ers data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < Ers (cmH2O/L) < 85, suggesting similar predictive capabilities within this clinically relevant Ers range. CONCLUSION The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.
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Affiliation(s)
| | | | - Xin Wang
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, 25200, Malaysia
| | - Matthew E Cove
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore
| | - J Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
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11
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Cushway J, Murphy L, Chase JG, Shaw GM, Desaive T. Modelling patient specific cardiopulmonary interactions. Comput Biol Med 2022; 151:106235. [PMID: 36334361 DOI: 10.1016/j.compbiomed.2022.106235] [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: 07/28/2022] [Revised: 09/19/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Mechanical ventilation is well known for having detrimental effects on the cardiovascular system, particularly when using high positive end-expiratory pressure. High positive end-expiratory pressure levels cause a decrease in stroke volume, which, under normal conditions, usually bring about a decrease in stressed blood volume. Stressed blood volume, defined as the total pressure generating volume of the cardiovascular system, has been shown to be a potential index of fluid responsiveness, making it a potentially important diagnostic tool. Generally, respiratory and haemodynamic care are provided independently of one another. However, that positive end-expiratory pressure alters both stroke volume and stressed blood volume suggests both the pulmonary and cardiovascular state should be conjointly optimised and used to guide positive end-expiratory pressure. However, the complex and patient-specific nature of cardiopulmonary interactions which occur during mechanical ventilation presents a challenge for accurate modelling of respiratory and cardiovascular interactions required to better optimise care. Previous models attempting to incorporate cardiopulmonary interactions have suffered from poor reliability at higher PEEP levels, largely due to an exaggerated effect of intrathoracic pressure on the cardiovascular system. A new parameter, alpha, is added to a previously validated cardiopulmonary model, to modulate the percentage of intrathoracic pressure applied to the vena cava and left ventricle. The new parameter aims to increase reliability under high PEEP conditions as well as provide a patient specific solution to modelling cardiopulmonary interactions. The results from the identified optimal alpha are compared to the original model to investigate how this new parameter may be used to create a more patient-specific cardiopulmonary model, which would be better suited for guidance of care in the ICU.
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Affiliation(s)
- James Cushway
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand; University of Liège (ULg), GIGA-Cardiovascular Sciences, Liège, Belgium.
| | - Liam Murphy
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand
| | - J Geoffrey Chase
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- University of Liège (ULg), GIGA-Cardiovascular Sciences, Liège, Belgium
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12
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Zhou C, Chase JG. Low-cost structured light imaging of regional volume changes for use in assessing mechanical ventilation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107176. [PMID: 36228494 DOI: 10.1016/j.cmpb.2022.107176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/21/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Optimal setting of mechanical ventilators is critical for improving outcomes. Accurate, predictive lung mechanics models are effective in optimizing MV settings, but only at a global level as they cannot estimate regional lung volume ventilation to assess the potential of local distension or under-ventilation. This study presents a low-cost structured light system for non-contact high resolution chest motion measurement to estimate regional lung volume changes. METHODS The system consists of a structured light projector and two cameras. A new pattern is designed to extract motion from sub-regions of the chest surface, and an efficient feature is proposed to provide a fast and accurate correspondence matching between two views. Reconstruction of 3D surface points is based on the matched points and stereo method. Asymmetric distribution of tidal volume into left and right lungs is estimated based on reconstructed regional chest expansion. A proof-of-concept experiment using a dummy model and two test lungs connected to a ventilator to provide differential chest expansion is conducted under tidal volumes of 400 ml, 500 ml and 600 ml, with results compared to the widely-used SURF and ORB methods. RESULTS Compared to the SURF and ORB methods, the proposed method is more computationally efficient with ∼40% less computational time cost, and higher accuracy for dense point correspondence. Finally, the proposed method estimated the region lung volumes with the maximum error of 8 ml under 600 ml tidal volume, indicating a good accuracy. CONCLUSIONS Surface reconstruction results in a proof-of-concept experiment with differential chest expansion show good performance for the proposed pattern and method in extracting the key information for regional chest expansion. The proposed method is generalizable, with potential for use in other applications.
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Affiliation(s)
- Cong Zhou
- School of Civil Aviation, Northwestern Polytechnical University, China; Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
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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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14
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Non-invasive over-distension measurements: data driven vs model-based. J Clin Monit Comput 2022; 37:389-398. [PMID: 35920951 DOI: 10.1007/s10877-022-00900-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/22/2022] [Indexed: 10/16/2022]
Abstract
Clinical measurements offer bedside monitoring aiming to minimise unintended over-distension, but have limitations and cannot be predicted for changes in mechanical ventilation (MV) settings and are only available in certain MV modes. This study introduces a non-invasive, real-time over-distension measurement, which is robust, predictable, and more intuitive than current methods. The proposed over-distension measurement, denoted as OD, is compared with the clinically proven stress index (SI). Correlation is analysed via R2 and Spearman rs. The OD safe range corresponding to the unit-less SI safe range (0.95-1.05) is calibrated by sensitivity and specificity test. Validation is fulfilled with 19 acute respiratory distress syndrome (ARDS) patients data (196 cases), including assessment across ARDS severity. Overall correlation between OD and SI yielded R2 = 0.76 and Spearman rs = 0.89. Correlation is higher considering only moderate and severe ARDS patients. Calibration of OD to SI yields a safe range defined: 0 ≤ OD ≤ 0.8 cmH2O. The proposed OD offers an efficient, general, real-time measurement of patient-specific lung mechanics, which is more intuitive and robust than SI. OD eliminates the limitations of SI in MV mode and its less intuitive lung status value. Finally, OD can be accurately predicted for new ventilator settings via its foundation in a validated predictive personalized lung mechanics model. Therefore, OD offers potential clinical value over current clinical methods.
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15
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Cushway J, Murphy L, Chase JG, Shaw GM, Desaive T. Physiological trend analysis of a novel cardio-pulmonary model during a preload reduction manoeuvre. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106819. [PMID: 35461125 DOI: 10.1016/j.cmpb.2022.106819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Mechanical ventilation causes adverse effects on the cardiovascular system. However, the exact nature of the effects on haemodynamic parameters is not fully understood. A recently developed cardio-vascular system model which incorporates cardio-pulmonary interactions is compared to the original 3-chamber cardiovascular model to investigate the exact effects of mechanical ventilation on haemodynamic parameters and to assess the trade-off of model complexity and model reliability between the 2 models. METHODS Both the cardio-pulmonary and three chamber models are used to identify cardiovascular system parameters from aortic pressure, left ventricular volume, airway flow and airway pressure measurements from 4 pigs during a preload reduction manoeuvre. Outputs and parameter estimations from both models are contrasted to assess the relative performance of each model and to further investigate the effects of mechanical ventilation on haemodynamic parameters. RESULTS Both models tracked measurements accurately as expected. There was no identifiable increase in error from the added complexity of the cardio-pulmonary model, with both models having a mean average error below 0.5% for all pigs. Identified left ventricle and vena cava elastances of the 3-chamber model was found to diverge exponentially with PEEP from identified left ventricle and vena cava elastances of the cardio-pulmonary model. The r2 of the fit for each pig ranged from 0.888 to 0.998 for left ventricle elastance divergence and from 0.905 to 0.999 for vena cava elastance divergence. All other identified parameters showed no significant difference between models. CONCLUSIONS Despite the increase in model complexity, there was no loss in the cardio-pulmonary model's ability to accurately estimate haemodynamic parameters and reproduce system dynamics. Furthermore, the cardio-pulmonary model was able to demonstrate how mechanical ventilation affected parameter estimations as PEEP was increased. The 3-chamber model was shown to produce parameter estimations which diverged exponentially with PEEP, while the cardiopulmonary model estimations remained more stable, suggesting its ability to produce more physiologically accurate parameter estimations under higher PEEP conditions.
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Affiliation(s)
- James Cushway
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand.
| | - Liam Murphy
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand
| | - J Geoffrey Chase
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Dept of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- University of Liège (ULg), GIGA-Cardiovascular Sciences, Liège, Belgium
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16
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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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17
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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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18
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Ang CYS, Chiew YS, Vu LH, Cove ME. Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106601. [PMID: 34973606 DOI: 10.1016/j.cmpb.2021.106601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Spontaneous breathing (SB) effort during mechanical ventilation (MV) is an important metric of respiratory drive. However, SB effort varies due to a variety of factors, including evolving pathology and sedation levels. Therefore, assessment of SB efforts needs to be continuous and non-invasive. This is important to prevent both over- and under-assistance with MV. In this study, a machine learning model, Convolutional Autoencoder (CAE) is developed to quantify the magnitude of SB effort using only bedside MV airway pressure and flow waveform. METHOD The CAE model was trained using 12,170,655 simulated SB flow and normal flow data (NB). The paired SB and NB flow data were simulated using a Gaussian Effort Model (GEM) with 5 basis functions. When the CAE model is given a SB flow input, it is capable of predicting a corresponding NB flow for the SB flow input. The magnitude of SB effort (SBEMag) is then quantified as the difference between the SB and NB flows. The CAE model was used to evaluate the SBEMag of 9 pressure control/ support datasets. Results were validated using a mean squared error (MSE) fitting between clinical and training SB flows. RESULTS The CAE model was able to produce NB flows from the clinical SB flows with the median SBEMag of the 9 datasets being 25.39% [IQR: 21.87-25.57%]. The absolute error in SBEMag using MSE validation yields a median of 4.77% [IQR: 3.77-8.56%] amongst the cohort. This shows the ability of the GEM to capture the intrinsic details present in SB flow waveforms. Analysis also shows both intra-patient and inter-patient variability in SBEMag. CONCLUSION A Convolutional Autoencoder model was developed with simulated SB and NB flow data and is capable of quantifying the magnitude of patient spontaneous breathing effort. This provides potential application for real-time monitoring of patient respiratory drive for better management of patient-ventilator interaction.
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Affiliation(s)
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia.
| | - Lien Hong Vu
- Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Health System, Singapore
| | - Matthew E Cove
- Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Health System, Singapore
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19
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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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20
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Wong JW, Chiew YS, Desaive T, Chase JG. Model-based patient matching for in-parallel pressure-controlled ventilation. Biomed Eng Online 2022; 21:11. [PMID: 35139858 PMCID: PMC8826717 DOI: 10.1186/s12938-022-00983-y] [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: 10/14/2021] [Accepted: 01/24/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV. METHODS The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation. RESULTS The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care. CONCLUSION This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.
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Affiliation(s)
- Jin Wai Wong
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | | | - Thomas Desaive
- GIGA-In Silico Medicine, University of Liege, Liege, Belgium
| | - J. Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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21
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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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22
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Lee JWW, Chiew YS, Wang X, Tan CP, Mat Nor MB, Cove ME, Damanhuri NS, Chase JG. Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106577. [PMID: 34936946 DOI: 10.1016/j.cmpb.2021.106577] [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: 09/24/2021] [Revised: 11/17/2021] [Accepted: 12/03/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses. METHODS This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume. RESULTS The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH2O and 0.55 [0.19 - 1.20] mL/kg were obtained. CONCLUSIONS Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.
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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
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Pahang, Malaysia
| | - Matthew E Cove
- Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Health System, Singapore
| | - Nor Salwa Damanhuri
- Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Pulau Pinang, Malaysia
| | - J Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
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Kim KT, Knopp J, Dixon B, Chase JG. Quantifying neonatal patient effort using non-invasive model-based methods. Med Biol Eng Comput 2022; 60:739-751. [PMID: 35043368 DOI: 10.1007/s11517-021-02491-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 12/15/2021] [Indexed: 10/19/2022]
Abstract
Patient-specific spontaneous breathing effort (SB) is common in invasively mechanically ventilated (MV) adult patients, and especially common in preterm neonates who are not typically sedated. However, there is no proven, ethically feasible and non-invasive method to quantify SB effort in neonates, creating the potential for model-based measures. Lung mechanics and SB effort are segregated using a basis function model to identify passive lung mechanics, and an additional time-varying elastance model to identify patient-specific SB effort and asynchrony as negative and positive added elastances, respectively. Data from ten preterm neonates on standard MV care in the neonatal intensive care unit (NICU) are used to assess this model-based approach, using area under the curve (AUC) for positive (asynchrony) and negative (SB effort) time-varying elastance. Median [interquartile-range (IQR)] of passive pulmonary lung elastance was 3.82 [2.09-5.80] cmH2O/ml. Median [IQR] AUC quantified SB effort was -0.32 [-0.43--0.12]cmH2O/ml. AUC quantified asynchrony was 0.00 [0.00-0.01]cmH2O/ml, and affected 28% of the 25,287 total breaths. This proof of concept model-based approach provides a non-invasive, computationally straightforward, and thus clinically feasible means to quantify patient-specific spontaneous breathing effort and asynchrony.
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Affiliation(s)
- Kyeong Tae Kim
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.
| | - Jennifer Knopp
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Bronwyn Dixon
- Neonatal Intensive Care Unit, Christchurch Women's Hospital, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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24
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Quantifying ventilator unloading in CPAP ventilation. Comput Biol Med 2022; 142:105225. [PMID: 35032739 DOI: 10.1016/j.compbiomed.2022.105225] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/04/2022] [Accepted: 01/04/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND The intrinsic (muscular) patient effort driving inspiration in non-invasive ventilation modes, such as continuous positive airway pressure (CPAP) therapy, has not been identified from non-invasive data. Current CPAP settings are based on clinical judgment and assessment of symptoms of respiratory distress. Non-optimal settings, including too much positive end expiratory pressure (PEEP) can cause unintended lung injury and ventilator unloading, where patient effort drops and the CPAP device enables too much work being imposed on the injured lung. Currently, there is no non-invasive means of quantifying or identifying these effects. METHODS A novel model-based method of ascertaining intrinsic patient work of breathing (WOB) in CPAP is developed based on linear single compartment and 2nd order b-spline models previously used in invasive ventilation modes. Results are compared to current clinical indications, such as total Imposed WOB from the CPAP device and beak length, the latter of which is the clinical metric used to indicate alveolar overdistension. Intrinsic and Imposed WOB are compared. The hypothesis is that ventilator unloading can be assessed as a decrease in Intrinsic WOB relative to Imposed WOB, as PEEP and associated ventilator unloading rise. This hypothesis is tested using 14 subjects from a CPAP trial of several breathing rates at two PEEP levels. RESULTS The ratio of Intrinsic to Imposed WOB, normalised per unit tidal volume, decreased with increasing PEEP (4-7 cm H2O), capturing the expected trend of ventilator unloading. Ventilator unloading was observed across all breathing rates. Beak length measurements showed no conclusive evidence of capturing overdistension at higher PEEP or ventilator unloading. CONCLUSIONS Patient Intrinsic WOB in CPAP was non-invasively quantified using model-based methods, based on pressure and flow measurements. The ratio of Intrinsic to Imposed WOB per unit tidal volume clearly and consistently showed ventilator unloading across all patients and breathing rates, with Intrinsic WOB decreasing with increasing PEEP. This trend was not observed in the current clinical metric of beak length. Non-invasively quantifying Intrinsic WOB and ventilator unloading is the critical first step to objectively optimising clinical CPAP settings, patient care, and outcomes.
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Muhamad Sauki NS, Damanhuri NS, Othman NA, Chiew Meng BC, Chiew YS, Mat Nor MB. Assessing the Asynchrony Event Based on the Ventilation Mode for Mechanically Ventilated Patients in ICU. Bioengineering (Basel) 2021; 8:bioengineering8120222. [PMID: 34940375 PMCID: PMC8698314 DOI: 10.3390/bioengineering8120222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/30/2021] [Accepted: 12/15/2021] [Indexed: 11/16/2022] Open
Abstract
Respiratory system modelling can assist clinicians in making clinical decisions during mechanical ventilation (MV) management in intensive care. However, there are some cases where the MV patients produce asynchronous breathing (asynchrony events) due to the spontaneous breathing (SB) effort even though they are fully sedated. Currently, most of the developed models are only suitable for fully sedated patients, which means they cannot be implemented for patients who produce asynchrony in their breathing. This leads to an incorrect measurement of the actual underlying mechanics in these patients. As a result, there is a need to develop a model that can detect asynchrony in real-time and at the bedside throughout the ventilated days. This paper demonstrates the asynchronous event detection of MV patients in the ICU of a hospital by applying a developed extended time-varying elastance model. Data from 10 mechanically ventilated respiratory failure patients admitted at the International Islamic University Malaysia (IIUM) Hospital were collected. The results showed that the model-based technique precisely detected asynchrony events (AEs) throughout the ventilation days. The patients showed an increase in AEs during the ventilation period within the same ventilation mode. SIMV mode produced much higher asynchrony compared to SPONT mode (p < 0.05). The link between AEs and the lung elastance (AUC Edrs) was also investigated. It was found that when the AEs increased, the AUC Edrs decreased and vice versa based on the results obtained in this research. The information of AEs and AUC Edrs provides the true underlying lung mechanics of the MV patients. Hence, this model-based method is capable of detecting the AEs in fully sedated MV patients and providing information that can potentially guide clinicians in selecting the optimal ventilation mode of MV, allowing for precise monitoring of respiratory mechanics in MV patients.
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Affiliation(s)
- Nur Sa’adah Muhamad Sauki
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh 13500, Malaysia; (N.S.M.S.); (N.A.O.); (B.C.C.M.)
| | - Nor Salwa Damanhuri
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh 13500, Malaysia; (N.S.M.S.); (N.A.O.); (B.C.C.M.)
- Correspondence: ; Tel.: +60-11-38715853
| | - Nor Azlan Othman
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh 13500, Malaysia; (N.S.M.S.); (N.A.O.); (B.C.C.M.)
| | - Belinda Chong Chiew Meng
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh 13500, Malaysia; (N.S.M.S.); (N.A.O.); (B.C.C.M.)
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
| | - Mohd Basri Mat Nor
- Department of Anaesthesiology and Intensive Care, School of Medicine, International Islamic University of Malaysia, Kuantan 25200, Malaysia;
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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] [MESH Headings] [Grants] [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
- School of Engineering, Monash University Malaysia, 47500, Subang Jaya, Selangor, Malaysia.
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, 47500, Subang Jaya, Selangor, Malaysia.
| | - Xin Wang
- School of Engineering, Monash University Malaysia, 47500, Subang Jaya, Selangor, Malaysia
| | - Chee Pin Tan
- School of Engineering, Monash University Malaysia, 47500, Subang Jaya, Selangor, Malaysia
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, 25200, Kuantan, Pahang, Malaysia
| | - Nor Salwa Damanhuri
- Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500, Bukit Bertajam, Pulau Pinang, Malaysia
| | - J Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch, 8041, New Zealand
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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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Wong JW, Chiew YS, Desaive T, Chase JG. Model-based Patient Matching for in-parallel Multiplexing Mechanical Ventilation Support. IFAC-PAPERSONLINE 2021; 54:121-126. [PMID: 38620762 PMCID: PMC8562132 DOI: 10.1016/j.ifacol.2021.10.242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Surges of COVID-19 infections could lead to insufficient supply of mechanical ventilators, and rationing of needed care. Multiplexing mechanical ventilators (co-MV) to serve multiple patients is a potential temporary solution. However, if patients are ventilated in parallel ventilation, there is currently no means to match ventilation requirements or patients, with no guidelines to date for co-MV. This research uses patient-specific clinically validated respiratory mechanics models to propose a method for patient matching and mechanical ventilator settings for two-patient co-MV under pressure control mode. The proposed method can simulate and estimate the resultant tidal volume of different combinations of co-ventilated patients. With both patients fulfilling the specified constraint under similar ventilation settings, the actual mechanical ventilator settings for co-MV are determined. This method allows clinicians to analyze in silico co-MV before clinical implementation.
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Affiliation(s)
- Jin Wai Wong
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | | | - Thomas Desaive
- GIGA-In Silico Medicine, University of Liege, Liege, Belgium
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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Agrawal DK, Smith BJ, Sottile PD, Albers DJ. A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms. Front Physiol 2021; 12:724046. [PMID: 34658911 PMCID: PMC8517122 DOI: 10.3389/fphys.2021.724046] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 09/01/2021] [Indexed: 12/31/2022] Open
Abstract
Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome.
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Affiliation(s)
- Deepak K. Agrawal
- Department of Bioengineering, University of Colorado Denver|Anschutz Medical Campus, Aurora, CO, United States
- Section of Informatics and Data Science, Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Bradford J. Smith
- Department of Bioengineering, University of Colorado Denver|Anschutz Medical Campus, Aurora, CO, United States
- Section of Pulmonary and Sleep Medicine, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Peter D. Sottile
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, United States
| | - David J. Albers
- Department of Bioengineering, University of Colorado Denver|Anschutz Medical Campus, Aurora, CO, United States
- Section of Informatics and Data Science, Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
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Quantifying patient spontaneous breathing effort using model-based methods. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102809] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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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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Morton SE, Knopp JL, Tawhai MH, Docherty P, Heines SJ, Bergmans DC, Möller K, Chase JG. Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105696. [PMID: 32798977 DOI: 10.1016/j.cmpb.2020.105696] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/30/2020] [Indexed: 06/11/2023]
Abstract
Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where inter- and intra- patient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH2O. Prediction errors for peak inspiratory volume for an increase of 16 cmH2O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care.
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Affiliation(s)
- Sophie E Morton
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L Knopp
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand
| | - Merryn H Tawhai
- Auckland Bioengineering Institute, Auckland University, Auckland, New Zealand
| | - Paul Docherty
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand
| | - 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
| | - Knut Möller
- Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - J Geoffrey Chase
- Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand.
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Howe SL, März M, Krüger-Ziolek S, Laufer B, Pretty C, Shaw GM, Desaive T, Möller K, Chase JG. Measuring lung mechanics of expiratory tidal breathing with non-invasive breath occlusion. Biomed Eng Online 2020; 19:32. [PMID: 32410675 PMCID: PMC7224083 DOI: 10.1186/s12938-020-00777-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 04/30/2020] [Indexed: 11/19/2022] Open
Abstract
Background and objective Lung mechanics measurements provide clinically useful information about disease progression and lung health. Currently, there are no commonly practiced methods to non-invasively measure both resistive and elastic lung mechanics during tidal breathing, preventing the important information provided by lung mechanics from being utilised. This study presents a novel method to easily assess lung mechanics of spontaneously breathing subjects using a dynamic elastance, single-compartment lung model. Methods A spirometer with a built-in shutter was used to occlude expiration during tidal breathing, creating exponentially decaying flow when the shutter re-opened. The lung mechanics measured were respiratory system elastance and resistance, separated from the exponentially decaying flow, and interrupter resistance calculated at shutter closure. Progressively increasing resistance was added to the spirometer mouthpiece to simulate upper airway obstruction. The lung mechanics of 17 healthy subjects were successfully measured through spirometry. Results N = 17 (8 female, 9 male) healthy subjects were recruited. Measured decay rates ranged from 5 to 42/s, subjects with large variation of decay rates showed higher muscular breathing effort. Lung elastance measurements ranged from 3.9 to 21.2 cmH\documentclass[12pt]{minimal}
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\begin{document}$$_2$$\end{document}2O/L, with no clear trend between change in elastance and added resistance. Resistance calculated from decay rate and elastance ranged from 0.15 to 1.95 cmH\documentclass[12pt]{minimal}
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\begin{document}$$_2$$\end{document}2Os/L. These very small resistance values are due to the airflow measured originating from low-resistance areas in the centre of airways. Occlusion resistance measurements were as expected for healthy subjects, and increased as expected as resistance was added. Conclusions This test was able to identify reasonable dynamic lung elastance and occlusion resistance values, providing new insight into expiratory breathing effort. Clinically, this lung function test could impact current practice. It does not require high levels of cooperation from the subject, allowing a wider cohort of patients to be assessed more easily. Additionally, this test can be simply implemented in a small standalone device, or with standard lung function testing equipment.
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Affiliation(s)
- Sarah L Howe
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Melanie März
- Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany
| | - Sabine Krüger-Ziolek
- Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany
| | - Bernhard Laufer
- Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany
| | - Chris Pretty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Geoffery M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA Cardiovascular Science, University of Liege, Liege, Belgium
| | - Knut Möller
- Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
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Howe SL, Chase JG, Redmond DP, Morton SE, Kim KT, Pretty C, Shaw GM, Tawhai MH, Desaive T. Inspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 186:105184. [PMID: 31715280 DOI: 10.1016/j.cmpb.2019.105184] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/29/2019] [Accepted: 11/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Model-based lung mechanics monitoring can provide clinically useful information for guiding mechanical ventilator treatment in intensive care. However, many methods of measuring lung mechanics are not appropriate for both fully and partially sedated patients, and are unable provide lung mechanics metrics in real-time. This study proposes a novel method of using lung mechanics identified during passive expiration to estimate inspiratory lung mechanics for spontaneously breathing patients. METHODS Relationships between inspiratory and expiratory modeled lung mechanics were identified from clinical data from 4 fully sedated patients. The validity of these relationships were assessed using data from a further 4 spontaneously breathing patients. RESULTS For the fully sedated patients, a linear relationship was identified between inspiratory and expiratory elastance, with slope 1.04 and intercept 1.66. The r value of this correlation was 0.94. No cohort-wide relationship was determined for airway resistance. Expiratory elastance measurements in spontaneously breathing patients were able to produce reasonable estimates of inspiratory elastance after adjusting for the identified difference between them. CONCLUSIONS This study shows that when conventional methods fail, typically ignored expiratory data may be able to provide clinicians with the information needed about patient condition to guide MV therapy.
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Affiliation(s)
- S L Howe
- University of Canterbury, 8041, Christchurch, New Zealand.
| | - J G Chase
- University of Canterbury, 8041, Christchurch, New Zealand
| | - D P Redmond
- University of Canterbury, 8041, Christchurch, New Zealand
| | - S E Morton
- University of Canterbury, 8041, Christchurch, New Zealand
| | - K T Kim
- University of Canterbury, 8041, Christchurch, New Zealand
| | - C Pretty
- University of Canterbury, 8041, Christchurch, New Zealand
| | - G M Shaw
- Christchurch Hospital, 8011, Christchurch, New Zealand
| | - M H Tawhai
- University of Auckland, Auckland, New Zealand
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Kim KT, Morton S, Howe S, Chiew YS, Knopp JL, Docherty P, Pretty C, Desaive T, Benyo B, Szlavecz A, Moeller K, Shaw GM, Chase JG. Model-based PEEP titration versus standard practice in mechanical ventilation: a randomised controlled trial. Trials 2020; 21:130. [PMID: 32007099 PMCID: PMC6995650 DOI: 10.1186/s13063-019-4035-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 12/29/2019] [Indexed: 11/12/2022] Open
Abstract
Background Positive end-expiratory pressure (PEEP) at minimum respiratory elastance during mechanical ventilation (MV) in patients with acute respiratory distress syndrome (ARDS) may improve patient care and outcome. The Clinical utilisation of respiratory elastance (CURE) trial is a two-arm, randomised controlled trial (RCT) investigating the performance of PEEP selected at an objective, model-based minimal respiratory system elastance in patients with ARDS. Methods and design The CURE RCT compares two groups of patients requiring invasive MV with a partial pressure of arterial oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio ≤ 200; one criterion of the Berlin consensus definition of moderate (≤ 200) or severe (≤ 100) ARDS. All patients are ventilated using pressure controlled (bi-level) ventilation with tidal volume = 6–8 ml/kg. Patients randomised to the control group will have PEEP selected per standard practice (SPV). Patients randomised to the intervention will have PEEP selected based on a minimal elastance using a model-based computerised method. The CURE RCT is a single-centre trial in the intensive care unit (ICU) of Christchurch hospital, New Zealand, with a target sample size of 320 patients over a maximum of 3 years. The primary outcome is the area under the curve (AUC) ratio of arterial blood oxygenation to the fraction of inspired oxygen over time. Secondary outcomes include length of time of MV, ventilator-free days (VFD) up to 28 days, ICU and hospital length of stay, AUC of oxygen saturation (SpO2)/FiO2 during MV, number of desaturation events (SpO2 < 88%), changes in respiratory mechanics and chest x-ray index scores, rescue therapies (prone positioning, nitric oxide use, extracorporeal membrane oxygenation) and hospital and 90-day mortality. Discussion The CURE RCT is the first trial comparing significant clinical outcomes in patients with ARDS in whom PEEP is selected at minimum elastance using an objective model-based method able to quantify and consider both inter-patient and intra-patient variability. CURE aims to demonstrate the hypothesized benefit of patient-specific PEEP and attest to the significance of real-time monitoring and decision-support for MV in the critical care environment. Trial registration Australian New Zealand Clinical Trial Registry, ACTRN12614001069640. Registered on 22 September 2014. (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366838&isReview=true) The CURE RCT clinical protocol and data usage has been granted by the New Zealand South Regional Ethics Committee (Reference number: 14/STH/132).
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Affiliation(s)
- Kyeong Tae Kim
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.
| | - Sophie Morton
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Sarah Howe
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | | | - Jennifer L Knopp
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Paul Docherty
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Christopher Pretty
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA Cardiovascular Science, University of Liege, Liege, Belgium
| | - Balazs Benyo
- Department of Control Engineering and Information, Budapest University of Technology and Economics, Budapest, Hungary
| | - Akos Szlavecz
- Department of Control Engineering and Information, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Institute of Technical Medicine (ITeM), HFU Furtwangen University, Villingen-Schwenningen, Germany
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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Kim KT, Knopp J, Dixon B, Chase JG. Mechanically ventilated premature babies have sex differences in specific elastance: A pilot study. Pediatr Pulmonol 2020; 55:177-184. [PMID: 31596060 DOI: 10.1002/ppul.24538] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 09/18/2019] [Indexed: 11/09/2022]
Abstract
OBJECTIVES A pilot study to compare pulmonary mechanics in a neonatal intensive care unit (NICU) cohort, specifically, comparing lung elastance between male and female infants in the NICU. HYPOTHESIS Anecdotally, male infants are harder to ventilate than females. We hypothesize that males have higher model-based elastance (converse: lower specific compliance) compared to females, reflecting underlying stiffer lungs. STUDY DESIGN A clinically validated, single-compartment model is used to identify specific elastance (inverse of specific compliance) and resistance for each breath. Specific elastance accounts for weight differences when comparing male and female infants. Relative percent breath-to-breath variability (%ΔE) in specific elastance is also compared. Level of asynchrony was also determined. PATIENT-SUBJECT SELECTION Ten invasively mechanically ventilated patients from Christchurch Women's Hospital. METHODOLOGY Airway pressure and flow data from 10 invasive mechanical ventilation (MV) infants from Christchurch Women's Hospital Neonatal Intensive Care Unit, New Zealand was prospectively recorded under standard MV care. Model-based specific elastance and resistance are identified for each breath, as well as relative percent breath-to-breath variability (%ΔE) in specific elastance. RESULTS Male infants overall had higher specific elastance compared to females infants (P ≤ .01), with median (interquartile range) for males of 1.91 (1.33-2.48) cmH2 O·kg/mL compared to 1.31 (0.86-2.02) cmH2 O·kg/mL in females. Male infants had lower variability with %ΔE of -0.03 (-7.56 to 8.01)% vs female infants of -0.59 (12.56-12.86)%. Males had 14.75% asynchronous breaths whereas females had 17.54%. CONCLUSION Overall, males had higher specific elastance and correspondingly lower breath-to-breath variability. These results indicate male and female infants may require different MV settings, mode, and monitoring.
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Affiliation(s)
- Kyeong Tae Kim
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer Knopp
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Bronwyn Dixon
- Neonatal Intensive Care Unit, Christchurch Women's Hospital, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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Kim KT, Knopp J, Dixon B, Chase G. Quantifying neonatal pulmonary mechanics in mechanical ventilation. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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de Bournonville S, Pironet A, Pretty C, Chase JG, Desaive T. Parameter estimation in a minimal model of cardio-pulmonary interactions. Math Biosci 2019; 313:81-94. [PMID: 31128126 DOI: 10.1016/j.mbs.2019.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 11/25/2022]
Abstract
Mechanical ventilation is a widely used breathing support for patients in intensive care. Its effects on the respiratory and cardiovascular systems are complex and difficult to predict. This work first presents a minimal mathematical model representing the mechanics of both systems and their interaction, in terms of flows, pressures and volumes. The aim of this model is to get insight on the two systems' status when mechanical ventilation settings, such as positive end-expiratory pressure, are changing. The parameters of the model represent cardiac elastances and vessel compliances and resistances. As a second step, these parameters are estimated from 16 experimental datasets. The data come from three pig experiments reproducing intensive care conditions, where a large range of positive end-expiratory pressures was imposed by the mechanical ventilator. The data used for parameter estimation is limited to information available in the intensive care unit, such as stroke volume, central venous pressure and systemic arterial pressure. The model is able to satisfactorily reproduce this experimental data, with mean relative errors ranging from 1 to 26%. The model also reproduces the dynamics of the cardio-vascular and respiratory systems, and their interaction. By looking at the estimated parameter values, one can quantitatively track how the two coupled systems mechanically react to changes in external conditions imposed by the ventilator. This work thus allows real-time, model-based management of ventilator settings.
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Affiliation(s)
- Sébastien de Bournonville
- Prometheus, Division of Skeletal Tissue Engineering, Katholieke Universiteit Leuven (KUL), Leuven, Belgium; GIGA-In Silico Medicine, University of Liège (ULg), Liège, Belgium.
| | - Antoine Pironet
- GIGA-In Silico Medicine, University of Liège (ULg), Liège, Belgium.
| | - Chris Pretty
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand.
| | - J Geoffrey Chase
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand.
| | - Thomas Desaive
- GIGA-In Silico Medicine, University of Liège (ULg), Liège, Belgium.
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Morton SE, Knopp JL, Chase JG, Docherty P, Howe SL, Möller K, Shaw GM, Tawhai M. Optimising mechanical ventilation through model-based methods and automation. ANNUAL REVIEWS IN CONTROL 2019; 48:369-382. [PMID: 36911536 PMCID: PMC9985488 DOI: 10.1016/j.arcontrol.2019.05.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 04/09/2019] [Accepted: 05/01/2019] [Indexed: 06/11/2023]
Abstract
Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.
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Affiliation(s)
- Sophie E Morton
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Jennifer L Knopp
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Paul Docherty
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Sarah L Howe
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Knut Möller
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Redmond DP, Chiew YS, Major V, Chase JG. Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 171:67-79. [PMID: 27697371 DOI: 10.1016/j.cmpb.2016.09.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 08/11/2016] [Accepted: 09/14/2016] [Indexed: 06/06/2023]
Abstract
Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.
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Affiliation(s)
- Daniel P Redmond
- Centre for Bioengineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - Yeong Shiong Chiew
- Centre for Bioengineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand; School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Subang Jaya, Selangor 47500, Malaysia.
| | - Vincent Major
- Centre for Bioengineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
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Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function. Ann Biomed Eng 2019; 47:1626-1641. [PMID: 30927170 DOI: 10.1007/s10439-019-02253-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/22/2019] [Indexed: 10/27/2022]
Abstract
Mechanical ventilation is a life-support therapy for intensive care patients suffering from respiratory failure. To reduce the current rate of ventilator-induced lung injury requires ventilator settings that are patient-, time-, and disease-specific. A common lung protective strategy is to optimise the level of positive end-expiratory pressure (PEEP) through a recruitment manoeuvre to prevent alveolar collapse at the end of expiration and to improve gas exchange through recruitment of additional alveoli. However, this process can subject parts of the lung to excessively high pressures or volumes. This research significantly extends and more robustly validates a previously developed pulmonary mechanics model to predict lung mechanics throughout recruitment manoeuvres. In particular, the process of recruitment is more thoroughly investigated and the impact of the inclusion of expiratory data when estimating peak inspiratory pressure is assessed. Data from the McREM trial and CURE pilot trial were used to test model predictive capability and assumptions. For PEEP changes of up to and including 14 cmH2O, the parabolic model was shown to improve peak inspiratory pressure prediction resulting in less than 10% absolute error in the CURE cohort and 16% in the McREM cohort. The parabolic model also better captured expiratory mechanics than the exponential model for both cohorts.
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Major VJ, Chiew YS, Shaw GM, Chase JG. Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation. Biomed Eng Online 2018; 17:169. [PMID: 30419903 PMCID: PMC6233601 DOI: 10.1186/s12938-018-0599-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 11/01/2018] [Indexed: 12/16/2022] Open
Abstract
Background Mechanical ventilation is an essential therapy to support critically ill respiratory failure patients. Current standards of care consist of generalised approaches, such as the use of positive end expiratory pressure to inspired oxygen fraction (PEEP–FiO2) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective. Discussion This review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions. Conclusion Engineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.
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Affiliation(s)
- Vincent J Major
- Department of Population Health, NYU Langone Health, New York, NY, USA.
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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Chiew YS, Tan CP, Chase JG, Chiew YW, Desaive T, Ralib AM, Mat Nor MB. Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 157:217-224. [PMID: 29477430 DOI: 10.1016/j.cmpb.2018.02.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2017] [Revised: 01/05/2018] [Accepted: 02/02/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation. METHODS An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn). RESULTS AND DISCUSSION Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%. CONCLUSION The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.
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Affiliation(s)
| | - Chee Pin Tan
- School of Engineering, Monash University, Subang Jaya, Malaysia.
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.
| | | | - Thomas Desaive
- GIGA Cardiovascular Science, University of Liege, Liege, Belgium.
| | - Azrina Md Ralib
- Department of Intensive Care, International Islamic University Malaysia Medical Centre, Kuantan, Malaysia.
| | - Mohd Basri Mat Nor
- Department of Intensive Care, International Islamic University Malaysia Medical Centre, Kuantan, Malaysia.
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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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Kannangara O, Dickson JL, Chase JG. Specific compliance: is it truly independent of lung volume? ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.ifacol.2018.11.625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Nieman GF, Satalin J, Andrews P, Aiash H, Habashi NM, Gatto LA. Personalizing mechanical ventilation according to physiologic parameters to stabilize alveoli and minimize ventilator induced lung injury (VILI). Intensive Care Med Exp 2017; 5:8. [PMID: 28150228 PMCID: PMC5289131 DOI: 10.1186/s40635-017-0121-x] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 01/26/2017] [Indexed: 12/15/2022] Open
Abstract
It has been shown that mechanical ventilation in patients with, or at high-risk for, the development of acute respiratory distress syndrome (ARDS) can be a double-edged sword. If the mechanical breath is improperly set, it can amplify the lung injury associated with ARDS, causing a secondary ventilator-induced lung injury (VILI). Conversely, the mechanical breath can be adjusted to minimize VILI, which can reduce ARDS mortality. The current standard of care ventilation strategy to minimize VILI attempts to reduce alveolar over-distension and recruitment-derecruitment (R/D) by lowering tidal volume (Vt) to 6 cc/kg combined with adjusting positive-end expiratory pressure (PEEP) based on a sliding scale directed by changes in oxygenation. Thus, Vt is often but not always set as a "one-size-fits-all" approach and although PEEP is often set arbitrarily at 5 cmH2O, it may be personalized according to changes in a physiologic parameter, most often to oxygenation. However, there is evidence that oxygenation as a method to optimize PEEP is not congruent with the PEEP levels necessary to maintain an open and stable lung. Thus, optimal PEEP might not be personalized to the lung pathology of an individual patient using oxygenation as the physiologic feedback system. Multiple methods of personalizing PEEP have been tested and include dead space, lung compliance, lung stress and strain, ventilation patterns using computed tomography (CT) or electrical impedance tomography (EIT), inflection points on the pressure/volume curve (P/V), and the slope of the expiratory flow curve using airway pressure release ventilation (APRV). Although many studies have shown that personalizing PEEP is possible, there is no consensus as to the optimal technique. This review will assess various methods used to personalize PEEP, directed by physiologic parameters, necessary to adaptively adjust ventilator settings with progressive changes in lung pathophysiology.
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Affiliation(s)
- Gary F. Nieman
- Department of Surgery, SUNY Upstate Medical University, Syracuse, NY USA
| | - Joshua Satalin
- Department of Surgery, SUNY Upstate Medical University, Syracuse, NY USA
- Cardiopulmonary Critical Care Lab, Department of Surgery, Upstate Medical University, 750 East Adams Street, Syracuse, NY 13210 USA
| | | | - Hani Aiash
- Department of Surgery, SUNY Upstate Medical University, Syracuse, NY USA
| | - Nader M. Habashi
- Department of Trauma Critical Care Medicine, R Adams Cowley Shock Trauma Center, University of Maryland, Baltimore, MD USA
| | - Louis A. Gatto
- Biological Sciences Department, Biological Sciences Department, SUNY Cortland, Cortland, NY USA
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Abstract
Respiratory disease is a significant problem worldwide, and it is a problem with increasing prevalence. Pathology in the upper airways and lung is very difficult to diagnose and treat, as response to disease is often heterogeneous across patients. Computational models have long been used to help understand respiratory function, and these models have evolved alongside increases in the resolution of medical imaging and increased capability of functional imaging, advances in biological knowledge, mathematical techniques and computational power. The benefits of increasingly complex and realistic geometric and biophysical models of the respiratory system are that they are able to capture heterogeneity in patient response to disease and predict emergent function across spatial scales from the delicate alveolar structures to the whole organ level. However, with increasing complexity, models become harder to solve and in some cases harder to validate, which can reduce their impact clinically. Here, we review the evolution of complexity in computational models of the respiratory system, including successes in translation of models into the clinical arena. We also highlight major challenges in modelling the respiratory system, while making use of the evolving functional data that are available for model parameterisation and testing.
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Affiliation(s)
- Alys R Clark
- 1 Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Haribalan Kumar
- 1 Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Kelly Burrowes
- 2 Department of Chemical and Materials Engineering, The University of Auckland, Auckland, New Zealand
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Langdon R, Docherty PD, Schranz C, Chase JG. Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics. Biomed Eng Online 2017; 16:126. [PMID: 29096634 PMCID: PMC5668972 DOI: 10.1186/s12938-017-0415-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 10/25/2017] [Indexed: 11/25/2022] Open
Abstract
Background For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. Methods and results Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH2O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH2O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). Conclusions Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.
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Affiliation(s)
- Ruby Langdon
- Department of Mechanical Engineering, University of Canterbury, Private bag 4800, Christchurch, 8140, New Zealand
| | - Paul D Docherty
- 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
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Rodgers GW, Lau Young JB, Desaive T, Shaw GM, Chase JG. A proof of concept study of acoustic sensing of lung recruitment during mechanical ventilation. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.08.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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