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Li Z, Pei Y, Wang Y, Tian Q. An enhanced respiratory mechanics model based on double-exponential and fractional calculus. Front Physiol 2023; 14:1273645. [PMID: 38111899 PMCID: PMC10726035 DOI: 10.3389/fphys.2023.1273645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/17/2023] [Indexed: 12/20/2023] Open
Abstract
We address mathematical modelling of respiratory mechanics and put forward a model based on double-exponential and fractional calculus for parameter estimation, model simulation, and evaluation based on actual data. Our model has been implemented on a publicly available executable code with adjustable parameters, making it suitable for different applications. Our analysis represents the first application of fractional calculus and double-exponential modelling to respiratory mechanics, and allows us to propose a hybrid model fitting experimental data in different ventilation modes. Furthermore, our model can be used to study the mechanical features of the respiratory system, improve the safety of ventilation techniques, reduce ventilation damages, and provide strong support for fast and adaptive determination of ventilation parameters.
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Affiliation(s)
- Zongwei Li
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yanbin Pei
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Yuqi Wang
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Qing Tian
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
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2
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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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3
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Stroh JN, Smith BJ, Sottile PD, Hripcsak G, Albers DJ. Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research. J Biomed Inform 2023; 137:104275. [PMID: 36572279 PMCID: PMC9788853 DOI: 10.1016/j.jbi.2022.104275] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 11/21/2022] [Accepted: 12/14/2022] [Indexed: 12/25/2022]
Abstract
Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.
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Affiliation(s)
- J N Stroh
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, USA; Department of Bioengineering, University of Colorado, Denver-Anschutz Medical Campus, Aurora, CO, USA.
| | - Bradford J Smith
- Department of Bioengineering, University of Colorado, Denver-Anschutz Medical Campus, Aurora, CO, USA; Section of Pulmonary and Sleep Medicine, Department of Pediatrics, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, USA
| | - Peter D Sottile
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - David J Albers
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, USA; Department of Bioengineering, University of Colorado, Denver-Anschutz Medical Campus, Aurora, CO, USA; Department of Biomedical Informatics, Columbia University, New York, NY, USA; Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, USA
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4
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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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5
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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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6
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Arn Ng Q, Yew Shuen Ang C, Shiong Chiew Y, Wang X, Pin Tan C, Basri Mat Nor M, Salwa Damanhuri N, Geoffrey Chase J. CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring. HARDWAREX 2022; 12:e00358. [PMID: 36117541 PMCID: PMC9474567 DOI: 10.1016/j.ohx.2022.e00358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 09/01/2022] [Accepted: 09/05/2022] [Indexed: 06/15/2023]
Abstract
Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.
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Affiliation(s)
- Qing Arn Ng
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor 47500, Malaysia
| | | | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor 47500, Malaysia
| | - Xin Wang
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor 47500, Malaysia
| | - Chee Pin Tan
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor 47500, Malaysia
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, Pahang 25200, Malaysia
| | - Nor Salwa Damanhuri
- Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500, Permatang Pauh, Pulau Pinang, Malaysia
| | - J. Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch 8041, New Zealand
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7
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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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8
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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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9
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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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10
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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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11
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Sun Q, Zhou C, Chase JG. Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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12
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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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13
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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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14
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Morton SE, Dickson J, Chase JG, Docherty P, Desaive T, Howe SL, Shaw GM, Tawhai M. A virtual patient model for mechanical ventilation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 165:77-87. [PMID: 30337083 DOI: 10.1016/j.cmpb.2018.08.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 07/24/2018] [Accepted: 08/08/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Mechanical ventilation (MV) is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause further lung damage due to heterogeneity of healthy and damaged alveoli. Varying positive-end-expiratory-pressure (PEEP) to a point of minimum elastance is a lung protective ventilator strategy. However, even low levels of PEEP can lead to ventilator induced lung injury for individuals with highly inflamed pulmonary tissue. Hence, models that could accurately predict peak inspiratory pressures after changes to PEEP could improve clinician confidence in attempting potentially beneficial treatment strategies. METHODS This study develops and validates a physiologically relevant respiratory model that captures elastance and resistance via basis functions within a well-validated single compartment lung model. The model can be personalised using information available at a low PEEP to predict lung mechanics at a higher PEEP. Proof of concept validation is undertaken with data from four patients and eight recruitment manoeuvre arms. RESULTS Results show low error when predicting upwards over the clinically relevant pressure range, with the model able to predict peak inspiratory pressure with less than 10% error over 90% of the range of PEEP changes up to 12 cmH2O. CONCLUSIONS The results provide an in-silico model-based means of predicting clinically relevant responses to changes in MV therapy, which is the foundation of a first virtual patient for MV.
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Affiliation(s)
- S E Morton
- Department of Mechanical Engineering, University of Canterbury, New Zealand.
| | - J Dickson
- Department of Mechanical Engineering, University of Canterbury, New Zealand.
| | - J G Chase
- Department of Mechanical Engineering, University of Canterbury, New Zealand.
| | - P Docherty
- Department of Mechanical Engineering, University of Canterbury, New Zealand.
| | - T Desaive
- GIGA Cardiovascular Science, University of Liege, Liege, Belgium.
| | - S L Howe
- Department of Mechanical Engineering, University of Canterbury, New Zealand.
| | - G M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand.
| | - M Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
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15
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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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16
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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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Development of a Predictive Pulmonary Elastance Model to Describe Lung Mechanics throughout Recruitment Manoeuvres. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.ifacol.2018.11.640] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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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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Laufer B, Kretschmer J, Docherty PD, Chiew YS, Möller K. Lung mechanics - airway resistance in the dynamic elastance model. HEALTH AND TECHNOLOGY 2016. [DOI: 10.1007/s12553-016-0172-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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Damanhuri NS, Chiew YS, Othman NA, Docherty PD, Pretty CG, Shaw GM, Desaive T, Chase JG. Assessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:175-185. [PMID: 27208532 DOI: 10.1016/j.cmpb.2016.03.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Revised: 02/17/2016] [Accepted: 03/29/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care. METHODS Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort. RESULTS The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort. CONCLUSION A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care.
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Affiliation(s)
- Nor Salwa Damanhuri
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand; Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia.
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand; Monash University, Malaysia.
| | - Nor Azlan Othman
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand; Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia.
| | - Paul D Docherty
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.
| | - Christopher G Pretty
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand.
| | | | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.
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21
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Major V, Corbett S, Redmond D, Beatson A, Glassenbury D, Chiew YS, Pretty C, Desaive T, Szlávecz Á, Benyó B, Shaw GM, Chase JG. Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2015.07.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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22
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Chiew YS, Pretty CG, Shaw GM, Chiew YW, Lambermont B, Desaive T, Chase JG. Feasibility of titrating PEEP to minimum elastance for mechanically ventilated patients. Pilot Feasibility Stud 2015; 1:9. [PMID: 28435689 PMCID: PMC5395899 DOI: 10.1186/s40814-015-0006-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 02/26/2015] [Indexed: 01/11/2023] Open
Abstract
Background Selecting positive end-expiratory pressure (PEEP) during mechanical ventilation is important, as it can influence disease progression and outcome of acute respiratory distress syndrome (ARDS) patients. However, there are no well-established methods for optimizing PEEP selection due to the heterogeneity of ARDS. This research investigates the viability of titrating PEEP to minimum elastance for mechanically ventilated ARDS patients. Methods Ten mechanically ventilated ARDS patients from the Christchurch Hospital Intensive Care Unit were included in this study. Each patient underwent a stepwise PEEP recruitment manoeuvre. Airway pressure and flow data were recorded using a pneumotachometer. Patient-specific respiratory elastance (Ers) and dynamic functional residual capacity (dFRC) at each PEEP level were calculated and compared. Optimal PEEP for each patient was identified by finding the minima of the PEEP-Ers profile. Results Median Ers and dFRC over all patients and PEEP values were 32.2 cmH2O/l [interquartile range (IQR) 25.0–45.9] and 0.42 l [IQR 0.11–0.87]. These wide ranges reflect patient heterogeneity and variable response to PEEP. The level of PEEP associated with minimum Ers corresponds to a high change of functional residual capacity, representing the balance between recruitment and minimizing the risk of overdistension. Conclusions Monitoring patient-specific Ers can provide clinical insight to patient-specific condition and response to PEEP settings. The level of PEEP associated with minimum-Ers can be identified for each patient using a stepwise PEEP recruitment manoeuvre. This ‘minimum elastance PEEP’ may represent a patient-specific optimal setting during mechanical ventilation. Trial registration Australian New Zealand Clinical Trials Registry: ACTRN12611001179921. Electronic supplementary material The online version of this article (doi:10.1186/s40814-015-0006-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yeong Shiong Chiew
- Department of Mechanical Engineering, University of Canterbury, Private Bag, 8140, Christchurch, New Zealand
| | - Christopher G Pretty
- Department of Mechanical Engineering, University of Canterbury, Private Bag, 8140, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Yeong Woei Chiew
- Western Medicine Division, Hospital Lam Hua EE, Pulau Penang, Malaysia
| | | | - Thomas Desaive
- GIGA Cardiovascular Science, University of Liege, Liege, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag, 8140, Christchurch, New Zealand
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Chiew YS, Pretty C, Docherty PD, Lambermont B, Shaw GM, Desaive T, Chase JG. Time-varying respiratory system elastance: a physiological model for patients who are spontaneously breathing. PLoS One 2015; 10:e0114847. [PMID: 25611069 PMCID: PMC4303416 DOI: 10.1371/journal.pone.0114847] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 11/14/2014] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E(drs)) model that can be used in spontaneously breathing patients to determine their respiratory mechanics. METHODS A time-varying respiratory elastance model is developed with a negative elastic component (E(demand)), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs) of every breathing cycle for each patient at different ventilation modes are presented for comparison. RESULTS At the start of every breathing cycle initiated by patient, E(drs) is < 0. This negativity is attributed from the E(demand) due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E(drs) trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of E(drs) (AUCE(drs)) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator. CONCLUSION The E(drs) model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.
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Affiliation(s)
- Yeong Shiong Chiew
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- * E-mail:
| | - Christopher Pretty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Paul D. Docherty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | | | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA Cardiovascular Science, University of Liege, Liege, Belgium
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Major V, Simon C, Redmond D, Beatson A, Glassenbury D, Chiew YS, Pretty C, Desaive T, Szlavecz A, Benyo B, Shaw GM, Chase JG. Assessing Respiratory Mechanics of Reverse-Triggered Breathing Cycles - Case Study of Two Mechanically Ventilated Patients. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.10.191] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management. Biomed Eng Online 2014; 13:140. [PMID: 25270094 PMCID: PMC4192763 DOI: 10.1186/1475-925x-13-140] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 09/20/2014] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment. IMPLEMENTATION CURE Soft is a desktop application developed in JAVA. It has two modes of operation, 1) Online real-time monitoring decision support and, 2) Offline for user education purposes, auditing, or reviewing patient care. The CURE Soft has been tested in mechanically ventilated patients with respiratory failure. The clinical protocol, software testing and use of the data were approved by the New Zealand Southern Regional Ethics Committee. RESULTS AND DISCUSSION Using CURE Soft, patient's respiratory mechanics response to treatment and clinical protocol were monitored. Results showed that the patient's respiratory elastance (Stiffness) changed with the use of muscle relaxants, and responded differently to ventilator settings. This information can be used to guide mechanical ventilation therapy and titrate optimal ventilator PEEP. CONCLUSION CURE Soft enables real-time calculation of model-based respiratory mechanics for mechanically ventilated patients. Results showed that the system is able to provide detailed, previously unavailable information on patient-specific respiratory mechanics and response to therapy in real-time. The additional insight available to clinicians provides the potential for improved decision-making, and thus improved patient care and outcomes.
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