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Ang CYS, Chiew YS, Wang X, Ooi EH, Cove ME, Chen Y, Zhou C, Chase JG. Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method? COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108323. [PMID: 39029417 DOI: 10.1016/j.cmpb.2024.108323] [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: 05/01/2024] [Revised: 06/27/2024] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND AND OBJECTIVE Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort. METHODS Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison. RESULTS Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN. CONCLUSION The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.
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Affiliation(s)
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Selangor, Malaysia; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Xin Wang
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Ean Hin Ooi
- 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
| | - Yuhong Chen
- Intensive Care Unit, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Cong Zhou
- 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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Arshad M, Mehmood K, Lazoglu I. Development of a non-invasive ventilator for emergency and beyond. Comput Biol Med 2023; 167:107670. [PMID: 37939406 DOI: 10.1016/j.compbiomed.2023.107670] [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: 04/04/2023] [Revised: 10/22/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
The SARS-CoV-2 pandemic led to the development and implementation of emergency ventilators owing to the shortage of ventilators globally. Using invasive ventilators for patient intubation has medical experts concerned about increasing mortality. Early intervention with oxygen and respiratory therapy reduces the need for intubation, increases survival rates, and reduces the stress of critical care ventilators in hospitals. This study explores the capabilities of an easy-to-build and accessible non-invasive ventilator during an emergency and the practical implementation of the ventilator beyond the scope of the emergency. The proposed system consists of a high-pressure turbine integrated with a microcontroller and pressure and flow sensors assembled in a portable design. The non-invasive pressure support system is tested with a single-chamber high-precision lung simulator capable of simulating multiple lung diseases. The system is operated in a spontaneous pressure support mode as a Bi-level Ventilator for varying degrees of pressure level and lung conditions. The proposed study implements two most commonly adapted non-invasive patient circuits, i.e., single passive limb leak circuit and single limb active circuit. Both circuits are tested with and without leakage compensation. Two clinically accepted ventilation modes, i.e., pressure support and volume-assured pressure support ventilation, are presented. The results demonstrate the feasibility of using this type of device for non-invasive respiratory support and highlight the need for further testing to assess its safety and effectiveness in various clinical settings.
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Affiliation(s)
- Munam Arshad
- Manufacturing and Automation Research Center, Koc University, Istanbul, 34450, Turkey
| | - Khunsha Mehmood
- Manufacturing and Automation Research Center, Koc University, Istanbul, 34450, Turkey
| | - Ismail Lazoglu
- Manufacturing and Automation Research Center, Koc University, Istanbul, 34450, Turkey.
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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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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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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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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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7
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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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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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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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Lee JWW, Chiew YS, Wang X, Mat Nor MB, Chase JG, Desaive T. Stochastic integrated model-based protocol for volume-controlled ventilation setting. Biomed Eng Online 2022; 21:13. [PMID: 35148759 PMCID: PMC8832735 DOI: 10.1186/s12938-022-00981-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 01/21/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration. METHODS A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each. RESULTS From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol. CONCLUSIONS Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.
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Affiliation(s)
- Jay Wing Wai Lee
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor Malaysia
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor Malaysia
| | - Xin Wang
- School of Engineering, Monash University Malaysia, Subang Jaya, Selangor Malaysia
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia
| | - J. Geoffrey Chase
- Center of Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA In-Silico Medicine, University of Liege, Liege, Belgium
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11
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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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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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