Stochastic integrated model-based protocol for volume-controlled ventilation setting
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-02-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-022-00981-0 |
| _version_ | 1818330594962046976 |
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| author | Jay Wing Wai Lee Yeong Shiong Chiew Xin Wang Mohd Basri Mat Nor J. Geoffrey Chase Thomas Desaive |
| author_facet | Jay Wing Wai Lee Yeong Shiong Chiew Xin Wang Mohd Basri Mat Nor J. Geoffrey Chase Thomas Desaive |
| author_sort | Jay Wing Wai Lee |
| collection | DOAJ |
| description | 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 E rs 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. |
| first_indexed | 2024-12-13T13:06:27Z |
| format | Article |
| id | doaj.art-6e636c781a114526aeb1b8cbfd770ebe |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-13T13:06:27Z |
| publishDate | 2022-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-6e636c781a114526aeb1b8cbfd770ebe2022-12-21T23:44:49ZengBMCBioMedical Engineering OnLine1475-925X2022-02-0121112110.1186/s12938-022-00981-0Stochastic integrated model-based protocol for volume-controlled ventilation settingJay Wing Wai Lee0Yeong Shiong Chiew1Xin Wang2Mohd Basri Mat Nor3J. Geoffrey Chase4Thomas Desaive5School of Engineering, Monash University MalaysiaSchool of Engineering, Monash University MalaysiaSchool of Engineering, Monash University MalaysiaKulliyah of Medicine, International Islamic University MalaysiaCenter of Bioengineering, University of CanterburyGIGA In-Silico Medicine, University of LiegeAbstract 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 E rs 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.https://doi.org/10.1186/s12938-022-00981-0Mechanical ventilationStochastic modellingRespiratory mechanicsDecision-makingModel-based protocolCritical care |
| spellingShingle | Jay Wing Wai Lee Yeong Shiong Chiew Xin Wang Mohd Basri Mat Nor J. Geoffrey Chase Thomas Desaive Stochastic integrated model-based protocol for volume-controlled ventilation setting BioMedical Engineering OnLine Mechanical ventilation Stochastic modelling Respiratory mechanics Decision-making Model-based protocol Critical care |
| title | Stochastic integrated model-based protocol for volume-controlled ventilation setting |
| title_full | Stochastic integrated model-based protocol for volume-controlled ventilation setting |
| title_fullStr | Stochastic integrated model-based protocol for volume-controlled ventilation setting |
| title_full_unstemmed | Stochastic integrated model-based protocol for volume-controlled ventilation setting |
| title_short | Stochastic integrated model-based protocol for volume-controlled ventilation setting |
| title_sort | stochastic integrated model based protocol for volume controlled ventilation setting |
| topic | Mechanical ventilation Stochastic modelling Respiratory mechanics Decision-making Model-based protocol Critical care |
| url | https://doi.org/10.1186/s12938-022-00981-0 |
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