Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients

Lee, Jay Wing Wai; Chiew, Yeong Shiong; Wang, Xin; Tan, Chee Pin; Nor, Mohd Basri Mat; Cove, Matthew E.; Damanhuri, Nor Salwa; Chase, J. Geoffrey

doi:10.1016/j.cmpb.2021.106577

Cited by 7 publications

(6 citation statements)

References 66 publications

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“…The single-compartment model is well-validated clinically [55,56]. Furthermore, as prior works by the author have utilised this model in the development of the stochastic model of E rs [44] and the VENT protocol [45,46], the same model is used in this study to allow a more direct comparison between the VENT and SiVENT protocol, enabling a better understanding of the impact the stochastic model alone has on the decision support system protocol. Further study could repeat this approach with more descriptive models better capturing lung mechanics, such as those that incorporate basis functions or nonlinear mechanics [30,34,57], or including patient-specific effort for assisted breathing modes [58].…”

Section: Discussionmentioning

confidence: 99%

“…The designed protocol is named the Stochastic integrated VENT protocol (SiVENT). It is an extension of the VENT protocol in [45,46]. The SiVENT protocol is divided into 3 phases.…”

Section: Decision Support System Protocol Designmentioning

confidence: 99%

“…The predicted 5th and 95th E rs values are used as inputs for the model-based VENT protocol [45,46]. The VENT protocol consists of 4 stages: (1) virtual ventilation (V); (2) eliminate/estimate (E); (3) narrowing objectives (N); and (4) tabulation of data (T).…”

Section: Phase 3: Vent Protocolmentioning

confidence: 99%

“…The result was a stochastic model of E rs with promising cross-validation results of 92.59% and 68.56% of forecasted E rs within the 5-95% and 25-75% prediction range, respectively. In a separate work [45,46], a decision support system protocol known as the 'VENT' protocol was developed, which utilises a physiological lung model together with well-established clinical rules to provide a narrowed range of recommended MV settings for clinicians. The previous VENT protocol showed potential in reducing an overwhelming number of possible MV settings to a safe narrowed range of settings that were all deemed safe according to literature recommendations of pressure and volume outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic integrated model-based protocol for volume-controlled ventilation setting

et al. 2022

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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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Section: Discussionmentioning

confidence: 99%

“…The designed protocol is named the Stochastic integrated VENT protocol (SiVENT). It is an extension of the VENT protocol in [45,46]. The SiVENT protocol is divided into 3 phases.…”

Section: Decision Support System Protocol Designmentioning

confidence: 99%

Section: Phase 3: Vent Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic integrated model-based protocol for volume-controlled ventilation setting

et al. 2022

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“…In this study, we demonstrate the model-based pairing process through simulation of virtual patients [ 40 – 43 ]. A virtual patient, Patient 1 is simulated as the first candidate considered for Co-MV.…”

Section: Methodsmentioning

confidence: 99%

Model-based patient matching for in-parallel pressure-controlled ventilation

et al. 2022

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Background Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV. Methods The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation. Results The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care. Conclusion This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.

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Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

Ang

Lee

Chiew

et al. 2022

Computer Methods and Programs in Biomedicine

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Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients

Cited by 7 publications

References 66 publications

Stochastic integrated model-based protocol for volume-controlled ventilation setting

Stochastic integrated model-based protocol for volume-controlled ventilation setting

Model-based patient matching for in-parallel pressure-controlled ventilation

Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

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