Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

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

doi:10.1007/s10439-021-02854-4

Cited by 17 publications

(14 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 shows the performance evaluation results for all 20 patients. Patients 10-13 have been omitted due to having less than 3 h of patient data within a single day of ventilation [44]. The initial number of available MV settings in VC ventilation is 189,000.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In previous works [44], a preliminary stochastic model of E rs was over time was developed. 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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The respiratory elastance,

is one key metric that can be used to detect and quantify the asynchrony events (AEs) of MV patients [ 21 , 22 ]. AEs appear when the patient’s breathing effort is not synchronized with the mechanical ventilation’s breathing support, resulting in a mismatch between airway pressure and flow.…”

Section: Discussionmentioning

confidence: 99%

“…This is on the condition that the value of

decreases day by day as depicted in Figure 7 or Patient 9. This suggests that the lung condition of this patient is showing signs of improvement, and the patient can be switched to another ventilation mode that is more suitable to the patient’s condition since increasing of AEs indicates that the patient may have the ability to breathe spontaneously [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Assessing the Asynchrony Event Based on the Ventilation Mode for Mechanically Ventilated Patients in ICU

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Modeling has provided insights into the mechanistic basis of RCT-derived associations between VILI indicators such as driving pressure and mortality, 46 and allowed different VILI indicators to be compared in terms of their suitability as “targets” for maximally protective ventilation. 47 Stochastic modeling has been used to create patient-specific models to predict future elastance ranges for a patient, yielding a range for minute volume that will minimize VILI, 48 though further clinical testing is needed to verify this approach. The problem of minimizing VILI while adequately oxygenating a patient can be framed as an “optimization problem,” in which values known to represent harm are given to the computational model as clear boundaries within which a solution representing safe ventilation is found.…”

Section: Acute Respiratory Distress Syndromementioning

confidence: 99%

Modeling Mechanical Ventilation In Silico—Potential and Pitfalls

Hannon

Mistry

Das

et al. 2022

Semin Respir Crit Care Med

View full text Add to dashboard Cite

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.

show abstract

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

Cited by 17 publications

References 74 publications

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

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

Assessing the Asynchrony Event Based on the Ventilation Mode for Mechanically Ventilated Patients in ICU

Modeling Mechanical Ventilation In Silico—Potential and Pitfalls

Contact Info

Product

Resources

About