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Mathematical and Computational Modelling of High Flow Nasal Oxygen Therapy


   School of Engineering

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  Prof D Bates  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

An exciting opportunity exists to join the Interdisciplinary Collaboration in Systems Medicine (ICSM) Research Group as a doctoral candidate. Working with an international team of clinicians and engineers, you will develop mathematical models of high-flow nasal oxygen therapy (HFNOT), an advanced form of non-invasive respiratory support that is increasingly widely used in the treatment of acute hypoxic respiratory failure (AHRF). Models will be coded in Matlab and incorporated into the ICSM Cardiopulmonary simulator, a world-leading simulation platform that has been in continuous development by our group for over 25 years [1-5].

In a recent publication in the British Journal of Anaesthesia [6,7], we used modelling and simulation to evaluate the effects of HFNOT compared to other forms of non-invasive respiratory support in a “virtual” cohort of 120 COVID-19 patients experiencing AHRF. This study suggested that HFNOT could deliver similar improvements in oxygenation while producing lower levels of lung stress and strain (risk factors for lung injury) than other forms of support. The proposed research will extend this study in a number of ways:

1. Individualised patient modelling and stratification using new patient data to investigate the effectiveness of HFNOT across different patient phenotypes.

2. More detailed modelling of HFNOT mechanisms when treating AHRF, including: effects of cardiogenic oscillations, dead space gas mixing, and micro-ventilation on CO2 clearance, effects of different cannulae bore sizes on oxygen delivery, effects of different flow rates on pressure support delivered, etc.

3. Investigating combining and transitioning between HFNOT and other forms of non-invasive respiratory support, such as continuous positive airway pressure (CPAP) or non-invasive ventilation (NIV), in terms of patient oxygenation as well as the mechanical forces produced inside the lung.


References

1. A. Das, P.P. Menon, J. Hardman and D.G. Bates, “Optimization of Ventilation Settings for Pulmonary Disease States”, IEEE Transactions on Biomedical Engineering, 60(6):1599-607, 2013.
2. M. Chikhani, A. Das, M. Haque, W. Wang, D.G. Bates, and J.G. Hardman, "High PEEP in ARDS: evaluating the trade-off between improved oxygenation and decreased oxygen delivery", British Journal of Anaesthesia, 117 (5): 650–8 (2016) DOI: 10.1093/bja/aew314, 2016.
3. S. Saffaran, A. Das, J.G. Hardman, N. Yehya and D.G. Bates, "High-fidelity Computational Simulation to Refine Strategies for Lung-Protective Ventilation in Paediatric Acute Respiratory Distress Syndrome", Intensive Care Medicine, https://doi.org/10.1007/s00134-019-05559-4, 2019
4. S. Saffaran, A. Das, J.G. Laffey, J.G. Hardman, N. Yehya and D.G. Bates, "Utility of driving pressure and mechanical power to guide protective ventilator settings in two cohorts of adult and pediatric patients with acute respiratory distress syndrome: A computational investigation", Critical Care Medicine, DOI:10.1097/CCM.0000000000004372, 2020.
5. L. Weaver, A. Das, S. Saffaran, N. Yehya, T.E. Scott, M. Chikhani, J.G. Laffey, J.G. Hardman, L. Camporota and D.G. Bates, "High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational study", Annals of Intensive Care, 11:109, 2021.
6. L. Weaver, A. Das, S. Saffaran, N. Yehya, M. Chikhani, T.E. Scott, J.G. Laffey, J.G. Hardman, L. Camporota and D.G. Bates, "Optimising respiratory support for early COVID-19 pneumonia: a computational modelling study", British Journal of Anaesthesia, 128(6):1052e1058, 2022.
7. V. Tsolaki and G.E. Zakynthinos, "Simulation to minimise patient self-inflicted lung injury: are we almost there? British Journal of Anaesthesia, Jun 18:S0007-0912(22)00245-8. doi: 10.1016/j.bja.2022.05.007, 2022.

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