Personalised Simulation Technologies for Optimising Treatment in the Intensive Care Unit: Realising Industrial and Medical Applications

  • Funded by UK Research and Innovation (UKRI)
  • Total publications:13 publications

Grant number: EP/P023444/1

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Key facts

  • Disease

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    UK Research and Innovation (UKRI)
  • Principle Investigator

  • Research Location

    United Kingdom, Europe
  • Lead Research Institution

    University of Warwick
  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Supportive care, processes of care and management

  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment


  • Age Group


  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable


In the UK, approximately 142,000 people are admitted to Intensive Care Units (ICU) each year. A large proportion of these patients have life-threatening pulmonary illness and require mechanical ventilation; the mortality rate in this group is around 35%, and even survival may bring ongoing suffering lasting years after discharge. Critical pulmonary disease thus has enormous financial impact and represents a significant burden of suffering for the general population. Despite years of research, there has been a lack of progress in our understanding of critical illness and in our ability to personalise treatment. Traditional clinical research approaches (using randomised clinical trials) have been costly and often inconclusive, and have provided disappointing improvements in critical care (diagnosis, survival, cost-effectiveness). The development of more effective personalised treatments for this patient population would therefore have significant national and global impact. In this project, we will develop novel methods for personalising and optimising the therapy delivered in the ICU. We will work closely with our business and clinical partners to transfer our high-fidelity modelling technologies from the research lab to the ICU, in order that real-time, personalised, patient simulation can be achieved with the aim of guiding the treatment of critical illness. This approach offers potentially "low-cost" improvements in patient-care, since it is based on smarter strategies and technologies that exploit and optimise multiple interventions, without requiring expensive new pharmaceuticals or devices. Using large-scale integration of incoming data streams from routine patient monitoring, our technology will allow us to establish a matched simulation of an individual patient's physiology. The resulting personalised bedside simulation will allow clinicians to test planned interventions and to estimate vital parameters in the patient that would otherwise be inaccessible. In addition to acting passively, the technology will proactively advise on optimised treatment strategies that are expected to improve patient outcome. The technology will scan the patient's treatment and physiological data continually, seeking potential improvements in management, and testing proposed treatment strategies by applying them to the personalised simulation and assessing outcome.Personalised optimisation of critical care treatment offers the opportunity to improve patient outcomes and reduce days spent receiving mechanical ventilation in the intensive care unit, and has the potential for enormous impact in terms of reducing patient suffering and healthcare expenditure. We will make this potential a reality by working closely with our business partner Medtronic (the world's largest standalone medical technology development company, and a leading ventilator manufacturer) and with our clinical partner Prof. Luigi Camporata, a consultant in intensive care medicine at Guy's and St Thomas' NHS Foundation Trust (one of the UK's leading centres for research on the treatment of critical illness).

Publicationslinked via Europe PMC

Last Updated:39 minutes ago

View all publications at Europe PMC

A computational cardiopulmonary physiology simulator accurately predicts individual patient responses to changes in mechanical ventilator settings.

Why Reduced Inspiratory Pressure Could Determine Success of Non-Invasive Ventilation in Acute Hypoxic Respiratory Failure.

Validation of at-the-bedside formulae for estimating ventilator driving pressure during airway pressure release ventilation using computer simulation.

Modeling Mechanical Ventilation In Silico-Potential and Pitfalls.

High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational modelling study.

Ventilation strategies for front of neck airway rescue: an in silico study.

In Silico Modeling of Coronavirus Disease 2019 Acute Respiratory Distress Syndrome: Pathophysiologic Insights and Potential Management Implications.

Management of primary blast lung injury: a comparison of airway pressure release versus low tidal volume ventilation.