Personalised Simulation Technologies for Optimising Treatment in the Intensive Care Unit

Respiratory Insufficiency Clinical Trial

— PSTOTICU  

Official title:

Personalised Simulation Technologies for Optimising Treatment in the Intensive Care Unit

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04297397
• Other study ID #: 266780PSTOTICU
• Secondary ID: EP/P023444/1
• Status: Completed
• Start date: February 20, 2020
• Est. completion date: May 31, 2022

Study information

• Verified date: July 2023
• Source: Guy's and St Thomas' NHS Foundation Trust
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This project aims to develop software models describing how critically ill patients respond to changes in their treatment whilst admitted to an Intensive Care Unit (ICU). We will use high performance computers to fit software models to the physiological and treatment data of patients receiving mechanical ventilation.

Description:

In the United Kingdom, approximately 142,000 people are admitted to ICU each year. A large proportion, 10 - 20%, of these patients have a life-threatening respiratory illness called Acute Respiratory Distress Syndrome (ARDS). These patients need specialist help with their breathing, from a machine called a ventilator. Only seven out of ten patients will survive this illness and even survival may bring ongoing problems, sometimes for a long time after leaving hospital. Accurate mathematical and computer models of ARDS, would allow investigation of the illness outside of the ICU and inside the virtual environment of a computer. Different treatments could be simulated on the same 'virtual' patient, or the same treatment on many different patients with varying degrees of illness. Development of these software models, requires collection of a library of data describing how patients respond to changes in their treatment. An example would be to describe how a patient's blood pressure responds to a change in the settings of their ventilator. The changes to a patient's ventilation would be made as part of the normal care provided by the doctors and nurses looking after them. Mathematical descriptions have been created before, from simpler data sets which were essentially single snapshots of a patient's condition and treatment. The investigators aim to capture sequences of snapshots over several hours, allowing them to build more accurate models. Guy's and St Thomas' NHS Foundation Trust (GSTFT) is the clinical partner of the project. Patients would be identified there by clinical researchers, who would then collect the data describing their treatment. This data would be anonymised before adding to the library of data to be shared with academic researchers. Academic members of the team at the University of Warwick and the University of Nottingham possess the engineering and mathematical expertise needed to develop the complex software models. They also provide the facility of a high performance computing cluster necessary for the difficult process of fitting models to the data. Once the software models have been built and used to examine the how treatment might be improved, the findings would be shared with clinical staff around the world, through the publication of articles in medical journals. It is possible that the insights gained by the modelling process might inform, change and improve how clinical staff use ventilators to support patients with ARDS.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 25
• Est. completion date: May 31, 2022
• Est. primary completion date: April 30, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 100 Years

Eligibility

Inclusion Criteria:

• At least 18 years of age
• Patients admitted to GSTFT Intensive Care between the dates 01/01/2010 and 31/03/2019
• Receiving mechanical ventilation

Exclusion Criteria:

• Pregnancy or lactation

Related Conditions & MeSH terms

• Acute Lung Injury
• Respiratory Distress Syndrome
• Respiratory Distress Syndrome, Adult
• Respiratory Distress Syndrome, Newborn
• Respiratory Insufficiency

Locations

Country	Name	City	State
United Kingdom	Guys & St. Thomas' NHS Foundation Trust	London

Sponsors (3)

Lead Sponsor	Collaborator
Guy's and St Thomas' NHS Foundation Trust	University of Nottingham, University of Warwick

Country where clinical trial is conducted

United Kingdom, 

References & Publications (4)

Chikhani M, Das A, Haque M, Wang W, Bates DG, Hardman JG. High PEEP in acute respiratory distress syndrome: quantitative evaluation between improved arterial oxygenation and decreased oxygen delivery. Br J Anaesth. 2016 Nov;117(5):650-658. doi: 10.1093/bja/aew314. — View Citation

Das A, Cole O, Chikhani M, Wang W, Ali T, Haque M, Bates DG, Hardman JG. Evaluation of lung recruitment maneuvers in acute respiratory distress syndrome using computer simulation. Crit Care. 2015 Jan 12;19(1):8. doi: 10.1186/s13054-014-0723-6. — View Citation

Das A, Haque M, Chikhani M, Cole O, Wang W, Hardman JG, Bates DG. Hemodynamic effects of lung recruitment maneuvers in acute respiratory distress syndrome. BMC Pulm Med. 2017 Feb 8;17(1):34. doi: 10.1186/s12890-017-0369-7. — View Citation

Flechelles O, Ho A, Hernert P, Emeriaud G, Zaglam N, Cheriet F, Jouvet PA. Simulations for mechanical ventilation in children: review and future prospects. Crit Care Res Pract. 2013;2013:943281. doi: 10.1155/2013/943281. Epub 2013 Mar 7. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Development of a simulation platform	Develop predictive physiological models and simulation platform in mechanically ventilated patients with ARDS	2 years
Secondary	Development of dynamic modelling with integration of real time ICU data streams	To integrate data-streams available in the ICU with our existing physiological modelling algorithms to enable real-time simulation of treatment response.	2 years
Secondary	Exploration of therapeutic intervention design space	To develop mathematical methods to explore the "design space" for a clinical support system.	2 years