Personalised Simulation Technologies for Optimising Treatment in the Intensive Care Unit

NCT ID: NCT04297397

Last Updated: 2023-07-11

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 25 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-02-20
• Study Completion Date: 2022-05-31

Brief 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.

Detailed 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.

Conditions

Respiratory Distress Syndrome, Adult; Respiratory Insufficiency

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of Nottingham

OTHER

Sponsor Role: collaborator

University of Warwick

OTHER

Sponsor Role: collaborator

Guy's and St Thomas' NHS Foundation Trust

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Guys & St. Thomas' NHS Foundation Trust

London, , United Kingdom

Countries

United Kingdom

References

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.

Reference Type: BACKGROUND

PMID: 27799180 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 25578295 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 28178996 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 23533735 (View on PubMed)

Provided Documents

Document Type: Study Protocol

View Document

Other Identifiers

EP/P023444/1

Identifier Type: OTHER

Identifier Source: secondary_id

266780PSTOTICU

Identifier Type: -

Identifier Source: org_study_id