Personalised Simulation Technologies for Optimising Treatment in the Intensive Care Unit

NCT04297397 | Completed

• 2 other identifiers: 266780PSTOTICUEP/P023444/1
• Study Type: observational
• Target: 25
• Locations: 1 country
• Sites: 1

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.

Conditions

Respiratory Distress Syndrome, Adult; Respiratory Insufficiency

Keywords

ICU; Patient Simulation; Critical Care; ARDS; Mechanical Ventilation

Outcome Measures

Primary Outcomes (1)

• Development of a simulation platform

  Develop predictive physiological models and simulation platform in mechanically ventilated patients with ARDS

  2 years

Secondary Outcomes (2)

• Development of dynamic modelling with integration of real time ICU data streams

  2 years

• Exploration of therapeutic intervention design space

  2 years

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Subjects will be drawn from the adult critical care population at GSTFT. Subjects of interest are those receiving mechanical ventilation.

You may qualify if:

• 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

You may not qualify if:

• Pregnancy or lactation

Sponsors & Collaborators

• Guy's and St Thomas' NHS Foundation Trust: lead
• University of Nottingham: collaborator
• University of Warwick: collaborator

Study Sites (1)

Guys & St. Thomas' NHS Foundation Trust

London, SE1 7EH, United Kingdom

Location

Related 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.

  PMID: 27799180 BACKGROUND

• 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.

  PMID: 25578295 BACKGROUND

• 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.

  PMID: 28178996 BACKGROUND

• 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.

  PMID: 23533735 BACKGROUND

MeSH Terms

Conditions

Respiratory Distress Syndrome; Respiratory Insufficiency

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Respiration Disorders

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 4, 2020
• First Posted: March 5, 2020
• Study Start: February 20, 2020
• Primary Completion: April 30, 2022
• Study Completion: May 31, 2022
• Last Updated: July 11, 2023

Record last verified: 2023-07

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)