Clinical Outcome Modelling of Rapid Dynamics in Acute Stroke

Stroke Clinical Trial

Official title:

Clinical Outcome Modelling of Rapid Dynamics in Acute Stroke With Joint-detail, Remote, Body Motion Analysis

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04641286
• Other study ID #: KCH20-069
• Secondary ID: MR/T005351/1
• Status: Recruiting
• Start date: July 7, 2021
• Est. completion date: January 2025

Study information

• Verified date: October 2023
• Source: King's College Hospital NHS Trust
• Contact: Lead Stroke Research Co-ordionator
• Phone: 02032999000
• Email: j.aeron-thomas[@]nhs.net
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Stroke - still the second commonest cause of death and principal cause of adult neurological disability in the Western World - is characterised by rapid changes over time and marked variability in outcomes. A patient may improve or deteriorate over minutes, and the resultant disability may range from an obvious complete paralysis to subtle, task dependent incoordination of a single limb.

Unlike many other neurological disorders, stroke can be exquisitely sensitive to prompt and intelligently tailored treatment, rewarding innovation in the delivery of care with real-world, tangible impact on patient outcomes. Optimal treatment therefore requires both detailed characterisation of the patient's clinical picture and its pattern of change over time.

Arguably the most important aspect of the patient's clinical picture -- body movement -- remains remarkably poorly documented: quantified only subjectively and at infrequent intervals in the patient's clinical evolution. The combination of artificial intelligence with high-performance computing now enables automatic extraction of a patient's skeletal frame resolved down to major joints, like that of a stick-man, to be delivered simply, safely, and inexpensively, without the use of cumbersome body worn markers. Central to this technology is patient privacy, with the skeletal frame extracted in real time, ensuring no video data, from which patients can be identified, to be stored or transmitted by the device.

Our motion categorisation system -- MoCat -- will be used to study the rapid dynamics of acute stroke, seamlessly embedded in the clinical stream. By quantifying the change in motor deficit over time we shall examine the relationship between these trajectories with clinical outcomes and develop predictive models that can support clinical management and optimise service delivery.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 5000
• Est. completion date: January 2025
• Est. primary completion date: January 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Putative diagnosis of an acute stroke

• Admission on the stroke unit

Exclusion Criteria:

• Under 18 years of age

Related Conditions & MeSH terms

• Stroke

Intervention

Other:
• Body motion categorisation
All patients will receive passive motion categorisation monitoring

Locations

Country	Name	City	State
United Kingdom	King's College Hospital NHS Foundation Trust	London

Sponsors (3)

Lead Sponsor	Collaborator
King's College Hospital NHS Trust	King's College London, University College, London

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Quantify the contribution of joint-level motor dynamics to high-dimensional, predictive models of major clinical outcomes in acute stroke through comparisons of predictive fidelity.	The predictive fidelity will be quantified by out-of-sample receiver operating characteristic curves for binary variables and mean squared error for real number variables.	Up to 24 weeks