Clinical Outcome Modelling of Rapid Dynamics in Acute Stroke

Sponsor
King's College Hospital NHS Trust (Other)
Overall Status
Recruiting
CT.gov ID
NCT04641286
Collaborator
King's College London (Other), University College, London (Other)
1,000
1
25.8
38.7

Study Details

Study Description

Brief 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.

Condition or Disease Intervention/Treatment Phase
  • Other: Body motion categorisation

Study Design

Study Type:
Observational
Anticipated Enrollment :
1000 participants
Observational Model:
Cohort
Time Perspective:
Prospective
Official Title:
Clinical Outcome Modelling of Rapid Dynamics in Acute Stroke With Joint-detail, Remote, Body Motion Analysis
Actual Study Start Date :
Jul 7, 2021
Anticipated Primary Completion Date :
Sep 1, 2023
Anticipated Study Completion Date :
Sep 1, 2023

Arms and Interventions

Arm Intervention/Treatment
Stroke

Individuals admitted to the Hyper Acute Stroke Unit.

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

Outcome Measures

Primary Outcome Measures

  1. 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. [Up to 24 weeks]

    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.

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years and Older
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
Inclusion Criteria:
  • Putative diagnosis of an acute stroke

  • Admission on the stroke unit

Exclusion Criteria:
  • Under 18 years of age

Contacts and Locations

Locations

Site City State Country Postal Code
1 King's College Hospital NHS Foundation Trust London United Kingdom

Sponsors and Collaborators

  • King's College Hospital NHS Trust
  • King's College London
  • University College, London

Investigators

  • Study Chair: Yee Mah, King's College Hospital NHS Trust

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
King's College Hospital NHS Trust
ClinicalTrials.gov Identifier:
NCT04641286
Other Study ID Numbers:
  • KCH20-069
  • MR/T005351/1
First Posted:
Nov 23, 2020
Last Update Posted:
Aug 17, 2022
Last Verified:
Aug 1, 2022
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms:

Study Results

No Results Posted as of Aug 17, 2022