Clinical Outcome Modelling of Rapid Dynamics in Acute Stroke | NCT04641286

Studies
Clinical Outcome Modelling of Rapid Dynamics in Acute Stroke

NCT04641286

Sponsor

King's College Hospital NHS Trust

Status

Recruiting

Last Update Posted

Oct 24, 2024Actual

Enrollment

8,000Estimated

Phase

Not provided

Conditions

Stroke

Interventions

Body motion categorisation

Countries

United Kingdom

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

ID	Type	Description	Link
MR/T005351/1	Other Grant/Funding Number	MRC

Status Module

Record Verification Date

Oct 2024

Overall Recruitment Status or Expanded Access Status

Recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jul 7, 2021Actual

Primary Completion Date

Feb 2028Estimated

Completion Date

Feb 2028Estimated

First Submitted Date

Sep 22, 2020

First Submission Date that Met QC Criteria

Nov 20, 2020

First Posted Date

Nov 23, 2020Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Oct 22, 2024

Last Update Posted Date

Oct 24, 2024Actual

Name	Class
King's College London	OTHER
University College, London	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

8,000Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Stroke		Individuals admitted to the Hyper Acute Stroke Unit.	Other: Body motion categorisation

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Body motion categorisation	Other	All patients will receive passive motion categorisation monitoring	Stroke

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
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

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Putative diagnosis of an acute stroke
Admission on the stroke unit

Exclusion Criteria:

Under 18 years of age

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Patients admitted to the stroke unit with a putative diagnosis of an acute stroke.

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Lead Stroke Research Co-ordionator	Contact	02032999000		kch-tr.kingsresearch@nhs.net

Overall Officials

Name	Affiliation	Role
Yee Mah	King's College Hospital NHS Trust	Study Chair

Locations

Facility	Status	City	State	ZIP	Country	Contacts
King's College Hospital NHS Foundation Trust	Recruiting	London			United Kingdom

Condition Browse Module

MeSH Terms

ID	Term
D020521	Stroke

Ancestor Terms

ID	Term
D002561	Cerebrovascular Disorders
D001927	Brain Diseases
D002493	Central Nervous System Diseases
D009422	Nervous System Diseases

Browse Leaves

Not provided

Browse Branches

Not provided