NCT04581031

Brief Summary

This is a pilot study to assess whether artificial intelligence (AI) combined with continuous vital signs monitoring from wearable sensors can predict clinically relevant outcomes in patients with suspected or confirmed Covid-19 infection on general medical wards.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
48

participants targeted

Target at P25-P50 for not_applicable covid19

Timeline
Completed

Started Jul 2020

Longer than P75 for not_applicable covid19

Geographic Reach
1 country

2 active sites

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

May 4, 2020

Completed
2 months until next milestone

Study Start

First participant enrolled

July 11, 2020

Completed
3 months until next milestone

First Posted

Study publicly available on registry

October 9, 2020

Completed
1.5 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 22, 2022

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

April 22, 2022

Completed
Last Updated

May 27, 2022

Status Verified

May 1, 2022

Enrollment Period

1.8 years

First QC Date

May 4, 2020

Last Update Submit

May 26, 2022

Conditions

COVID-19

Outcome Measures

Primary Outcomes (1)

  • Development of an AI model to predict clinically relevant outcomes for ward-based patients with COVID-19 monitored for up to 20 days. Metrics to be employed depend on the algorithm used but include, Log-Loss, precision and/or recall and confusion matrix.

    1 year

Secondary Outcomes (2)

  • Performance of the wearable vital signs sensor as measured by the percentage of possible data capture that is actually obtained

    1 year

  • Look for evidence of circadian disruption in the vital signs of the enrolled patients.

    1 year

Study Arms (1)

Wearable monitors - Isansys Patient Status Engine

OTHER

All patients will wear the continuous vital sign monitoring sensors.

Device: Continuous vital sign monitoring - Isansys Patient Status EngineOther: Machine Learning/AI Algorithm

Interventions

Continuous vital sign monitoring - Isansys Patient Status EngineDEVICE

CE marked wearable continuous vital signs monitors

Wearable monitors - Isansys Patient Status Engine
Machine Learning/AI AlgorithmOTHER

Patient data will be subjected to machine learning/AI algorithms to determine whether algorithms may be beneficial as an early indication of patient's condition worsening.

Wearable monitors - Isansys Patient Status Engine

Eligibility Criteria

Age16 Years+
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

  • Participants are eligible to be included in the study only if all of the following criteria apply:
  • Adult (aged 16 years or older), hospital inpatients
  • Suspected or confirmed COVID-19 infection (nasopharyngeal swab sent or planned):
  • Positive nasopharyngeal swab during this admission OR
  • Nasopharyngeal swab pending during this admission and the treating team suspect COVID-19 OR
  • Negative nasopharyngeal swab during this admission but the treating team continue to suspect COVID-19 OR
  • Positive nasopharyngeal swab in the last 7 days
  • Emergency admission to hospital within the last 72 hours and/or a positive nasopharyngeal test within the last 72 hours taken from a patient who was already an inpatient at the time the swab was taken.
  • Symptoms consistent with COVID-19 infection at the time of admission or when swab taken: cough, shortness of breath, alteration to sense of taste or smell, fevers or other symptoms in keeping with COVID-19 in the opinion of the study team.
  • For full active treatment (including escalation to critical care)
  • The patient is at risk of deterioration (as evidenced by a requirement for supplementary oxygen)

You may not qualify if:

  • Participants are excluded from the study if any of the following criteria apply:
  • Patients unable to give informed consent.
  • Patients with a life expectancy of \<24hours.
  • Known allergy or history of contact dermatitis to medical adhesives.
  • Patients with pacemakers, implantable defibrillators or neurostimulators.
  • Patients with an arterio-venous fistula in either arm.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

The Christie NHS Foundation Trust

Manchester, M20 4BX, United Kingdom

Location

Manchester University NHS Foundation Trust

Manchester, United Kingdom

Location

Related Publications (1)

  • Wilson AJ, Parker AJ, Kitchen GB, Martin A, Hughes-Noehrer L, Nirmalan M, Peek N, Martin GP, Thistlethwaite FC. The completeness, accuracy and impact on alerts, of wearable vital signs monitoring in hospitalised patients. BMC Digit Health. 2025;3(1):13. doi: 10.1186/s44247-025-00151-x. Epub 2025 Apr 15.

    PMID: 40242279DERIVED

MeSH Terms

Conditions

COVID-19

Condition Hierarchy (Ancestors)

Pneumonia, ViralPneumoniaRespiratory Tract InfectionsInfectionsVirus DiseasesCoronavirus InfectionsCoronaviridae InfectionsNidovirales InfectionsRNA Virus InfectionsLung DiseasesRespiratory Tract Diseases

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Masking Details
The treating team on the ward will be blinded to the observations recorded by the wearable vital signs sensors
Purpose
OTHER
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

May 4, 2020

First Posted

October 9, 2020

Study Start

July 11, 2020

Primary Completion

April 22, 2022

Study Completion

April 22, 2022

Last Updated

May 27, 2022

Record last verified: 2022-05

Data Sharing

IPD Sharing
Will not share

Locations

GB(2)