COntinuous Signs Monitoring In Covid-19 Patients

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

48 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-07-11

Study Completion Date

2022-04-22

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

COVID-19 Surveillance Based on Smart Wearable Device

NCT04459637

COVID-19

COMPLETED

Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms

NCT04510441

Coronavirus

UNKNOWN

Feasibility of Remote Evaluation and Monitoring of Acoustic Pathophysiological Signals With External Sensor Technology in Covid-19

NCT04695821

Covid19

UNKNOWN NA

International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms

NCT05334680

COVID-19

UNKNOWN

SENSING-AI Cohort for Long COVID Patients

NCT05204615

Post-acute COVID-19 Syndrome

COMPLETED

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Adult patients on general medical wards with COVID-19 infection considered to be at high risk of deterioration will be asked to wear vital signs sensors for the duration of their hospital stay. These sensors are an established method of recording patient vital signs and are CE marked. Patients enrolled in the study will continue to receive routine medical care as directed by their treating team.

All data recorded from the wearable sensors in this study will be analysed in conjunction with routine data collected during the patient's treatment. Several models will be created using deep learning AI techniques with the aim of reliably predicting several important clinical outcomes. The study will identify whether continuous monitoring alone can improve identification of deteriorating patients compared to traditional vital signs and if the addition of AI technology / algorithms can provide even earlier identification.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

COVID-19

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

OTHER

Blinding Strategy

NONE

The treating team on the ward will be blinded to the observations recorded by the wearable vital signs sensors

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Wearable monitors - Isansys Patient Status Engine

All patients will wear the continuous vital sign monitoring sensors.

Group Type OTHER

Continuous vital sign monitoring - Isansys Patient Status Engine

Intervention Type DEVICE

CE marked wearable continuous vital signs monitors

Machine Learning/AI Algorithm

Intervention Type OTHER

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.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Continuous vital sign monitoring - Isansys Patient Status Engine

CE marked wearable continuous vital signs monitors

Intervention Type DEVICE

Machine Learning/AI Algorithm

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.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

Participants are eligible to be included in the study only if all of the following criteria apply:

1. Adult (aged 16 years or older), hospital inpatients
2. Suspected or confirmed COVID-19 infection (nasopharyngeal swab sent or planned):

1. Positive nasopharyngeal swab during this admission OR
2. Nasopharyngeal swab pending during this admission and the treating team suspect COVID-19 OR
3. Negative nasopharyngeal swab during this admission but the treating team continue to suspect COVID-19 OR
4. Positive nasopharyngeal swab in the last 7 days
3. 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.
4. 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.
5. For full active treatment (including escalation to critical care)
6. The patient is at risk of deterioration (as evidenced by a requirement for supplementary oxygen)

Exclusion Criteria

Participants are excluded from the study if any of the following criteria apply:

1. Patients unable to give informed consent.
2. Patients with a life expectancy of \<24hours.
3. Known allergy or history of contact dermatitis to medical adhesives.
4. Patients with pacemakers, implantable defibrillators or neurostimulators.
5. Patients with an arterio-venous fistula in either arm.

Minimum Eligible Age

16 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No