COVID-19COSMIC-19NCT04581031
COntinuous Signs Monitoring In Covid-19 Patients
A study testing Continuous vital sign monitoring - Isansys Patient Status Engine and Machine Learning/AI Algorithm in people with COVID-19, run by The Christie NHS Foundation Trust at 2 sites in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- COVID-19
- What is being tested?
- Continuous vital sign monitoring - Isansys Patient Status Engine and Machine Learning/AI Algorithm
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 48 participants
- Where
- 2 sitesUnited Kingdom
- Study dates
- 11 Jul 2020to 22 Apr 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- 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
Show all 11Show fewer
- 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)
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country.
United Kingdom2 sites
The Christie NHS Foundation Trust
Manchester, M20 4BX
Manchester University NHS Foundation Trust
Manchester
About this study
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.
Read the detailed description
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
- COVID-19
- Being tested
- Continuous vital sign monitoring - Isansys Patient Status Engine, Machine Learning/AI Algorithm
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Continuous vital sign monitoring - Isansys Patient Status EngineDevice
CE marked wearable continuous vital signs monitors
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.
| Study group | Role | What they receive |
|---|---|---|
| Wearable monitors - Isansys Patient Status Engine | Other | All patients will wear the continuous vital sign monitoring sensors. Receives: Device: Continuous vital sign monitoring - Isansys Patient Status Engine; Other: Machine Learning/AI Algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Measured: 1 year
Look for evidence of circadian disruption in the vital signs of the enrolled patients.
To investigate whether circadian rhythm disruption is involved in COVID-19
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Oct 2020
- Study start11 Jul 2020
- Primary completion22 Apr 2022
- Study completion22 Apr 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for COVID-19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 48 participants (actual)
Sponsor and investigators
- Lead sponsor
- The Christie NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- Manchester University NHS Foundation Trust, Aptus Clinical Ltd., Zenzium Ltd.
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04581031
- Sponsor's protocol ID
- CFTSp187
- Acronym
- COSMIC-19
- Status last verified
- May 2022
- First submitted
- 4 May 2020
- Last updated
- 27 May 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 40242279
Study at a glance
From the registry record, last updated by the sponsor on 27 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- COntinuous Signs Monitoring In Covid-19 Patients
- ClinicalTrials.gov ID
- NCT04581031
- Acronym / sponsor ID
- COSMIC-19 / CFTSp187
- Conditions
- COVID-19
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 48 participants (actual)
- Lead sponsor
- The Christie NHS Foundation TrustUniversity, hospital or other
- Collaborators
- Manchester University NHS Foundation Trust, Aptus Clinical Ltd., Zenzium Ltd.
- Study start
- 11 Jul 2020 (actual)
- Primary completion
- 22 Apr 2022 (actual)
- Study completion
- 22 Apr 2022 (actual)
- First posted
- 9 Oct 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Wearable monitors - Isansys Patient Status Engine | Other | Device: Continuous vital sign monitoring - Isansys Patient Status Engine, Other: Machine Learning/AI AlgorithmAll patients will wear the continuous vital sign monitoring sensors. |
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (11 listed)
- 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):
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Continuous vital sign monitoring - Isansys Patient Status EngineCE marked wearable continuous vital signs monitors | Device | Wearable monitors - Isansys Patient Status Engine |
| Machine Learning/AI AlgorithmPatient 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. | Other | Wearable monitors - Isansys Patient Status Engine |
Primary outcome
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.
Time frame: 1 year
2 secondary outcomes
Performance of the wearable vital signs sensor as measured by the percentage of possible data capture that is actually obtained
Time frame: 1 year
Look for evidence of circadian disruption in the vital signs of the enrolled patients.
Time frame: 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 May 2022.
- Derived from this study: PubMed 40242279Wilson 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Continuous vital sign monitoring - Isansys Patient Status Engine and Machine Learning/AI Algorithm in people with COVID-19, run by The Christie NHS Foundation Trust at 2 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 11 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 May 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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. (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04581031, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 May 2022.