COntinuous Signs Monitoring In Covid-19 Patients

Study Overview
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Brief Summary
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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
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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.

Official Title
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COntinuous Signs Monitoring In Covid-19 Patients

Conditions
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COVID-19

Intervention / Treatment
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* Device: Continuous vital sign monitoring - Isansys Patient Status Engine
* Other: Machine Learning/AI Algorithm


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 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) Exclusion Criteria: 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.

Ages Eligible for Study
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Minimum Age: 16 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Other: Wearable monitors - Isansys Patient Status Engine<br>All patients will wear the continuous vital sign monitoring sensors. | Device: Continuous vital sign monitoring - Isansys Patient Status Engine<br>* CE marked wearable continuous vital signs monitors<br>Other: Machine Learning/AI Algorithm<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | To investigate whether circadian rhythm disruption is involved in COVID-19 | 1 year |