Covid19 Clinical Trial
— CARPOfficial title:
COVID-19 Advanced Respiratory Physiology (CARP) Study
The anticipated second wave of COVID-19 cases will present healthcare system challenges, including requirement to monitor large numbers of patients for deteriorating respiratory failure. Rising respiratory rate can identify deterioration requiring escalation of care. However constant monitoring of respiratory rate can be challenging outwith critical care units due to feasibility and inaccuracy of intermittent measurements. Wearable biosensors which allows for remote patient monitoring of RR is therefore attractive, particularly when combined in a dashboard with clinical summary data. This would establish source data and infrastructure for the training and validation of machine-learning models, with decision support risk-predictions prioritising alerts and clinician reviews.
Status | Recruiting |
Enrollment | 150 |
Est. completion date | September 29, 2021 |
Est. primary completion date | December 30, 2020 |
Accepts healthy volunteers | |
Gender | All |
Age group | N/A and older |
Eligibility | Inclusion Criteria: - Inclusion criteria - Adult patients with respiratory failure of any cause requiring hospital admission, oxygen therapy and routine NEWS-2 physiology observations. - CARP detailed sub-study Adult patients with respiratory failure who are suitable for opportunistically acquired serial detailed physiology measurements taken alongside routine clinical care by study team. - CARP follow-up remote-monitoring sub-study Adult patients with respiratory failure who have provided informed consent and have a smartphone to connect to Fitbit and Lenus accounts for postdischarge wearable device data capture. Exclusion Criteria: - Exclusion criteria • Lack of capacity or inability to comprehend informed consent. |
Country | Name | City | State |
---|---|---|---|
United Kingdom | NHS Greater Glasgow and Clyde | Glasgow |
Lead Sponsor | Collaborator |
---|---|
NHS Greater Glasgow and Clyde | Altair Medical, Storm ID |
United Kingdom,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Machine-learning model development | Developing risk-predictions for clinically significant deteriorations | 1 year |
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