COPD
Conditions
Brief summary
The goal of this clinical investigation is to explore the efficacy and economics of a novel medical device system which, through the remote monitoring and evaluation of acoustic pathophysiological parameters in long-term respiratory conditions, flags patients who are beginning to deteriorate for clinical review, in order to reduce time-to-intervention in respiratory disease flareups. The main question it aims to answer are: Would the use of the Senti-AI and Senti-Wear Device System reduce time-to-intervention in respiratory disease flareups? Participants will wear the Senti-Wear device up to twice per day as tolerated for 12 weeks. The Senti-AI subsystem will generate FLAREs (flags for review) and these will retrospectively be compared to the standard of care to evaluate whether acting on the FLARES would have reduced time-to-intervention.
Detailed description
This clinical investigation seeks to establish whether or not the Senti-Wear device system with the Senti-AI subsystem is effective for autonomously monitoring people with long-term respiratory illness with COPD. The aim of this autonomous monitoring is to generate flags for review (FLARES) if the system detects a worsening (deterioration) in lung sounds which might indicate a worsening respiratory illness. If effective, this device system will enable people with long-term respiratory illnesses to receive earlier intervention in disease flare-ups, ensuring these patients are on the right medication at the right time, avoiding hospital admissions, and staying well at home. 200 patients with COPD will be recruited. The design of this study is comparative. All 200 participants will undertake patient-sought care as usual, where patients seek medical aid once starting to feel more unwell. Over a six-month period, we will collect data on what care is sought and when this changes treatment. Participants will also use the Senti-Wear device to capture their breath and heart sounds twice a day over this six-month period. Participants, clinicians, and the research team will be blinded to the device output FLARES until the end of the data collection period. At the end of the data collection period, we will compare this with Senti-AI FLARES, to determine whether acting on FLARES would have led to early intervention in disease flareups. The study activities will take place in the participants' own homes. The study will recruit around Manchester and Liverpool, UK. The study is being funded by Senti-Tech limited, the manufacturer of the Senti-Wear with Senti-AI device system.
Interventions
DEVICESenti Wear device system with Senti-AI subsystem
This device is a smart garment, with a similar form to a T-Shirt, jacket or tabard, embedded with ten sensor modules (nine acoustic sensors, one kinetic sensor - all able to detect signals from the chest wall and underlying structures, including the lungs) encased in silicone; the device is charged via a charging port in the garment. The device has two modes of operation: recording mode and charging mode. The device is internally (battery) powered during recording mode and mains-powered during charging mode. The device is accompanied by cloud-based software to listen to both contemporaneous and historically recorded breath sounds, for each Senti patient. To use the device, the user puts the garment on (overhead, like a T-Shirt, before joining the back piece to the front like a tabard). Additionally, Senti-AI adds an anomaly burden score and an alert flag for review against each acoustic record for each patient.
Sponsors
Senti Tech Ltd
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Masking description
Participants, clinicians, and the research team will be blinded to the device output FLARES until the end of the data collection period
Intervention model description
This question will be answered by a double-blinded, comparative, device efficacy investigation, comparing the standard of care with a virtual arm simulating care delivered by acting on FLAREs generated by the Senti-AI subsystem.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Diagnosis of COPD (on GP record is acceptable) and at least one COPD exacerbation requiring steroid prescription in the last three years
Exclusion criteria
* Participants with significant cognitive impairment or limiting physical disabilities - to the extent that they are not able to manage their own Activities of Daily Living; except where sufficient family or care support is available to manage the device functions (in which case, the device may be used with caution). * Participants with existing pressure sores across the thorax and/or torso. * Participants under the age of 18. * Participants with a height outside the range (150-189cm) * Participants with a waist circumference outside the range (62-169cm) * Participants with a known sensitivity or allergy to any of the components of the device. * Participants unable to give their consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC at 24 hours, 48 hours, 72 hours, 5 days, and 10 days prior to a definitive change in treatment | 12 weeks | Area under the Receiver Operator Characteristic curve (AUC) for FLAREs at 24 hours, 48 hours, 72 hours, 5 days, and 10 days prior to a definitive change in treatment being initiated (such as antibiotics/steroids prescription). This outcome measure is clinically meaningful as this metric quantifies the performance of the system for reducing time-to-intervention in respiratory disease flareups by specific time intervals. Earlier intervention in respiratory disease flareups, in turn, will improve outcomes, reduce hospitalisations, and improve quality of life. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Average time difference between FLAREs and change in treatment | 12 weeks | Average time difference between true-positive FLARE to change in treatment. Change in treatment is defined as antibiotic/steroid/nebuliser acute prescription or change in respiratory preventative medication. |
| FLAREs False Positive Rate | 12 weeks | False Positives (Proportion of FLARES where there was no change in care in the following three weeks under the standard of care). As if the false positive rate is too high, then this will increase burden on services through unnecessary clinical reviews. |
| Device utilisation | 12 weeks | Cumulative time device was worn and recorded data over. This will provide an idea of device usability, uptake, and churn. |
| Number of A&E attendances | 12 weeks | Number of A&E attendances |
| Number of hospital admissions | 12 weeks | Number of hospital admissions |
| Total sum length of stay of any hospital admissions | 12 weeks | Total sum length of stay of any hospital admissions |
| Number of GP visits | 12 weeks | Number of GP visits |
| Number of specialist respiratory appointments | 12 weeks | Number of specialist respiratory appointments |
| Respiratory Symptomology | 12 weeks | Correlation between FLARES and change in symptomatology. As this will provide supportive evidence of the algorithm's efficacy. |
| Usability | 12 weeks | Technical and usability device-related issues - qualitative thematic reporting of issues reported by users related to operating the device. As this will drive improvements in future generations of the device. |
| ADEs | 12 weeks | Adverse device events. As this will quantify the risks associated with use of the device. |
Countries
United Kingdom
Outcome results
None listed