Clinical trial NCT06802003: Predicting Acute Exacerbations of COPD Using Wearable Devices and Remote Monitoring Technology With AI/ML Models
Official title: Early Prediction of Acute Exacerbations of COPD Using Wearable and Portable Remote Monitoring Technology With AI/ML Empowered Platforms: A Prospective Clinical Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: COPD
AE COPD
Keywords: acute exacerbation of COPD (AECOPD)
chronic obstructive pulmonary disease (COPD)
artificial intelligence/machine learning (AI/ML)
remote patient monitoring (RPM)
biometric wearable device
oscillometry
Lead sponsor: McGill University Health Centre/Research Institute of the McGill University Health Centre
Sponsor class: OTHER
Collaborators: name: Restech Srl
First posted: 2025-01-30
Enrollment: 50
Sex: ALL
Minimum age (years): 40
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with COPD with a documented history of frequent exacerbations.

While there is no specific method to estimate sample sizes for machine learning-based clinical research, based on two prior COPD studies measuring respiratory rate (RR) differences between stable-state and peak exacerbation phases, it was estimated that detecting a 4 breaths/min difference in RR (effect size 0.74) requires 17 prospectively collected exacerbation events, with alpha set to 0.05, power set to 0.8, and two-tailed analysis. Detecting a more subtle difference (i.e. 2 breaths/min; effect size 0.36) would require 63 events.

Assuming each participant experiences two exacerbations annually, 32 participants would meet these requirements. To account for the long observation period, the potential for non-events, and a 25% attrition rate, up to 50 participants will be recruited for this one-year study.

Brief summary: This study is aimed to collect real-time physiological data using two wearable devices (a biometric ring and a biometric wristband), daily lung mechanical measurements by a handheld oscillometer, and participant-reported symptoms in patients with COPD remotely from their home environment. The data will be used to train and validate artificial intelligence and machine learning (AI/ML) models to predict COPD exacerbations in advance of their actual occurrence. The data will also be used to test the new severity classification system for exacerbations of COPD, as well as to determine important relationships between physiological measurements from the wearable devices, the handheld oscillometer, the self-reported symptoms, and the tests performed at the baseline visit.

Detailed description: A single-site, prospective, observational cohort study to collect high-quality multidimensional data from the wearable/portable devices, as well as symptom and exacerbation data, in high-risk patients with frequent exacerbations in order to develop a COPD exacerbation predictive model.

Eligibility criteria: Inclusion Criteria:

* Males/females, age ≥ 40, former/current smokers with ≥10 pack-year smoking history
* FEV1/FVC \< 0.7, with 80% \< FEV1 ≤50% (moderate, 'GOLD 2') 50% \< FEV1 ≤ 30% (severe, 'GOLD 3') or FEV1 \< 30% (very severe, 'GOLD 4') COPD
* History of 2 or more exacerbations in the preceding 12 months requiring corticosteroids, antibiotics, or both
* Ability to provide informed consent
* Ability to access internet at least once daily

Exclusion Criteria:

* No existing COPD diagnosis
* Any medical/cognitive/functional condition which renders inability to operate research equipment/devices, and/or to complete daily symptom response

Primary outcomes:
measure: Respiration
timeFrame: Daily/nightly for 12 months
measure: Cardiovascular
timeFrame: Daily/nightly for 12 months
measure: Oxygen level
timeFrame: Daily/nightly for 12 months
measure: Step count
timeFrame: Daily/nightly for 12 months
measure: Sleep duration
timeFrame: Nightly for 12 months
measure: Rapid eye movement (REM) sleep
timeFrame: Nightly for 12 months
measure: Deep sleep
timeFrame: Nightly for 12 months
measure: Body temperature
timeFrame: Nightly for 12 months
measure: Resistance at 5 Hz (R5)
timeFrame: Daily for 12 months
measure: Reactance at 5 Hz (X5)
timeFrame: Daily for 12 months
measure: Intra-breath difference between expiratory and inspiratory reactance (ΔXrs)
timeFrame: Daily for 12 months
measure: Tidal volume (Vt)
timeFrame: Daily for 12 months
measure: Respiratory flows
timeFrame: Daily for 12 months
measure: Respiratory rate (RR)
timeFrame: Daily for 12 months
measure: Minute ventilation (Ve)
timeFrame: Daily for 12 months
measure: Heart rate (HR) prior oscillometry test
timeFrame: Daily for 12 months
measure: Blood oxygen saturation (SpO2) prior oscillometry test
timeFrame: Daily for 12 months
measure: Daily symptom questionnaire
timeFrame: Daily for 12 months
measure: Weekly exacerbation questionnaire
timeFrame: Weekly for 12 months
measure: Calories
timeFrame: Daily/nightly for 12 months
measure: Metabolic equivalents
timeFrame: Daily/nightly for 12 months
measure: Movement intensity
timeFrame: Daily/nightly for 12 months
measure: Sleep efficiency
timeFrame: Nightly for 12 months
measure: Sleep oncet
timeFrame: Nightly for 12 months
measure: Sleep disturbance
timeFrame: Nightly for 12 months
Secondary outcomes:
measure: COPD Assessment Test (CAT)
timeFrame: Once (baseline visit)
measure: 6-Minute Walk Test (6MWT)
timeFrame: Once (baseline visit)
measure: System Usability Scale (SUS)
timeFrame: Through study completion, 1 year.
Other outcomes:
Locations:
city: Montreal
country: Canada
facility: McGill University Health Centre
state: Quebec
Study officials:
References: