Using Clinical Prediction Models to Improve Treatment for Patients With Chronic Obstructive Pulmonary Disease (COPD)

University of British Columbia

Status: Enrolling

Conditions: Chronic Obstructive Pulmonary Disease

Treatments: Other: Comparator; Other: ACCEPT Decision Intervention

Study type: Interventional

Funder types: Other

Identifiers

NCT05309356

F20-05804 (Other Grant/Funding Number)

H21-02348

Details and patient eligibility

About

Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease of the lungs that affects more than 2.5 million Canadians. Patients with COPD experience episodes of lung attacks (or exacerbations). During these attacks, patients experience an intense increase in symptoms, such as breathlessness and cough. It is challenging to decide which patients should be put on treatments that would reduce the risk of such lung attacks. The digitization of health records in many clinics and hospitals means complex risk prediction algorithms can be used to predict the risk of lung attacks to enable personalized care. In this study, our team will implement a risk prediction tool (called ACCEPT) into the electronic health records in two teaching hospitals in Vancouver, British Columbia (BC), Canada. A clinical study will be conducted to evaluate if the use of this tool results in patients with COPD receiving better care with better outcomes, and if they are more satisfied with the care they are receiving.

Full description

COPD is a heterogenous and progressive disease of the airways that affects millions of people worldwide. However, current treatment guidelines fail to provide personalised, patient-centered disease management. In contrast, precision medicine emphasizes the tailoring of disease management to patient characteristics and values to optimize patient care and outcomes. Clinical prediction models (CPMs) are major enablers of precision medicine, and facilitate targeted therapies to patients who will benefit the most from them.

The investigators developed a CPM called ACCEPT that improves risk stratification for COPD patients by predicting the risk of exacerbation at an individual level and thereby enabling personalized, preventive disease management. Using a stepped wedged cluster randomized controlled trial (RCT), the investigators aim to evaluate the impact of integrating ACCEPT into routine COPD care at two outpatient respiratory clinics in Vancouver, British Columbia, Canada.

The 'stepped wedged' RCT has a cross-over design, with treatment assignment done in a uni-directional, staggered format that will provide opportunities to control for time trend. The total duration of the study is 30 months. There will be a one-month phase in period with patient recruitment and data collection starting on month two. The last physician assignment will occur in month 18, and patient recruitment will continue until month 24. Follow-up data will be collected until month 30 to ensure six months of follow-up data for all patients.

Primary and secondary outcomes will be analysed using generalized estimating equations to account for possible clustering of endpoints (multiple visits for each physician). Further, following the intention to treat principle, clusters (physicians) will be analyzed according to their randomized crossover time irrespective of whether crossover was achieved at the desired time.

Enrollment: 1,130 estimated patients

Sex: All

Ages: 35+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Are a legal Canadian resident
• Aged 35 years and older
• Can speak English
• Have a diagnosis of COPD

Exclusion criteria

• Are under 35 years of age

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Crossover Assignment

Masking: None (Open label)

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1,130 participants in 2 patient groups

Usual care (Control)

Active Comparator group

Description:

Routine COPD patient care.

Treatment:

Other: Comparator

ACCEPT Decision Intervention

Experimental group

Description:

Clinical prediction model (ACCEPT)-based treatment recommendations: The ACCEPT tool will display the predicted risk of exacerbations, and the corresponding treatment recommendations to the physicians. These recommendations will be provided in a non-mandatory 'directive' format where the physician can override the recommendation, but is required to provide a justification (pre-set choices and a free text).

Treatment:

Other: ACCEPT Decision Intervention