COPD Exacerbation Modelling Using Unobtrusive Sensors - the TOLIFE Clinical Study A

Study Overview
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Brief Summary
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This work is a multicentric prospective cohort study designed to improve chronic obstructive pulmonary disease (COPD) treatment and management. The study involves 150 patients diagnosed with COPD who are at risk of exacerbations. These patients are recruited from three tertiary hospitals in Spain, Germany, and Italy. The study will last 18 months, with a 12-month follow-up duration for each patient. The primary objective of this study is to develop and test Artificial Intelligence (AI)-based models that can predict moderate-to-severe COPD exacerbations early on. This will be done by analyzing daily-life data collected from unobtrusive sensors that monitor patients' psycho-physiological and environmental signals. By accurately predicting exacerbations, the study aims to support clinicians in providing more precise, optimized, and personalized treatment to COPD patients. A secondary objective is to train and test AI-based models to estimate the 12-month dynamics of health-related quality of life (HRQoL) in COPD patients. This will involve analyzing data related to the patients' functional exercise capacity, dyspnea (difficulty breathing), and health-related quality of life, as measured by the Clinical COPD Questionnaire (CCQ) score and the COPD Assessment Test (CAT) score.

Official Title
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COPD Exacerbation Modelling Study Using Daily-life Data From Unobtrusive Sensors - the TOLIFE Clinical Study A

Conditions
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Chronic Obstructive Pulmonary Disease (COPD)

Intervention / Treatment
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* Other: Variations in daily life activity signals detected by unobtrusive sensors


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Diagnosis of COPD (FEV1/FVC < 0.7) for at least a year. Participants with moderate-to-very severe COPD (FEV1 ≤ 80% predicted) Documented history of ≥ 1 moderate (treated with SABDs and oral corticosteroids ± antibiotics) or severe COPD exacerbation* within 12 months before enrolment. Adults aged 40 or over. Able to walk 4 meters independently with or without walking aids. Anticipated availability for repeated study visits over 12 months. Willingness to use smart sensors. Able to read and write in the first language in the respective location. Capable of giving signed informed consent, which includes compliance with the requirements and restrictions listed in the informed consent form (ICF) and the study protocol. Exclusion Criteria: Occurrence of any of the following within three months before informed consent: myocardial infarction, hospitalization for unstable angina, stroke, coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), implantation of a cardiac resynchronization therapy device (CRTD). Uncontrolled congestive heart disease (NYHA class >3). Having undergone major lung surgery (e.g., lung transplant) Primary respiratory diseases other than COPD. Participant who has experienced a major respiratory infection or exacerbation within 2 weeks before screening Lung volume reduction within six months before screening. Active treatment for cancer or other malignant diseases that in the opinion of the investigator, have an impact on the patient's quality of life such as to prevent adherence to the study. Acute psychosis or major psychiatric disorders or continued substance abuse. Severe disease that limits survival to 1 year. Patients with severe cognitive impairment (MMSE < 18). Substantial limitations in mobility due to factors other than COPD. Inability to follow the study procedures (e.g., due to language problems, psychological disorders) or unable to read, understand and fill in a questionnaire.

Ages Eligible for Study
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Minimum Age: 40 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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Arms and Interventions

| Intervention/Treatment |
| --- |
|Other: Variations in daily life activity signals detected by unobtrusive sensors|Patients will be equipped with unobtrusive devices for a duration of 12 months. Throughout this period, they will attend scheduled visits every three months following the baseline visit. These assessments will focus on determining the frequency of exacerbations, evaluating exercise capacity, measuring the severity of dyspnea, and assessing health-related quality of life. The data gathered from the sensors embedded in these unobtrusive devices will be instrumental in developing AI-based models. These models aim to accurately predict COPD exacerbations and effectively estimate the progression of the previously mentioned health outcome.|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Occurrence of moderate-to-severe COPD exacerbations | Occurrence of moderate-to-severe COPD exacerbations at months 3, 6, 9, and 12, through medical records | Months 3, 6, 9, and 12 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in the health-related quality of life | Health-related quality of life at baseline, months 3, 6, 9, and 12, using the Clinical COPD Questionnaire (CCQ) and the COPD Assessment Test (CAT). The CCQ score ranges from 0 to 6, and the CAT score ranges from 0 to 40. Both questionnaires use higher scores to indicate a more severe impact of COPD on a patient's life. | Baseline, Month 3, 6, 9 and 12 | 
| Changes in the functional exercise capacity | Changes in the functional exercise capacity at baseline, month 3, 6, 9 and 12, as measured by the Six-minute walking distance | Baseline, Month 3, 6, 9 and 12 | 
| Changes in the dyspnoea severity grade | Changes in the dyspnoea severity grade at baseline, month 3, 6, 9, and 12, as measured by the modified Medical Research Council (MRC) Dyspnoea scale. The mMRC Dyspnoea scale ranges from 0 to 4, with higher scores indicating more severe dyspnea. | Baseline, Month 3, 6, 9 and 12 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Chronic Obstructive Pulmonary Disease (COPD), Exacerbation, Remote patient monitoring, Predictive modeling