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| Name | Class |
|---|---|
| University of Pisa | OTHER |
| LungenClinic Grosshansdorf | OTHER |
| Hospital del Mar Research Institute | UNKNOWN |
| Fondazione C.N.R./Regione Toscana "G. Monasterio", Pisa, Italy |
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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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Variations in daily life activity signals detected by unobtrusive sensors | Other | 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. |
| 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 |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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This study targets COPD patients at risk of moderate-to-severe exacerbations. Participants will be recruited from three European tertiary care hospitals: Hospital del Mar in Barcelona, Spain; the Pulmonary Research Institute in Grosshansdorf, Germany; and Azienda Ospedaliera Universitaria Pisana in Pisa, Italy. Recruitment sources include research registers, hospital records, and referrals.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Judith Garcia-Aymerich, MD; PhD | Contact | +342147380 | judith.garcia@isglobal.org |
| Name | Affiliation | Role |
|---|---|---|
| Judith Garcia-Aymerich | Barcelona Institute for Global Health | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Pulmonary Research Institute | Recruiting | Großhansdorf | Schleswig-Holstein | 22927 | Germany |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 42242832 | Derived | Vasquez-Andrade R, Alcaraz-Serrano V, Buekers J, Bufano P, Laurino M, Celi A, Watz H, Gea J, Carbonaro N, Tognetti A, Garcia-Aymerich J. Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study. BMJ Open Respir Res. 2026 Jun 4;13(1):e003942. doi: 10.1136/bmjresp-2025-003942. |
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| ID | Term |
|---|---|
| D029424 | Pulmonary Disease, Chronic Obstructive |
| ID | Term |
|---|---|
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002908 | Chronic Disease |
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| OTHER_GOV |
| Universidad Politecnica de Madrid | OTHER |
| TIMELEX | UNKNOWN |
| Avvale | UNKNOWN |
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| 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 |
| Azienda Ospedaliero Universitaria Pisana | Not yet recruiting | Pisa | 56126 | Italy |
|
| Hospital del Mar Research Institute | Recruiting | Barcelona | 08003 | Spain |
|
| D020969 |
| Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |