Chronic Obstructive Pulmonary DiseaseBigCOPDataNCT04206098
Chart Review of Patients With COPD, Using Electronic Medical Records and Artificial Intelligence
An observational study of Chronic Obstructive Pulmonary Disease, run by Sociedad Española de Neumología y Cirugía Torácica at 11 sites in 4 countries.
Trial summary
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the World since 2003. Many people suffer from this disease or its complications for many years and die prematurely. Read more about this study
- Who is it for?
- Ages 35 to 99 years
- Conditions the study is for
- Chronic Obstructive Pulmonary Disease
- What is being tested?
- Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)
- How many people
- 2,500,000 participants
- Where
- 11 sitesin 4 countries
- Study dates
- 8 Jan 2020to 30 Dec 2020
- How long will I take part?
- 5 Years
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 35 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: COPD patients that may experience acute exacerbations requiring emergency department and /or a hospital admission
Who may be able to join
- Subjects aged ≥ 35-year-old, smokers or former smokers of more than 10 pack-years.
- Had a diagnosis of COPD (a post-bronchodilator ratio forced expiratory volume in the first second (FEV1) / forced vital capacity (FVC) < 0.70, and the presence of respiratory symptoms such as cough, sputum, and dyspnoea).
- Admitted for ''respiratory disease'' [respiratory infection or pleural effusion (OR) respiratory failure (OR) right/left heart failure (OR) chronic bronchitis (OR) bronchospasms (AND) [historical diagnosis of COPD (OR) a documented FEV1/FVC < 0.70 in the absence of other obstructive diseases, such as asthma or bronchiolitis].
Who cannot join
- Patients with a specific diagnosis upon admission of pulmonary oedema, pneumonia, radiological infiltration, pulmonary embolism, pneumothorax, rib fractures, aspiration, or any other associated respiratory or of non-respiratory condition, such as major cardiopathy with chronic heart failure, extended neoplasia, liver or kidney failure.
Trial locations and contacts
11 sites in 4 countries.
11 sites in 4 countries
Spain8 sites, 1 recruiting
- Active, not recruiting
Hospital Universitario de Guadalajara
Alcalá de Henares
- Active, not recruiting
Hospital Universitario Príncipe de Asturias
Alcalá de Henares
- Active, not recruiting
Hospital Universitario Vall d'Hebron
Barcelona
- Recruiting
Hospital Universitario La Princesa
Madrid
JB Soriano, MD, +34915202200, jbsoriano2@gmail.com
JB Soriano, MD
- Active, not recruiting
Hospital Universitario Son Espases
Palma de Mallorca
- Active, not recruiting
Hospital Universitario Santiago de Compostela
Santiago de Compostela
- Active, not recruiting
Hospital Universitario Virgen del Rocio
Seville
- Active, not recruiting
Hospital Arnau de Vilanova
Valencia
Austria1 site, 1 recruiting
- Not yet recruiting
Kepler Universitäts Klinikum
Linz
Bernd Lamprecht, MD, +43 (0) 5 7680 83 6910, bernd.lamprecht@kepleruniklinikum.at
Bernd Lamprecht, MD
Switzerland1 site, 1 recruiting
- Not yet recruiting
HUG
Geneva
Salim G Chucri, +41765677688, scuc@hcuge.ch
Salim G Chucri, MD
United Kingdom1 site, 1 recruiting
- Not yet recruiting
Queen Elizabeth Hospital University
Birmingham, B9 5SS
Robert A Stockley, MA, +441213716808, rob.stockley@uhb.nhs.uk
Robert A Stockley
About this study
Official title: Chart Review of Patients With COPD, Using Electronic Medical Records and Artificial Intelligence: BigCOPData
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the World since 2003. Many people suffer from this disease or its complications for many years and die prematurely. In the European Union, the total direct costs of respiratory diseases are estimated to be around 6% of the total healthcare budget, with COPD accounting for 56% (38.6 billion Euros) of the costs of respiratory diseases.
In the natural history of COPD, many patients may experience acute exacerbations (AECOPD) that are described as episodes of sustained worsening of the respiratory symptoms that result in additional therapy. These episodes of exacerbation that often require been seen in the emergency department and/or a hospital admission are associated with significant morbidity and mortality; they are responsible for a significant portion of the economic burden of the disease too. The pharmacological approach used in the management of AECOPD (inhaled bronchodilators, corticosteroids, and antibiotics), has the objective to minimize the negative impact of the current exacerbation and to prevent subsequent events.
Read more
Despite the collaborative effort between the European Respiratory Society, the American Thoracic Society, and others to provide clinical recommendations for the prevention of AECOPD, there is still a considerable number of patients that are prone to suffer from recurrent exacerbations and to experience a more severe impairment in health status.
Based on all the above, the aim is to identify the factors potentially associated with hospital admission in patients with AECOPD in English, French, German, and Spanish, speaking countries, and to develop a predictive model that predicts the risk of hospitalization in this group of patients, by using artificial intelligence. In this study proposes to take advantage of SAVANA, a new clinical platform, created in the context of the era of electronic medical records (EMRs), to analyse the information included in the electronic medical files (i.e., big data). This clinical platform is a powerful free-text analysis engine, capable of meaningfully interpreting the contents of the EMRs, regardless of the management system in which they operate. In this context, this machine learning analytical method can be used to build a flexible, customized and automated predictive model using the information available in EMRs.
Read the detailed description
The study will be conducted in accordance with legal and regulatory requirements, as well as with scientific purpose, value and rigor and follow generally accepted research practices described in the International Conference Harmonization (ICH) Guideline for Good Clinical Practice, the Helsinki Declaration in its latest edition, Good Pharmacoepidemiology Practices, and applicable local regulations.
To maintain patient confidentiality, demographic and personal identifying information (e.g., initials, date of birth, etc.) will not be collected; only age will be collected. In no case Savana staff will handle a correspondence table between the anonymized patient codes and their EMRs. Only the healthcare centre can identify patients. In any case, SEPAR, the study sponsor, or its partners, will not have gto EMRs. It will only access a report, which will contain aggregated information on the data obtained as described in this protocol. The final results will be published.
According to the European Data Protection Authority, an anonymous clinical record is released from its status as personal data, so that the General Data Protection Regulation no longer applies. The anonymization is performed at each site by the owner of the information (so that nobody else has access to that information and so that it is not possible to track it).
All actions will be taken in accordance with the Code of Good Data Protection Practices for Big Data Projects of the European Data Protection Authority, the European General Data Protection Regulation or another that may replace it.
In addition, clinical records will never be stored in a location other than the institution where it is implemented. Savana does not use EMRs from individual patients, but aggregate clinical information, which is also encrypted and secured. The aggregation of the data ensures the impossibility of identifying patients or individual centres. The system is based on the processing of a large amount of information (Big Data), so that the impact of random errors is minimized. The use of this software is possible nowadays since there has been a notable improvement in the implementation of EMRs, which may result in the use of this software in significant investments for the use and better knowledge of the health system.
In summary, this new technology allows a complete dissociation between the data obtained for the current study and the personal data of the patient, since this information is obtained in an aggregated and completely anonymous form. This clearly represents an advance in data protection in the context of classical observational epidemiological studies.
Therefore, only aggregated and completely anonymous data will be obtained, completely dissociated from the personal data of each patient and centre. The confidentiality of patient records will be maintained at all times. All study reports will contain only aggregated data and will not identify patients, doctors or individual centres. At no time during the study, the sponsor will receive information that may allow the identification of a patient or individual centre.
- Conditions
- Chronic Obstructive Pulmonary Disease
- Being tested
- Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)
- Keywords
- COPD
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)Other
Develop a descriptive predictive model fo factors that influence clinical characteristic of patients that require hospital admission
| Study group | Role | What they receive |
|---|---|---|
| Sex | Male/Female Receives: Other: Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of factors associated with hospital admission of patients with AECOPD
Number of factors associated with hospital admission of patients with AECOPD, using EMRs and artificial intelligence learning tool
Measured: 5 years
Secondary outcomes (8)
Number of clinical characteristics of COPD hospitalized patients
Number of clinical characteristics of COPD patients that require hospital admission
Measured: 5 years
Number of comorbidities of COPD patients hospitalized per sex
Number and description of comorbidities associated with hospitalized COPD patients, presented per sex, such as cardiovascular disease, anxiety, depression, gastroesophageal reflux.
Measured: 5 years
Number of patients with increased eosinophil blood counts
Number of patients with COPD hospitalized with increased eosinophil blood counts.
Measured: 5 years
Number of patients with COPD hospitalized with elevated inflammatory parameters
Number of COPD patients hospitalized with elevated inflammatory parameters such as white cell counts, neutrophil count and C-reactive protein, presented in a descriptive model
Measured: 5 years
Number of clinical phenotypes identified in COPD patients hospitalized
Number of clinical phenotypes of patients with COPD that exacerbate and require hospital admissions
Measured: 5 years
Number of COPD patients hospitalized do not follow treatment recommendations
Number of COPD patients hospitalized do not follow 100% treatment recommendations within the previous 6 weeks
Measured: 5 years
Number of Risk on COPD patients hospitalized
Number of identified risks per COPD patient hospitalized, such as Spanish Guide COPD (GesEPOC), the Dyspnoea, Eosinopenia, Consolidation, Acidemia and Atrial Fibrillation [DECAF] Score, or another multicomponent index.
Measured: 5 years
Number of biologic biomarkers different to eosinophil count
Number of biologic biomarkers (different to eosinophil count) associated to hospitalization and/or rehospitalizations due to COPD exacerbations
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Dec 2019
- Study start8 Jan 2020
- Primary completion1 Oct 2020 (estimated)
- Study completion30 Dec 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chronic Obstructive Pulmonary DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 2,500,000 participants (estimated)
- Target duration
- 5 Years
Sponsor and investigators
- Lead sponsor
- Sociedad Española de Neumología y Cirugía Torácica
- Sponsor type
- University, hospital or other
- Collaborators
- SAVANA, European Commission
- Responsible party
- Sponsor
- Investigators
- JB Soriano, MD, Principal investigator, Sociedad Española de Neumología y Cirugía Torácica
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04206098
- Sponsor's protocol ID
- H2020
- Acronym
- BigCOPData
- Status last verified
- Dec 2019
- First submitted
- 13 Dec 2019
- Last updated
- 13 Jan 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Final results of the study will be disseminated in the form of a manuscript/s in the peer-reviewed literature. In addition, where relevant, data from potential interim analyses will be presented at (a) relevant congress(es).
Study at a glance
From the registry record, last updated by the sponsor on 13 Jan 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Chart Review of Patients With COPD, Using Electronic Medical Records and Artificial Intelligence: BigCOPData
- ClinicalTrials.gov ID
- NCT04206098
- Acronym / sponsor ID
- BigCOPData / H2020
- Conditions
- Chronic Obstructive Pulmonary Disease
- Keywords
- COPD
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,500,000 participants (estimated)
- Lead sponsor
- Sociedad Española de Neumología y Cirugía TorácicaUniversity, hospital or other
- Collaborators
- SAVANA, European Commission
- Study start
- 8 Jan 2020 (actual)
- Primary completion
- 1 Oct 2020 (estimated)
- Study completion
- 30 Dec 2020 (estimated)
- First posted
- 20 Dec 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the World since 2003. Many people suffer from this disease or its complications for many years and die prematurely. In the European Union, the total direct costs of respiratory diseases are estimated to be around 6% of the total healthcare budget, with COPD accounting for 56% (38.6 billion Euros) of the costs of respiratory diseases.
In the natural history of COPD, many patients may experience acute exacerbations (AECOPD) that are described as episodes of sustained worsening of the respiratory symptoms that result in additional therapy. These episodes of exacerbation that often require been seen in the emergency department and/or a hospital admission are associated with significant morbidity and mortality; they are responsible for a significant portion of the economic burden of the disease too. The pharmacological approach used in the management of AECOPD (inhaled bronchodilators, corticosteroids, and antibiotics), has the objective to minimize the negative impact of the current exacerbation and to prevent subsequent events.
Despite the collaborative effort between the European Respiratory Society, the American Thoracic Society, and others to provide clinical recommendations for the prevention of AECOPD, there is still a considerable number of patients that are prone to suffer from recurrent exacerbations and to experience a more severe impairment in health status.
Based on all the above, the aim is to identify the factors potentially associated with hospital admission in patients with AECOPD in English, French, German, and Spanish, speaking countries, and to develop a predictive model that predicts the risk of hospitalization in this group of patients, by using artificial intelligence. In this study proposes to take advantage of SAVANA, a new clinical platform, created in the context of the era of electronic medical records (EMRs), to analyse the information included in the electronic medical files (i.e., big data). This clinical platform is a powerful free-text analysis engine, capable of meaningfully interpreting the contents of the EMRs, regardless of the management system in which they operate. In this context, this machine learning analytical method can be used to build a flexible, customized and automated predictive model using the information available in EMRs.
Detailed description
The study will be conducted in accordance with legal and regulatory requirements, as well as with scientific purpose, value and rigor and follow generally accepted research practices described in the International Conference Harmonization (ICH) Guideline for Good Clinical Practice, the Helsinki Declaration in its latest edition, Good Pharmacoepidemiology Practices, and applicable local regulations.
To maintain patient confidentiality, demographic and personal identifying information (e.g., initials, date of birth, etc.) will not be collected; only age will be collected. In no case Savana staff will handle a correspondence table between the anonymized patient codes and their EMRs. Only the healthcare centre can identify patients. In any case, SEPAR, the study sponsor, or its partners, will not have gto EMRs. It will only access a report, which will contain aggregated information on the data obtained as described in this protocol. The final results will be published.
According to the European Data Protection Authority, an anonymous clinical record is released from its status as personal data, so that the General Data Protection Regulation no longer applies. The anonymization is performed at each site by the owner of the information (so that nobody else has access to that information and so that it is not possible to track it).
All actions will be taken in accordance with the Code of Good Data Protection Practices for Big Data Projects of the European Data Protection Authority, the European General Data Protection Regulation or another that may replace it.
In addition, clinical records will never be stored in a location other than the institution where it is implemented. Savana does not use EMRs from individual patients, but aggregate clinical information, which is also encrypted and secured. The aggregation of the data ensures the impossibility of identifying patients or individual centres. The system is based on the processing of a large amount of information (Big Data), so that the impact of random errors is minimized. The use of this software is possible nowadays since there has been a notable improvement in the implementation of EMRs, which may result in the use of this software in significant investments for the use and better knowledge of the health system.
In summary, this new technology allows a complete dissociation between the data obtained for the current study and the personal data of the patient, since this information is obtained in an aggregated and completely anonymous form. This clearly represents an advance in data protection in the context of classical observational epidemiological studies.
Therefore, only aggregated and completely anonymous data will be obtained, completely dissociated from the personal data of each patient and centre. The confidentiality of patient records will be maintained at all times. All study reports will contain only aggregated data and will not identify patients, doctors or individual centres. At no time during the study, the sponsor will receive information that may allow the identification of a patient or individual centre.
Study design
- Observational model
- Other
- Time perspective
- Other
- Target follow-up
- 5 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Sex | – | Other: Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)Male/Female |
Who may qualify
- Ages
- 35 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- COPD patients that may experience acute exacerbations requiring emergency department and /or a hospital admission
Key inclusion criteria (3 listed)
- Subjects aged ≥ 35-year-old, smokers or former smokers of more than 10 pack-years.
- Had a diagnosis of COPD (a post-bronchodilator ratio forced expiratory volume in the first second (FEV1) / forced vital capacity (FVC) < 0.70, and the presence of respiratory symptoms such as cough, sputum, and dyspnoea).
- Admitted for ''respiratory disease'' [respiratory infection or pleural effusion (OR) respiratory failure (OR) right/left heart failure (OR) chronic bronchitis (OR) bronchospasms (AND) [historical diagnosis of COPD (OR) a documented FEV1/FVC < 0.70 in the absence of other obstructive diseases, such as asthma or bronchiolitis].
Key exclusion criteria (1 listed)
- Patients with a specific diagnosis upon admission of pulmonary oedema, pneumonia, radiological infiltration, pulmonary embolism, pneumothorax, rib fractures, aspiration, or any other associated respiratory or of non-respiratory condition, such as major cardiopathy with chronic heart failure, extended neoplasia, liver or kidney failure.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)Develop a descriptive predictive model fo factors that influence clinical characteristic of patients that require hospital admission | Other | Sex |
Primary outcome
Number of factors associated with hospital admission of patients with AECOPD
Time frame: 5 years
Number of factors associated with hospital admission of patients with AECOPD, using EMRs and artificial intelligence learning tool
8 secondary outcomes
Number of clinical characteristics of COPD hospitalized patients
Time frame: 5 years
Number of comorbidities of COPD patients hospitalized per sex
Time frame: 5 years
Number of patients with increased eosinophil blood counts
Time frame: 5 years
Number of patients with COPD hospitalized with elevated inflammatory parameters
Time frame: 5 years
Number of clinical phenotypes identified in COPD patients hospitalized
Time frame: 5 years
Number of COPD patients hospitalized do not follow treatment recommendations
Time frame: 5 years
Number of Risk on COPD patients hospitalized
Time frame: 5 years
Number of biologic biomarkers different to eosinophil count
Time frame: 5 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Jan 2020.
- Individual participant dataSharing planned: Undecided. Final results of the study will be disseminated in the form of a manuscript/s in the peer-reviewed literature. In addition, where relevant, data from potential interim analyses will be presented at (a) relevant congress(es).
Related trials for Chronic Obstructive Pulmonary Disease
Active, not recruiting
Symptoms and Functions in Patients With COPD and Chronic Bronchitis Switching From CIG to THS
COPD, Smoking
Not yet recruitingPhase 2/3
An Open-Label Extension Study to Evaluate Inhaled Treprostinil SMI in Participants With Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease (PH-COPD)
Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease
Not yet recruiting
The Aim of the Study is to Decipher the Alterations in Signalling Pathways of Skeletal Muscle Stem Cells (Satellite Cells (SCs)) in Individuals With COPD, as Well as to Examine the Effect of Exercise Training on SCs.
COPD (Chronic Obstructive Pulmonary Disease), Sarcopenia in Elderly
Not yet recruitingPhase 2
Study of Treprostinil Administered Via Soft Mist Inhaler (SMI) in Participants With Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease (PH-COPD)
Pulmonary Hypertension Associated With Chronic Obstructive Pulmonary Disease
RecruitingPhase 3
A Study to Investigate the Efficacy and Safety of Belumosudil Compared With Best Available Therapy in Participants Aged 12 Years or Older With Chronic Graft-versus-host Disease
Chronic Graft Versus Host Disease
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
JB Soriano, MD principal investigator
Sociedad Española de Neumología y Cirugía Torácica
11 sites in 4 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Obstructive Pulmonary Disease, run by Sociedad Española de Neumología y Cirugía Torácica at 11 sites in 4 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 13 Jan 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 11 sites in Spain, Austria, Switzerland, United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact JB Soriano, MD (+34915202200, jbsoriano2@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of factors associated with hospital admission of patients with AECOPD (time frame: 5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Jan 2020.