COPDNCT04978922
Impact of the Artificial Intelligence in a Telemonitoring Programme of COPD Patients With Multiple Hospitalizations
A study testing Machine Learning: ML (Artificial Intelligence System) in people with COPD, run by Dr. Cristobal Esteban at 1 site in Spain.
Trial summary
Given the current situation concerning healthcare, population demographics and economy, it seems required to look for new approaches in the health system. The use of new technologies must be the main factor for this change. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- COPD
- What is being tested?
- Machine Learning: ML (Artificial Intelligence System)
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 345 participants
- Where
- 1 siteSpain
- Study dates
- 1 Jan 2018to 1 Jun 2022
- Phase
- Interventional
- 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 18 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Having a COPD (COPD was confirmed if the post-bronchodilator forced expiratory volume in one second (FEV1) divided by the forced vital capacity (FVC) was less than 0.7 (FEV1/FVC<70%)
- Having been admitted at least twice in the previous year or three times in the two previous years for a COPD exacerbation (eCOPD).
Who cannot join
- Another significant respiratory disease.
- An active neoplasm.
- A terminal clinical situation.
- Inability to carry out any of the measurements of the project.
- Unwillingness to take part in the study.
Trial locations and contacts
1 site in 1 country.
Spain1 site, 1 recruiting
- Recruiting
Hospital Galdakao Usansolo
Galdakao, Vizcaya, 48960
Cristobal Esteban, MD, +34-944007002, cristobal.est@gmail.com
About this study
Official title: Impact of the Artificial Intelligence (Machine Learning) in a Telemonitoring Programme of COPD Patients With Multiple Hospitalizations (telEPOC)
Given the current situation concerning healthcare, population demographics and economy, it seems required to look for new approaches in the health system. The use of new technologies must be the main factor for this change.
GENERAL OBJECTIVE:
Read more
To determine the impact that the application of an artificial intelligence system (Machine Learning) could have on an active telemonitoring programme of readmitted COPD patients.
Particular objectives: to determine the changes in:
- The use of healthcare resources.
- Patients´ quality of life.
- Costs.
- Load of work.
- Daily clinical practice.
- Inflammation markers
METHODS:
Based on the telEPOC programme and Machine Learning developement in this project, non-randomized intervention study, with two branches: intervention (Galdakao hospital) and control (Cruces and Basurto hospital).
Sample size of at least 115 patients per hospital (115 in the intervention branch and 230 in the control branch). A 2-year follow-up.
Uni and multivariate statistics will be applied.
Read the detailed description
Telemonitoring programmes are an alternative to the traditional systems of patients' control, specially in chronic diseases. This kind of tools are also important because of the aging of the population, the increase in chronic diseases and the consequent increase in costs of maintenance of the health systems. On the other hand, nowadays these chronic patients are especially attended because of exacerbations, fundamentally in emergencies and hospitalization, and also in in-person scheduled consultations when patients are stable. Then, a closer attention is more desirable by the point of view of clinic, management, and costs.
COPD (Chronic Obstructive Pulmonary Disease) is a highly prevalent disease. Moreover, it has a high consumption of sanitary resources and costs, 50% of whom are due to hospitalizations.
Furthermore, exacerbations in COPD and specially the severe ones, have important consequences for patients (decrease of pulmonary function, worsening of quality of life and increase in mortality).
Because of that, telemonitoring appears to be a solution to improve the control of these patients and improve the consumption of resources. In Galdakao Hospital in Spain, it was initiated a telemonitoring programme in COPD patients who re-admit to hospital. Its primary objective was tos reduce readmissions because of COPD exacerbations and it could demonstrate a significant decrease in the use of sanitary resources (hospitalizations, visit to emergences department, readmissions and average stay days). It also demonstrated a less worsening in clinical symptoms and quality of life in more severe patients.
However, there are three factors that are very important in chronic diseases: the increase in aging people, the increase of people with chronic diseases and the fast evolution of technology, specially the recollection and information processing systems.
Machine Learning (ML) is the most important part of de Artificial Intelligence, and its objective is the learning of a computer. The computer writes its own programmes to solve problems that we do not know how to solve. When works are difficult, like doing predictions in medical scenarios, ML algorithms need a high quantity of dates to get the learning. Most medical data bases have inconveniences that come from human intervention, like missing data, wrong values, etc. Because of that, programmes based on telemedicine appears to be an ideal platform for ML algorithms. This is because telemedicine systems normally produce a periodic flow of collected data by electronic ways and they are directly saved in a data base. This constant flow of dates and the low participation of people in the recollection and storage of them, give high quality to data bases, which ML algorithms can use to do the best predictions.
Because of that, TelEPOC (the Telemonitoring program in a COPD cohort, in Galdakao Hospital) shows to be the best option to use in its data the ML algorithms, due to the quality and the quantity of generated data, and also because of the utility of those predictions in the clinical practice.
In this situation, the question is if investigators could anticipate to an exacerbation or how much they could anticipate a manifestation of an exacerbation. To test this hypothesis, it is presented here a project that uses Artificial Intelligence (ML).
Investigators previously did a test of this system, that gave promising results. That prototype was trained with retrospective data that TelEPOC programme had recollected before and it was based on an ML algorithm called Random Forests. With this probe they got a ROC curve (receiver operating characteristic curve) of 0,8 in prediction of suffering an exacerbation in following three days. Currently in Galdakao Hospital there is developing a ML system in the TelEPOC programme. Its objective is to anticipate to an alarm (exacerbation).
Whit this purpose investigators consider a lot of additional questions that can be investigated, like for example: how can affect the arrival of this technology in the diary clinical practice? In this project the use of ML can change the way of focus the clinical assistance. There are tools than can predict de evolution of the patients. Another question is that if investigators anticipate an exacerbation, they could change pathogenic basis (inflammatory mediators) that round a COPD exacerbation.
Investigators considerate this initiative like pioneer in this field of COPD and chronic diseases.
- Conditions
- COPD
- Being tested
- Machine Learning: ML (Artificial Intelligence System)
- Keywords
- COPD exacerbation, Telemedicine, Intervention Studies, Patient Readmission, Machine Learning
- 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.
Machine Learning: ML (Artificial Intelligence System)Device
To applicate an artificial intelligence system (Machine Learning: ML) on an active telemonitoring programme of readmitted COPD patients (TelEPOC)
| Study group | Role | What they receive |
|---|---|---|
| TelEPOC with Machine Learning (ML) | Experimental | Hospital with an active telemonitoring programme of readmitted COPD patients (TelEPOC) after application of an artificial intelligence system (Machine Learning: ML).
* TelEPOC: The program consisted of: 1) Educational program about COPD. This educational program was carried-out by a respiratory nurse in two 30-minute speeches to the patient and career, once at their inclusion in the program and again 1 year later. 2) Training in using the device (smart phone) that supported the telemonitoring. 3) Daily phone calls to make self-confident the patient during the first week. Afterwards the phone calls were established according to the capacity of the patient to manage on their own. Receives: Device: Machine Learning: ML (Artificial Intelligence System) |
| TelEPOC without ML | No intervention | Hospitals with an active telemonitoring programme of readmitted COPD patients (TelEPOC) without the application of an artificial intelligence system (Machine Learning: ML). |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of resources after the implementation of ML (Machine Learning) added to a telemedicine system in readmitted COPD patients (telEPOC).
- Number of hospitalizations (hospital base data).
- Days of hospital staying ((hospital base data).
- Emergency visits (hospital base data).
- Readmissions (hospital base data).
- Visits to pneumology consultation in last 2 years (hospital base data).
Measured: 2 years
Change in quality of life in patients after the implementation of ML (in patients that generate alarms)
-CAT (COPD assessment test): impact of COPD on health status. 8 items (cough, phlegm, chest tightness, breathlessness, limited activities, confidence leaving home, sleeplessness and energy), scaling from 1 to 5. Higher scores denote a more severe impact of COPD on a patient's life.
Measured: 2 years
Changes in quality of life in patients after the implementation of ML (in patients that generate alarms)
- EuroQol-5d questionnaire: measure of health for clinical and economic appraisal. 2 parts:
- 5 dimensions descriptive system (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression). Each of them has 3 levels of severity (no problems -1 point- , some problems -2 points- or moderate-severe problems - 3 points-). Having more points represents a worse situation.
- A visual analog scale for a more general evaluation. It is a vertical scale, ranging from 0 (worst imaginable state of health) to 100 (best imaginable state of health). In it, the individual must mark the point on the vertical line that best reflects the assessment of their global health status today.
Measured: 2 years
Cost of the implementation of ML in relation to the standard telemonitoring programmes
- Economic evaluation, inlcuding all the interventions carried out inherent to the program, ranging from phone calls, patient displacement for consultation, drug use, hospitalizations and visits to emergencioes, primary and specialized care (hospital base data).
Measured: 2 years
Workload of nurses
- The time that must be spend every day managing the alarms after adding ML.
Measured: 2 years
Changes in clinical diary practice after including ML
- Exercise capacity (six minutes walking test)
Measured: 2 years
Change in clinical diary practice after including ML
- Physical activity (pedometer)
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jul 2021
- Study start1 Jan 2018
- Primary completion1 Jan 2022 (estimated)
- Study completion1 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for COPDStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 345 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Dr. Cristobal Esteban
- Sponsor type
- Other government
- Collaborators
- Osakidetza
- Responsible party
- Sponsor-investigator, Dr. Cristobal Esteban, Hospital Galdakao-Usansolo
- Investigators
- Cristobal Esteban, MD, Principal investigator, Osakidetza
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04978922
- Sponsor's protocol ID
- PI18/01797
- Status last verified
- Jul 2021
- First submitted
- 11 Apr 2021
- Last updated
- 27 Jul 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Mathers CD, Loncar D. Updated projections of global mortality and burden of disease, 2002-2030.World Health Organization, http//www.who.int/healthinfo/statistics/bodprojectionspaper.pdf
World Health Organization. Chronic Obstructive Pulmonary Disease (COPD). Avalaible from: http:// www.who.int/respiratory/copd/en/index.html
Donaldson GC, Seemungal TA, Bhowmik A, Wedzicha JA. Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax. 2002 Oct;57(10):847-52. doi: 10.1136/thorax.57.10.847.
PubMed 12324669Esteban C, Quintana JM, Moraza J, Aburto M, Egurrola M, Espana PP, Perez-Izquierdo J, Aguirre U, Aizpiri S, Capelastegui A. Impact of hospitalisations for exacerbations of COPD on health-related quality of life. Respir Med. 2009 Aug;103(8):1201-8. doi: 10.1016/j.rmed.2009.02.002. Epub 2009 Mar 9.
PubMed 19272762Soler-Cataluna JJ, Martinez-Garcia MA, Roman Sanchez P, Salcedo E, Navarro M, Ochando R. Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease. Thorax. 2005 Nov;60(11):925-31. doi: 10.1136/thx.2005.040527. Epub 2005 Jul 29.
PubMed 16055622Pinnock H, Hanley J, McCloughan L, Todd A, Krishan A, Lewis S, Stoddart A, van der Pol M, MacNee W, Sheikh A, Pagliari C, McKinstry B. Effectiveness of telemonitoring integrated into existing clinical services on hospital admission for exacerbation of chronic obstructive pulmonary disease: researcher blind, multicentre, randomised controlled trial. BMJ. 2013 Oct 17;347:f6070. doi: 10.1136/bmj.f6070.
PubMed 24136634Bolton CE, Waters CS, Peirce S, Elwyn G; EPSRC and MRC Grand Challenge Team. Insufficient evidence of benefit: a systematic review of home telemonitoring for COPD. J Eval Clin Pract. 2011 Dec;17(6):1216-22. doi: 10.1111/j.1365-2753.2010.01536.x. Epub 2010 Sep 16.
PubMed 20846317Jordan R, Adab P, Jolly K. Telemonitoring for patients with COPD. BMJ. 2013 Oct 17;347:f5932. doi: 10.1136/bmj.f5932. No abstract available.
PubMed 24136632Esteban C, Moraza J, Iriberri M, Aguirre U, Goiria B, Quintana JM, Aburto M, Capelastegui A. Outcomes of a telemonitoring-based program (telEPOC) in frequently hospitalized COPD patients. Int J Chron Obstruct Pulmon Dis. 2016 Nov 24;11:2919-2930. doi: 10.2147/COPD.S115350. eCollection 2016.
PubMed 27920519Esteban C, Schmidt D, Krompaß D y Tresp V. Predicting sequences of clinical events by using a personalized temporal latent embedding model. Proceedings of the IEEE International Conference on Healthcare Informatics, 2015
SPARRA: Scottish Patients at Risk of Readmission and Admission - A report on development work to extend the algorithm's applicability to patients of all ages, Information Services Division, NHS National Services Scotland. June 2008
Esteban C, Moraza J, Sancho F et al. Sistema de Alerta Temprana para el programa telEPOC mediante Machine Learning. Congreso Internacional SEPAR 2015 , Gran Canaria, España, Junio 2015.
Esteban C, Moraza J, Sancho F et al. Machine Learning for COPD exacerbation prediction. European Respiratory Journal 2015;46:Issue suppl 59
Noell G, Cosio BG, Faner R, Monso E, Peces-Barba G, de Diego A, Esteban C, Gea J, Rodriguez-Roisin R, Garcia-Nunez M, Pozo-Rodriguez F, Kalko SG, Agusti A. Multi-level differential network analysis of COPD exacerbations. Eur Respir J. 2017 Sep 27;50(3):1700075. doi: 10.1183/13993003.00075-2017. Print 2017 Sep.
PubMed 28954781
Study at a glance
From the registry record, last updated by the sponsor on 27 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact of the Artificial Intelligence (Machine Learning) in a Telemonitoring Programme of COPD Patients With Multiple Hospitalizations (telEPOC)
- ClinicalTrials.gov ID
- NCT04978922
- Acronym / sponsor ID
- PI18/01797
- Conditions
- COPD
- Keywords
- COPD exacerbation, Telemedicine, Intervention Studies, Patient Readmission, Machine Learning
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 345 participants (estimated)
- Lead sponsor
- Dr. Cristobal EstebanOther government
- Collaborators
- Osakidetza
- Study start
- 1 Jan 2018 (actual)
- Primary completion
- 1 Jan 2022 (estimated)
- Study completion
- 1 Jun 2022 (estimated)
- First posted
- 27 Jul 2021 (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.
Given the current situation concerning healthcare, population demographics and economy, it seems required to look for new approaches in the health system. The use of new technologies must be the main factor for this change.
GENERAL OBJECTIVE:
To determine the impact that the application of an artificial intelligence system (Machine Learning) could have on an active telemonitoring programme of readmitted COPD patients.
Particular objectives: to determine the changes in:
- The use of healthcare resources.
- Patients´ quality of life.
- Costs.
- Load of work.
- Daily clinical practice.
- Inflammation markers
METHODS:
Based on the telEPOC programme and Machine Learning developement in this project, non-randomized intervention study, with two branches: intervention (Galdakao hospital) and control (Cruces and Basurto hospital).
Sample size of at least 115 patients per hospital (115 in the intervention branch and 230 in the control branch). A 2-year follow-up.
Uni and multivariate statistics will be applied.
Detailed description
Telemonitoring programmes are an alternative to the traditional systems of patients' control, specially in chronic diseases. This kind of tools are also important because of the aging of the population, the increase in chronic diseases and the consequent increase in costs of maintenance of the health systems. On the other hand, nowadays these chronic patients are especially attended because of exacerbations, fundamentally in emergencies and hospitalization, and also in in-person scheduled consultations when patients are stable. Then, a closer attention is more desirable by the point of view of clinic, management, and costs.
COPD (Chronic Obstructive Pulmonary Disease) is a highly prevalent disease. Moreover, it has a high consumption of sanitary resources and costs, 50% of whom are due to hospitalizations.
Furthermore, exacerbations in COPD and specially the severe ones, have important consequences for patients (decrease of pulmonary function, worsening of quality of life and increase in mortality).
Because of that, telemonitoring appears to be a solution to improve the control of these patients and improve the consumption of resources. In Galdakao Hospital in Spain, it was initiated a telemonitoring programme in COPD patients who re-admit to hospital. Its primary objective was tos reduce readmissions because of COPD exacerbations and it could demonstrate a significant decrease in the use of sanitary resources (hospitalizations, visit to emergences department, readmissions and average stay days). It also demonstrated a less worsening in clinical symptoms and quality of life in more severe patients.
However, there are three factors that are very important in chronic diseases: the increase in aging people, the increase of people with chronic diseases and the fast evolution of technology, specially the recollection and information processing systems.
Machine Learning (ML) is the most important part of de Artificial Intelligence, and its objective is the learning of a computer. The computer writes its own programmes to solve problems that we do not know how to solve. When works are difficult, like doing predictions in medical scenarios, ML algorithms need a high quantity of dates to get the learning. Most medical data bases have inconveniences that come from human intervention, like missing data, wrong values, etc. Because of that, programmes based on telemedicine appears to be an ideal platform for ML algorithms. This is because telemedicine systems normally produce a periodic flow of collected data by electronic ways and they are directly saved in a data base. This constant flow of dates and the low participation of people in the recollection and storage of them, give high quality to data bases, which ML algorithms can use to do the best predictions.
Because of that, TelEPOC (the Telemonitoring program in a COPD cohort, in Galdakao Hospital) shows to be the best option to use in its data the ML algorithms, due to the quality and the quantity of generated data, and also because of the utility of those predictions in the clinical practice.
In this situation, the question is if investigators could anticipate to an exacerbation or how much they could anticipate a manifestation of an exacerbation. To test this hypothesis, it is presented here a project that uses Artificial Intelligence (ML).
Investigators previously did a test of this system, that gave promising results. That prototype was trained with retrospective data that TelEPOC programme had recollected before and it was based on an ML algorithm called Random Forests. With this probe they got a ROC curve (receiver operating characteristic curve) of 0,8 in prediction of suffering an exacerbation in following three days. Currently in Galdakao Hospital there is developing a ML system in the TelEPOC programme. Its objective is to anticipate to an alarm (exacerbation).
Whit this purpose investigators consider a lot of additional questions that can be investigated, like for example: how can affect the arrival of this technology in the diary clinical practice? In this project the use of ML can change the way of focus the clinical assistance. There are tools than can predict de evolution of the patients. Another question is that if investigators anticipate an exacerbation, they could change pathogenic basis (inflammatory mediators) that round a COPD exacerbation.
Investigators considerate this initiative like pioneer in this field of COPD and chronic diseases.
Study design
- Primary purpose
- Prevention
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| TelEPOC with Machine Learning (ML) | Experimental | Device: Machine Learning: ML (Artificial Intelligence System)Hospital with an active telemonitoring programme of readmitted COPD patients (TelEPOC) after application of an artificial intelligence system (Machine Learning: ML). * TelEPOC: The program consisted of: 1) Educational program about COPD. This educational program was carried-out by a respiratory nurse in two 30-minute speeches to the patient and career, once at their inclusion in the program and again 1 year later. 2) Training in using the device (smart phone) that supported the telemonitoring. 3) Daily phone calls to make self-confident the patient during the first week. Afterwards the phone calls were established according to the capacity of the patient to manage on their own. |
| TelEPOC without ML | No intervention | –Hospitals with an active telemonitoring programme of readmitted COPD patients (TelEPOC) without the application of an artificial intelligence system (Machine Learning: ML). |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Having a COPD (COPD was confirmed if the post-bronchodilator forced expiratory volume in one second (FEV1) divided by the forced vital capacity (FVC) was less than 0.7 (FEV1/FVC<70%)
- Having been admitted at least twice in the previous year or three times in the two previous years for a COPD exacerbation (eCOPD).
Key exclusion criteria (5 listed)
- Another significant respiratory disease.
- An active neoplasm.
- A terminal clinical situation.
- Inability to carry out any of the measurements of the project.
- Unwillingness to take part in the study.
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 |
|---|---|---|
| Machine Learning: ML (Artificial Intelligence System)To applicate an artificial intelligence system (Machine Learning: ML) on an active telemonitoring programme of readmitted COPD patients (TelEPOC) | Device | TelEPOC with Machine Learning (ML) |
Primary outcome
Number of resources after the implementation of ML (Machine Learning) added to a telemedicine system in readmitted COPD patients (telEPOC).
Time frame: 2 years
* Number of hospitalizations (hospital base data). * Days of hospital staying ((hospital base data). * Emergency visits (hospital base data). * Readmissions (hospital base data). * Visits to pneumology consultation in last 2 years (hospital base data).
Change in quality of life in patients after the implementation of ML (in patients that generate alarms)
Time frame: 2 years
-CAT (COPD assessment test): impact of COPD on health status. 8 items (cough, phlegm, chest tightness, breathlessness, limited activities, confidence leaving home, sleeplessness and energy), scaling from 1 to 5. Higher scores denote a more severe impact of COPD on a patient's life.
Changes in quality of life in patients after the implementation of ML (in patients that generate alarms)
Time frame: 2 years
* EuroQol-5d questionnaire: measure of health for clinical and economic appraisal. 2 parts: * 5 dimensions descriptive system (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression). Each of them has 3 levels of severity (no problems -1 point- , some problems -2 points- or moderate-severe problems - 3 points-). Having more points represents a worse situation. * A visual analog scale for a more general evaluation. It is a vertical scale, ranging from 0 (worst imaginable state of health) to 100 (best imaginable state of health). In it, the individual must mark the point on the vertical line that best reflects the assessment of their global health status today.
Cost of the implementation of ML in relation to the standard telemonitoring programmes
Time frame: 2 years
- Economic evaluation, inlcuding all the interventions carried out inherent to the program, ranging from phone calls, patient displacement for consultation, drug use, hospitalizations and visits to emergencioes, primary and specialized care (hospital base data).
Workload of nurses
Time frame: 2 years
- The time that must be spend every day managing the alarms after adding ML.
Changes in clinical diary practice after including ML
Time frame: 2 years
- Exercise capacity (six minutes walking test)
Change in clinical diary practice after including ML
Time frame: 2 years
- Physical activity (pedometer)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jul 2021.
- BackgroundMathers CD, Loncar D. Updated projections of global mortality and burden of disease, 2002-2030.World Health Organization, http//www.who.int/healthinfo/statistics/bodprojectionspaper.pdf
- BackgroundWorld Health Organization. Chronic Obstructive Pulmonary Disease (COPD). Avalaible from: http:// www.who.int/respiratory/copd/en/index.html
- Background: PubMed 12324669Donaldson GC, Seemungal TA, Bhowmik A, Wedzicha JA. Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax. 2002 Oct;57(10):847-52. doi: 10.1136/thorax.57.10.847.
- Background: PubMed 19272762Esteban C, Quintana JM, Moraza J, Aburto M, Egurrola M, Espana PP, Perez-Izquierdo J, Aguirre U, Aizpiri S, Capelastegui A. Impact of hospitalisations for exacerbations of COPD on health-related quality of life. Respir Med. 2009 Aug;103(8):1201-8. doi: 10.1016/j.rmed.2009.02.002. Epub 2009 Mar 9.
- Background: PubMed 16055622Soler-Cataluna JJ, Martinez-Garcia MA, Roman Sanchez P, Salcedo E, Navarro M, Ochando R. Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease. Thorax. 2005 Nov;60(11):925-31. doi: 10.1136/thx.2005.040527. Epub 2005 Jul 29.
- Background: PubMed 24136634Pinnock H, Hanley J, McCloughan L, Todd A, Krishan A, Lewis S, Stoddart A, van der Pol M, MacNee W, Sheikh A, Pagliari C, McKinstry B. Effectiveness of telemonitoring integrated into existing clinical services on hospital admission for exacerbation of chronic obstructive pulmonary disease: researcher blind, multicentre, randomised controlled trial. BMJ. 2013 Oct 17;347:f6070. doi: 10.1136/bmj.f6070.
- Background: PubMed 20846317Bolton CE, Waters CS, Peirce S, Elwyn G; EPSRC and MRC Grand Challenge Team. Insufficient evidence of benefit: a systematic review of home telemonitoring for COPD. J Eval Clin Pract. 2011 Dec;17(6):1216-22. doi: 10.1111/j.1365-2753.2010.01536.x. Epub 2010 Sep 16.
- Background: PubMed 24136632Jordan R, Adab P, Jolly K. Telemonitoring for patients with COPD. BMJ. 2013 Oct 17;347:f5932. doi: 10.1136/bmj.f5932. No abstract available.
- Background: PubMed 27920519Esteban C, Moraza J, Iriberri M, Aguirre U, Goiria B, Quintana JM, Aburto M, Capelastegui A. Outcomes of a telemonitoring-based program (telEPOC) in frequently hospitalized COPD patients. Int J Chron Obstruct Pulmon Dis. 2016 Nov 24;11:2919-2930. doi: 10.2147/COPD.S115350. eCollection 2016.
- BackgroundEsteban C, Schmidt D, Krompaß D y Tresp V. Predicting sequences of clinical events by using a personalized temporal latent embedding model. Proceedings of the IEEE International Conference on Healthcare Informatics, 2015
- BackgroundSPARRA: Scottish Patients at Risk of Readmission and Admission - A report on development work to extend the algorithm's applicability to patients of all ages, Information Services Division, NHS National Services Scotland. June 2008
- BackgroundEsteban C, Moraza J, Sancho F et al. Sistema de Alerta Temprana para el programa telEPOC mediante Machine Learning. Congreso Internacional SEPAR 2015 , Gran Canaria, España, Junio 2015.
- Individual participant dataSharing planned: No
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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.
Cristobal Esteban, MD principal investigator
Osakidetza
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Machine Learning: ML (Artificial Intelligence System) in people with COPD, run by Dr. Cristobal Esteban at 1 site in Spain.
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 2 inclusion and 5 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 27 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. 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 Cristobal Esteban, MD (+34 94 400 7002, cristobal.est@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of resources after the implementation of ML (Machine Learning) added to a telemedicine system in readmitted COPD patients (telEPOC). (time frame: 2 years). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04978922, which also lists 14 linked publications.
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 27 Jul 2021.