Impact of the Artificial Intelligence in a Telemonitoring Programme of COPD Patients With Multiple Hospitalizations

NCT ID: NCT04978922

Last Updated: 2021-07-27

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 345 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2018-01-01
• Study Completion Date: 2022-06-01

Brief 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.

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.

Conditions

COPD

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: SINGLE

Study Groups

TelEPOC with Machine Learning (ML)

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.

Group Type: EXPERIMENTAL

Machine Learning: ML (Artificial Intelligence System)

Intervention Type: DEVICE

To applicate an artificial intelligence system (Machine Learning: ML) on an active telemonitoring programme of readmitted COPD patients (TelEPOC)

TelEPOC without ML

Hospitals with an active telemonitoring programme of readmitted COPD patients (TelEPOC) without the application of an artificial intelligence system (Machine Learning: ML).

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

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)

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• 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).

Exclusion Criteria

• 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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Osakidetza

OTHER

Sponsor Role: collaborator

Dr. Cristobal Esteban

OTHER_GOV

Sponsor Role: lead

Responsible Party

Dr. Cristobal Esteban

MD

Responsibility Role: SPONSOR_INVESTIGATOR

Principal Investigators

Cristobal Esteban, MD

Role: PRINCIPAL_INVESTIGATOR

Osakidetza

Locations

Hospital Galdakao Usansolo

Galdakao, Vizcaya, Spain

Site Status: RECRUITING

Countries

Spain

Central Contacts

Cristobal Esteban, MD

Role: CONTACT

cristobal.est@gmail.com

+34 94 400 7002

Facility Contacts

Cristobal Esteban, MD

Role: primary

cristobal.est@gmail.com

+34-944007002

References

Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006 Nov;3(11):e442. doi: 10.1371/journal.pmed.0030442.

Reference Type: BACKGROUND

PMID: 17132052 (View on PubMed)

World Health Organization. Chronic Obstructive Pulmonary Disease (COPD). Avalaible from: http:// www.who.int/respiratory/copd/en/index.html

Reference Type: BACKGROUND

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.

Reference Type: BACKGROUND

PMID: 12324669 (View on PubMed)

Esteban 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.

Reference Type: BACKGROUND

PMID: 19272762 (View on PubMed)

Soler-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.

Reference Type: BACKGROUND

PMID: 16055622 (View on PubMed)

Pinnock 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.

Reference Type: BACKGROUND

PMID: 24136634 (View on PubMed)

Bolton 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.

Reference Type: BACKGROUND

PMID: 20846317 (View on PubMed)

Jordan R, Adab P, Jolly K. Telemonitoring for patients with COPD. BMJ. 2013 Oct 17;347:f5932. doi: 10.1136/bmj.f5932. No abstract available.

Reference Type: BACKGROUND

PMID: 24136632 (View on PubMed)

Esteban 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.

Reference Type: BACKGROUND

PMID: 27920519 (View on PubMed)

Esteban 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

Reference Type: BACKGROUND

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

Reference Type: BACKGROUND

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.

Reference Type: BACKGROUND

Esteban C, Moraza J, Sancho F et al. Machine Learning for COPD exacerbation prediction. European Respiratory Journal 2015;46:Issue suppl 59

Reference Type: BACKGROUND

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.

Reference Type: BACKGROUND

PMID: 28954781 (View on PubMed)

Other Identifiers

PI18/01797

Identifier Type: -

Identifier Source: org_study_id