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

NCT04978922 | Unknown

• 1 other identifier: PI18/01797
• Study Type: interventional
• Target: 345
• Locations: 1 country
• Sites: 1

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.

Conditions

COPD

Keywords

COPD exacerbation; Telemedicine; Intervention Studies; Patient Readmission; Machine Learning

Outcome Measures

Primary Outcomes (7)

• 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).

  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.

  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.

  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).

  2 years

• Workload of nurses

  \- The time that must be spend every day managing the alarms after adding ML.

  2 years

• Changes in clinical diary practice after including ML

  \- Exercise capacity (six minutes walking test)

  2 years

• Change in clinical diary practice after including ML

  \- Physical activity (pedometer)

  2 years

Study Arms (2)

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.

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).

Interventions

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)

TelEPOC with Machine Learning (ML)

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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).

You may not qualify if:

• 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.

Sponsors & Collaborators

• Dr. Cristobal Esteban: lead
• Osakidetza: collaborator

Study Sites (1)

Hospital Galdakao Usansolo

Galdakao, Vizcaya, 48960, Spain

RECRUITING

Related Publications (14)

• 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.

  PMID: 17132052 BACKGROUND

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

  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.

  PMID: 12324669 BACKGROUND

• 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.

  PMID: 19272762 BACKGROUND

• 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.

  PMID: 16055622 BACKGROUND

• 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.

  PMID: 24136634 BACKGROUND

• 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.

  PMID: 20846317 BACKGROUND

• 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.

  PMID: 24136632 BACKGROUND

• 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.

  PMID: 27920519 BACKGROUND

• 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

  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

  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.

  BACKGROUND

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

  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.

  PMID: 28954781 BACKGROUND

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Cristobal Esteban, MD

  Osakidetza

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Cristobal Esteban, MD

CONTACT

+34 94 400 7002; cristobal.est@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Only data entry people and biostatisticians were masking
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: MD

Study Record Dates

• First Submitted: April 11, 2021
• First Posted: July 27, 2021
• Study Start: January 1, 2018
• Primary Completion: January 1, 2022
• Study Completion: June 1, 2022
• Last Updated: July 27, 2021

Record last verified: 2021-07

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)