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

Study Overview
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Brief Summary
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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
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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.

Official Title
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Impact of the Artificial Intelligence (Machine Learning) in a Telemonitoring Programme of COPD Patients With Multiple Hospitalizations (telEPOC)

Conditions
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COPD

Intervention / Treatment
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* Device: Machine Learning: ML (Artificial Intelligence System)


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Prevention
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: TelEPOC with Machine Learning (ML)<br>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)<br>* To applicate an artificial intelligence system (Machine Learning: ML) on an active telemonitoring programme of readmitted COPD patients (TelEPOC)<br>|
| No Intervention: TelEPOC without ML<br>Hospitals with an active telemonitoring programme of readmitted COPD patients (TelEPOC) without the application of an artificial intelligence system (Machine Learning: ML). | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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COPD exacerbation, Telemedicine, Intervention Studies, Patient Readmission, Machine Learning