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Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive, chronic respiratory disease with a significant socioeconomic burden. Exacerbations, the sudden and sustained worsening of symptoms, can lead to hospitalization and reduce quality of life. Major limitations of previous telemon...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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JMIR Publications
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360891/ https://www.ncbi.nlm.nih.gov/pubmed/28270380 http://dx.doi.org/10.2196/jmir.7207 |
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author | Shah, Syed Ahmar Velardo, Carmelo Farmer, Andrew Tarassenko, Lionel |
author_facet | Shah, Syed Ahmar Velardo, Carmelo Farmer, Andrew Tarassenko, Lionel |
author_sort | Shah, Syed Ahmar |
collection | PubMed |
description | BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive, chronic respiratory disease with a significant socioeconomic burden. Exacerbations, the sudden and sustained worsening of symptoms, can lead to hospitalization and reduce quality of life. Major limitations of previous telemonitoring interventions for COPD include low compliance, lack of consensus on what constitutes an exacerbation, limited numbers of patients, and short monitoring periods. We developed a telemonitoring system based on a digital health platform that was used to collect data from the 1-year EDGE (Self Management and Support Programme) COPD clinical trial aiming at daily monitoring in a heterogeneous group of patients with moderate to severe COPD. OBJECTIVE: The objectives of the study were as follows: first, to develop a systematic and reproducible approach to exacerbation identification and to track the progression of patient condition during remote monitoring; and second, to develop a robust algorithm able to predict COPD exacerbation, based on vital signs acquired from a pulse oximeter. METHODS: We used data from 110 patients, with a combined monitoring period of more than 35,000 days. We propose a finite-state machine–based approach for modeling COPD exacerbation to gain a deeper insight into COPD patient condition during home monitoring to take account of the time course of symptoms. A robust algorithm based on short-period trend analysis and logistic regression using vital signs derived from a pulse oximeter is also developed to predict exacerbations. RESULTS: On the basis of 27,260 sessions recorded during the clinical trial (average usage of 5.3 times per week for 12 months), there were 361 exacerbation events. There was considerable variation in the length of exacerbation events, with a mean length of 8.8 days. The mean value of oxygen saturation was lower, and both the pulse rate and respiratory rate were higher before an impending exacerbation episode, compared with stable periods. On the basis of the classifier developed in this work, prediction of COPD exacerbation episodes with 60%-80% sensitivity will result in 68%-36% specificity. CONCLUSIONS: All 3 vital signs acquired from a pulse oximeter (pulse rate, oxygen saturation, and respiratory rate) are predictive of COPD exacerbation events, with oxygen saturation being the most predictive, followed by respiratory rate and pulse rate. Combination of these vital signs with a robust algorithm based on machine learning leads to further improvement in positive predictive accuracy. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 40367841; http://www.isrctn.com/ISRCTN40367841 (Archived by WebCite at http://www.webcitation.org/6olpMWNpc) |
format | Online Article Text |
id | pubmed-5360891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-53608912017-04-06 Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System Shah, Syed Ahmar Velardo, Carmelo Farmer, Andrew Tarassenko, Lionel J Med Internet Res Original Paper BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive, chronic respiratory disease with a significant socioeconomic burden. Exacerbations, the sudden and sustained worsening of symptoms, can lead to hospitalization and reduce quality of life. Major limitations of previous telemonitoring interventions for COPD include low compliance, lack of consensus on what constitutes an exacerbation, limited numbers of patients, and short monitoring periods. We developed a telemonitoring system based on a digital health platform that was used to collect data from the 1-year EDGE (Self Management and Support Programme) COPD clinical trial aiming at daily monitoring in a heterogeneous group of patients with moderate to severe COPD. OBJECTIVE: The objectives of the study were as follows: first, to develop a systematic and reproducible approach to exacerbation identification and to track the progression of patient condition during remote monitoring; and second, to develop a robust algorithm able to predict COPD exacerbation, based on vital signs acquired from a pulse oximeter. METHODS: We used data from 110 patients, with a combined monitoring period of more than 35,000 days. We propose a finite-state machine–based approach for modeling COPD exacerbation to gain a deeper insight into COPD patient condition during home monitoring to take account of the time course of symptoms. A robust algorithm based on short-period trend analysis and logistic regression using vital signs derived from a pulse oximeter is also developed to predict exacerbations. RESULTS: On the basis of 27,260 sessions recorded during the clinical trial (average usage of 5.3 times per week for 12 months), there were 361 exacerbation events. There was considerable variation in the length of exacerbation events, with a mean length of 8.8 days. The mean value of oxygen saturation was lower, and both the pulse rate and respiratory rate were higher before an impending exacerbation episode, compared with stable periods. On the basis of the classifier developed in this work, prediction of COPD exacerbation episodes with 60%-80% sensitivity will result in 68%-36% specificity. CONCLUSIONS: All 3 vital signs acquired from a pulse oximeter (pulse rate, oxygen saturation, and respiratory rate) are predictive of COPD exacerbation events, with oxygen saturation being the most predictive, followed by respiratory rate and pulse rate. Combination of these vital signs with a robust algorithm based on machine learning leads to further improvement in positive predictive accuracy. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 40367841; http://www.isrctn.com/ISRCTN40367841 (Archived by WebCite at http://www.webcitation.org/6olpMWNpc) JMIR Publications 2017-03-07 /pmc/articles/PMC5360891/ /pubmed/28270380 http://dx.doi.org/10.2196/jmir.7207 Text en ©Syed Ahmar Shah, Carmelo Velardo, Andrew Farmer, Lionel Tarassenko. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.03.2017. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Shah, Syed Ahmar Velardo, Carmelo Farmer, Andrew Tarassenko, Lionel Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System |
title | Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System |
title_full | Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System |
title_fullStr | Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System |
title_full_unstemmed | Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System |
title_short | Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System |
title_sort | exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360891/ https://www.ncbi.nlm.nih.gov/pubmed/28270380 http://dx.doi.org/10.2196/jmir.7207 |
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