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Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations
BACKGROUND: COPDPredict™ is a novel digital application dedicated to providing early warning of imminent COPD (chronic obstructive pulmonary disease) exacerbations for prompt intervention. Exacerbation prediction algorithms are based on a decision tree model constructed from percentage thresholds fo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232856/ https://www.ncbi.nlm.nih.gov/pubmed/34188465 http://dx.doi.org/10.2147/COPD.S309372 |
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author | Patel, Neil Kinmond, Kathryn Jones, Pauline Birks, Pamela Spiteri, Monica A |
author_facet | Patel, Neil Kinmond, Kathryn Jones, Pauline Birks, Pamela Spiteri, Monica A |
author_sort | Patel, Neil |
collection | PubMed |
description | BACKGROUND: COPDPredict™ is a novel digital application dedicated to providing early warning of imminent COPD (chronic obstructive pulmonary disease) exacerbations for prompt intervention. Exacerbation prediction algorithms are based on a decision tree model constructed from percentage thresholds for disease state changes in patient-reported wellbeing, forced expiratory volume in one second (FEV(1)) and C-reactive protein (CRP) levels. Our study determined the validity of COPDPredict™ to identify exacerbations and provide timely notifications to patients and clinicians compared to clinician-defined episodes. METHODS: In a 6-month prospective observational study, 90 patients with COPD and frequent exacerbations registered wellbeing self-assessments daily using COPDPredict™ App and measured FEV(1) using connected spirometers. CRP was measured using finger-prick testing. RESULTS: Wellbeing self-assessment submissions showed 98% compliance. Ten patients did not experience exacerbations and treatment was unchanged. A total of 112 clinician-defined exacerbations were identified in the remaining 80 patients: 52 experienced 1 exacerbation; 28 had 2.2±0.4 episodes. Sixty-two patients self-managed using prescribed rescue medication. In 14 patients, exacerbations were more severe but responded to timely escalated treatment at home. Four patients attended the emergency room; with 2 hospitalised for <72 hours. Compared to the 6 months pre-COPDPredict™, hospitalisations were reduced by 98% (90 vs 2, p<0.001). COPDPredict™ identified COPD-related exacerbations at 7, 3 days (median, IQR) prior to clinician-defined episodes, sending appropriate alerts to patients and clinicians. Cross-tabulation demonstrated sensitivity of 97.9% (95% CI 95.7–99.2), specificity of 84.0% (95% CI 82.6–85.3), positive and negative predictive value of 38.4% (95% CI 36.4–40.4) and 99.8% (95% CI 99.5–99.9), respectively. CONCLUSION: High sensitivity indicates that if there is an exacerbation, COPDPredict™ informs patients and clinicians accurately. The high negative predictive value implies that when an exacerbation is not indicated by COPDPredict™, risk of an exacerbation is low. Thus, COPDPredict™ provides safe, personalised, preventative care for patients with COPD. |
format | Online Article Text |
id | pubmed-8232856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-82328562021-06-28 Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations Patel, Neil Kinmond, Kathryn Jones, Pauline Birks, Pamela Spiteri, Monica A Int J Chron Obstruct Pulmon Dis Original Research BACKGROUND: COPDPredict™ is a novel digital application dedicated to providing early warning of imminent COPD (chronic obstructive pulmonary disease) exacerbations for prompt intervention. Exacerbation prediction algorithms are based on a decision tree model constructed from percentage thresholds for disease state changes in patient-reported wellbeing, forced expiratory volume in one second (FEV(1)) and C-reactive protein (CRP) levels. Our study determined the validity of COPDPredict™ to identify exacerbations and provide timely notifications to patients and clinicians compared to clinician-defined episodes. METHODS: In a 6-month prospective observational study, 90 patients with COPD and frequent exacerbations registered wellbeing self-assessments daily using COPDPredict™ App and measured FEV(1) using connected spirometers. CRP was measured using finger-prick testing. RESULTS: Wellbeing self-assessment submissions showed 98% compliance. Ten patients did not experience exacerbations and treatment was unchanged. A total of 112 clinician-defined exacerbations were identified in the remaining 80 patients: 52 experienced 1 exacerbation; 28 had 2.2±0.4 episodes. Sixty-two patients self-managed using prescribed rescue medication. In 14 patients, exacerbations were more severe but responded to timely escalated treatment at home. Four patients attended the emergency room; with 2 hospitalised for <72 hours. Compared to the 6 months pre-COPDPredict™, hospitalisations were reduced by 98% (90 vs 2, p<0.001). COPDPredict™ identified COPD-related exacerbations at 7, 3 days (median, IQR) prior to clinician-defined episodes, sending appropriate alerts to patients and clinicians. Cross-tabulation demonstrated sensitivity of 97.9% (95% CI 95.7–99.2), specificity of 84.0% (95% CI 82.6–85.3), positive and negative predictive value of 38.4% (95% CI 36.4–40.4) and 99.8% (95% CI 99.5–99.9), respectively. CONCLUSION: High sensitivity indicates that if there is an exacerbation, COPDPredict™ informs patients and clinicians accurately. The high negative predictive value implies that when an exacerbation is not indicated by COPDPredict™, risk of an exacerbation is low. Thus, COPDPredict™ provides safe, personalised, preventative care for patients with COPD. Dove 2021-06-21 /pmc/articles/PMC8232856/ /pubmed/34188465 http://dx.doi.org/10.2147/COPD.S309372 Text en © 2021 Patel et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Patel, Neil Kinmond, Kathryn Jones, Pauline Birks, Pamela Spiteri, Monica A Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations |
title | Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations |
title_full | Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations |
title_fullStr | Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations |
title_full_unstemmed | Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations |
title_short | Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations |
title_sort | validation of copdpredict™: unique combination of remote monitoring and exacerbation prediction to support preventative management of copd exacerbations |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232856/ https://www.ncbi.nlm.nih.gov/pubmed/34188465 http://dx.doi.org/10.2147/COPD.S309372 |
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