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Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model

PURPOSE: To characterise patients with chronic obstructive pulmonary disease (COPD) who are rehospitalised for an acute exacerbation, to estimate the cost of these hospitalisations, to characterise high risk patient sub groups and to identify factors potentially associated with the risk of rehospita...

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Autores principales: Cavailles, Arnaud, Melloni, Boris, Motola, Stéphane, Dayde, Florent, Laurent, Marie, Le Lay, Katell, Caumette, Didier, Luciani, Laura, Lleu, Pierre Louis, Berthon, Geoffrey, Flament, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198446/
https://www.ncbi.nlm.nih.gov/pubmed/32431495
http://dx.doi.org/10.2147/COPD.S236787
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author Cavailles, Arnaud
Melloni, Boris
Motola, Stéphane
Dayde, Florent
Laurent, Marie
Le Lay, Katell
Caumette, Didier
Luciani, Laura
Lleu, Pierre Louis
Berthon, Geoffrey
Flament, Thomas
author_facet Cavailles, Arnaud
Melloni, Boris
Motola, Stéphane
Dayde, Florent
Laurent, Marie
Le Lay, Katell
Caumette, Didier
Luciani, Laura
Lleu, Pierre Louis
Berthon, Geoffrey
Flament, Thomas
author_sort Cavailles, Arnaud
collection PubMed
description PURPOSE: To characterise patients with chronic obstructive pulmonary disease (COPD) who are rehospitalised for an acute exacerbation, to estimate the cost of these hospitalisations, to characterise high risk patient sub groups and to identify factors potentially associated with the risk of rehospitalisation. PATIENTS AND METHODS: This was a retrospective study using the French National Hospital Discharge Database. All patients aged ≥40 years hospitalised for an acute exacerbation of COPD between 2015 and 2016 were identified and followed for six months. Patients with at least one rehospitalisation for acute exacerbation of COPD constituted the rehospitalisation analysis population. A machine learning model was built to study the factors associated with the risk of rehospitalisation using decision tree analysis. A direct cost analysis was performed from the perspective of national health insurance. RESULTS: A total of 143,006 eligible patients were hospitalised for an acute exacerbation of COPD (AECOPD) in 2015–2016 (mean age: 74 years; 62.1% men). 25,090 (18.8%) were rehospitalised for another exacerbation within six months. In this study, 8.5% of patients died during or immediately following the index hospitalisation and 10.5% died during or immediately after rehospitalisation (p <0.001). The specific cost of these rehospitalisations was € 5304. The overall total cost per patient of all AECOPD-related stays was € 9623, being significantly higher in patients who were rehospitalised (€ 16,275) compared to those who were not (€ 8208). In decision tree analysis, the most important driver of rehospitalisation was hospitalisation in the previous two years (contributing 85% of the information). CONCLUSION: Rehospitalisations for acute exacerbations of COPD carry a high epidemiological and economic burden. Since hospitalisation for an acute exacerbation is the most important determinant of future rehospitalisations, management of COPD needs to focus on interventions aimed at decreasing the rehospitalisation risk of in order to lower the burden of disease.
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spelling pubmed-71984462020-05-19 Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model Cavailles, Arnaud Melloni, Boris Motola, Stéphane Dayde, Florent Laurent, Marie Le Lay, Katell Caumette, Didier Luciani, Laura Lleu, Pierre Louis Berthon, Geoffrey Flament, Thomas Int J Chron Obstruct Pulmon Dis Original Research PURPOSE: To characterise patients with chronic obstructive pulmonary disease (COPD) who are rehospitalised for an acute exacerbation, to estimate the cost of these hospitalisations, to characterise high risk patient sub groups and to identify factors potentially associated with the risk of rehospitalisation. PATIENTS AND METHODS: This was a retrospective study using the French National Hospital Discharge Database. All patients aged ≥40 years hospitalised for an acute exacerbation of COPD between 2015 and 2016 were identified and followed for six months. Patients with at least one rehospitalisation for acute exacerbation of COPD constituted the rehospitalisation analysis population. A machine learning model was built to study the factors associated with the risk of rehospitalisation using decision tree analysis. A direct cost analysis was performed from the perspective of national health insurance. RESULTS: A total of 143,006 eligible patients were hospitalised for an acute exacerbation of COPD (AECOPD) in 2015–2016 (mean age: 74 years; 62.1% men). 25,090 (18.8%) were rehospitalised for another exacerbation within six months. In this study, 8.5% of patients died during or immediately following the index hospitalisation and 10.5% died during or immediately after rehospitalisation (p <0.001). The specific cost of these rehospitalisations was € 5304. The overall total cost per patient of all AECOPD-related stays was € 9623, being significantly higher in patients who were rehospitalised (€ 16,275) compared to those who were not (€ 8208). In decision tree analysis, the most important driver of rehospitalisation was hospitalisation in the previous two years (contributing 85% of the information). CONCLUSION: Rehospitalisations for acute exacerbations of COPD carry a high epidemiological and economic burden. Since hospitalisation for an acute exacerbation is the most important determinant of future rehospitalisations, management of COPD needs to focus on interventions aimed at decreasing the rehospitalisation risk of in order to lower the burden of disease. Dove 2020-04-30 /pmc/articles/PMC7198446/ /pubmed/32431495 http://dx.doi.org/10.2147/COPD.S236787 Text en © 2020 Cavailles et al. http://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/). 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
Cavailles, Arnaud
Melloni, Boris
Motola, Stéphane
Dayde, Florent
Laurent, Marie
Le Lay, Katell
Caumette, Didier
Luciani, Laura
Lleu, Pierre Louis
Berthon, Geoffrey
Flament, Thomas
Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model
title Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model
title_full Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model
title_fullStr Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model
title_full_unstemmed Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model
title_short Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model
title_sort identification of patient profiles with high risk of hospital re-admissions for acute copd exacerbations (aecopd) in france using a machine learning model
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198446/
https://www.ncbi.nlm.nih.gov/pubmed/32431495
http://dx.doi.org/10.2147/COPD.S236787
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