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Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database
BACKGROUND: Reducing unplanned rehospitalizations is one of the priorities of health care policies in France and other Western countries. An easy-to-use algorithm for identifying patients at higher risk of rehospitalizations would help clinicians prioritize actions and care concerning discharge tran...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6414180/ https://www.ncbi.nlm.nih.gov/pubmed/30861004 http://dx.doi.org/10.1371/journal.pone.0210714 |
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author | Pauly, Vanessa Mendizabal, Hélène Gentile, Stéphanie Auquier, Pascal Boyer, Laurent |
author_facet | Pauly, Vanessa Mendizabal, Hélène Gentile, Stéphanie Auquier, Pascal Boyer, Laurent |
author_sort | Pauly, Vanessa |
collection | PubMed |
description | BACKGROUND: Reducing unplanned rehospitalizations is one of the priorities of health care policies in France and other Western countries. An easy-to-use algorithm for identifying patients at higher risk of rehospitalizations would help clinicians prioritize actions and care concerning discharge transitions. Our objective was to develop a predictive unplanned 30-day all-cause rehospitalization risk score based on the French hospital medico-administrative database. METHODS: This was a retrospective cohort study of all 2015 discharges from acute-care inpatient hospitalizations in a tertiary-care university center comprising four hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization via emergency departments, and we collected sociodemographic, clinical, and hospital characteristics based on hospitalization database computed for reimbursement of fees. We derived a predictive rehospitalization risk score using a split-sample design and multivariate logistic regression, and we compared the discriminative properties with the LACE index risk-score. RESULT: Our analysis included 118,650 hospitalizations, of which 4,127 (3.5%) led to rehospitalizations via emergency departments. Variables independently associated with rehospitalization were age, gender, state-funded medical assistance, as well as disease category and severity, Charlson comorbidity index, hospitalization via emergency departments, length of stay (LOS), and previous hospitalizations 6 months before. The predictive rehospitalization risk score yielded satisfactory discriminant properties (C statistic: 0.74) exceeding the LACE index (0.66). CONCLUSION: Our findings indicate that the possibility of unplanned rehospitalization remains high for some patient characteristics, indicating that targeted interventions could be beneficial for patients at the greatest risk. We developed an easy-to-use predictive rehospitalization risk-score of unplanned 30-day all-cause rehospitalizations with satisfactory discriminant properties. Future works should, however, explore if other data from electronic medical records and other databases could improve the accuracy of our predictive rehospitalization risk score based on medico-administrative data. |
format | Online Article Text |
id | pubmed-6414180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64141802019-04-02 Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database Pauly, Vanessa Mendizabal, Hélène Gentile, Stéphanie Auquier, Pascal Boyer, Laurent PLoS One Research Article BACKGROUND: Reducing unplanned rehospitalizations is one of the priorities of health care policies in France and other Western countries. An easy-to-use algorithm for identifying patients at higher risk of rehospitalizations would help clinicians prioritize actions and care concerning discharge transitions. Our objective was to develop a predictive unplanned 30-day all-cause rehospitalization risk score based on the French hospital medico-administrative database. METHODS: This was a retrospective cohort study of all 2015 discharges from acute-care inpatient hospitalizations in a tertiary-care university center comprising four hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization via emergency departments, and we collected sociodemographic, clinical, and hospital characteristics based on hospitalization database computed for reimbursement of fees. We derived a predictive rehospitalization risk score using a split-sample design and multivariate logistic regression, and we compared the discriminative properties with the LACE index risk-score. RESULT: Our analysis included 118,650 hospitalizations, of which 4,127 (3.5%) led to rehospitalizations via emergency departments. Variables independently associated with rehospitalization were age, gender, state-funded medical assistance, as well as disease category and severity, Charlson comorbidity index, hospitalization via emergency departments, length of stay (LOS), and previous hospitalizations 6 months before. The predictive rehospitalization risk score yielded satisfactory discriminant properties (C statistic: 0.74) exceeding the LACE index (0.66). CONCLUSION: Our findings indicate that the possibility of unplanned rehospitalization remains high for some patient characteristics, indicating that targeted interventions could be beneficial for patients at the greatest risk. We developed an easy-to-use predictive rehospitalization risk-score of unplanned 30-day all-cause rehospitalizations with satisfactory discriminant properties. Future works should, however, explore if other data from electronic medical records and other databases could improve the accuracy of our predictive rehospitalization risk score based on medico-administrative data. Public Library of Science 2019-03-12 /pmc/articles/PMC6414180/ /pubmed/30861004 http://dx.doi.org/10.1371/journal.pone.0210714 Text en © 2019 Pauly et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Pauly, Vanessa Mendizabal, Hélène Gentile, Stéphanie Auquier, Pascal Boyer, Laurent Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database |
title | Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database |
title_full | Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database |
title_fullStr | Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database |
title_full_unstemmed | Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database |
title_short | Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database |
title_sort | predictive risk score for unplanned 30-day rehospitalizations in the french universal health care system based on a medico-administrative database |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6414180/ https://www.ncbi.nlm.nih.gov/pubmed/30861004 http://dx.doi.org/10.1371/journal.pone.0210714 |
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