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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...

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Autores principales: Pauly, Vanessa, Mendizabal, Hélène, Gentile, Stéphanie, Auquier, Pascal, Boyer, Laurent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
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.
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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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