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Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data

BACKGROUND: This study aims at gathering evidence about the relation between 30-day mortality and 30-day unplanned readmission and patient and hospital factors. By definition, we refer to 30-day mortality and 30-day unplanned readmission as the number of deaths and non-programmed hospitalizations fo...

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Autores principales: Roshanghalb, Afsaneh, Mazzali, Cristina, Lettieri, Emanuele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936032/
https://www.ncbi.nlm.nih.gov/pubmed/31888610
http://dx.doi.org/10.1186/s12913-019-4818-2
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author Roshanghalb, Afsaneh
Mazzali, Cristina
Lettieri, Emanuele
author_facet Roshanghalb, Afsaneh
Mazzali, Cristina
Lettieri, Emanuele
author_sort Roshanghalb, Afsaneh
collection PubMed
description BACKGROUND: This study aims at gathering evidence about the relation between 30-day mortality and 30-day unplanned readmission and patient and hospital factors. By definition, we refer to 30-day mortality and 30-day unplanned readmission as the number of deaths and non-programmed hospitalizations for any cause within 30 days after the incident heart failure (HF). In particular, the focus is on the role played by hospital-level factors. METHODS: A multi-level logistic model that combines patient- and hospital-level covariates has been developed to better disentangle the role played by the two groups of covariates. Later on, hospital outliers in term of better-than-expected/worst-than-expected performers have been identified by comparing expected cases vs. observed cases. Hospitals performance in terms of 30-day mortality and 30-day unplanned readmission rates have been visualized through the creation of funnel plots. Covariates have been selected coherently to past literature. Data comes from the hospital discharge forms for Heart Failure patients in the Lombardy Region (Northern Italy). Considering incident cases for HF in the timespan 2010–2012, 78,907 records for adult patients from 117 hospitals have been collected after quality checks. RESULTS: Our results show that 30-day mortality and 30-day unplanned readmissions are explained by hospital-level covariates, paving the way for the design and implementation of evidence-based improvement strategies. While the percentage of surgical DRG (OR = 1.001; CI (1.000–1.002)) and the hospital type of structure (Research hospitals vs. non-research public hospitals (OR = 0.62; CI (0.48–0.80)) and Non-research private hospitals vs. non-research hospitals OR = 0.75; CI (0.63–0.90)) are significant for mortality, the mean length of stay (OR = 0.96; CI (0.95–0.98)) is significant for unplanned readmission, showing that mortality and readmission rates might be improved through different strategies. CONCLUSION: Our results confirm that hospital-level covariates do affect quality of care, and that 30-day mortality and 30-day unplanned readmission are affected by different managerial choices. This confirms that hospitals should be accountable for their “added value” to quality of care.
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spelling pubmed-69360322019-12-31 Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data Roshanghalb, Afsaneh Mazzali, Cristina Lettieri, Emanuele BMC Health Serv Res Research Article BACKGROUND: This study aims at gathering evidence about the relation between 30-day mortality and 30-day unplanned readmission and patient and hospital factors. By definition, we refer to 30-day mortality and 30-day unplanned readmission as the number of deaths and non-programmed hospitalizations for any cause within 30 days after the incident heart failure (HF). In particular, the focus is on the role played by hospital-level factors. METHODS: A multi-level logistic model that combines patient- and hospital-level covariates has been developed to better disentangle the role played by the two groups of covariates. Later on, hospital outliers in term of better-than-expected/worst-than-expected performers have been identified by comparing expected cases vs. observed cases. Hospitals performance in terms of 30-day mortality and 30-day unplanned readmission rates have been visualized through the creation of funnel plots. Covariates have been selected coherently to past literature. Data comes from the hospital discharge forms for Heart Failure patients in the Lombardy Region (Northern Italy). Considering incident cases for HF in the timespan 2010–2012, 78,907 records for adult patients from 117 hospitals have been collected after quality checks. RESULTS: Our results show that 30-day mortality and 30-day unplanned readmissions are explained by hospital-level covariates, paving the way for the design and implementation of evidence-based improvement strategies. While the percentage of surgical DRG (OR = 1.001; CI (1.000–1.002)) and the hospital type of structure (Research hospitals vs. non-research public hospitals (OR = 0.62; CI (0.48–0.80)) and Non-research private hospitals vs. non-research hospitals OR = 0.75; CI (0.63–0.90)) are significant for mortality, the mean length of stay (OR = 0.96; CI (0.95–0.98)) is significant for unplanned readmission, showing that mortality and readmission rates might be improved through different strategies. CONCLUSION: Our results confirm that hospital-level covariates do affect quality of care, and that 30-day mortality and 30-day unplanned readmission are affected by different managerial choices. This confirms that hospitals should be accountable for their “added value” to quality of care. BioMed Central 2019-12-30 /pmc/articles/PMC6936032/ /pubmed/31888610 http://dx.doi.org/10.1186/s12913-019-4818-2 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Roshanghalb, Afsaneh
Mazzali, Cristina
Lettieri, Emanuele
Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data
title Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data
title_full Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data
title_fullStr Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data
title_full_unstemmed Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data
title_short Multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data
title_sort multi-level models for heart failure patients’ 30-day mortality and readmission rates: the relation between patient and hospital factors in administrative data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936032/
https://www.ncbi.nlm.nih.gov/pubmed/31888610
http://dx.doi.org/10.1186/s12913-019-4818-2
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