Cargando…

Multi-state modelling of heart failure care path: A population-based investigation from Italy

BACKGROUND: How different risk profiles of heart failure (HF) patients can influence multiple readmissions and outpatient management is largely unknown. We propose the application of two multi-state models in real world setting to jointly evaluate the impact of different risk factors on multiple hos...

Descripción completa

Detalles Bibliográficos
Autores principales: Gasperoni, Francesca, Ieva, Francesca, Barbati, Giulia, Scagnetto, Arjuna, Iorio, Annamaria, Sinagra, Gianfranco, Di Lenarda, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5462433/
https://www.ncbi.nlm.nih.gov/pubmed/28591172
http://dx.doi.org/10.1371/journal.pone.0179176
_version_ 1783242515733807104
author Gasperoni, Francesca
Ieva, Francesca
Barbati, Giulia
Scagnetto, Arjuna
Iorio, Annamaria
Sinagra, Gianfranco
Di Lenarda, Andrea
author_facet Gasperoni, Francesca
Ieva, Francesca
Barbati, Giulia
Scagnetto, Arjuna
Iorio, Annamaria
Sinagra, Gianfranco
Di Lenarda, Andrea
author_sort Gasperoni, Francesca
collection PubMed
description BACKGROUND: How different risk profiles of heart failure (HF) patients can influence multiple readmissions and outpatient management is largely unknown. We propose the application of two multi-state models in real world setting to jointly evaluate the impact of different risk factors on multiple hospital admissions, Integrated Home Care (IHC) activations, Intermediate Care Unit (ICU) admissions and death. METHODS AND FINDINGS: The first model (model 1) concerns only hospitalizations as possible events and aims at detecting the determinants of repeated hospitalizations. The second model (model 2) considers both hospitalizations and ICU/IHC events and aims at evaluating which profiles are associated with transitions in intermediate care with respect to repeated hospitalizations or death. Both are characterized by transition specific covariates, adjusting for risk factors. We identified 4,904 patients (4,129 de novo and 775 worsening heart failure, WHF) hospitalized for HF from 2009 to 2014. 2,714 (55%) patients died. Advanced age and higher morbidity load increased the rate of dying and of being rehospitalized (model 1), decreased the rate of being discharged from hospital (models 1 and 2) and increased the rate of inactivation of IHC (model 2). WHF was an important risk factor associated with hospital readmission. CONCLUSION: Multi-state models enable a better identification of two patterns of HF patients. Once adjusted for age and comorbidity load, the WHF condition identifies patients who are more likely to be readmitted to hospital, but does not represent an increasing risk factor for activating ICU/IHC. This highlights different ways to manage specific patients’ patterns of care. These results provide useful healthcare support to patients’ management in real world context. Our study suggests that the epidemiology of the considered clinical characteristics is more nuanced than traditionally presented through a single event.
format Online
Article
Text
id pubmed-5462433
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-54624332017-06-22 Multi-state modelling of heart failure care path: A population-based investigation from Italy Gasperoni, Francesca Ieva, Francesca Barbati, Giulia Scagnetto, Arjuna Iorio, Annamaria Sinagra, Gianfranco Di Lenarda, Andrea PLoS One Research Article BACKGROUND: How different risk profiles of heart failure (HF) patients can influence multiple readmissions and outpatient management is largely unknown. We propose the application of two multi-state models in real world setting to jointly evaluate the impact of different risk factors on multiple hospital admissions, Integrated Home Care (IHC) activations, Intermediate Care Unit (ICU) admissions and death. METHODS AND FINDINGS: The first model (model 1) concerns only hospitalizations as possible events and aims at detecting the determinants of repeated hospitalizations. The second model (model 2) considers both hospitalizations and ICU/IHC events and aims at evaluating which profiles are associated with transitions in intermediate care with respect to repeated hospitalizations or death. Both are characterized by transition specific covariates, adjusting for risk factors. We identified 4,904 patients (4,129 de novo and 775 worsening heart failure, WHF) hospitalized for HF from 2009 to 2014. 2,714 (55%) patients died. Advanced age and higher morbidity load increased the rate of dying and of being rehospitalized (model 1), decreased the rate of being discharged from hospital (models 1 and 2) and increased the rate of inactivation of IHC (model 2). WHF was an important risk factor associated with hospital readmission. CONCLUSION: Multi-state models enable a better identification of two patterns of HF patients. Once adjusted for age and comorbidity load, the WHF condition identifies patients who are more likely to be readmitted to hospital, but does not represent an increasing risk factor for activating ICU/IHC. This highlights different ways to manage specific patients’ patterns of care. These results provide useful healthcare support to patients’ management in real world context. Our study suggests that the epidemiology of the considered clinical characteristics is more nuanced than traditionally presented through a single event. Public Library of Science 2017-06-07 /pmc/articles/PMC5462433/ /pubmed/28591172 http://dx.doi.org/10.1371/journal.pone.0179176 Text en © 2017 Gasperoni 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
Gasperoni, Francesca
Ieva, Francesca
Barbati, Giulia
Scagnetto, Arjuna
Iorio, Annamaria
Sinagra, Gianfranco
Di Lenarda, Andrea
Multi-state modelling of heart failure care path: A population-based investigation from Italy
title Multi-state modelling of heart failure care path: A population-based investigation from Italy
title_full Multi-state modelling of heart failure care path: A population-based investigation from Italy
title_fullStr Multi-state modelling of heart failure care path: A population-based investigation from Italy
title_full_unstemmed Multi-state modelling of heart failure care path: A population-based investigation from Italy
title_short Multi-state modelling of heart failure care path: A population-based investigation from Italy
title_sort multi-state modelling of heart failure care path: a population-based investigation from italy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5462433/
https://www.ncbi.nlm.nih.gov/pubmed/28591172
http://dx.doi.org/10.1371/journal.pone.0179176
work_keys_str_mv AT gasperonifrancesca multistatemodellingofheartfailurecarepathapopulationbasedinvestigationfromitaly
AT ievafrancesca multistatemodellingofheartfailurecarepathapopulationbasedinvestigationfromitaly
AT barbatigiulia multistatemodellingofheartfailurecarepathapopulationbasedinvestigationfromitaly
AT scagnettoarjuna multistatemodellingofheartfailurecarepathapopulationbasedinvestigationfromitaly
AT iorioannamaria multistatemodellingofheartfailurecarepathapopulationbasedinvestigationfromitaly
AT sinagragianfranco multistatemodellingofheartfailurecarepathapopulationbasedinvestigationfromitaly
AT dilenardaandrea multistatemodellingofheartfailurecarepathapopulationbasedinvestigationfromitaly