Cargando…

A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care

INTRODUCTION: We sought to derive literature-based summary estimates of readmission to the ICU and hospital mortality among patients discharged alive from the ICU. METHODS: We searched MEDLINE, Embase, CINAHL and the Cochrane Central Register of Controlled Trials from inception to March 2013, as wel...

Descripción completa

Detalles Bibliográficos
Autores principales: Hosein, F Shaun, Roberts, Derek J, Turin, Tanvir Chowdhury, Zygun, David, Ghali, William A, Stelfox, Henry T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312433/
https://www.ncbi.nlm.nih.gov/pubmed/25551448
http://dx.doi.org/10.1186/s13054-014-0715-6
_version_ 1782355117969768448
author Hosein, F Shaun
Roberts, Derek J
Turin, Tanvir Chowdhury
Zygun, David
Ghali, William A
Stelfox, Henry T
author_facet Hosein, F Shaun
Roberts, Derek J
Turin, Tanvir Chowdhury
Zygun, David
Ghali, William A
Stelfox, Henry T
author_sort Hosein, F Shaun
collection PubMed
description INTRODUCTION: We sought to derive literature-based summary estimates of readmission to the ICU and hospital mortality among patients discharged alive from the ICU. METHODS: We searched MEDLINE, Embase, CINAHL and the Cochrane Central Register of Controlled Trials from inception to March 2013, as well as the reference lists in the publications of the included studies. We selected cohort studies of ICU discharge prognostic factors that in which readmission to the ICU or hospital mortality among patients discharged alive from the ICU was reported. Two reviewers independently abstracted the number of patients readmitted to the ICU and hospital deaths among patients discharged alive from the ICU. Fixed effects and random effects models were used to estimate the pooled cumulative incidence of ICU readmission and the pooled cumulative incidence of hospital mortality. RESULTS: The analysis included 58 studies (n = 2,073,170 patients). The majority of studies followed patients until hospital discharge (n = 46 studies) and reported readmission to the ICU (n = 46 studies) or hospital mortality (n = 49 studies). The cumulative incidence of ICU readmission was 4.0 readmissions (95% confidence interval (CI), 3.9 to 4.0) per 100 patient discharges using fixed effects pooling and 6.3 readmissions (95% CI, 5.6 to 6.9) per 100 patient discharges using random effects pooling. The cumulative incidence of hospital mortality was 3.3 deaths (95% CI, 3.3 to 3.3) per 100 patient discharges using fixed effects pooling and 6.8 deaths (95% CI, 6.1 to 7.6) per 100 patient discharges using random effects pooling. There was significant heterogeneity for the pooled estimates, which was partially explained by patient, institution and study methodological characteristics. CONCLUSIONS: Using current literature estimates, for every 100 patients discharged alive from the ICU, between 4 and 6 patients on average will be readmitted to the ICU and between 3 and 7 patients on average will die prior to hospital discharge. These estimates can inform the selection of benchmarks for quality metrics of transitions of patient care between the ICU and the hospital ward.
format Online
Article
Text
id pubmed-4312433
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-43124332015-02-01 A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care Hosein, F Shaun Roberts, Derek J Turin, Tanvir Chowdhury Zygun, David Ghali, William A Stelfox, Henry T Crit Care Research INTRODUCTION: We sought to derive literature-based summary estimates of readmission to the ICU and hospital mortality among patients discharged alive from the ICU. METHODS: We searched MEDLINE, Embase, CINAHL and the Cochrane Central Register of Controlled Trials from inception to March 2013, as well as the reference lists in the publications of the included studies. We selected cohort studies of ICU discharge prognostic factors that in which readmission to the ICU or hospital mortality among patients discharged alive from the ICU was reported. Two reviewers independently abstracted the number of patients readmitted to the ICU and hospital deaths among patients discharged alive from the ICU. Fixed effects and random effects models were used to estimate the pooled cumulative incidence of ICU readmission and the pooled cumulative incidence of hospital mortality. RESULTS: The analysis included 58 studies (n = 2,073,170 patients). The majority of studies followed patients until hospital discharge (n = 46 studies) and reported readmission to the ICU (n = 46 studies) or hospital mortality (n = 49 studies). The cumulative incidence of ICU readmission was 4.0 readmissions (95% confidence interval (CI), 3.9 to 4.0) per 100 patient discharges using fixed effects pooling and 6.3 readmissions (95% CI, 5.6 to 6.9) per 100 patient discharges using random effects pooling. The cumulative incidence of hospital mortality was 3.3 deaths (95% CI, 3.3 to 3.3) per 100 patient discharges using fixed effects pooling and 6.8 deaths (95% CI, 6.1 to 7.6) per 100 patient discharges using random effects pooling. There was significant heterogeneity for the pooled estimates, which was partially explained by patient, institution and study methodological characteristics. CONCLUSIONS: Using current literature estimates, for every 100 patients discharged alive from the ICU, between 4 and 6 patients on average will be readmitted to the ICU and between 3 and 7 patients on average will die prior to hospital discharge. These estimates can inform the selection of benchmarks for quality metrics of transitions of patient care between the ICU and the hospital ward. BioMed Central 2014-12-31 2014 /pmc/articles/PMC4312433/ /pubmed/25551448 http://dx.doi.org/10.1186/s13054-014-0715-6 Text en © Hosein et al.; licensee BioMed Central. 2014 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 work is properly credited. 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
Hosein, F Shaun
Roberts, Derek J
Turin, Tanvir Chowdhury
Zygun, David
Ghali, William A
Stelfox, Henry T
A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care
title A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care
title_full A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care
title_fullStr A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care
title_full_unstemmed A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care
title_short A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care
title_sort meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312433/
https://www.ncbi.nlm.nih.gov/pubmed/25551448
http://dx.doi.org/10.1186/s13054-014-0715-6
work_keys_str_mv AT hoseinfshaun ametaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT robertsderekj ametaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT turintanvirchowdhury ametaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT zygundavid ametaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT ghaliwilliama ametaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT stelfoxhenryt ametaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT hoseinfshaun metaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT robertsderekj metaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT turintanvirchowdhury metaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT zygundavid metaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT ghaliwilliama metaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare
AT stelfoxhenryt metaanalysistoderiveliteraturebasedbenchmarksforreadmissionandhospitalmortalityafterpatientdischargefromintensivecare