Cargando…

The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data

BACKGROUND: This large-scale analysis pools individual data about the Clinical Frailty Scale (CFS) to predict outcome in the intensive care unit (ICU). METHODS: A systematic search identified all clinical trials that used the CFS in the ICU (PubMed searched until 24th June 2020). All patients who we...

Descripción completa

Detalles Bibliográficos
Autores principales: Bruno, Raphael Romano, Wernly, Bernhard, Bagshaw, Sean M., van den Boogaard, Mark, Darvall, Jai N., De Geer, Lina, de Gopegui Miguelena, Pablo Ruiz, Heyland, Daren K., Hewitt, David, Hope, Aluko A., Langlais, Emilie, Le Maguet, Pascale, Montgomery, Carmel L., Papageorgiou, Dimitrios, Seguin, Philippe, Geense, Wytske W., Silva-Obregón, J. Alberto, Wolff, Georg, Polzin, Amin, Dannenberg, Lisa, Kelm, Malte, Flaatten, Hans, Beil, Michael, Franz, Marcus, Sviri, Sigal, Leaver, Susannah, Guidet, Bertrand, Boumendil, Ariane, Jung, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155148/
https://www.ncbi.nlm.nih.gov/pubmed/37133796
http://dx.doi.org/10.1186/s13613-023-01132-x
_version_ 1785036271628845056
author Bruno, Raphael Romano
Wernly, Bernhard
Bagshaw, Sean M.
van den Boogaard, Mark
Darvall, Jai N.
De Geer, Lina
de Gopegui Miguelena, Pablo Ruiz
Heyland, Daren K.
Hewitt, David
Hope, Aluko A.
Langlais, Emilie
Le Maguet, Pascale
Montgomery, Carmel L.
Papageorgiou, Dimitrios
Seguin, Philippe
Geense, Wytske W.
Silva-Obregón, J. Alberto
Wolff, Georg
Polzin, Amin
Dannenberg, Lisa
Kelm, Malte
Flaatten, Hans
Beil, Michael
Franz, Marcus
Sviri, Sigal
Leaver, Susannah
Guidet, Bertrand
Boumendil, Ariane
Jung, Christian
author_facet Bruno, Raphael Romano
Wernly, Bernhard
Bagshaw, Sean M.
van den Boogaard, Mark
Darvall, Jai N.
De Geer, Lina
de Gopegui Miguelena, Pablo Ruiz
Heyland, Daren K.
Hewitt, David
Hope, Aluko A.
Langlais, Emilie
Le Maguet, Pascale
Montgomery, Carmel L.
Papageorgiou, Dimitrios
Seguin, Philippe
Geense, Wytske W.
Silva-Obregón, J. Alberto
Wolff, Georg
Polzin, Amin
Dannenberg, Lisa
Kelm, Malte
Flaatten, Hans
Beil, Michael
Franz, Marcus
Sviri, Sigal
Leaver, Susannah
Guidet, Bertrand
Boumendil, Ariane
Jung, Christian
author_sort Bruno, Raphael Romano
collection PubMed
description BACKGROUND: This large-scale analysis pools individual data about the Clinical Frailty Scale (CFS) to predict outcome in the intensive care unit (ICU). METHODS: A systematic search identified all clinical trials that used the CFS in the ICU (PubMed searched until 24th June 2020). All patients who were electively admitted were excluded. The primary outcome was ICU mortality. Regression models were estimated on the complete data set, and for missing data, multiple imputations were utilised. Cox models were adjusted for age, sex, and illness acuity score (SOFA, SAPS II or APACHE II). RESULTS: 12 studies from 30 countries with anonymised individualised patient data were included (n = 23,989 patients). In the univariate analysis for all patients, being frail (CFS ≥ 5) was associated with an increased risk of ICU mortality, but not after adjustment. In older patients (≥ 65 years) there was an independent association with ICU mortality both in the complete case analysis (HR 1.34 (95% CI 1.25–1.44), p < 0.0001) and in the multiple imputation analysis (HR 1.35 (95% CI 1.26–1.45), p < 0.0001, adjusted for SOFA). In older patients, being vulnerable (CFS 4) alone did not significantly differ from being frail. After adjustment, a CFS of 4–5, 6, and ≥ 7 was associated with a significantly worse outcome compared to CFS of 1–3. CONCLUSIONS: Being frail is associated with a significantly increased risk for ICU mortality in older patients, while being vulnerable alone did not significantly differ. New Frailty categories might reflect its “continuum” better and predict ICU outcome more accurately. Trial registration: Open Science Framework (OSF: https://osf.io/8buwk/). GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13613-023-01132-x.
format Online
Article
Text
id pubmed-10155148
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-101551482023-05-05 The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data Bruno, Raphael Romano Wernly, Bernhard Bagshaw, Sean M. van den Boogaard, Mark Darvall, Jai N. De Geer, Lina de Gopegui Miguelena, Pablo Ruiz Heyland, Daren K. Hewitt, David Hope, Aluko A. Langlais, Emilie Le Maguet, Pascale Montgomery, Carmel L. Papageorgiou, Dimitrios Seguin, Philippe Geense, Wytske W. Silva-Obregón, J. Alberto Wolff, Georg Polzin, Amin Dannenberg, Lisa Kelm, Malte Flaatten, Hans Beil, Michael Franz, Marcus Sviri, Sigal Leaver, Susannah Guidet, Bertrand Boumendil, Ariane Jung, Christian Ann Intensive Care Research BACKGROUND: This large-scale analysis pools individual data about the Clinical Frailty Scale (CFS) to predict outcome in the intensive care unit (ICU). METHODS: A systematic search identified all clinical trials that used the CFS in the ICU (PubMed searched until 24th June 2020). All patients who were electively admitted were excluded. The primary outcome was ICU mortality. Regression models were estimated on the complete data set, and for missing data, multiple imputations were utilised. Cox models were adjusted for age, sex, and illness acuity score (SOFA, SAPS II or APACHE II). RESULTS: 12 studies from 30 countries with anonymised individualised patient data were included (n = 23,989 patients). In the univariate analysis for all patients, being frail (CFS ≥ 5) was associated with an increased risk of ICU mortality, but not after adjustment. In older patients (≥ 65 years) there was an independent association with ICU mortality both in the complete case analysis (HR 1.34 (95% CI 1.25–1.44), p < 0.0001) and in the multiple imputation analysis (HR 1.35 (95% CI 1.26–1.45), p < 0.0001, adjusted for SOFA). In older patients, being vulnerable (CFS 4) alone did not significantly differ from being frail. After adjustment, a CFS of 4–5, 6, and ≥ 7 was associated with a significantly worse outcome compared to CFS of 1–3. CONCLUSIONS: Being frail is associated with a significantly increased risk for ICU mortality in older patients, while being vulnerable alone did not significantly differ. New Frailty categories might reflect its “continuum” better and predict ICU outcome more accurately. Trial registration: Open Science Framework (OSF: https://osf.io/8buwk/). GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13613-023-01132-x. Springer International Publishing 2023-05-03 /pmc/articles/PMC10155148/ /pubmed/37133796 http://dx.doi.org/10.1186/s13613-023-01132-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Bruno, Raphael Romano
Wernly, Bernhard
Bagshaw, Sean M.
van den Boogaard, Mark
Darvall, Jai N.
De Geer, Lina
de Gopegui Miguelena, Pablo Ruiz
Heyland, Daren K.
Hewitt, David
Hope, Aluko A.
Langlais, Emilie
Le Maguet, Pascale
Montgomery, Carmel L.
Papageorgiou, Dimitrios
Seguin, Philippe
Geense, Wytske W.
Silva-Obregón, J. Alberto
Wolff, Georg
Polzin, Amin
Dannenberg, Lisa
Kelm, Malte
Flaatten, Hans
Beil, Michael
Franz, Marcus
Sviri, Sigal
Leaver, Susannah
Guidet, Bertrand
Boumendil, Ariane
Jung, Christian
The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
title The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
title_full The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
title_fullStr The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
title_full_unstemmed The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
title_short The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
title_sort clinical frailty scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155148/
https://www.ncbi.nlm.nih.gov/pubmed/37133796
http://dx.doi.org/10.1186/s13613-023-01132-x
work_keys_str_mv AT brunoraphaelromano theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT wernlybernhard theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT bagshawseanm theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT vandenboogaardmark theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT darvalljain theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT degeerlina theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT degopeguimiguelenapabloruiz theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT heylanddarenk theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT hewittdavid theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT hopealukoa theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT langlaisemilie theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT lemaguetpascale theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT montgomerycarmell theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT papageorgioudimitrios theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT seguinphilippe theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT geensewytskew theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT silvaobregonjalberto theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT wolffgeorg theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT polzinamin theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT dannenberglisa theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT kelmmalte theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT flaattenhans theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT beilmichael theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT franzmarcus theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT svirisigal theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT leaversusannah theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT guidetbertrand theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT boumendilariane theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT jungchristian theclinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT brunoraphaelromano clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT wernlybernhard clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT bagshawseanm clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT vandenboogaardmark clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT darvalljain clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT degeerlina clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT degopeguimiguelenapabloruiz clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT heylanddarenk clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT hewittdavid clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT hopealukoa clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT langlaisemilie clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT lemaguetpascale clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT montgomerycarmell clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT papageorgioudimitrios clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT seguinphilippe clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT geensewytskew clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT silvaobregonjalberto clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT wolffgeorg clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT polzinamin clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT dannenberglisa clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT kelmmalte clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT flaattenhans clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT beilmichael clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT franzmarcus clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT svirisigal clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT leaversusannah clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT guidetbertrand clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT boumendilariane clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata
AT jungchristian clinicalfrailtyscaleformortalitypredictionofoldacutelyadmittedintensivecarepatientsametaanalysisofindividualpatientleveldata