Cargando…

Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal

BACKGROUND: Several prediction models of survival after in-hospital cardiac arrest (IHCA) have been published, but no overview of model performance and external validation exists. We performed a systematic review of the available prognostic models for outcome prediction of attempted resuscitation fo...

Descripción completa

Detalles Bibliográficos
Autores principales: Grandbois van Ravenhorst, Casey, Schluep, Marc, Endeman, Henrik, Stolker, Robert-Jan, Hoeks, Sanne Elisabeth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862512/
https://www.ncbi.nlm.nih.gov/pubmed/36670450
http://dx.doi.org/10.1186/s13054-023-04306-y
_version_ 1784875110207848448
author Grandbois van Ravenhorst, Casey
Schluep, Marc
Endeman, Henrik
Stolker, Robert-Jan
Hoeks, Sanne Elisabeth
author_facet Grandbois van Ravenhorst, Casey
Schluep, Marc
Endeman, Henrik
Stolker, Robert-Jan
Hoeks, Sanne Elisabeth
author_sort Grandbois van Ravenhorst, Casey
collection PubMed
description BACKGROUND: Several prediction models of survival after in-hospital cardiac arrest (IHCA) have been published, but no overview of model performance and external validation exists. We performed a systematic review of the available prognostic models for outcome prediction of attempted resuscitation for IHCA using pre-arrest factors to enhance clinical decision-making through improved outcome prediction. METHODS: This systematic review followed the CHARMS and PRISMA guidelines. Medline, Embase, Web of Science were searched up to October 2021. Studies developing, updating or validating a prediction model with pre-arrest factors for any potential clinical outcome of attempted resuscitation for IHCA were included. Studies were appraised critically according to the PROBAST checklist. A random-effects meta-analysis was performed to pool AUROC values of externally validated models. RESULTS: Out of 2678 initial articles screened, 33 studies were included in this systematic review: 16 model development studies, 5 model updating studies and 12 model validation studies. The most frequently included pre-arrest factors included age, functional status, (metastatic) malignancy, heart disease, cerebrovascular events, respiratory, renal or hepatic insufficiency, hypotension and sepsis. Only six of the developed models have been independently validated in external populations. The GO-FAR score showed the best performance with a pooled AUROC of 0.78 (95% CI 0.69–0.85), versus 0.59 (95%CI 0.50–0.68) for the PAM and 0.62 (95% CI 0.49–0.74) for the PAR. CONCLUSIONS: Several prognostic models for clinical outcome after attempted resuscitation for IHCA have been published. Most have a moderate risk of bias and have not been validated externally. The GO-FAR score showed the most acceptable performance. Future research should focus on updating existing models for use in clinical settings, specifically pre-arrest counselling. Systematic review registration PROSPERO CRD42021269235. Registered 21 July 2021. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-023-04306-y.
format Online
Article
Text
id pubmed-9862512
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-98625122023-01-22 Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal Grandbois van Ravenhorst, Casey Schluep, Marc Endeman, Henrik Stolker, Robert-Jan Hoeks, Sanne Elisabeth Crit Care Research BACKGROUND: Several prediction models of survival after in-hospital cardiac arrest (IHCA) have been published, but no overview of model performance and external validation exists. We performed a systematic review of the available prognostic models for outcome prediction of attempted resuscitation for IHCA using pre-arrest factors to enhance clinical decision-making through improved outcome prediction. METHODS: This systematic review followed the CHARMS and PRISMA guidelines. Medline, Embase, Web of Science were searched up to October 2021. Studies developing, updating or validating a prediction model with pre-arrest factors for any potential clinical outcome of attempted resuscitation for IHCA were included. Studies were appraised critically according to the PROBAST checklist. A random-effects meta-analysis was performed to pool AUROC values of externally validated models. RESULTS: Out of 2678 initial articles screened, 33 studies were included in this systematic review: 16 model development studies, 5 model updating studies and 12 model validation studies. The most frequently included pre-arrest factors included age, functional status, (metastatic) malignancy, heart disease, cerebrovascular events, respiratory, renal or hepatic insufficiency, hypotension and sepsis. Only six of the developed models have been independently validated in external populations. The GO-FAR score showed the best performance with a pooled AUROC of 0.78 (95% CI 0.69–0.85), versus 0.59 (95%CI 0.50–0.68) for the PAM and 0.62 (95% CI 0.49–0.74) for the PAR. CONCLUSIONS: Several prognostic models for clinical outcome after attempted resuscitation for IHCA have been published. Most have a moderate risk of bias and have not been validated externally. The GO-FAR score showed the most acceptable performance. Future research should focus on updating existing models for use in clinical settings, specifically pre-arrest counselling. Systematic review registration PROSPERO CRD42021269235. Registered 21 July 2021. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-023-04306-y. BioMed Central 2023-01-20 /pmc/articles/PMC9862512/ /pubmed/36670450 http://dx.doi.org/10.1186/s13054-023-04306-y 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Grandbois van Ravenhorst, Casey
Schluep, Marc
Endeman, Henrik
Stolker, Robert-Jan
Hoeks, Sanne Elisabeth
Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal
title Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal
title_full Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal
title_fullStr Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal
title_full_unstemmed Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal
title_short Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal
title_sort prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862512/
https://www.ncbi.nlm.nih.gov/pubmed/36670450
http://dx.doi.org/10.1186/s13054-023-04306-y
work_keys_str_mv AT grandboisvanravenhorstcasey prognosticmodelsforoutcomepredictionfollowinginhospitalcardiacarrestusingprearrestfactorsasystematicreviewmetaanalysisandcriticalappraisal
AT schluepmarc prognosticmodelsforoutcomepredictionfollowinginhospitalcardiacarrestusingprearrestfactorsasystematicreviewmetaanalysisandcriticalappraisal
AT endemanhenrik prognosticmodelsforoutcomepredictionfollowinginhospitalcardiacarrestusingprearrestfactorsasystematicreviewmetaanalysisandcriticalappraisal
AT stolkerrobertjan prognosticmodelsforoutcomepredictionfollowinginhospitalcardiacarrestusingprearrestfactorsasystematicreviewmetaanalysisandcriticalappraisal
AT hoekssanneelisabeth prognosticmodelsforoutcomepredictionfollowinginhospitalcardiacarrestusingprearrestfactorsasystematicreviewmetaanalysisandcriticalappraisal