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...
Autores principales: | , , , , |
---|---|
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 |