Cargando…

Clinical assessment of patients with chest pain; a systematic review of predictive tools

BACKGROUND: The clinical assessment of patients with chest pain of recent onset remains difficult. This study presents a critical review of clinical predictive tools for the assessment of patients with chest pain. METHODS: Systematic review of observational studies and estimation of probabilities of...

Descripción completa

Detalles Bibliográficos
Autores principales: Ayerbe, Luis, González, Esteban, Gallo, Valentina, Coleman, Claire L., Wragg, Andrew, Robson, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721048/
https://www.ncbi.nlm.nih.gov/pubmed/26790953
http://dx.doi.org/10.1186/s12872-016-0196-4
_version_ 1782411168483115008
author Ayerbe, Luis
González, Esteban
Gallo, Valentina
Coleman, Claire L.
Wragg, Andrew
Robson, John
author_facet Ayerbe, Luis
González, Esteban
Gallo, Valentina
Coleman, Claire L.
Wragg, Andrew
Robson, John
author_sort Ayerbe, Luis
collection PubMed
description BACKGROUND: The clinical assessment of patients with chest pain of recent onset remains difficult. This study presents a critical review of clinical predictive tools for the assessment of patients with chest pain. METHODS: Systematic review of observational studies and estimation of probabilities of coronary artery disease (CAD) in patients with chest pain. Searches were conducted in PubMed, Embase, Scopus, and Web of Science to identify studies reporting tools, with at least three variables from clinical history, physical examination or ECG, produced with multivariate analysis, to estimate probabilities of CAD in patients with chest pain of recent onset, published from inception of the database to the 31(st) July 2015. The references of previous relevant reviews were hand searched. The methodological quality was assessed with standard criteria. Since the incidence of CAD has changed in the past few decades, the date of publication was acknowledged to be relevant in order to use the tool in clinical practice, and more recent papers were considered more relevant. Probabilities of CAD according to the studies of highest quality were estimated and the evidence provided was graded. RESULTS: Twelve papers were included out of the 19126 references initially identified. The methodological quality of all of them was high. The clinical characteristics of the chest pain, age, past medical history of cardiovascular disease, gender, and abnormalities in the ECG were the predictors of CAD most commonly reported across the studies. The most recent papers, with highest methodological quality, and most practical for use in clinical settings, reported prediction or exclusion of CAD with area under the curve 0.90 in Primary Care, 0.91 in Emergency department, and 0.79 in Cardiology. These papers provide evidence of high level (1B) and the recommendation to use their results in the management of patients with chest pain is strong (A). CONCLUSIONS: The risk of CAD can be estimated on clinical grounds in patients with chest pain in different clinical settings with high accuracy. The estimation of probabilities of CAD presented in these studies could be used for a better management of patients with chest pain and also in the development of future predictive tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12872-016-0196-4) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4721048
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-47210482016-01-22 Clinical assessment of patients with chest pain; a systematic review of predictive tools Ayerbe, Luis González, Esteban Gallo, Valentina Coleman, Claire L. Wragg, Andrew Robson, John BMC Cardiovasc Disord Research Article BACKGROUND: The clinical assessment of patients with chest pain of recent onset remains difficult. This study presents a critical review of clinical predictive tools for the assessment of patients with chest pain. METHODS: Systematic review of observational studies and estimation of probabilities of coronary artery disease (CAD) in patients with chest pain. Searches were conducted in PubMed, Embase, Scopus, and Web of Science to identify studies reporting tools, with at least three variables from clinical history, physical examination or ECG, produced with multivariate analysis, to estimate probabilities of CAD in patients with chest pain of recent onset, published from inception of the database to the 31(st) July 2015. The references of previous relevant reviews were hand searched. The methodological quality was assessed with standard criteria. Since the incidence of CAD has changed in the past few decades, the date of publication was acknowledged to be relevant in order to use the tool in clinical practice, and more recent papers were considered more relevant. Probabilities of CAD according to the studies of highest quality were estimated and the evidence provided was graded. RESULTS: Twelve papers were included out of the 19126 references initially identified. The methodological quality of all of them was high. The clinical characteristics of the chest pain, age, past medical history of cardiovascular disease, gender, and abnormalities in the ECG were the predictors of CAD most commonly reported across the studies. The most recent papers, with highest methodological quality, and most practical for use in clinical settings, reported prediction or exclusion of CAD with area under the curve 0.90 in Primary Care, 0.91 in Emergency department, and 0.79 in Cardiology. These papers provide evidence of high level (1B) and the recommendation to use their results in the management of patients with chest pain is strong (A). CONCLUSIONS: The risk of CAD can be estimated on clinical grounds in patients with chest pain in different clinical settings with high accuracy. The estimation of probabilities of CAD presented in these studies could be used for a better management of patients with chest pain and also in the development of future predictive tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12872-016-0196-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-20 /pmc/articles/PMC4721048/ /pubmed/26790953 http://dx.doi.org/10.1186/s12872-016-0196-4 Text en © Ayerbe et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Article
Ayerbe, Luis
González, Esteban
Gallo, Valentina
Coleman, Claire L.
Wragg, Andrew
Robson, John
Clinical assessment of patients with chest pain; a systematic review of predictive tools
title Clinical assessment of patients with chest pain; a systematic review of predictive tools
title_full Clinical assessment of patients with chest pain; a systematic review of predictive tools
title_fullStr Clinical assessment of patients with chest pain; a systematic review of predictive tools
title_full_unstemmed Clinical assessment of patients with chest pain; a systematic review of predictive tools
title_short Clinical assessment of patients with chest pain; a systematic review of predictive tools
title_sort clinical assessment of patients with chest pain; a systematic review of predictive tools
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721048/
https://www.ncbi.nlm.nih.gov/pubmed/26790953
http://dx.doi.org/10.1186/s12872-016-0196-4
work_keys_str_mv AT ayerbeluis clinicalassessmentofpatientswithchestpainasystematicreviewofpredictivetools
AT gonzalezesteban clinicalassessmentofpatientswithchestpainasystematicreviewofpredictivetools
AT gallovalentina clinicalassessmentofpatientswithchestpainasystematicreviewofpredictivetools
AT colemanclairel clinicalassessmentofpatientswithchestpainasystematicreviewofpredictivetools
AT wraggandrew clinicalassessmentofpatientswithchestpainasystematicreviewofpredictivetools
AT robsonjohn clinicalassessmentofpatientswithchestpainasystematicreviewofpredictivetools