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Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension

OBJECTIVE: To provide an overview of the currently available risk prediction models (RPMs) for cardiovascular diseases (CVDs), diabetes and hypertension, and to compare their effectiveness in proper recognition of patients at risk of developing these diseases. DESIGN: Umbrella systematic review. DAT...

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Autores principales: Lucaroni, Francesca, Cicciarella Modica, Domenico, Macino, Mattia, Palombi, Leonardo, Abbondanzieri, Alessio, Agosti, Giulia, Biondi, Giorgia, Morciano, Laura, Vinci, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937066/
https://www.ncbi.nlm.nih.gov/pubmed/31862737
http://dx.doi.org/10.1136/bmjopen-2019-030234
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author Lucaroni, Francesca
Cicciarella Modica, Domenico
Macino, Mattia
Palombi, Leonardo
Abbondanzieri, Alessio
Agosti, Giulia
Biondi, Giorgia
Morciano, Laura
Vinci, Antonio
author_facet Lucaroni, Francesca
Cicciarella Modica, Domenico
Macino, Mattia
Palombi, Leonardo
Abbondanzieri, Alessio
Agosti, Giulia
Biondi, Giorgia
Morciano, Laura
Vinci, Antonio
author_sort Lucaroni, Francesca
collection PubMed
description OBJECTIVE: To provide an overview of the currently available risk prediction models (RPMs) for cardiovascular diseases (CVDs), diabetes and hypertension, and to compare their effectiveness in proper recognition of patients at risk of developing these diseases. DESIGN: Umbrella systematic review. DATA SOURCES: PubMed, Scopus, Cochrane Library. ELIGIBILITY CRITERIA: Systematic reviews or meta-analysis examining and comparing performances of RPMs for CVDs, hypertension or diabetes in healthy adult (18–65 years old) population, published in English language. DATA EXTRACTION AND SYNTHESIS: Data were extracted according to the following parameters: number of studies included, intervention (RPMs applied/assessed), comparison, performance, validation and outcomes. A narrative synthesis was performed. Data were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY SELECTION: 3612 studies were identified. After title/abstract screening and removal of duplicate articles, 37 studies met the eligibility criteria. After reading the full text, 13 were deemed relevant for inclusion. Three further papers from the reference lists of these articles were then added. STUDY APPRAISAL: The methodological quality of the included studies was assessed using the AMSTAR tool. RISK OF BIAS IN INDIVIDUAL STUDIES: Risk of Bias evaluation was carried out using the ROBIS tool. RESULTS: Sixteen studies met the inclusion criteria: six focused on diabetes, two on hypertension and eight on CVDs. Globally, prediction models for diabetes and hypertension showed no significant difference in effectiveness. Conversely, some promising differences among prediction tools were highlighted for CVDs. The Ankle-Brachial Index, in association with the Framingham tool, and QRISK scores provided some evidence of a certain superiority compared with Framingham alone. LIMITATIONS: Due to the significant heterogeneity of the studies, it was not possible to perform a meta-analysis. The electronic search was limited to studies in English and to three major international databases (MEDLINE/PubMed, Scopus and Cochrane Library), with additional works derived from the reference list of other studies; grey literature with unpublished documents was not included in the search. Furthermore, no assessment of potential adverse effects of RPMs was carried out. CONCLUSIONS: Consistent evidence is available only for CVD prediction: the Framingham score, alone or in combination with the Ankle-Brachial Index, and the QRISK score can be confirmed as the gold standard. Further efforts should not be concentrated on creating new scores, but rather on performing external validation of the existing ones, in particular on high-risk groups. Benefits could be further improved by supplementing existing models with information on lifestyle, personal habits, family and employment history, social network relationships, income and education. PROSPERO REGISTRATION NUMBER: CRD42018088012.
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spelling pubmed-69370662020-01-06 Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension Lucaroni, Francesca Cicciarella Modica, Domenico Macino, Mattia Palombi, Leonardo Abbondanzieri, Alessio Agosti, Giulia Biondi, Giorgia Morciano, Laura Vinci, Antonio BMJ Open Public Health OBJECTIVE: To provide an overview of the currently available risk prediction models (RPMs) for cardiovascular diseases (CVDs), diabetes and hypertension, and to compare their effectiveness in proper recognition of patients at risk of developing these diseases. DESIGN: Umbrella systematic review. DATA SOURCES: PubMed, Scopus, Cochrane Library. ELIGIBILITY CRITERIA: Systematic reviews or meta-analysis examining and comparing performances of RPMs for CVDs, hypertension or diabetes in healthy adult (18–65 years old) population, published in English language. DATA EXTRACTION AND SYNTHESIS: Data were extracted according to the following parameters: number of studies included, intervention (RPMs applied/assessed), comparison, performance, validation and outcomes. A narrative synthesis was performed. Data were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY SELECTION: 3612 studies were identified. After title/abstract screening and removal of duplicate articles, 37 studies met the eligibility criteria. After reading the full text, 13 were deemed relevant for inclusion. Three further papers from the reference lists of these articles were then added. STUDY APPRAISAL: The methodological quality of the included studies was assessed using the AMSTAR tool. RISK OF BIAS IN INDIVIDUAL STUDIES: Risk of Bias evaluation was carried out using the ROBIS tool. RESULTS: Sixteen studies met the inclusion criteria: six focused on diabetes, two on hypertension and eight on CVDs. Globally, prediction models for diabetes and hypertension showed no significant difference in effectiveness. Conversely, some promising differences among prediction tools were highlighted for CVDs. The Ankle-Brachial Index, in association with the Framingham tool, and QRISK scores provided some evidence of a certain superiority compared with Framingham alone. LIMITATIONS: Due to the significant heterogeneity of the studies, it was not possible to perform a meta-analysis. The electronic search was limited to studies in English and to three major international databases (MEDLINE/PubMed, Scopus and Cochrane Library), with additional works derived from the reference list of other studies; grey literature with unpublished documents was not included in the search. Furthermore, no assessment of potential adverse effects of RPMs was carried out. CONCLUSIONS: Consistent evidence is available only for CVD prediction: the Framingham score, alone or in combination with the Ankle-Brachial Index, and the QRISK score can be confirmed as the gold standard. Further efforts should not be concentrated on creating new scores, but rather on performing external validation of the existing ones, in particular on high-risk groups. Benefits could be further improved by supplementing existing models with information on lifestyle, personal habits, family and employment history, social network relationships, income and education. PROSPERO REGISTRATION NUMBER: CRD42018088012. BMJ Publishing Group 2019-12-19 /pmc/articles/PMC6937066/ /pubmed/31862737 http://dx.doi.org/10.1136/bmjopen-2019-030234 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Public Health
Lucaroni, Francesca
Cicciarella Modica, Domenico
Macino, Mattia
Palombi, Leonardo
Abbondanzieri, Alessio
Agosti, Giulia
Biondi, Giorgia
Morciano, Laura
Vinci, Antonio
Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension
title Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension
title_full Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension
title_fullStr Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension
title_full_unstemmed Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension
title_short Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension
title_sort can risk be predicted? an umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937066/
https://www.ncbi.nlm.nih.gov/pubmed/31862737
http://dx.doi.org/10.1136/bmjopen-2019-030234
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