Cargando…

Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review

BACKGROUND: Atrial fibrillation (AF) is the arrhythmia most commonly diagnosed in clinical practice. It is associated with significant morbidity and mortality. Prevalence of AF and complications of AF, estimated by hospitalisations, have increased dramatically in the last decade. Being able to predi...

Descripción completa

Detalles Bibliográficos
Autores principales: Dretzke, Janine, Chuchu, Naomi, Chua, Winnie, Fabritz, Larissa, Bayliss, Susan, Kotecha, Dipak, Deeks, Jonathan J., Kirchhof, Paulus, Takwoingi, Yemisi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712856/
https://www.ncbi.nlm.nih.gov/pubmed/31462304
http://dx.doi.org/10.1186/s13643-019-1128-z
_version_ 1783446767756378112
author Dretzke, Janine
Chuchu, Naomi
Chua, Winnie
Fabritz, Larissa
Bayliss, Susan
Kotecha, Dipak
Deeks, Jonathan J.
Kirchhof, Paulus
Takwoingi, Yemisi
author_facet Dretzke, Janine
Chuchu, Naomi
Chua, Winnie
Fabritz, Larissa
Bayliss, Susan
Kotecha, Dipak
Deeks, Jonathan J.
Kirchhof, Paulus
Takwoingi, Yemisi
author_sort Dretzke, Janine
collection PubMed
description BACKGROUND: Atrial fibrillation (AF) is the arrhythmia most commonly diagnosed in clinical practice. It is associated with significant morbidity and mortality. Prevalence of AF and complications of AF, estimated by hospitalisations, have increased dramatically in the last decade. Being able to predict AF would allow tailoring of management strategies and a focus on primary or secondary prevention. Models predicting recurrent AF would have particular clinical use for the selection of rhythm control therapy. There are existing prognostic models which combine several predictors or risk factors to generate an individualised estimate of risk of AF. The aim of this systematic review is to summarise and compare model performance measures and predictive accuracy across different models and populations at risk of developing incident or recurrent AF. METHODS: Methods tailored to systematic reviews of prognostic models will be used for study identification, risk of bias assessment and synthesis. Studies will be eligible for inclusion where they report an internally or externally validated model. The quality of studies reporting a prognostic model will be assessed using the Prediction Study Risk Of Bias Assessment Tool (PROBAST). Studies will be narratively described and included variables and predictive accuracy compared across different models and populations. Meta-analysis of model performance measures for models validated in similar populations will be considered where possible. DISCUSSION: To the best of our knowledge, this will be the first systematic review to collate evidence from all studies reporting on validated prognostic models, or on the impact of such models, in any population at risk of incident or recurrent AF. The review may identify models which are suitable for impact assessment in clinical practice. Should gaps in the evidence be identified, research recommendations relating to model development, validation or impact assessment will be made. Findings will be considered in the context of any models already used in clinical practice, and the extent to which these have been validated. SYSTEMATIC REVIEW REGISTRATION: PROSPERO (CRD42018111649).
format Online
Article
Text
id pubmed-6712856
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-67128562019-09-04 Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review Dretzke, Janine Chuchu, Naomi Chua, Winnie Fabritz, Larissa Bayliss, Susan Kotecha, Dipak Deeks, Jonathan J. Kirchhof, Paulus Takwoingi, Yemisi Syst Rev Protocol BACKGROUND: Atrial fibrillation (AF) is the arrhythmia most commonly diagnosed in clinical practice. It is associated with significant morbidity and mortality. Prevalence of AF and complications of AF, estimated by hospitalisations, have increased dramatically in the last decade. Being able to predict AF would allow tailoring of management strategies and a focus on primary or secondary prevention. Models predicting recurrent AF would have particular clinical use for the selection of rhythm control therapy. There are existing prognostic models which combine several predictors or risk factors to generate an individualised estimate of risk of AF. The aim of this systematic review is to summarise and compare model performance measures and predictive accuracy across different models and populations at risk of developing incident or recurrent AF. METHODS: Methods tailored to systematic reviews of prognostic models will be used for study identification, risk of bias assessment and synthesis. Studies will be eligible for inclusion where they report an internally or externally validated model. The quality of studies reporting a prognostic model will be assessed using the Prediction Study Risk Of Bias Assessment Tool (PROBAST). Studies will be narratively described and included variables and predictive accuracy compared across different models and populations. Meta-analysis of model performance measures for models validated in similar populations will be considered where possible. DISCUSSION: To the best of our knowledge, this will be the first systematic review to collate evidence from all studies reporting on validated prognostic models, or on the impact of such models, in any population at risk of incident or recurrent AF. The review may identify models which are suitable for impact assessment in clinical practice. Should gaps in the evidence be identified, research recommendations relating to model development, validation or impact assessment will be made. Findings will be considered in the context of any models already used in clinical practice, and the extent to which these have been validated. SYSTEMATIC REVIEW REGISTRATION: PROSPERO (CRD42018111649). BioMed Central 2019-08-28 /pmc/articles/PMC6712856/ /pubmed/31462304 http://dx.doi.org/10.1186/s13643-019-1128-z Text en © The Author(s). 2019 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 Protocol
Dretzke, Janine
Chuchu, Naomi
Chua, Winnie
Fabritz, Larissa
Bayliss, Susan
Kotecha, Dipak
Deeks, Jonathan J.
Kirchhof, Paulus
Takwoingi, Yemisi
Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review
title Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review
title_full Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review
title_fullStr Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review
title_full_unstemmed Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review
title_short Prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review
title_sort prognostic models for predicting incident or recurrent atrial fibrillation: protocol for a systematic review
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712856/
https://www.ncbi.nlm.nih.gov/pubmed/31462304
http://dx.doi.org/10.1186/s13643-019-1128-z
work_keys_str_mv AT dretzkejanine prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT chuchunaomi prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT chuawinnie prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT fabritzlarissa prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT baylisssusan prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT kotechadipak prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT deeksjonathanj prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT kirchhofpaulus prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview
AT takwoingiyemisi prognosticmodelsforpredictingincidentorrecurrentatrialfibrillationprotocolforasystematicreview