Evidence map›Paper›PMID 31462304›Full record

ArticleSystematic reviews2019

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

Janine Dretzke, Naomi Chuchu, Winnie Chua, Larissa Fabritz, Susan Bayliss, Dipak Kotecha, Jonathan J Deeks, Paulus Kirchhof, Yemisi Takwoingi

Abstract read
In one paragraph

Article in Systematic reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2020
    Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Janine DretzkeInstitute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. j.dretzke@bham.ac.uk.ORCID 0000-0002-2591-6918
Naomi ChuchuInstitute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Winnie ChuaInstitute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Larissa FabritzInstitute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Susan BaylissInstitute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Dipak KotechaInstitute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Jonathan J DeeksInstitute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Paulus KirchhofInstitute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Yemisi TakwoingiInstitute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Funding

Department of Health CDF-2015-08-074
6 · The paper itself

Abstract

backgroundAtrial 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.

methodsMethods 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 ).

Indexed as

Atrial FibrillationIncidenceModels, StatisticalPrimary PreventionPrognosisRecurrenceReproducibility of ResultsSecondary PreventionSystematic Reviews as TopicAtrial fibrillationIncidenceModel performanceModel validationPrognostic modelRecurrenceRisk scoreSystematic review

Identifiers

PMID31462304
PMCPMC6712856

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.