Evidence map›Paper›PMID 38950915›Full record

SynthesisBMJ evidence-based medicine2025

Use of digital patient decision-support tools for atrial fibrillation treatments: a systematic review and meta-analysis.

Aileen Zeng, Queenie Tang, Edel O'Hagan, Kirsten McCaffery, Kiran Ijaz, Juan C Quiroz, Ahmet Baki Kocaballi, Dana Rezazadegan, Ritu Trivedi, Joyce Siette and 5 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMJ evidence-based medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Article
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  8. 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

15 authors.

Aileen ZengWestmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-7528-8630
Queenie Tang *Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia.
Edel O'Hagan *Westmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.
Kirsten McCafferySydney Health Literacy Lab, School of Public Health, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia.
Kiran IjazAffective Interactions lab, School of Architecture, Design and Planning, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0001-8722-6595
Juan C QuirozCentre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.
Ahmet Baki KocaballiSchool of Computer Science, Faculty of Engineering & Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia.
Dana RezazadeganDepartment of Computing Technologies, Swinburne University of Technology, Melbourne, Victoria, Australia.
Ritu TrivediWestmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.
Joyce SietteThe MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, New South Wales, Australia.
Timothy ShawWestmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.
Meredith MakehamThe University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia.
Aravinda ThiagalingamWestmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.
Clara K ChowWestmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.
Liliana LaranjoWestmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia liliana.laranjo@sydney.edu.au.ORCID 0000-0003-1020-3402

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF. STUDY

designSystematic review and meta-analysis. ELIGIBILITY CRITERIA: Eligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF. INFORMATION SOURCES: We searched MEDLINE, EMBASE and Scopus from 2005 to 2023.Risk-of-bias (RoB) assessment: We assessed RoB using the Cochrane Risk of Bias Tool 2 for RCTs and cluster RCT and the ROBINS-I tool for quasi-experimental studies. SYNTHESIS OF

resultsWe used random effects meta-analysis to synthesise decisional conflict and patient knowledge outcomes reported in RCTs. We performed narrative synthesis for all outcomes. The main outcomes of interest were decisional conflict and patient knowledge.

results13 articles, reporting on 11 studies (4 RCTs, 1 cluster RCT and 6 quasi-experimental) met the inclusion criteria. There were 2714 participants across all studies (2372 in RCTs), of which 26% were women and the mean age was 71 years. Socioeconomically disadvantaged groups were poorly represented in the included studies. Seven studies (n=2508) focused on non-valvular AF and the mean CHAD2DS2-VASc across studies was 3.2 and for HAS-BLED 1.9. All tools focused on decisions regarding thromboembolic stroke prevention and most enabled calculation of individualised stroke risk. Tools were heterogeneous in features and functions; four tools were patient decision aids. The readability of content was reported in one study. Meta-analyses showed a reduction in decisional conflict (4 RCTs (n=2167); standardised mean difference -0.19; 95% CI -0.30 to -0.08; p=0.001; I

conclusionsIn the context of stroke prevention in AF, digital patient decision-support tools likely reduce decisional conflict and may result in little to no change in patient knowledge, compared with usual care. Future studies should leverage digital capabilities for increased personalisation and interactivity of the tools, with better consideration of health literacy and equity aspects. Additional robust trials and implementation studies are warranted. PROSPERO REGISTRATION NUMBER: CRD42020218025.

Indexed as

Atrial FibrillationDecision Support TechniquesPatient ParticipationHumansRandomized Controlled Trials as TopicCardiovascular DiseasesEvidence-Based Practice

Identifiers

PMID38950915
PMCPMC11874357

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.