SynthesisHeart (British Cardiac Society)2022
Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis.
Synthesis in Heart (British Cardiac Society), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Non-antiarrhythmic pharmacotherapy in cardio-renal-metabolic disease and incident atrial fibrillation: a trial meta-analysis.European heart journal · 2026Pooled it
- Risk prediction models for detecting a new diagnosis of heart failure within 5 years in the community: a systematic review.BMJ open · 2026Pooled it
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Predictive value of NT pro BNP for new-onset atrial fibrillation in heart failure and preserved ejection fraction.ESC heart failure · 2024Trial
- Article
- Prediction of Atrial Fibrillation Occurrence With Handheld Mobile Electrocardiogram: Deep Learning Model Development Using Real-World Data.JMIR medical informatics · 2026Article
- Stratifying prognosis in heart failure patients with reduced ejection fraction and atrial cardiomyopathy.ESC heart failure · 2026Article
- Article
- Machine learning for sudden cardiac death prediction among older adults using community-based electronic health records.BMC public health · 2025Article
- Article
- The mCJACC. Advances · 2025Article
- Evidence Gaps and Lessons in the Early Detection of Atrial Fibrillation: A Prospective Study in a Primary Care Setting (PREFATE Study).Biomedicines · 2025Article
- Screening and detection of atrial fibrillation in primary care: current practice and future perspectives.European heart journal supplements : journal of the European Society of Cardiology · 2024Article
- Simple risk scoring using sinus rhythm electrocardiograms predicts the incidence of atrial fibrillation in the general population.Scientific reports · 2024Article
- Epidemiology, prognosis, and challenges in the management of hyperthyroidism-related atrial fibrillation.European thyroid journal · 2024Review
- Article
- Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure.Bioengineering (Basel, Switzerland) · 2023Article
- Prediction of short-term atrial fibrillation risk using primary care electronic health records.Heart (British Cardiac Society) · 2023Article
- What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key.European heart journal. Quality of care & clinical outcomes · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveAtrial fibrillation (AF) is common and is associated with an increased risk of stroke. We aimed to systematically review and meta-analyse multivariable prediction models derived and/or validated in electronic health records (EHRs) and/or administrative claims databases for the prediction of incident AF in the community.
methodsOvid Medline and Ovid Embase were searched for records from inception to 23 March 2021. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation.
resultsEleven studies met inclusion criteria, describing nine prediction models, with four eligible for meta-analysis including 9 289 959 patients. The CHADS (Congestive heart failure, Hypertension, Age>75, Diabetes mellitus, prior Stroke or transient ischemic attack) (summary c-statistic 0.674; 95% CI 0.610 to 0.732; 95% PI 0.526-0.815), CHA
conclusionsModels externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021245093.
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Registered trials
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.