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Record W7117463482 · doi:10.3390/jcm15010188

Visceral Embolic Events in Atrial Fibrillation: A Systematic Review and Meta-Analysis of Incidence, Mortality, and Risk Prediction

2025· article· en· W7117463482 on OpenAlexaffabout
Yazan Jumah Alalwani, Waleed Abdullah Alharthi, Hadeel Khalid Bin-Shuiel, Raghad Adel Badoghaish, Saja Fawzi Alzanbaqi, Lamees Naji Alsaleh, Fatimah Essam Alzaid, Mustafa Abdulwahab AlShayeb, Fatimah Reda Algawez, Rama Khalid Alsulaim, Shomoukh Abdullah Alnabtawi, Ahmed Y. Azzam, Eiman Mohammed AlShammari

Bibliographic record

VenueJournal of Clinical Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsAtrial fibrillationIncidence (geometry)PoolingCohort studyCohortSubgroup analysisProspective cohort study

Abstract

fetched live from OpenAlex

Background: Visceral embolic events (VEE), including mesenteric, splenic, and renal infarctions, represent understudied complications of atrial fibrillation (AF) often subsumed within broader systemic embolic event categories. The 2024 European Society of Cardiology guidelines introduced the CHA2DS2-VA score, removing female sex as a risk modifier, with potential implications for non-cerebral embolic risk stratification. We systematically synthesized evidence on VEE incidence, mortality, and risk predictors in AF patients. Methods: We searched PubMed, Embase, Scopus, and Web of Science through September 2024 for studies reporting VEE in AF populations. Study quality was assessed using the Newcastle–Ottawa Scale (NOS). Due to substantial heterogeneity when pooling all prevalence studies (I2 = 99.6%), we performed event-definition-based subgroup analyses. Random-effects meta-analyses were conducted using DerSimonian–Laird methods with 95% prediction intervals. Sensitivity analyses excluded studies with NOS scores < 7 to assess robustness. Results: We identified 12 studies including 329,128 patients. Quality assessment revealed a mean NOS score of 6.7 ± 1.6 (range: 4–9), with 75% of studies achieving good-to-excellent ratings. For non-AMI visceral embolic events (splenic, renal, mesenteric infarctions), subgroup meta-analysis of three studies (n = 548) yielded a pooled prevalence of 1.6% (95% CI: 0.0–3.2%, I2 = 45.4%, p = 0.160), representing a 54.2 percentage point reduction in heterogeneity compared to pooling all event types. Sensitivity analysis excluding moderate-quality studies confirmed robust findings (pooled prevalence 2.7%, 95% CI: 0.0–6.8%, I2 = 70.3%). Incidence rates ranged from 0.36 to 3.48 per 1000 person-years across three cohort studies (I2 = 99.4%), reflecting temporal and geographic variation. Mortality varied substantially by patient population: 64.0% in-hospital mortality among elderly patients with concurrent acute myocardial infarction (AMI) versus 17.4% in younger cohorts with isolated non-AMI VEE. Potential predictors included left atrial enlargement (OR range: 2.1–4.3), elevated D-dimer (OR: 3.2), and higher CHA2DS2-VASc scores (OR: 1.3 per point increase), though validation in independent cohorts is lacking. Conclusions: Visceral embolic events occur in approximately 1–6% of AF patients, with mean prevalence of 1.6% for non-AMI events based on moderate-heterogeneity meta-analysis. Event-definition-based subgrouping successfully reduced heterogeneity from 99.6% to 45.4%, providing the first reliable pooled estimate for this outcome. Mortality ranges widely (17–64%) depending on concurrent AMI and patient age. Potential predictors including left atrial enlargement and elevated D-dimer require prospective validation before clinical implementation. These findings suggest VEE may warrant enhanced clinical awareness and individualized risk assessment strategies in AF management, pending validation in prospective studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.038
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.042
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.220
GPT teacher head0.505
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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