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Epidemiology of subclinical atrial fibrillation in patients with cardiac implantable electronic devices: a systematic review and meta-analysis

2021· review· en· W3206217532 on OpenAlexaff
G F Romiti, Bernadette Corica, Marco Borgi, Marco Vitolo, Kazuo Miyazawa, Jeff S. Healey, Deirdre A. Lane, Giuseppe Boriani, Stefania Basili, G Y H Lip, Marco Proietti

Bibliographic record

VenueEuropean Heart Journal · 2021
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAtrial fibrillationMeta-analysisIncidence (geometry)Subclinical infectionEpidemiologyInternal medicinePublication biasCardiologyStudy heterogeneityStroke (engine)Systematic reviewMEDLINE

Abstract

fetched live from OpenAlex

Abstract Background Sub-clinical atrial fibrillation (SCAF) and atrial high-rate episodes (AHREs), seen as high-frequency atrial tachyarrhythmias in patients with cardiac implantable electronic devices (CIEDs), have gained prominence as determinants of clinical atrial fibrillation (AF) and increased stroke risk. As a result, several studies investigating their role in predicting the onset of AF and AHRE-related outcomes have been conducted but uncertainty exists on the epidemiology of AHRE. Purpose To estimate the incidence of SCAF, according to presence of AHREs in patients with CIEDs, through a systematic review and meta-analysis of the available literature. Methods PubMed and EMBASE were searched from inception to 27th January 2021 for all studies documenting the incidence of AHREs in patients with CIEDs. We included all studies with ≥100 patients reporting data on AHREs incidence. Pooled prevalence and incidence rates were computed; we also performed meta-regressions for pooled incidence rates, according to relevant study-level characteristics. This study was registered in PROSPERO: CRD42019106994. Results Among the 2,515 results retrieved, we included 51 studies in the systematic review and meta-analysis, with a total of 68,414 patients. Meta-analysis of included studies showed a pooled prevalence of 28.2% (95% CI: 24.3–32.5%, I2=99%), with a pooled incidence rate (IR) of 15 new AHRE cases per 100 patient-years (95% CI: 12–19, I2=100%). Given the large heterogeneity showed in the pooled estimates we performed additional analyses. Regarding pooled prevalence, we performed several subgroup analyses, according to various studies baseline characteristics, which did not show any significant difference in any of the subgroups examined. Regarding IR, a multivariable meta-regression analysis showed that decreasing follow-up time and increasing age were the only factors significantly associated with AHRE incidence, explaining a large proportion of heterogeneity (R2=68%, p<0.001; Figure 1, Panel A and B respectively). Accordingly, the AHRE IR was highest at 1 year follow-up and in the oldest subjects. Presence of SCAF was significantly associated with older age, higher CHA2DS2-VASc score, and higher prevalence of hypertension, heart failure and history of cerebrovascular disease. Conclusions This systematic review and meta-regression demonstrated that SCAF is very common in patients with CIEDs, with an overall IR for AHREs of up to 15 per 100 patient-years; increasing with age and decreasing with longer follow-up time. Presence of SCAF was associated with an overall higher clinical risk profile compared to those subjects without SCAF. Funding Acknowledgement Type of funding sources: None. Figure 1. Meta-regression for AHRE Incidence

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)0.013
metaresearch head score (Gemma)0.030
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: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.030
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.042
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
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 head0.234
GPT teacher head0.437
Teacher spread0.203 · 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
GenreReview

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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