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Record W2964250327 · doi:10.1002/jmri.26876

Left atrial vortex size and velocity distributions by 4D flow MRI in patients with paroxysmal atrial fibrillation: Associations with age and CHA<sub>2</sub>DS<sub>2</sub>‐VASc risk score

2019· article· en· W2964250327 on OpenAlexafffund
Julio García, Hana Sheitt, Michael Bristow, Carmen Lydell, Andrew G. Howarth, Bobak Heydari, Frank S. Prato, Maria Drangova, Rebecca E. Thornhill, Pablo B. Nery, Stephen B. Wilton, Allan C. Skanes, James A. White

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsOttawa HospitalUniversity of OttawaLibin Cardiovascular Institute of AlbertaWestern UniversityAlberta Children's HospitalUniversity of Calgary
FundersMitacsHealth Research Board
KeywordsMedicineAtrial fibrillationCardiologyInternal medicineVentriclePopulationUnivariate analysisPulmonary veinMultivariate analysis

Abstract

fetched live from OpenAlex

Background Characterization of left atrial (LA) hemodynamics in paroxysmal atrial fibrillation (PAF) may provide valuable insights for thromboembolic risk. Purpose To evaluate LA vortex formation and velocity distributions by 4D flow MRI and identify associations with age, LA/LV (left ventricle) function, and established risk scores. Study Type Prospective clinical. Population Patients with PAF (n = 45, 46 ± 14 years) and healthy controls (n = 15, 54 ± 9 years) were enrolled. MRI Sequences 3T standardized cardiac MRI protocol inclusive of 4D flow MRI. Assessment Flow analysis planes were prescribed at each pulmonary vein. Velocity distribution analysis and vortex size quantification by the Lambda2 (λ2) method were performed in the LA. Statistics Pearson or Spearman's correlation coefficients, r, were calculated to identify relationships between 4D flow‐derived LA parameters and age, LA/LV function, and CHA2DS2‐VASc stroke risk score. Univariate and multivariate determinants of stroke risk were assessed using linear regressions. To compare parameters within multiple groups, one‐way analysis of variance or Kruskal–Wallis was used. Results LA vortice sizes were observed in all subjects using λ2 showing inverse correlations with peak pulmonary vein inflow velocities (P < 0.05), and positive correlations with LA volume (P < 0.05). Vortex size was elevated in PAF at all phases of the cardiac cycle, being most prominent at end early diastole (3.98 ± 1.84 cm3 vs. 6.93 ± 3.11 cm3, P = 0.001). Velocity distribution analysis showed a greater incidence of flow stasis among patients with PAF (P < 0.05). In univariate regression, vortex size was associated with the CHA2DS2‐VASc risk score at peak systole (0.457 ± 0.038, P ≤ 0.001). However, in multivariate regression age was the dominant determinant of stroke risk (0.348 ± 0.012, P = 0.006). Data Conclusion This study demonstrated that LA vortex size is increased among low‐risk patients with PAF and is associated with the CHA2DS2‐VASc risk score. Age remained the dominant determinant of stroke risk. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:871–884.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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 designObservational
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".

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Citations53
Published2019
Admission routes2
Has abstractyes

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