Abstract WP270: Can Left Atrial Volume Index Be Used to Identify Patients With Asymptomatic Atrial Fibrillation After Acute Ischemic Stroke or Transient Ischemic Attack?
Bibliographic record
Abstract
Introduction: Detecting atrial fibrillation (AF) after an ischemic stroke may be challenging. We aimed to determine whether using the left atrial volume index (LAVi) can help screen patients at risk of having AF after an ischemic stroke or transient ischemic attack (TIA). Methods: All adult patients who were admitted to our comprehensive stroke center with ischemic strokes or TIA from January 2017 to January 2018 were retrospectively analyzed. Demographics, risk factors, stroke etiology and transthoracic echocardiography (TTE) data were collected. AF detection was based on either in-hospital cardiac telemetry and/or a 14-day or 30-day ambulatory ECG monitoring. Reference values for LAVi were derived from 2015 Guidelines from the American Society of Echocardiography. TTE and ECG parameters were analyzed using Fisher’s exact test for categorical variables and non-parametric tests for continuous variables. Results: We identified 334 patients with ischemic strokes and TIA without documented AF (66 ± 11 years, 49% females, 70% White, 23% African American, 6% Other). The mean total days of cardiac monitoring was 9.8 ± 7.2 days and it was lower in patients with AF vs without AF (3.8 ± 5.0 days vs. 10.6 ± 7.1 days, p < 0.0001). Newly diagnosed AF was detected in 37 of 334 patients (11%). Of the 241 patients with normal left atrium (LA), 15 (6%) were diagnosed with AF; 10 of 49 (20%) with mildly dilated LA; 5 of 23 (22%) with moderately dilated LA; 7 of 21 (33%) with severely dilated LA. The proportions of AF detection differed significantly between the LA categories ( p < 0.0001). The mean LAVi was higher in patients with AF vs without AF (40.0 ± 13.8 mL/m 2 vs. 28 ± 10.5 mL/m 2 , p < 0.001), and in those with cardioembolic strokes (34.7 ± 13.3 mL/m 2 , p < 0.0001). The mean LAVi for non-cardioembolic strokes including cryptogenic, small vessel and large vessel were 29.3 ± 10.2 mL/m 2 , 28.6 ± 8.5 mL/m 2 , 26.2 ± 12.8 mL/m 2 , respectively. Conclusion: Our data demonstrates a significant association between higher LAVi on routine TTE and detecting AF using non-invasive ECG monitoring in patients with ischemic strokes or TIA. Use of LAVi measured during the stroke hospitalization may help identify patients who would benefit from additional non-invasive cardiac monitoring post-discharge.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".