Atrial fibrillation in unexplained syncope: observations from the Reveal LINQ registry
Bibliographic record
Abstract
Abstract Background Implantable loop recorders (ILRs) have come to play an important role in the workup of patients with recurrent syncope of uncertain origin. In addition to detecting bradyarrhythmias related to syncope, which is the main diagnostic focus in these patients, ILRs are also capable of uncovering subclinical atrial fibrillation (AF). Purpose We sought to determine the percentage of patients monitored with an ILR for unexplained syncope who have AF detected and to describe clinical actions taken in these patients. Methods Patients enrolled in the Reveal LINQ Registry who received an ILR for unexplained syncope and had at least one follow-up form were included. The device automatically detects AF episodes lasting ≥2 minutes. Patients were considered to have AF based on an AF diagnosis made by the treating physician during follow-up or if device-detected AF was adjudicated as true AF by an external reviewer. AF detection rates were calculated using Kaplan-Meier methods. Results In total, 498 patients (aged 61.8±20.0 years, 49.6% female, CHA2DS2VASc score 2.2±1.7) were included and followed for 22±12 months. A history of AF was present in 97 (20%) patients, while 401 patients had no history. By 18 months, the incidence of AF was 70.9% (95% CI, 60.8%, 80.3%) in patients with a history of AF and 21.4% (95% CI, 17.4%, 26.1%) in patients without (Figure). AF detection in those with (30.4%) and without (30.1%) syncope during follow-up was similar. By the end of follow-up, and among patients with newly detected AF, 29/86 (33.7%) were on oral anticoagulation, 7 (8.1%) underwent AF ablation, 6 (7.0%) underwent other type of ablation, and 2 (2.3%) received cardioversion. Other actions among the whole cohort included implant of an IPG, ICD, or CRT in 98/498 (19.7%). Conclusion Among patients monitored with ILRs to determine the cause of recurrent syncope episodes, approximately 1 in 5 patients had new AF detected. In addition to improving the management of patients with syncope, ILR data served to support AF-related clinical decisions. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Medtronic Inc Incidence of AF according to baseline AF
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".