Abstract 14972: Sub-clinical Atrial Fibrillation in Elderly Primary Care Patients Without Clinical Atrial Fibrillation
Bibliographic record
Abstract
Introduction: Sub-clinical AF has been reported in 10% of pacemaker patients (≥ 6 minutes, with 3 months of monitoring) and 16% of patients following cryptogenic stroke (≥ 30 seconds, with 1 month of monitoring). It is unknown how common sub-clinical AF is among other patient groups, including the elderly. These data are needed to give context to the detection of sub-clinical AF in clinical practice. Methods: We prospectively investigated the prevalence of sub-clinical AF among individuals ≥ 80 years, without known AF or symptoms of arrhythmia, attending primary care clinics. Subjects had a history of hypertension and at least one of the following: diabetes, BMI ≥ 30, sleep apnea, smoking, coronary disease, heart failure or left ventricular hypertrophy. Patients were recruited from 7 Ontario family practice clinics (n=119) and one general medicine clinic (n=10). Patients underwent 30 days of continuous, non-invasive ambulatory ECG monitoring using a device with automatic AF detection (Vitaphone 3100). The primary outcome was a composite of atrial flutter (AFL) or AF ≥ 6 minutes in duration. Those without AF were invited to complete an additional 30 days of monitoring. Results: Of 129 patients screened and consented, 100 patients initiated monitoring for an average monitoring duration of 36± 21 days. The mean (SD) age was 84 ± 3 years and systolic blood pressure was 138 ± 17 mmHg; 50% had coronary disease, 28% had diabetes and 6% had heart failure. Only 4% had a history of prior stroke. Thirty days of monitoring was completed by 57% of patients and 31% completed an additional 30 days. AFL or AF ≥ 30 seconds duration was documented in 19/100 patients; ≥ 6 minutes in 15; ≥ 30 minutes in 12; ≥ 6 hours in 8 and ≥ 24 hours in 2. Shorter episodes of atrial tachycardia lasting less than 30 seconds were observed in 47 patients. Conclusions: In this prospective, outpatient study, using non-invasive ECG monitoring, we found AFL or AF ≥ 6 minutes in 15% of elderly individuals with stroke risk factors. This high background prevalence of AFL/AF among elderly patients suggests a possible role for AF screening in this population; but also should be taken into consideration when interpreting the prevalence of AFL/AF in other populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".