Atrial fibrillation in the pacemaker clinic.
Bibliographic record
Abstract
BACKGROUND: Electrocardiographic (ECG) recognition of the underlying rhythm in patients with ventricular pacing can be difficult. Atrial fibrillation (AF) in particular may go unreported. OBJECTIVES: To compare the underlying atrial rhythm determined in the pacemaker clinic with the 12-lead ECG interpretation of the atrial rhythm in those who were continuously paced in the ventricle. It was intended to determine whether long term anticoagulation therapy was related to whether AF was diagnosed before or after pacemaker implantation. METHODS: Pacemaker clinic patients were enrolled if they had a 100% paced ventricular rhythm. The underlying rhythm was determined using pacemaker programming manoeuvres. A 12-lead ECG was recorded on all patients within 10 min of their pacemaker assessment and interpreted by one of the several geographic full-time cardiologists at the centre. All cardiologists were blinded to the results of pacemaker assessment and to the clinical history. RESULTS: Fifty-six patients were enrolled. At the pacemaker clinic, 37 were determined to be in AF and three were in atrial flutter (AFL). Of these 40 patients with AF/AFL, 28 were correctly identified as such on the 12-lead ECG interpretation. Twelve of the 40 were interpreted only as having an 'electronic ventricular pacemaker' (EVP). Sixteen of the 40 patients (40.0%) with AF or AFL were not taking warfarin. Twenty-two of 25 patients with an AF/AFL diagnosis before pacemaker implantation were taking warfarin, compared with two of 15 patients with AF/AFL diagnosis after pacemaker implantation (P<0.0001). CONCLUSION: These results show that the underlying rhythm in patients with ventricular pacing is frequently unrecognized by routine ECG interpretation. This may be of particular importance in the AF/AFL population as a potential contributor to the underuse of warfarin, especially when AF develops after pacemaker implantation. The pacemaker clinic may be in a position to play an important role in the identification of these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".