Accuracy of Atrial Tachyarrhythmia Detection in Implantable Devices with Arrhythmia Therapies
Bibliographic record
Abstract
The clinical application of atrial tachyarrhythmia (AT) episode data stored by implantable devices is presently limited by the high proportion of inappropriate detection. We quantified the percentage of inappropriate AT detection in two implantable devices with AT diagnostics and therapies via meta-analysis of stored AT episodes from a number of clinical trials. The AT500 and GEM III AT, contain dual chamber logic to discriminate AT from ventricular tachycardia and far-field R wave (FFRW) oversensing using dual chamber bipolar electrograms. A subset of data from four clinical trials of 1,142 patients was considered. Manual analysis was performed on 21,553 stored episodes with atrial EGM and marker channel from 409 patients with stored episodes and the market-released device detection configuration. The percentage of episodes with inappropriate detection and termination was evaluated and compared between septal and nonseptal lead locations. The percentage of inappropriately detected episodes receiving ATP therapy was also determined. The percentage of episodes appropriately detected and the percentage of net episode duration (i.e., burden) recorded by the device were also determined from a separate analysis of 24-hour Holter recordings from a subset of 40 patients from one trial. Adjusted estimates of the percentage of appropriate [corrected] detection were 95.3% (93.5-96.7; 95% CI) for AT500 and 95.7% (84.3-98.9) for GEM III AT. Inappropriate detection was primarily due to FFRW oversensing or brief runs of premature atrial contractions (PACs). The device detected 100% of the sustained atrial arrhythmia episodes and 95.3% (range 76.1-99.9) of the net AT duration observed on the Holter recordings. AT detection was not influenced by atrial lead location. Appropriate detection of normal sinus rhythm at episode termination was 83.7% (80.7-86.3) for AT500 and 92.1% (84.5-96.2) for GEM III AT. Accurate detection and discrimination of FFRWs validates the reliability of AT diagnostic data and decreases the risk of inappropriate device therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".