Atrial Response to Ventricular Antitachycardia Pacing Discriminates Mechanism of 1:1 Atrioventricular Tachycardia
Bibliographic record
Abstract
BACKGROUND: Inappropriate shocks from implantable cardioverter defibrillators (ICD) remain a significant clinical problem despite device discrimination algorithms. The atrial response to antitachycardia pacing (ATP) may determine the mechanism of 1:1 A:V tachycardia. METHODS: For this study we refer to sinus tachycardia, atrial tachycardia (AT), atrial fibrillation, and flutter as atrial tachycardia (AT), and all other tachycardia as "non-AT." Three atrial response patterns during the burst of ATP were determined. The atrial cycle length (ACL) may be unchanged (type 1) indicating AT. The ACL may show variation during ATP (type 2) indicating variable VA block and does not discriminate between an AT and a non-AT mechanism, in which case a default diagnosis of non-AT is made. The ACL may accelerate to the ATP cycle length (type 3) indicating entrainment. A VAAV response at the end of ATP was considered diagnostic of AT (type 3A) whereas a VAV or VVA response was considered a non-AT mechanism (type 3B). This algorithm was applied to ICD tracings from 68 episodes of spontaneous 1:1 A:V tachycardia that had 136 sequences of ATP administered. The rhythm "truth" was determined by consensus of two experienced clinicians. RESULTS: The algorithm correctly identified AT with a sensitivity of 71.9% (95% CI: 67.1-73.6), and specificity of 95% (83.5-99.1). The PPV was 97.2% (90.9-99.5), and NPV 58.5% (51.4-61.0). Kappa was 0.57 (0.43-0.62). If used clinically the algorithm would have aborted 53.3% (8/15) of inappropriate shocks delivered into an AT-mechanism tachycardia and would not have withheld a shock for any episode of VT. CONCLUSION: Analysis of atrial response patterns during and after ventricular ATP can successfully discriminate tachycardia mechanism and may reduce inappropriate ICD shocks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".