Combined Atrial and Ventricular Antitachycardia Pacing as a Novel Method of Rhythm Discrimination
Bibliographic record
Abstract
BACKGROUND: Inappropriate and unnecessary implantable cardioverter-defibrillator shocks continue to be highly prevalent. METHODS AND RESULTS: We prospectively evaluated a new algorithm for discriminating supraventricular (SVT) and ventricular (VT) tachycardias with 1:1 atrioventricular association that is based on the response of the arrhythmia to simultaneous or convergent dual-chamber antitachycardia pacing. Patients undergoing dual-chamber cardioverter-defibrillator implantation were randomized to the simultaneous atrioventricular and convergent atrioventricular arms with crossover at 3 months. Sixty-three patients had 1407 1:1 antitachycardia pacing sequences suitable for analysis (1381 1:1 SVT episodes in 32 patients and 26 1:1 VT episodes in 6 patients). Antitachycardia pacing terminated 66 of 1381 SVT (5%; generalized estimating equations adjusted, 23.8%) and 20 of 26 VT (77%; generalized estimating equation adjusted, 68.6%) episodes. After the exclusion of sinus tachycardia, the new software terminated 40 of 57 (70%; generalized estimating equation adjusted, 70.2%) SVT episodes. The new algorithm terminated or correctly classified 1379 of 1381 SVT sequences for an overall specificity of 99.9% (generalized estimating equation adjusted, 99.8%) and 23 of 26 VT for an overall sensitivity of 88.5% (generalized estimating equation adjusted, 82.1%). There were no statistically significant differences between the simultaneous and the convergent atrioventricular antitachycardia pacing sequences in their ability to confirm VT or reject SVT. No significant proarrhythmias were noted. CONCLUSIONS: We describe here a new pacing algorithm in dual-chamber defibrillators that can terminate arrhythmias or discriminate between 1:1 SVT and VT if the arrhythmia persists. Testing this new algorithm in larger patient populations is warranted. CLINICAL TRIAL REGISTRATION INFORMATION- URL: http://ftp.resource.org/gpo.gov/register/2007/2007_15297.pdf. IDE No. G060230.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".