Initial Clinical Experience With a New Automated Antitachycardia Pacing Algorithm
Bibliographic record
Abstract
BACKGROUND: Antitachycardia pacing (ATP) in implantable cardioverter-defibrillators (ICD) decreases patient shock burden but has recognized limitations. A new automated ATP (AATP) based on electrophysiological first principles was designed. The study objective was to assess the feasibility and safety of AATP in ambulatory ICD patients. METHODS AND RESULTS: Enrolled patients had dual chamber or cardiac resynchronization therapy ICDs, history of ≥1 ICD-treated ventricular tachycardias (VT)/ventricular fibrillation episode, or a recorded, sustained monomorphic VT. Detection was set to ventricular fibrillation number of intervals to detect=24/32, VT number of intervals to detect≥16, and a fast VT zone of 240 to 320 ms. AATP prescribed the components and delivery of successive ATP sequences in real time, using the same settings for all patients. ICD datalogs were uploaded every ≈3 months, at unscheduled visits, exit, and death. Episodes and adverse events were adjudicated by separate committees. Results were adjusted (generalized estimating equations) for multiple episodes. AATP was downloaded into the ICDs of 144 patients (121 men), aged 67.4±11.9 years, left ventricular ejection fraction 33.1±13.6% (n=137), and treated 1626 episodes in 49 patients during 14.5±5.1 months of follow-up. Datalogs permitted adjudication of 702 episodes, including 669 sustained monomorphic VT, 20 polymorphic VT, 10 supraventricular tachycardia, and 3 malsensing episodes. AATP terminated 39 of 69 (59% adjusted) sustained monomorphic VT in the fast VT zone, 509 of 590 (85% adjusted) in the VT zone, and 6 of 10 in the ventricular fibrillation zone. No supraventricular tachycardias converted to VT or ventricular fibrillation. No anomalous AATP behavior was observed. CONCLUSIONS: The new AATP algorithm safely generated ATP sequences and controlled therapy progression in all zones without need for individualized programing.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".