Antitachycardia pacing success in implantable cardioverter-defibrillators by patient, device, and programming characteristics
Bibliographic record
Abstract
BACKGROUND: Antitachycardia pacing (ATP) is an established implantable cardioverter-defibrillator (ICD) therapy that terminates ventricular tachycardias (VTs) without painful ICD shocks. However, factors influencing ATP success are not well understood. OBJECTIVE: The purpose of this study was to examine ATP success rates by patient, device, and programming characteristics. METHODS: This retrospective analysis of the PainFree SmartShock Technology study included spontaneous ATP-treated monomorphic VT episodes. ATP success rates were calculated for various factors. Also, the relationship of ATP programming on shock burden and syncope were investigated. RESULTS: Of the 2770 enrolled patients (2200 [79%] male; mean age 65 years), 1699 (61%) received an ICD and 1071 (39%) a cardiac resynchronization therapy - defibrillator. ATP had >80% rate of success for terminating VTs overall, with similar rates observed between ICD and cardiac resynchronization therapy - defibrillator devices (82.2% vs 80.3%, respectively; P = .81) as well as between primary and secondary prevention patients with ICDs (77.2% vs 83.9% respectively; P = .25). Arrhythmias with a median cycle length of ≥320 ms had a significantly higher ATP success rate (88.0%; 95% confidence interval 84.8%-90.6%). The cumulative percentage of ATP success increased from 71% at 1 ATP sequence delivered to 87% at ≥8 sequences delivered. Programming more ATP sequences was associated with lower shock burden (P = .0005). There was no evidence that more sequences were associated with higher rates of syncope (P = .16). CONCLUSION: Delivering more ATP sequences resulted in a higher overall success of terminating VTs, while programming more ATP was associated with decreased shock burden and no evidence of increased syncope or acceleration. This suggests that more ATP sequences should be programmed when possible, but confirmation in prospective studies will be necessary.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".