Real World Performance of an Individualized Antitachycardia Pacing Algorithm
Bibliographic record
Abstract
ABSTRACT Background A novel individualized antitachycardia pacing (IATP) algorithm using the post-pacing interval for real-time control has been introduced. Performance information is limited to a small safety and feasibility study with additional single-center and case studies. A larger-scale analysis is needed to better understand algorithm performance. Methods De-identified remote monitoring transmissions from devices with the IATP therapy applied were randomly selected. Rhythms were classified and effects of the novel algorithm were assessed. For monomorphic ventricular tachycardias (MVTs) proportions of successful therapy, shock-free episodes, and acceleration were calculated using Generalized Estimating Equations to correct for multiple episodes and compute statistics of the algorithm’s performance. Results There were 2259 MVT episodes in 336 patients. IATP succeeded in 87.1% of MVT episodes with 89.9% of MVT episodes ultimately free of shock therapy. Based on multivariate analysis, significant factors in therapy success were programming of at least the recommended number of sequences (90% at least recommended vs 73%, p=0.00088) and female sex (95% for female vs 86%, p=0.002). A trend to higher success was found for MVT with cycle length of 320ms or greater (90% vs. 83%, p=0.10). The IATP accelerated 3.6% of MVT episodes. None of the available factors was significantly associated with acceleration in the multivariate analysis. Conclusions The IATP algorithm succeeded in large proportion of MVT episodes and with low acceleration in patients randomly selected from remote monitoring transmissions. Using at least the recommended number of sequences had the strongest association with successful therapy. CLINICAL PERSPECTIVE What is known A novel method for delivering antitachycardia pacing therapy using real-time feedback has been shown safe and feasible. Comparisons in computational studies and smaller case series suggest the novel method’s performance is superior to other antitachycardia pacing methods. What the study adds Under real-world programming and use, as evaluated on a large de-identified set of remote monitoring transmissions, the novel antitachycardia pacing algorithm treated monomorphic ventricular tachycardia episodes with high efficacy and a low proportion of acceleration. The strongest factor associated with successful therapy was programming at least the recommended number of pacing attempts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".