Improving SVT Discrimination in Single‐Chamber ICDs: A New Electrogram Morphology‐Based Algorithm
Bibliographic record
Abstract
INTRODUCTION: Wide-spread adoption of ICD therapy has focused efforts on improving the quality of life for patients by reducing "inappropriate" shock therapies. To this end, distinguishing supraventricular tachycardia from ventricular tachycardia remains a major challenge for ICDs. More sophisticated discrimination algorithms based on ventricular electrogram morphology have been made practicable by the increased computational ability of modern ICDs. METHODS AND RESULTS: We report results from a large prospective study (1,122 pts) of a new ventricular electrogram morphology tachycardia discrimination algorithm (Wavelet Dynamic Discrimination, Medtronic, Minneapolis, MN, USA) operating at minimal algorithm setting (RV coil-can electrogram, match threshold of 70%). This is a nonrandomized cohort study of ICD patients using the morphology discrimination of the Wavelet algorithm to distinguish SVT and VT/VF. The Wavelet criterion was required ON in all patients and all other supraventricular tachycardia discriminators were required to be OFF. Spontaneous episodes (N = 2,235) eligible for ICD therapy were adjudicated for detection algorithm performance. The generalized estimating equations method was used to remove bias introduced when an individual patient contributes multiple episodes. Inappropriate therapies for supraventricular tachycardia were reduced by 78% (90% CI: 72.8-82.9%) for episodes within the range of rates where Wavelet was programmed to discriminate. Sensitivity for sustained ventricular tachycardia was 98.6% (90% CI: 97-99.3%) without the use of high-rate time out. CONCLUSIONS: Results from this prospective study of the Wavelet electrogram morphology discrimination algorithm operating as the sole discriminator in the ON mode demonstrate that inappropriate therapy for supraventricular tachycardia in a single-chamber ICD can be dramatically reduced compared to rate detection alone.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".