SVT discrimination algorithms significantly reduce the rate of inappropriate therapy in the setting of modern‐day delayed high‐rate detection programming
Bibliographic record
Abstract
BACKGROUND: Contemporary implantable cardioverter-defibrillator (ICD) programming involving delayed high-rate detection and use of supraventricular tachycardia (SVT) discriminators has significantly reduced the rate of inappropriate shocks. The extent to which SVT algorithms alone reduce inappropriate therapies is poorly understood. METHODS AND RESULTS: PainFree SST enrolled 2770 patients with a single- or dual-chamber ICD or cardiac resynchronization defibrillator. Patients were followed for 22 ± 9 months with SVT discriminators on in 96% of patients. Sustained ventricular tachyarrhythmias and SVT episodes were adjudicated by an independent physician committee. For this analysis, all episodes were subjected to postprocessing computer simulation with SVT discriminators off with and without delayed high-rate detection criteria (ventricular fibrillation zone only, 30/40 at 320 ms). There were 3282 adjudicated SVT episodes of which 115 resulted in an ICD shock and 113 received only ATP (2-year inappropriate shock and therapy rates of 3.1% and 4.1%). Therapy was appropriately withheld for the remaining 3054 SVT episodes. With both SVT discriminators and delayed high-rate detection simulated off, the 2-year inappropriate therapy rate would have been 22.9% (hazard ratio [HR] = 6.24; 95% confidence interval [CI]: 5.20-7.49). With SVT discriminators simulated off and delayed high-rate detection simulated on in all patients, the 2-year rate would have been 6.4% (HR = 1.63; CI: 1.44-1.85). CONCLUSIONS: The use of SVT discriminators has a significant role in reducing the rate of inappropriate ICD therapy even in the setting of delayed high-rate detection settings. Deactivating SVT discriminators would have resulted in an overall increase in the inappropriate ICD therapy rate by 63% and 524% with and without delayed high-rate detection programming, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".