Novel ventricular tachyarrhythmia detection enhancement detects undertreated life-threatening arrhythmias
Bibliographic record
Abstract
BACKGROUND: Ventricular tachyarrhythmias (VTA) with low and varying signal amplitudes and morphologies may not be successfully identified utilizing traditional implantable cardioverter-defibrillator algorithms. OBJECTIVE: Develop and validate a novel algorithm (VF Therapy Assurance, VFTA) to improve detection and timely delivery of high-voltage therapy (HVT) for these arrhythmias. METHODS: Arrhythmia detection was simulated on recorded VTA electrograms (EGMs) utilizing Abbott's Merlin.net database. EGMs where an HVT occurred only when VFTA was enabled, or where VFTA provided an HVT >30 seconds earlier than without VFTA, were readjudicated with physician review. As VFTA never prevents detection or therapy, EGMs where VFTA did not activate or alter HVT were not adjudicated. RESULTS: Among 564,353 recorded VTA EGMs from 20,000 devices, VFTA altered HVT in 105 EGMs from 67 devices. Physician adjudication determined that 81.9% (86/105) of these EGMs were true undertreated VTA episodes and would have received appropriate HVT with VFTA enabled. Furthermore, 65% of the episodes (56/86) were ventricular fibrillation, were polymorphic, did not self-terminate during the recording window, or were not amenable antitachycardia pacing. Of those, 87.5% (49/56) would not have elicited HVT without VFTA. Overall, VFTA provided new or earlier appropriate HVT in 0.27% (53/20,000) of devices with an increase in inappropriate HVT in 0.07% (14/20,000) devices. CONCLUSION: The VFTA algorithm successfully identifies VTA missed by traditional detection algorithms, owing to undersensed ventricular signals resulting in the rate falling below the programmed detection rate. The use of VFTA increases the likelihood of delivering life-saving HVT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".