Intracardiac Electrogram T-Wave Alternans/Variability Increases Before Spontaneous Ventricular Tachyarrhythmias in Implantable Cardioverter-Defibrillator Patients
Bibliographic record
Abstract
BACKGROUND: T-wave alternans (TWA) increases before ventricular tachycardia (VT) or fibrillation (VF), suggesting that it may warn of VT/VF in implantable cardioverter-defibrillator patients. Recently, we described a method for measuring alternans and nonalternans variability (TWA/V) from electrograms (EGMs) stored in implantable cardioverter-defibrillators before VT/VF. The goal of this prospective, multicenter study was to determine whether EGM TWA/V was greater before VT/VF than at baseline. METHODS AND RESULTS: We enrolled 63 implantable cardioverter-defibrillator patients. TWA/V was computed from stored EGMs before spontaneous VT/VF and from sequential windows of 8 pairs of beats using 4 different control recordings: baseline rhythm, rapid pacing at 105 bpm, segments of ambulatory Holter EGMs matched to the time of VT/VF episodes, and EGMs before spontaneous supraventricular tachycardia. During follow-up, 28 patients had 166 episodes of VT/VF. TWA/V was greater before VT/VF (62.9 ± 3.1 μV; n = 28) than during baseline rhythm (12.8 ± 1.8 μV; P < 0.0001; n = 62), during rapid pacing (14.5 ± 2.0 μV; P < 0.0001; n = 52), before supraventricular tachycardia (27.5 ± 6.1 μV; P < 0.0001; n = 9), or during time-matched ambulatory controls (12.3 ± 3.5 μV; P < 0.0001; n = 16). By logistic regression, the odds of VT/VF increased by a factor of 2.2 for each 10-μV increment in TWA/V (P < 0.0001). CONCLUSIONS: In implantable cardioverter-defibrillator patients, EGM TWA/V is greater before spontaneous VT/VF than in control recordings. Future implantable cardioverter-defibrillators that measure EGM TWA/V continuously may warn patients and initiate pacing therapies to prevent VT/VF.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".