Differential pacing from two sites to diagnose risk of ventricular arrhythmia and death
Bibliographic record
Abstract
Abstract Background QRS abnormalities may not be apparent in sinus rhythm in electrically stable cardiomyopathy patients who can have quiescent but highly arrhythmogenic substrate. Here, we test the hypothesis that differential changes in QRS construction during right‐ventricular apex pacing (RVP) as opposed to atrial pacing (AP) will identify latent substrate for ventricular arrhythmias (VA) and death. Methods Forty patients with cardiomyopathy free of VA underwent baseline 114‐electrode body‐surface electrocardiogram during AP (100 beats per minute [bpm]) and RVP (100 and 120 bpm). The filtered‐averaged QRS at each electrode was deconstructed into individual intra‐QRS and post‐QRS ventricular myopotentials (VMP). The primary outcome was VA or death. Prognostic accuracy of VMP was validated using V1 to V6 leads in another prospective cohort of 44‐cardiomyopathy patients. Results Twenty‐six patients were eligible for initial analysis. After 5 ± 2 years of follow‐up, eight (31%) patients had VA (VAPos) while rest were uneventful (VANeg). During AP100, VAPos patients expressed more VMP than VANeg patients (16 ± 1 vs 12 ± 1, P = 0.02). RVP100 and RVP120 in VAPos patients introduced an additional 5.5 ± 0.5 and 6.0 ± 0.5 VMP (P < 0.0001 vs AP100). The relative change with RVP120 versus AP100 in VANeg patients exceeded VAPos patients by 1.2 ± 0.5 VMP (P = 0.03). Increment in VMP count of <8 in lead‐V5 with RVP120 compared to AP100 best predicted VA (area under curve 0.81, P = 0.01). In the validation cohort, primary outcome occurred in 13 (33%) patients. Native QRS features and AP100 alone failed to predict primary outcome. Patients with increment in VMP count of <8 in lead‐V5 with RVP120 versus AP100 had 7.9‐fold increased risk of primary outcome (95% confidence interval 1.01, 61.61; P = 0.049). Conclusion Cardiomyopathy patients at risk of VA or death perturb the QRS less than low‐risk patients with differential pacing. This functional response may be useful to identify arrhythmogenic substrate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".