The clinical utility of procainamide‐induced late potentials on the signal averaged ECG
Bibliographic record
Abstract
BACKGROUND: Late potentials (LPs) identified on the signal averaged electrocardiogram (SAECG) are a marker for an increased risk of arrhythmias in Brugada syndrome (BrS). Procainamide is a sodium channel blocker used to diagnose BrS. The effects of Procainamide on the SAECG in those with BrS and the significance of Procainamide-induced LPs are unknown. METHODS: Procainamide provocation was performed for suspected BrS with 12-lead and SAECG pre- and post-infusion. Filtered QRS duration (fQRSd), duration of low amplitude signals <40 μV (LAS40) and root-mean-square voltage in the terminal 40 ms (RMS40) were determined. RESULTS: Data from 150 patients were included in the analysis (mean age 44.5 years, 109 males). Procainamide increased fQRSd (Pre 118.8 ± 10.5 ms, post 121.2 ± 10.2 ms, p < 0.001) and LAS40 (Pre 38.7 ± 9.8 ms, post 40.2 ± 10.5 ms, p = 0.005) and decreased RMS40 (Pre 24.6 ± 12 ms, post 22.8 ± 12 ms, p = 0.002). LPs were present in 68/150 (45%) at baseline. Fifteen patients with negative baseline SAECGs had LPs unmasked by Procainamide, but six patients had LPs at baseline that were no longer present following Procainamide. Comparing those with normal hearts (n = 48) to those with a final diagnosis of BrS (n = 38), Procainamide prolonged fQRSd to a greater extent in those with BrS. Comparing those with Procainamide-induced LPs to those with no LPs at any time did not highlight any aspect of phenotype and did not correlate with a history of ventricular arrhythmias. CONCLUSIONS: Procainamide influences the SAECG, provoking LPs in a small proportion of patients. However, there is no evidence that Procainamide-induced LPs provide additional diagnostic information or aid risk stratification.
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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".