Variability of the Diagnostic ECG Pattern in an ICD Patient Population with Brugada Syndrome
Bibliographic record
Abstract
INTRODUCTION: The spontaneous presence of a coved-type ECG is considered as an important risk factor in Brugada syndrome. However, diagnosis making and risk stratification may be hampered by the dynamic nature of the ECG abnormalities. The objective of this study was to determine the variability and predictive value of the electrocardiogram in Brugada patients implanted with a cardioverter-defibrillator (ICD). METHODS AND RESULTS: We analyzed consecutive 12-lead ECGs from 89 ICD patients (44 +/- 14 years, 69 males) with Brugada syndrome. A total of 1,161 ECGs were included for analysis (13 +/- 8 ECGs/patient). Twenty-four percent of the ECGs/patient were coved-type I, 25% saddleback-type II or III, and 51% normal. Fifty-seven patients had a diagnostic coved-type ECG spontaneously (group A), 32 patients only after drug challenge (group B). In group A, 38% of the ECGs/patient were diagnostic, 25% saddleback-type, and 37% normal. Fifty-five group A patients (96%) had transient normalization and/or conversion to saddleback-type ECGs. During a mean follow-up of 48 +/- 35 months, 16 patients (18%) experienced appropriate shocks. All patients with appropriate shocks had spontaneous diagnostic ECGs. They tended to have more coved-type ECGs (36 vs 22%, respectively, P = 0.05) than patients without appropriate shocks. CONCLUSIONS: Analysis of serial ECG recordings in an ICD patient population shows that the Brugada-ECG pattern is highly variable over time. In patients with spontaneous coved-type ECG, only every third ECG is diagnostic and every third ECG normal. Patients with spontaneous coved-type ST-segment elevation have a high incidence of appropriate shocks. Spontaneous saddleback-type electrocardiograms are not helpful for 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".