Temporal Variability in Electrocardiographic Indices in Subjects With Brugada Patterns
Bibliographic record
Abstract
Background: Patients with Brugada electrocardiographic (ECG) patterns have differing levels of arrhythmic risk. We hypothesized that temporal variations in certain ECG markers may provide additional value for risk stratification. The present study evaluated the relationship between temporal variability of ECG markers and arrhythmic outcomes in patients with Brugada pattern ECG. Comparisons were made between low-risk asymptomatic subjects versus high-risk symptomatic patients with a history of syncope, ventricular tachycardia (VT) or ventricular fibrillation (VF). Methods: A total of 81 patients presenting with Brugada ECG patterns were recruited. Serial ECGs and electronic health records from January 2004 to April 2019 were analyzed. Temporal variability of QRS interval, J point-Tpeak interval (JTp), Tpeak-Tend interval (Tp-e) and ST elevation (STe) in precordial leads V1-3, in addition to RR-interval from lead II, was assessed using standard deviation and difference between maximum and minimum values over the serial ECGs. Results: Patients presenting with type 1 Brugada ECG pattern initially had significantly higher variability in JTp from lead V2 (SD: 33.5±13.8 vs. 25.2±11.5 ms, P=0.009; max-min: 98.6±46.2 vs. 78.3±47.6 ms, P=0.047) and STe in lead V1 (SD: 0.117±0.122 vs. 0.053±0.030 mV; P=0.004). Significantly higher variability in Tp-e interval measured from lead V3 was observed in the VT/VF group compared to the syncope and asymptomatic groups (SD: 20.5±8.5 vs. 16.6±7.3 and 14.7±9.8 ms; P=0.044; max-min: 70.2±28.9 vs. 56.3±29.0 and 43.5±28.5 ms; P=0.011). Conclusions: Temporal variability in ECG indices provides additional value for risk stratification in patients with Brugada pattern.
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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 | 0.003 |
| 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 | 0.000 | 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".