Abstract 21148: Stress-Induced Cardiac Repolarization Changes Are Associated With Mental Stress-Induced Mycoardial Ischemia
Bibliographic record
Abstract
Introduction: Mental Stress-Induced Myocardial Ischemia (MSIMI) can occur in patients with coronary artery disease (CAD) during acute mental stress challenge and is associated with an increased risk of adverse cardiovascular events. Adverse dynamic changes in repolarization with stress may also be particularly important when assessing risk of MSIMI. Hypothesis: In subjects with CAD, dynamic ECG repolarization changes during stress and recovery are associated with MSIMI. Methods: We studied 419 individuals with CAD who underwent mental stress challenge via a standardized speech stressor. Digital 12-lead ECGs were collected at baseline during resting , 1 minute after speech started, immediately after speech, and during recovery (5 minutes after speech ended). Repolarization metrics, including QRS-T angle, T-axis, ST depression, and T wave area were quantified in all 12 leads. Heart rate was also included. Subjects with baseline artifact were excluded. Changes amongst baseline, stress, and recovery were analyzed. Forward selection with alpha=0.01 was used to help guide the final model. MSIMI was assessed via Tc99m myocardial perfusion imaging (read by experienced clinicians). Results: The mean (SD) age was 56 (10), 38% were women, and 17% had MSIMI. The most significant predictors of MSIMI, based on the multivariate model, are summarized in the table. The C-statistic was 0.73, but reduced to 0.71 after leave-one-out cross validation. Goodness of fit was adequate. No significant race or gender differences were noted. Conclusion: Baseline and dynamic ECG repolarization changes in recovery (but not peak stress) contribute to MSIMI classification, and may be useful for risk stratification of arrhythmia risk due to emotional triggers. While the mechanisms regarding the relationship of MSIMI and ECG metrics during recovery are unknown, they could be due to hysteresis phenomena and/or known autonomic changes during recovery from stress.
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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) | 0.007 | 0.001 |
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".