Association of stress-induced autonomic dysfunction with heart failure in individuals with stable coronary artery disease
Bibliographic record
Abstract
Background: Heart failure (HF) is a common complication in individuals with coronary artery disease (CAD). Autonomic effects of psychological stress may play an important, under-recognized role in this relationship. We hypothesized that stress-induced autonomic dysfunction, measured by change in low-frequency heart rate variability (HRV) during mental stress challenge, associates with increased HF risk. Methods: We examined 662 participants with stable CAD and no known diagnosis of HF who underwent mental stress challenge via a standardized speaking task in conjunction with Holter monitoring. We evaluated HRV in 5-minute windows and examined its change from rest to stress as our primary exposure. Repeated events Cox proportional hazard models were used to examine incident and recurrent acute HF in the outpatient and inpatient setting. Results: The mean age was 58 years, 35 % were women, and 43 % self-identified as Black. In models adjusted for age, sex, race, comorbidities, ejection fraction, and resting low-frequency HRV, each standard deviation decrease (negative change) in low-frequency HRV change from rest to stress was associated with an increased risk of incident and recurrent acute HF (HR 1.39 [95 % CI 1.02-1.90], p = 0.035) over a median follow-up of 5.7 years. These estimates for HF risk were higher than those of resting HRV. Conclusion: Greater decreases in low-frequency HRV change during acute mental stress challenge independently associate with higher risks of future HF development in individuals with stable CAD and had stronger effect sizes than resting HRV alone, highlighting an important role of stress autonomic pathways in the pathogenesis of HF.
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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 | 0.002 |
| 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".