Happy heart syndrome: role of positive emotional stress in takotsubo syndrome
Bibliographic record
Abstract
AIMS: Takotsubo syndrome (TTS) is typically provoked by negative stressors such as grief, anger, or fear leading to the popular term 'broken heart syndrome'. However, the role of positive emotions triggering TTS remains unclear. The aim of the present study was to analyse the prevalence and characteristics of patients with TTS following pleasant events, which are distinct from the stressful or undesirable episodes commonly triggering TTS. METHODS AND RESULTS: Takotsubo syndrome patients with preceding pleasant events were compared to those with negative emotional triggers from the International Takotsubo Registry. Of 1750 TTS patients, we identified a total of 485 with a definite emotional trigger. Of these, 4.1% (n = 20) presented with pleasant preceding events and 95.9% (n = 465) with unequivocal negative emotional events associated with TTS. Interestingly, clinical presentation of patients with 'happy heart syndrome' was similar to those with the 'broken heart syndrome' including symptoms such as chest pain [89.5% (17/19) vs. 90.2% (412/457), P = 1.0]. Similarly, electrocardiographic parameters, laboratory findings, and 1-year outcome did not differ. However, in a post hoc analysis, a disproportionate higher prevalence of midventricular involvement was noted in 'happy hearts' compared with 'broken hearts' (35.0 vs. 16.3%, P = 0.030). CONCLUSION: Our data illustrate that TTS can be triggered by not only negative but also positive life events. While patient characteristics were similar between groups, the midventricular TTS type was more prevalent among the 'happy hearts' than among the 'broken hearts'. Presumably, despite their distinct nature, happy and sad life events may share similar final common emotional pathways, which can ultimately trigger TTS.
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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 | 0.001 |
| 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) | 0.002 | 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".