Relation of Ejection Fraction and Inducible Ventricular Tachycardia to Mode of Death in Patients With Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Fifty percent of deaths in patients with coronary disease occur suddenly. Although many factors correlate with increased mortality, there is little information regarding the influence of these factors on mode of death. As such, optimum methods to determine patients most likely to benefit from implantable defibrillator therapy are unclear. METHODS AND RESULTS: We analyzed the relation of ejection fraction and inducible ventricular tachyarrhythmias to mode of death in all 1791 patients enrolled in the Multicenter Unsustained Tachycardia Trial who did not receive antiarrhythmic therapy. Total mortality and arrhythmic deaths/cardiac arrests occurred more frequently in patients with ejection fraction <30% than in those with ejection fraction of 30% to 40%. The percentage of deaths classified as arrhythmic was similar in patients with ejection fraction <30% or > or =30%. The relative contribution of arrhythmic events to total mortality was significantly higher in patients with inducible tachyarrhythmia (58% of deaths in inducible patients versus 46% in noninducible patients, P=0.004). The higher percentage of events that were arrhythmic among patients with inducible tachyarrhythmia appeared more distinct among patients with an ejection fraction > or =30% (61% of events were arrhythmic among inducible patients with ejection fraction > or =30% and only 42% among noninducible patients, P=0.002). CONCLUSIONS: Both low ejection fraction and inducible tachyarrhythmias identify patients with coronary disease at increased mortality risk. Ejection fraction does not discriminate between modes of death, whereas inducible tachyarrhythmia identifies patients for whom death, if it occurs, is significantly more likely to be arrhythmic, especially if ejection fraction is > or =30%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".