Prognostic impact of demographic factors and clinical features on the mode of death in high-risk patients after myocardial infarction - A combined analysis from multicenter trials
Bibliographic record
Abstract
BACKGROUND: Contemporary information is lacking on the effect of demographic features and clinical features on the specific mode of mortality after myocardial infarction (MI) in the thrombolytic era. HYPOTHESIS: The aims of this study were (1) to examine the risk and trend of a different mode of mortality (i.e., all-cause, arrhythmic, and nonarrhythmic cardiac mortality) in high-risk patients post MI with reduced left ventricular ejection fraction (LVEF) or ventricular arrhythmias; and (2) to assess the predictive value of demographic and clinical variables in the prediction of specific modes of death in high-risk patients post MI in the thrombolytic era. METHODS: In all, 3,431 patients receiving placebo (2,700 men, median age 64 +/- 11 years) from the EMIAT, CAMIAT, SWORD, TRACE, and DIAMOND-MI studies, with LVEF < 40% or ventricular arrhythmia were pooled. Risk factors for mortality among patients surviving > or = 45 days after MI up to 2 years were examined using Cox regression. Short-term survival (from onset of MI to Day 44 after MI) was also examined for TRACE and DIAMOND-MI, in which patients were recruited within 2 weeks of MI. RESULTS: After adjustment for treatment and study effects, age, previous MI/angina, increased heart rate, and higher New York Heart Association functional class increased the risk of all-cause, arrhythmic, and cardiac mortality. Male gender, history of hypertension, low baseline systolic blood pressure, and Q wave were predictive of all-cause and arrhythmic mortality, whereas diabetes was only predictive of all-cause mortality. Smoking habit and atrial fibrillation had no prognostic value. Similar parameters were also predictive of short-term mortality, but not identical. CONCLUSIONS: Our study has shown that in high-risk patients post MI, who have been preselected using LVEF or frequent ventricular premature beats, demographic and clinical features are powerful predictors of mortality in the thrombolytic era. We propose that demographic and clinical factors should be considered when designing risk stratification or survival studies, or when identifying high-risk patients for prophylactic implantable cardiodefibrillator therapy.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".