Electrocardiographic Predictors of Coronary Heart Disease and Sudden Cardiac Deaths in Men and Women Free From Cardiovascular Disease in the Atherosclerosis Risk in Communities Study
Bibliographic record
Abstract
BACKGROUND: We evaluated predictors of coronary heart disease (CHD) death and sudden cardiac death (SCD) in the Atherosclerosis Risk in Communities (ARIC) study. METHODS AND RESULTS: The study population included 13 621 men and women 45 to 65 years of age free from manifest cardiovascular disease at entry. Hazard ratios from Cox regression with 95% confidence intervals were computed for 18 dichotomized repolarization-related ECG variables. The average follow-up was 14 years. Independent predictors of CHD death in men were TaVR- and rate-adjusted QTend (QTea), with a 2-fold increased risk for both, and spatial angles between mean QRS and T vectors and between Tpeak (Tp) and normal R reference vectors [θ(Rm|Tm) and θ(Tp|Tref), respectively], with a >1.5-fold increased risk for both. In women, independent predictors of the risk of CHD death were θ(Rm|Tm), with a 2-fold increased risk for θ(Rm|Tm), and θ(Tp|Tref), with a 1.7-fold increased risk. Independent predictors of SCD in men were θ(Tp|Tref) and QTea, with a 2-fold increased risk, and θ(Tinit|Tterm), with a 1.6-fold increased risk. In women, θ(Tinit|Tterm) was an independent predictor of SCD, with a >3-fold increased risk, and θ(Rm|Tm) and TV1 were >2-fold for both. CONCLUSIONS: θ(Rm|Tm) and θ(Tp|Tref), reflecting different aspects of ventricular repolarization, were independent predictors of CHD death and SCD, and TaVR and TV1 were also independent predictors. The risk levels for independent predictors for both CHD death and SCD were stronger in women than in men, and QTea was a significant predictor in men but not in women.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".