Heart Rate Turbulence and T‐Wave Alternans in Patients with Coronary Artery Disease: The Influence of Diabetes
Bibliographic record
Abstract
BACKGROUND: Patients with diabetes mellitus (DM) have a higher risk of sudden cardiac death. Factors associated with the risk profiles of coronary artery disease (CAD) patients with DM are not well established. Heart rate turbulence (HRT) and T-wave alternans (TWA) are often used to predict arrhythmia events. METHODS AND RESULTS: HRT and TWA were measured in two independent groups: the ARTEMIS cohort study and the REFINE-ICD randomized trial. ARTEMIS assesses risk 3-12 months after coronary angiography in patients with CAD. The initial 1001 patients in ARTEMIS, 526 with and 475 without DM, are included in this analysis. REFINE-ICD compares usual care versus usual care plus ICD therapy in patients with left ventricular (LV) ejection fraction (EF) values of 36-50% assessed 2-15 months after myocardial infarction. The initial 275 patients screened in REFINE ICD are included in this analysis. Abnormal HRT plus TWA was more common in patients with versus without DM in ARTEMIS (125/526, 24% vs 63/475, 13%; P < 0.001) and REFINE-ICD (43/55, 78% vs 55/220, 25%; P < 0.001), respectively. Abnormal HRT plus TWA was also more common in patients with LVEF values < 50% (28%) vs ≥ 50% (18%; P < 0.001) in ARTEMIS and LVEF values below the population median of 42% (60/138, 43%) versus above the median (38/137, 28%; P < 0.01) in REFINE-ICD. CONCLUSIONS: Abnormal HRT plus TWA is more common in CAD patients with DM compared with the patients without DM and is related to the severity of LV dysfunction. CLINICAL TRIAL REGISTRATION INFORMATION: http://www.clinicaltrials.gov, NCT01426685; http://www.clinicaltrials.gov, NCT00673842.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".