Atrial fibrillation and clinical outcomes 1 to 3 years after myocardial infarction
Bibliographic record
Abstract
Objective Atrial fibrillation (AF) and myocardial infarction (MI) are commonly comorbid and associated with adverse outcomes. Little is known about the impact of AF on quality of life and outcomes post-MI. We compared characteristics, quality of life and clinical outcomes in stable patients post-MI with/without AF. Methods/results The prospective, international, observational TIGRIS (long Term rIsk, clinical manaGement and healthcare Resource utilization of stable coronary artery dISease) registry included 8406 patients aged ≥50 years with ≥1 atherothrombotic risk factor who were 1–3 years post-MI. Patient characteristics were summarised by history of AF. Quality of life was assessed at baseline using EQ-5D. Clinical outcomes over 2 years of follow-up were compared. History of AF was present in 702/8277 (8.5%) registry patients and incident AF was diagnosed in 244/7575 (3.2%) over 2 years. Those with AF were older and had more comorbidities than those without AF. After multivariable adjustment, patients with AF had lower self-reported quality-of-life scores (EQ-5D UK-weighted index, visual analogue scale, usual activities and pain/discomfort) than those without AF. CHA2DS2-VASc score ≥2 was present in 686/702 (97.7%) patients with AF, although only 348/702 (49.6%) were on oral anticoagulants at enrolment. Patients with AF had higher rates of all-cause hospitalisation (adjusted rate ratio 1.25 [1.06–1.46], p=0.008) over 2 years than those without AF, but similar rates of mortality. Conclusions In stable patients post-MI, those with AF were commonly undertreated with oral anticoagulants, had poorer quality of life and had increased risk of clinical outcomes than those without AF. Trial registration number ClinicalTrials: NCT01866904 .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".