Risk factors associated with myocardial infarction in venous thromboembolism patients
Bibliographic record
Abstract
BACKGROUND: Although risk factors for MI have been described in the general population, there is a lack of data on the assessment of risk factors associated with MI in venous thromboembolism (VTE) patients. OBJECTIVE: The purpose of this study was to identify risk factors associated with MI in VTE patients. PATIENTS AND METHODS: Health insurance claims between January 2004 and September 2008 from the Ingenix IMPACT database were analyzed. Patients aged ≥18 years were identified as of the date of their first VTE diagnosis with ≥1 year of continuous insurance coverage before the index VTE. The risk of MI for VTE patients with 1, 2, and ≥3 major risk factors as identified by published guidelines was calculated. Multivariate Cox proportional hazard models were conducted to identify the most predictive risk factors associated with MI. RESULTS: A total of 177,885 VTE patients were identified; 4412 (2.5%) developed an MI during a mean follow-up period of 1.3 years. Previous MI, age (≥65 years), and coronary artery disease were the most predictive risk factors of MI with adjusted hazard ratios (HRs; 95% CI) of 5.47 (5.01-5.97), 1.78 (1.66-1.91), and 1.60 (1.48-1.74), respectively. Adjusted HRs (95% CI) for VTE patients with 1, 2, and ≥3 major risk factors relative to no major risk factor were 2.34 (1.94-2.81), 3.21 (2.67-3.85), and 6.93 (5.85-8.22), respectively. LIMITATIONS: These included possible inaccuracies or omissions in diagnoses, classification bias such as the identification of false-positive MI events, and the likely undercoding of some risk factors such as social issues. CONCLUSIONS: Traditional major cardiovascular risk factors are also predictive of MI in VTE patients. Having multiple major risk factors significantly increases the probability of developing MI events in VTE patients.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".