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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".