Risk of Acute Myocardial Infarction Among Hepatitis C Virus (HCV)-Positive and HCV-Negative Men at Various Lipid Levels: Results From ERCHIVES
Bibliographic record
Abstract
BACKGROUND: Risk of acute myocardial infarction (AMI) among hepatitis C virus (HCV)-positive versus HCV-negative persons with similar lipid levels is unknown. We determined incident AMI rates among HCV-positive and HCV-negative men among various lipid strata. METHODS: We created a propensity score matched (PSM) cohort and a low cardiovascular disease (CVD) risk cohort. Primary outcome was incident AMI rates by HCV status in each lipid strata using National Cholesterol Program guidelines for lipid strata. RESULTS: We identified 85863 HCV-positive and HCV-negative men in the PSM population. The incidence rates/1000 patient-years (95% confidence interval [CI]) for AMI among total cholesterol (TC) 200-239 stratum were 5.3 (4.89, 5.71) for HCV-positive versus 4.71 (4.42, 5) for HCV-negative men (P = .02) and for TC >240 mg/dL were 7.38 (6.49, 8.26) versus 6.17 (5.64, 6.71) (P = .02). For low-density lipoprotein cholesterol (LDL) of 130-159 mg/dL, AMI rates were 5.44 (4.97, 5.91) for HCV-positive and 4.81 (4.48, 5.14) for HCV-negative men (P = .03). The rise in risk with increasing lipid levels was greater in younger HCV-positive than in HCV-negative men (e.g., TC > 240 mg/dL: age >50 HR 1.38 [HCV-positive] and 1.12 [HCV-negative]; age ≤50 HR 1.6 [HCV-positive] and 1.29 [HCV-negative]), and more profoundly altered in HCV-positive men by lipid lowering therapy (change in HR with lipid-lowering therapy for TC >240 mg/dL from 1.82 to 1.19 [HCV-positive] from 1.48 to 1.03 [HCV-negative]). CONCLUSIONS: HCV-positive men have a higher risk of AMI than HCV-negative men at higher TC/LDL levels; this risk is more pronounced at a younger age. Lipid lowering therapy significantly reduces this risk, with more profound reduction among HCV-positive versus HCV-negative men at similar lipid levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".