930. HCV Treatment Is Associated With a Reduced Risk of Cardiovascular Disease Events: Results From ERCHIVES
Bibliographic record
Abstract
Abstract Background Studies reporting on the association between HCV and cardiovascular disease (CVD), and effect of HCV treatment upon future risk of CVD have shown mixed results. Methods Within ERCHIVES (Electronically Retrieved Cohort of HCV Infected Veterans), we identified all persons treated for ≥7 weeks and propensity-score-matched group who never received HCV treatment. We excluded those with HIV, HBV, or previously diagnosed CVD. Incidence rate (per 1,000 person-years) and risk factors for CVD events (Cox proportional hazards analysis) were determined for various treatment groups. CVD events were identified using ICD-9CM/ICD-10 codes. Kaplan–Meier plots were generated to show and compare CVD-free survival by treatment status and attainment of SVR. Results Among 32,575 treated and same number of untreated persons in the final dataset, median age was 58 years, 27% were Black race, and 96% were male. The incidence rate for CVD events/1,000 person-years (95% CI) among the treated was 19.10 (17.79, 20.50) vs. 32.37 (30.51, 34.33) among the untreated (P < 0.01). Treatment with a DAA regimen (vs. PEG/RBV; HR [95% CI] 0.68 [0.53,0.88]) and achieving SVR (HR [95% CI] 0.76 [0.63,0.92]) were associated with a lower risk of incidence CVD event (table). Kaplan–Meier curves demonstrated that untreated persons had a shorter CVD event-free survival during 30 months of follow-up compared with the treated persons. (figure; log-rank P < 0.0001) Conclusion HCV treatment is associated with a reduction in incident CVD events. Directly acting antiviral regimens (vs. PEG/RBV) and attainment of SVR (vs. no SVR) are associated with a lower risk of incident CVD events. Disclosures A. Ajwad Butt, Gilead: Grant Investigator, Research grant.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".