Risk of Cardiovascular Disease Associated with HCV and HBV Coinfection among Antiretroviral-Treated HIV-Infected Individuals
Bibliographic record
Abstract
BACKGROUND: The increased risk for cardiovascular disease (CVD) in HIV is well established. Despite high prevalence of viral hepatitis coinfection with HIV, there are few studies on the risk of CVD amongst antiretroviral therapy (ART)-treated coinfected patients. METHODS: Ontario HIV Treatment Network Cohort Study participants who initiated ART without prior CVD events were analysed. HBV and HCV coinfection were identified by serology and RNA test results. CVD was defined as any of: coronary artery disease including atherosclerosis, chronic ischaemic heart disease and arteriosclerotic vascular disease; myocardial infarction; congestive heart failure; cerebrovascular accident or stroke; coronary bypass; angioplasty; and sudden cardiac death. The impact of HBV and HCV coinfection on time to CVD was assessed using multivariable competing risk models accounting for left truncation between ART initiation and study enrolment. RESULTS: A total of 3,416 HIV-monoinfected, 432 HIV-HBV- and 736 HIV-HCV-coinfected individuals were followed for a median (IQR) of 2.32 years (1.36-8.02). Over the study period, 167 CVD events and 613 deaths were documented. After adjustment for age, gender, race, year initiating ART, weight and smoking status, HBV was not associated with time to CVD onset (aHR=1.05, 95% CI [0.63, 1.74]; P=0.86). There was an elevated risk of CVD for HCV-coinfected individuals, which approached statistical significance (aHR=1.44, 95% CI [0.97, 2.13]; P=0.07). CONCLUSIONS: Our results are consistent with a moderate increase of CVD among individuals with HIV-HCV coinfection relative to those with HIV infection alone, lending support to consideration of initiation of HCV antiviral treatment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".