Cardiovascular disease risk in patients with hepatitis C infection: Results from two general population health surveys in Canada and the United States (2007-2017)
Bibliographic record
Abstract
The role of hepatitis C virus (HCV) infection in increasing the risk of cardiovascular disease (CVD) is controversial. The objective of the present study is to estimate the 10-year risk of CVD in HCV- positive subjects and describe their profile of cardiometabolic risk markers compared to HCV-negative subjects. We conducted a cross-sectional study to estimate 10-year CVD risk, calculated using the Framingham Risk Score (FRS), in participants from the Canadian Health Measures Survey (CHMS; 2007-2015, n = 10,115) and the US-National Health and Nutrition Examination Survey (NHANES; 2007-2016, n = 16,668). Subjects included in our analysis were aged 30 to 74 years with no prior history of CVD. FRS estimates, sociodemographic and cardiometabolic risk factors were compared between HCV- positive and -negative subjects in the two surveys. HCV-positive subjects had a distinct sociodemographic profile compared to their HCV-negative counterparts. Cardiometabolic risk factors, inflammatory markers and serum levels of micronutrients were comparable between the two survey populations, both in HCV-positive and -negative subjects. The average FRS in HCV-positive patients was in the range of "intermediate" 10-year CVD risk (i.e., 10-20%) and was significantly higher (P<0.01) than their HCV-negative counterparts who were within the "low" 10-year CVD risk range (i.e., ≤10%). Using a multivariable linear regression model adjusted for ethnicity, number of metabolic syndrome components and BMI, HCV infection was significantly associated with a 2.5-3.5% absolute risk increase of 10-year CVD (P<0.01). The results of the present study suggest a potential association between HCV infection and risk of subclinical and clinical CVD. The expansion of anti-HCV therapy may also contribute to reduced CVD risk and burden in patients with chronic HCV infection and should be explored further in other datasets and population modelling studies.
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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 | 0.001 |
| 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 | 0.000 |
| 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".