Evaluating the Cardiovascular Risk in an Aging Population of People With HIV: The Impact of Hepatitis C Virus Coinfection
Bibliographic record
Abstract
Background People with HIV (PWH) are at an increased risk of cardiovascular disease (CVD) with an unknown added impact of hepatitis C virus (HCV) coinfection. We aimed to identify whether HCV coinfection increases the risk of type 1 myocardial infarction (T1MI) and if the risk differs by age. Methods and Results We used data from NA‐ACCORD (North American AIDS Cohort Collaboration on Research and Design) from January 1, 2000, to December 31, 2017, PWH (aged 40–79 years) who had initiated antiretroviral therapy. The primary outcome was an adjudicated T1MI event. Those who started direct‐acting HCV antivirals were censored at the time of initiation. Crude incidence rates per 1000 person‐years were calculated for T1MI by calendar time. Discrete time‐to‐event analyses with complementary log–log models were used to estimate adjusted hazard ratios and 95% CIs for T1MI among those with and without HCV. Among 23 361 PWH, 4677 (20%) had HCV. There were 89 (1.9%) T1MIs among PWH with HCV and 314 (1.7%) among PWH without HCV. HCV was not associated with increased T1MI risk in PWH (adjusted hazard ratio, 0.98 [95% CI, 0.74–1.30]). However, the risk of T1MI increased with age and was amplified in those with HCV (adjusted hazard ratio per 10‐year increase in age, 1.85 [95% CI, 1.38–2.48]) compared with those without HCV (adjusted hazard ratio per 10‐year increase in age,1.30 [95% CI, 1.13–1.50]; P <0.001, test of interaction). Conclusions HCV coinfection was not significantly associated with increased T1MI risk; however, the risk of T1MI with increasing age was greater in those with HCV compared with those without, and HCV status should be considered when assessing CVD risk in aging PWH.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".