Increased Risk of Myocardial Infarction in HIV-Infected Individuals in North America Compared With the General Population
Bibliographic record
Abstract
BACKGROUND: Previous studies of cardiovascular disease (CVD) among HIV-infected individuals have been limited by the inability to validate and differentiate atherosclerotic type 1 myocardial infarctions (T1MIs) from other events. We sought to define the incidence of T1MIs and risk attributable to traditional and HIV-specific factors among participants in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) and compare adjusted incidence rates (IRs) to the general population Atherosclerosis Risk in Communities (ARIC) cohort. METHODS: We ascertained and adjudicated incident MIs among individuals enrolled in 7 NA-ACCORD cohorts between 1995 and 2014. We calculated IRs, adjusted incidence rate ratios (aIRRs), and 95% confidence intervals of risk factors for T1MI using Poisson regression. We compared aIRRs of T1MIs in NA-ACCORD with those from ARIC. RESULTS: Among 29,169 HIV-infected individuals, the IR for T1MIs was 2.57 (2.30 to 2.86) per 1000 person-years, and the aIRR was significantly higher compared with participants in ARIC [1.30 (1.09 to 1.56)]. In multivariable analysis restricted to HIV-infected individuals and including traditional CVD risk factors, the rate of T1MI increased with decreasing CD4 count [≥500 cells/μL: ref; 350-499 cells/μL: aIRR = 1.32 (0.98 to 1.77); 200-349 cells/μL: aIRR = 1.37 (1.01 to 1.86); 100-199 cells/μL: aIRR = 1.60 (1.09 to 2.34); <100 cells/μL: aIRR = 2.19 (1.44 to 3.33)]. Risk associated with detectable HIV RNA [<400 copies/mL: ref; ≥400 copies/mL: aIRR = 1.36 (1.06 to 1.75)] was significantly increased only when CD4 was excluded. CONCLUSIONS: The higher incidence of T1MI in HIV-infected individuals and increased risk associated with lower CD4 count and detectable HIV RNA suggest that early suppressive antiretroviral treatment and aggressive management of traditional CVD risk factors are necessary to maximally reduce MI risk.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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".