Cardiovascular Events in an Inner-City HIV Clinic and Relationship to Abacavir Versus Tenofovir Disoproxil Fumarate-Containing Antiretroviral Regimens
Bibliographic record
Abstract
Following cardiovascular events (CVE) among people living with HIV (PLWH) is essential. Abacavir (ABC)'s impact on CVE challenges clinicians. We characterized CVE at our HIV clinic associated with ABC versus tenofovir disoproxil fumarate (TDF). This was a retrospective study of PLWH who started combination antiretroviral therapy with no prior CVE. Patients were evaluated as antiretroviral naive or antiretroviral experienced. Regimens included the following: always-ABC, always-TDF, first-ABC-switched-to-TDF, and first-TDF-switched-to-ABC regimens. Frequencies, rates, and Poisson regression were used to analyze CVE (cardiovascular/cerebrovascular) and were stratified with an a priori cutoff of before or after January 1, 2009. 1,440/2,852 patients were antiretroviral naive; 658 on always-ABC regimens, 1,186 on always-TDF regimens, 737 first-ABC-switched-to-TDF regimens, and 271 first-TDF-switched-to-ABC regimens. Seventy seven CVE occurred overall [16 naive vs. 61 experienced (p < .0001)]. Sixty events were cardiovascular and 17 cerebrovascular (p < .0001). Sixty-nine CVE occurred before 2009 and eight after (p < .0001). There were 5.65 CVE-per-1,000-years [95% confidence interval (CI) 3.23–9.87] in the always-ABC, 1.95 CVE-per-1,000-years (95% CI 1.08–3.51) in the always-TDF, 2.01 CVE-per-1,000-years (95% CI 1.14–3.56) in the ABC-switched-to-TDF, and 1.82 CVE-per-1,000-years (95% CI 0.77–4.30) in TDF-switched-to-ABC (p <.01). Multivariable Poisson regression incidence rate ratios (IRRs) revealed that being on ABC-only (IRR 2.89; 95% CI 2.13–3.94), age (IRR 1.06 per year; 95% CI 1.04–1.07), and smoking (IRR for current 2.81; 95% CI 1.97–3.99; IRR for former 2.49; 95% CI 1.72–3.61) increased risk of CVE. Thus, in our clinic, CVE rates were increased in those on ABC and adds to the body of literature suggesting concern.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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, 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".