HIV Viremia and Risk of Stroke Among People Living with HIV Who Are Using Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Rates of stroke are higher in people living with HIV compared with age-matched uninfected individuals. Causes of elevated stroke risk, including the role of viremia, are poorly defined. METHODS: Between 1 January 2006 and 31 December 2014, we identified incident strokes among people living with HIV on antiretroviral therapy at five sites across the United States. We considered three parameterizations of viral load (VL) including (1) baseline (most recent VL before study entry), (2) time-updated, and (3) cumulative VL (copy-days/mL of virus). We used Cox proportional hazards models to estimate hazard ratios (HRs) for stroke risk comparing the 75th percentile ("high VL") to the 25th percentile ("low VL") of baseline and time-updated VL. We used marginal structural Cox models, with most models adjusted for traditional stroke risk factors, to estimate HRs for stroke associated with cumulative VL. RESULTS: Among 15,974 people living with HIV, 139 experienced a stroke (113 ischemic; 18 hemorrhagic; eight were unknown type) over a median follow-up of 4.2 years. Median baseline VL was 38 copies/mL (interquartile interval: 24, 3,420). High baseline VL was associated with increased risk of both ischemic (HR: 1.3; 95% CI = 0.96-1.7) and hemorrhagic stroke (HR: 3.1; 95% CI = 1.6-5.9). In time-updated models, high VL was also associated with an increased risk of any stroke (HR: 1.8; 95% CI = 1.4-2.3). We observed no association between cumulative VL and stroke risk. CONCLUSIONS: Our findings are consistent with the hypothesis that elevated HIV VL may increase stroke risk, regardless of previous VL levels.
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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 | 0.000 | 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) | 0.001 | 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".