Depression as a Risk Factor for Incident Ischemic Stroke Among HIV‐Positive Veterans in the Veterans Aging Cohort Study
Bibliographic record
Abstract
Background HIV infection and depression are each associated with increased ischemic stroke risk. Whether depression is a risk factor for stroke within the HIV population is unknown. Methods and Results We analyzed data on 106 333 (33 528 HIV‐positive; 72 805 HIV‐negative) people who were free of baseline cardiovascular disease from an observational cohort of HIV‐positive people and matched uninfected veterans in care from April 1, 2003 through December 31, 2014. International Classification of Diseases, Ninth Revision ( ICD‐9 ) codes from medical records were used to determine baseline depression and incident stroke. Depression occurred in 19.5% of HIV‐positive people. After a median of 9.2 years of follow‐up, stroke rates were highest among people with both HIV and depression and lowest among those with neither condition. In Cox proportional hazard models, depression was associated with an increased risk of stroke for HIV‐positive people after adjusting for sociodemographic characteristics and cerebrovascular risk factors (hazard ratio [HR], 1.18; 95% CI: 1.03–1.34; 0.014). The depression‐stroke relationship was attenuated by alcohol use disorders, cocaine use, and baseline antidepressant use, and unaffected by combined antiretroviral therapy use or individual antiretroviral agents. A numerically higher HR of depression on stroke was found among those younger than 60 years. Conclusions Depression is associated with an increased risk of stroke among HIV‐positive people after adjusting for sociodemographic characteristics, traditional cerebrovascular risk factors, and HIV‐specific factors. Alcohol use disorders, cocaine use, and baseline antidepressant use accounted for some of the observed stroke risk. Depression may be a novel, independent risk factor for ischemic stroke in HIV, particularly among younger people.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".