Factors associated with discontinuation of antiretroviral therapy in HIV-infected patients with alcohol problems
Bibliographic record
Abstract
Although mortality rates among HIV-infected populations have declined with the advent of combination antiretroviral therapy (ART), patients with substance use disorders have benefited less from these therapies. While adherence to ART has been well studied, less is known about factors associated with discontinuation of ART. The aim of this study is to investigate predictors of discontinuation of ART in HIV-infected patients with alcohol problems, focusing on their substance use and depressive symptoms. The study cohort (n=266) was prospectively assessed with biannual standardised interviews between 2001 and 2005. Four predictor variables (cocaine, heroin, heavy alcohol use and substantial depressive symptoms) were assessed six months prior to the outcome (ART discontinuation). Longitudinal logistic regression models examined the association between predictor variables and ART discontinuation adjusting for age, gender, race/ethnicity, homelessness, CD4, HIV RNA and HIV Symptom Index. Subjects were 77% male; 43% black; 22% homeless; 45% used cocaine; 20% used heroin; 29% had heavy alcohol use; and 40% had substantial depressive symptoms. Discontinuation occurred in 135 (17%) of the observations (n=743). In bivariate analyses, cocaine use, heroin use and depressive symptoms were significantly associated with ART discontinuation but heavy alcohol use was not. In the multivariable model, substantial depressive symptoms (adjusted odds ratio (AOR)=1.66; 95% confidence interval (CI): 1.04, 2.65) but not cocaine (AOR=1.28; 95%CI: 0.76, 2.16) or heroin use (AOR=1.27 95%CI: 0.66, 2.44), remained significantly associated with ART discontinuation. Among HIV-infected adults with alcohol problems, depressive symptoms, but not substance use, predicted subsequent ART discontinuation. Recognition and treatment of depressive symptoms in this population may result in better maintenance of ART and its associated clinical benefits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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, 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".