Association of the VACS Index With Hospitalization Among People With HIV in the NA-ACCORD
Bibliographic record
Abstract
BACKGROUND: People with HIV (PWH) have a higher hospitalization rate than the general population. The Veterans Aging Cohort Study (VACS) Index at study entry well predicts hospitalization in PWH, but it is unknown if the time-updated parameter improves hospitalization prediction. We assessed the association of parameterizations of the VACS Index 2.0 with the 5-year risk of hospitalization. SETTING: PWH ≥30 years old with at least 12 months of antiretroviral therapy (ART) use and contributing hospitalization data from 2000 to 2016 in North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) were included. Three parameterizations of the VACS Index 2.0 were assessed and categorized by quartile: (1) "baseline" measurement at study entry; (2) time-updated measurements; and (3) cumulative scores calculated using the trapezoidal rule. METHODS: Discrete-time proportional hazard models estimated the crude and adjusted associations (and 95% confidence intervals [CIs]) of the VACS Index parameterizations and all-cause hospitalizations. The Akaike information criterion (AIC) assessed the model fit with each of the VACS Index parameters. RESULTS: Among 7289 patients, 1537 were hospitalized. Time-updated VACS Index fitted hospitalization best with a more distinct dose-response relationship [score <43: reference; score 43-55: aHR = 1.93 (95% CI: 1.66 to 2.23); score 55-68: aHR = 3.63 (95% CI: 3.12 to 4.23); score ≥68: aHR = 9.98 (95% CI: 8.52 to 11.69)] than study entry and cumulative VACS Index after adjusting for known risk factors. CONCLUSIONS: Time-updated VACS Index 2.0 had the strongest association with hospitalization and best fit to the data. Health care providers should consider using it when assessing hospitalization risk among PWH.
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 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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".