Highly active antiretroviral therapy and hospital readmission: comparison of a matched cohort
Bibliographic record
Abstract
BACKGROUND: Despite the known efficacy of highly active antiretroviral therapy (HAART), a large proportion of potentially-eligible HIV-infected patients do not access, and may stand to benefit from this treatment. In order to quantify these benefits in terms of reductions in hospitalizations and hospitalization costs, we sought to determine the impact of HAART on hospital readmission among HIV-infected patients hospitalized at St. Paul's Hospital (SPH) in Vancouver, BC, Canada. METHODS: All patients admitted to a specialized HIV/AIDS ward at SPH (Apr. 1997-Oct. 2002) were selected and classified as being on HAART or not on HAART based upon their initial admission. Patients were then matched by their propensity scores, which were calculated based on patients' sociodemographics such as age, gender, injection drug use (IDU) status, and AIDS indication, and followed up for one year. Multivariate logistic regression was used to estimate the difference in the odds of hospital readmission between patients on and not on HAART. RESULTS: Out of a total 1084 patients admitted to the HIV/AIDS ward between 1997 and 2002, 662 were matched according to their propensity score; 331 patients each on and not on HAART. Multivariate logistic regression revealed that patients on HAART had lower odds of AIDS hospital readmission (OR, 0.61; 95% CI, 0.42-0.89) compared to patients not on HAART. Odds of readmission among patients on HAART were also significantly lower for non-IDU related readmission (OR, 0.73; 95% CI, 0.53-0.99) and overall readmission (OR, 0.72; 95% CI, 0.53-0.98). CONCLUSION: Propensity score matching allowed us to reliably estimate the association between exposure (on or not on HAART) and outcome (readmitted to hospital). We found that HIV-infected patients who were potentially eligible for, but not on HAART had higher odds of being readmitted to hospital compared to those on HAART. Given the low level of uptake (31%) of HAART observed in our pre-matched hospitalized cohort, a large potential to achieve clinical benefits, reduce hospitalization costs and possibly slow disease progression from improved HAART uptake still exists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".